Datasets:

suvadityamuk

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huggingface-transformers-code-dataset

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# coding=utf-8 # Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable, Optional, Tuple import flax import flax.linen as nn import jax import jax.numpy as jnp import numpy as np from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen import partitioning as nn_partitioning from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import ( FlaxBaseModelOutput, FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, FlaxMaskedLMOutput, FlaxMultipleChoiceModelOutput, FlaxQuestionAnsweringModelOutput, FlaxSequenceClassifierOutput, FlaxTokenClassifierOutput, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_electra import ElectraConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/electra-small-discriminator" _CONFIG_FOR_DOC = "ElectraConfig" remat = nn_partitioning.remat @flax.struct.dataclass class FlaxElectraForPreTrainingOutput(ModelOutput): """ Output type of [`ElectraForPreTraining`]. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None ELECTRA_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`ElectraConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ELECTRA_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`numpy.ndarray` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class FlaxElectraEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" config: ElectraConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.word_embeddings = nn.Embed( self.config.vocab_size, self.config.embedding_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.position_embeddings = nn.Embed( self.config.max_position_embeddings, self.config.embedding_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.token_type_embeddings = nn.Embed( self.config.type_vocab_size, self.config.embedding_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings.__call__ def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True): # Embed inputs_embeds = self.word_embeddings(input_ids.astype("i4")) position_embeds = self.position_embeddings(position_ids.astype("i4")) token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4")) # Sum all embeddings hidden_states = inputs_embeds + token_type_embeddings + position_embeds # Layer Norm hidden_states = self.LayerNorm(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->Electra class FlaxElectraSelfAttention(nn.Module): config: ElectraConfig causal: bool = False dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.head_dim = self.config.hidden_size // self.config.num_attention_heads if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError( "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` " " : {self.config.num_attention_heads}" ) self.query = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.key = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.value = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,)) @nn.compact # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic=True, output_attentions: bool = False, ): # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.query(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.key(key_value_states) value_states = self.value(key_value_states) else: # self_attention key_states = self.key(hidden_states) value_states = self.value(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.config.attention_probs_dropout_prob > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attention_probs_dropout_prob, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) # Mask heads if we want to if layer_head_mask is not None: attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,)) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->Electra class FlaxElectraSelfOutput(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, hidden_states, input_tensor, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->Electra class FlaxElectraAttention(nn.Module): config: ElectraConfig causal: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): self.self = FlaxElectraSelfAttention(self.config, causal=self.causal, dtype=self.dtype) self.output = FlaxElectraSelfOutput(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states=None, init_cache=False, deterministic=True, output_attentions: bool = False, ): # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length) # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length) attn_outputs = self.self( hidden_states, attention_mask, layer_head_mask=layer_head_mask, key_value_states=key_value_states, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attn_output = attn_outputs[0] hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attn_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->Electra class FlaxElectraIntermediate(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.activation = ACT2FN[self.config.hidden_act] def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->Electra class FlaxElectraOutput(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_output, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + attention_output) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->Electra class FlaxElectraLayer(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.attention = FlaxElectraAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype) self.intermediate = FlaxElectraIntermediate(self.config, dtype=self.dtype) self.output = FlaxElectraOutput(self.config, dtype=self.dtype) if self.config.add_cross_attention: self.crossattention = FlaxElectraAttention(self.config, causal=False, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, ): # Self Attention attention_outputs = self.attention( hidden_states, attention_mask, layer_head_mask=layer_head_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = attention_outputs[0] # Cross-Attention Block if encoder_hidden_states is not None: cross_attention_outputs = self.crossattention( attention_output, attention_mask=encoder_attention_mask, layer_head_mask=layer_head_mask, key_value_states=encoder_hidden_states, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] hidden_states = self.intermediate(attention_output) hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attention_outputs[1],) if encoder_hidden_states is not None: outputs += (cross_attention_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->Electra class FlaxElectraLayerCollection(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): if self.gradient_checkpointing: FlaxElectraCheckpointLayer = remat(FlaxElectraLayer, static_argnums=(5, 6, 7)) self.layers = [ FlaxElectraCheckpointLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] else: self.layers = [ FlaxElectraLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None # Check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.shape[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for " f" {head_mask.shape[0]}." ) for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, head_mask[i] if head_mask is not None else None, encoder_hidden_states, encoder_attention_mask, init_cache, deterministic, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->Electra class FlaxElectraEncoder(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): self.layer = FlaxElectraLayerCollection( self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing, ) def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.layer( hidden_states, attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class FlaxElectraGeneratorPredictions(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dense = nn.Dense(self.config.embedding_size, dtype=self.dtype) def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = ACT2FN[self.config.hidden_act](hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class FlaxElectraDiscriminatorPredictions(nn.Module): """Prediction module for the discriminator, made up of two dense layers.""" config: ElectraConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype) self.dense_prediction = nn.Dense(1, dtype=self.dtype) def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = ACT2FN[self.config.hidden_act](hidden_states) hidden_states = self.dense_prediction(hidden_states).squeeze(-1) return hidden_states class FlaxElectraPreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ElectraConfig base_model_prefix = "electra" module_class: nn.Module = None def __init__( self, config: ElectraConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, gradient_checkpointing: bool = False, **kwargs, ): module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing def enable_gradient_checkpointing(self): self._module = self.module_class( config=self.config, dtype=self.dtype, gradient_checkpointing=True, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.init_weights def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") token_type_ids = jnp.zeros_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) attention_mask = jnp.ones_like(input_ids) head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} if self.config.add_cross_attention: encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,)) encoder_attention_mask = attention_mask module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states, encoder_attention_mask, return_dict=False, ) else: module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False ) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length), dtype="i4") attention_mask = jnp.ones_like(input_ids, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, past_key_values: dict = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # init input tensors if not passed if token_type_ids is None: token_type_ids = jnp.ones_like(input_ids) if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if head_mask is None: head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} if self.config.add_cross_attention: # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxElectraAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] else: outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, ) return outputs class FlaxElectraModule(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): self.embeddings = FlaxElectraEmbeddings(self.config, dtype=self.dtype) if self.config.embedding_size != self.config.hidden_size: self.embeddings_project = nn.Dense(self.config.hidden_size, dtype=self.dtype) self.encoder = FlaxElectraEncoder( self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing ) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask: Optional[np.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): embeddings = self.embeddings( input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic ) if hasattr(self, "embeddings_project"): embeddings = self.embeddings_project(embeddings) return self.encoder( embeddings, attention_mask, head_mask=head_mask, deterministic=deterministic, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) @add_start_docstrings( "The bare Electra Model transformer outputting raw hidden-states without any specific head on top.", ELECTRA_START_DOCSTRING, ) class FlaxElectraModel(FlaxElectraPreTrainedModel): module_class = FlaxElectraModule append_call_sample_docstring(FlaxElectraModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC) class FlaxElectraTiedDense(nn.Module): embedding_size: int dtype: jnp.dtype = jnp.float32 precision = None bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros def setup(self): self.bias = self.param("bias", self.bias_init, (self.embedding_size,)) def __call__(self, x, kernel): x = jnp.asarray(x, self.dtype) kernel = jnp.asarray(kernel, self.dtype) y = lax.dot_general( x, kernel, (((x.ndim - 1,), (0,)), ((), ())), precision=self.precision, ) bias = jnp.asarray(self.bias, self.dtype) return y + bias class FlaxElectraForMaskedLMModule(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.electra = FlaxElectraModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing ) self.generator_predictions = FlaxElectraGeneratorPredictions(config=self.config, dtype=self.dtype) if self.config.tie_word_embeddings: self.generator_lm_head = FlaxElectraTiedDense(self.config.vocab_size, dtype=self.dtype) else: self.generator_lm_head = nn.Dense(self.config.vocab_size, dtype=self.dtype) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): outputs = self.electra( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] prediction_scores = self.generator_predictions(hidden_states) if self.config.tie_word_embeddings: shared_embedding = self.electra.variables["params"]["embeddings"]["word_embeddings"]["embedding"] prediction_scores = self.generator_lm_head(prediction_scores, shared_embedding.T) else: prediction_scores = self.generator_lm_head(prediction_scores) if not return_dict: return (prediction_scores,) + outputs[1:] return FlaxMaskedLMOutput( logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""Electra Model with a `language modeling` head on top.""", ELECTRA_START_DOCSTRING) class FlaxElectraForMaskedLM(FlaxElectraPreTrainedModel): module_class = FlaxElectraForMaskedLMModule append_call_sample_docstring(FlaxElectraForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC) class FlaxElectraForPreTrainingModule(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.electra = FlaxElectraModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing ) self.discriminator_predictions = FlaxElectraDiscriminatorPredictions(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.electra( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.discriminator_predictions(hidden_states) if not return_dict: return (logits,) + outputs[1:] return FlaxElectraForPreTrainingOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Electra model with a binary classification head on top as used during pretraining for identifying generated tokens. It is recommended to load the discriminator checkpoint into that model. """, ELECTRA_START_DOCSTRING, ) class FlaxElectraForPreTraining(FlaxElectraPreTrainedModel): module_class = FlaxElectraForPreTrainingModule FLAX_ELECTRA_FOR_PRETRAINING_DOCSTRING = """ Returns: Example: ```python >>> from transformers import AutoTokenizer, FlaxElectraForPreTraining >>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator") >>> model = FlaxElectraForPreTraining.from_pretrained("google/electra-small-discriminator") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ``` """ overwrite_call_docstring( FlaxElectraForPreTraining, ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_ELECTRA_FOR_PRETRAINING_DOCSTRING, ) append_replace_return_docstrings( FlaxElectraForPreTraining, output_type=FlaxElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC ) class FlaxElectraForTokenClassificationModule(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.electra = FlaxElectraModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing ) classifier_dropout = ( self.config.classifier_dropout if self.config.classifier_dropout is not None else self.config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.electra( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.classifier(hidden_states) if not return_dict: return (logits,) + outputs[1:] return FlaxTokenClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Electra model with a token classification head on top. Both the discriminator and generator may be loaded into this model. """, ELECTRA_START_DOCSTRING, ) class FlaxElectraForTokenClassification(FlaxElectraPreTrainedModel): module_class = FlaxElectraForTokenClassificationModule append_call_sample_docstring( FlaxElectraForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC, ) def identity(x, **kwargs): return x class FlaxElectraSequenceSummary(nn.Module): r""" Compute a single vector summary of a sequence hidden states. Args: config ([`PretrainedConfig`]): The config used by the model. Relevant arguments in the config class of the model are (refer to the actual config class of your model for the default values it uses): - **summary_use_proj** (`bool`) -- Add a projection after the vector extraction. - **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes (otherwise to `config.hidden_size`). - **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output, another string or `None` will add no activation. - **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation. - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation. """ config: ElectraConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.summary = identity if hasattr(self.config, "summary_use_proj") and self.config.summary_use_proj: if ( hasattr(self.config, "summary_proj_to_labels") and self.config.summary_proj_to_labels and self.config.num_labels > 0 ): num_classes = self.config.num_labels else: num_classes = self.config.hidden_size self.summary = nn.Dense(num_classes, dtype=self.dtype) activation_string = getattr(self.config, "summary_activation", None) self.activation = ACT2FN[activation_string] if activation_string else lambda x: x # noqa F407 self.first_dropout = identity if hasattr(self.config, "summary_first_dropout") and self.config.summary_first_dropout > 0: self.first_dropout = nn.Dropout(self.config.summary_first_dropout) self.last_dropout = identity if hasattr(self.config, "summary_last_dropout") and self.config.summary_last_dropout > 0: self.last_dropout = nn.Dropout(self.config.summary_last_dropout) def __call__(self, hidden_states, cls_index=None, deterministic: bool = True): """ Compute a single vector summary of a sequence hidden states. Args: hidden_states (`jnp.ndarray` of shape `[batch_size, seq_len, hidden_size]`): The hidden states of the last layer. cls_index (`jnp.ndarray` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*): Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token. Returns: `jnp.ndarray`: The summary of the sequence hidden states. """ # NOTE: this doest "first" type summary always output = hidden_states[:, 0] output = self.first_dropout(output, deterministic=deterministic) output = self.summary(output) output = self.activation(output) output = self.last_dropout(output, deterministic=deterministic) return output class FlaxElectraForMultipleChoiceModule(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.electra = FlaxElectraModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing ) self.sequence_summary = FlaxElectraSequenceSummary(config=self.config, dtype=self.dtype) self.classifier = nn.Dense(1, dtype=self.dtype) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): num_choices = input_ids.shape[1] input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None # Model outputs = self.electra( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] pooled_output = self.sequence_summary(hidden_states, deterministic=deterministic) logits = self.classifier(pooled_output) reshaped_logits = logits.reshape(-1, num_choices) if not return_dict: return (reshaped_logits,) + outputs[1:] return FlaxMultipleChoiceModelOutput( logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ ELECTRA Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ELECTRA_START_DOCSTRING, ) class FlaxElectraForMultipleChoice(FlaxElectraPreTrainedModel): module_class = FlaxElectraForMultipleChoiceModule # adapt docstring slightly for FlaxElectraForMultipleChoice overwrite_call_docstring( FlaxElectraForMultipleChoice, ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) append_call_sample_docstring( FlaxElectraForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC, ) class FlaxElectraForQuestionAnsweringModule(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.electra = FlaxElectraModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing ) self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.electra( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.qa_outputs(hidden_states) start_logits, end_logits = logits.split(self.config.num_labels, axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: return (start_logits, end_logits) + outputs[1:] return FlaxQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ ELECTRA Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ELECTRA_START_DOCSTRING, ) class FlaxElectraForQuestionAnswering(FlaxElectraPreTrainedModel): module_class = FlaxElectraForQuestionAnsweringModule append_call_sample_docstring( FlaxElectraForQuestionAnswering, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, ) class FlaxElectraClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" config: ElectraConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype) classifier_dropout = ( self.config.classifier_dropout if self.config.classifier_dropout is not None else self.config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__(self, hidden_states, deterministic: bool = True): x = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x, deterministic=deterministic) x = self.dense(x) x = ACT2FN["gelu"](x) # although BERT uses tanh here, it seems Electra authors used gelu x = self.dropout(x, deterministic=deterministic) x = self.out_proj(x) return x class FlaxElectraForSequenceClassificationModule(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.electra = FlaxElectraModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing ) self.classifier = FlaxElectraClassificationHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.electra( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.classifier(hidden_states, deterministic=deterministic) if not return_dict: return (logits,) + outputs[1:] return FlaxSequenceClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Electra Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ELECTRA_START_DOCSTRING, ) class FlaxElectraForSequenceClassification(FlaxElectraPreTrainedModel): module_class = FlaxElectraForSequenceClassificationModule append_call_sample_docstring( FlaxElectraForSequenceClassification, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC, ) class FlaxElectraForCausalLMModule(nn.Module): config: ElectraConfig dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.electra = FlaxElectraModule( config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing ) self.generator_predictions = FlaxElectraGeneratorPredictions(config=self.config, dtype=self.dtype) if self.config.tie_word_embeddings: self.generator_lm_head = FlaxElectraTiedDense(self.config.vocab_size, dtype=self.dtype) else: self.generator_lm_head = nn.Dense(self.config.vocab_size, dtype=self.dtype) def __call__( self, input_ids, attention_mask: Optional[jnp.ndarray] = None, token_type_ids: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): outputs = self.electra( input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] prediction_scores = self.generator_predictions(hidden_states) if self.config.tie_word_embeddings: shared_embedding = self.electra.variables["params"]["embeddings"]["word_embeddings"]["embedding"] prediction_scores = self.generator_lm_head(prediction_scores, shared_embedding.T) else: prediction_scores = self.generator_lm_head(prediction_scores) if not return_dict: return (prediction_scores,) + outputs[1:] return FlaxCausalLMOutputWithCrossAttentions( logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( """ Electra Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks. """, ELECTRA_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForCausalLM with Bert->Electra class FlaxElectraForCausalLM(FlaxElectraPreTrainedModel): module_class = FlaxElectraForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyway. # Thus, we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxElectraForCausalLM, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _CONFIG_FOR_DOC, )

transformers/src/transformers/models/electra/modeling_flax_electra.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch ERNIE model.""" import math import warnings from dataclasses import dataclass from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_ernie import ErnieConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "nghuyong/ernie-1.0-base-zh" _CONFIG_FOR_DOC = "ErnieConfig" ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "nghuyong/ernie-1.0-base-zh", "nghuyong/ernie-2.0-base-en", "nghuyong/ernie-2.0-large-en", "nghuyong/ernie-3.0-base-zh", "nghuyong/ernie-3.0-medium-zh", "nghuyong/ernie-3.0-mini-zh", "nghuyong/ernie-3.0-micro-zh", "nghuyong/ernie-3.0-nano-zh", "nghuyong/ernie-gram-zh", "nghuyong/ernie-health-zh", # See all ERNIE models at https://huggingface.co/models?filter=ernie ] class ErnieEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) self.use_task_id = config.use_task_id if config.use_task_id: self.task_type_embeddings = nn.Embedding(config.task_type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, task_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values_length: int = 0, ) -> torch.Tensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings # add `task_type_id` for ERNIE model if self.use_task_id: if task_type_ids is None: task_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) task_type_embeddings = self.task_type_embeddings(task_type_ids) embeddings += task_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings # Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Ernie class ErnieSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in ErnieModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Ernie class ErnieSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Ernie class ErnieAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = ErnieSelfAttention(config, position_embedding_type=position_embedding_type) self.output = ErnieSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Ernie class ErnieIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Ernie class ErnieOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Ernie class ErnieLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = ErnieAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = ErnieAttention(config, position_embedding_type="absolute") self.intermediate = ErnieIntermediate(config) self.output = ErnieOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Ernie class ErnieEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([ErnieLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Ernie class ErniePooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output # Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Ernie class ErniePredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Ernie class ErnieLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = ErniePredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Ernie class ErnieOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = ErnieLMPredictionHead(config) def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: prediction_scores = self.predictions(sequence_output) return prediction_scores # Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->Ernie class ErnieOnlyNSPHead(nn.Module): def __init__(self, config): super().__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output): seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score # Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->Ernie class ErniePreTrainingHeads(nn.Module): def __init__(self, config): super().__init__() self.predictions = ErnieLMPredictionHead(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, sequence_output, pooled_output): prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class ErniePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ErnieConfig base_model_prefix = "ernie" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) @dataclass # Copied from transformers.models.bert.modeling_bert.BertForPreTrainingOutput with Bert->Ernie class ErnieForPreTrainingOutput(ModelOutput): """ Output type of [`ErnieForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None prediction_logits: torch.FloatTensor = None seq_relationship_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None ERNIE_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ErnieConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ERNIE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) task_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare Ernie Model transformer outputting raw hidden-states without any specific head on top.", ERNIE_START_DOCSTRING, ) class ErnieModel(ErniePreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. """ # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Ernie def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = ErnieEmbeddings(config) self.encoder = ErnieEncoder(config) self.pooler = ErniePooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() # Copied from transformers.models.bert.modeling_bert.BertModel.get_input_embeddings def get_input_embeddings(self): return self.embeddings.word_embeddings # Copied from transformers.models.bert.modeling_bert.BertModel.set_input_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value # Copied from transformers.models.bert.modeling_bert.BertModel._prune_heads def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, task_type_ids=task_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """ Ernie Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. """, ERNIE_START_DOCSTRING, ) class ErnieForPreTraining(ErniePreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] # Copied from transformers.models.bert.modeling_bert.BertForPreTraining.__init__ with Bert->Ernie,bert->ernie def __init__(self, config): super().__init__(config) self.ernie = ErnieModel(config) self.cls = ErniePreTrainingHeads(config) # Initialize weights and apply final processing self.post_init() # Copied from transformers.models.bert.modeling_bert.BertForPreTraining.get_output_embeddings def get_output_embeddings(self): return self.cls.predictions.decoder # Copied from transformers.models.bert.modeling_bert.BertForPreTraining.set_output_embeddings def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=ErnieForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, next_sentence_label: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], ErnieForPreTrainingOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. Returns: Example: ```python >>> from transformers import AutoTokenizer, ErnieForPreTraining >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh") >>> model = ErnieForPreTraining.from_pretrained("nghuyong/ernie-1.0-base-zh") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) total_loss = None if labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss if not return_dict: output = (prediction_scores, seq_relationship_score) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return ErnieForPreTrainingOutput( loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """Ernie Model with a `language modeling` head on top for CLM fine-tuning.""", ERNIE_START_DOCSTRING ) class ErnieForCausalLM(ErniePreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.__init__ with BertLMHeadModel->ErnieForCausalLM,Bert->Ernie,bert->ernie def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `ErnieForCausalLM` as a standalone, add `is_decoder=True.`") self.ernie = ErnieModel(config, add_pooling_layer=False) self.cls = ErnieOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.get_output_embeddings def get_output_embeddings(self): return self.cls.predictions.decoder # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.set_output_embeddings def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.prepare_inputs_for_generation def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, use_cache=True, **model_kwargs ): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past_key_values is used if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] return { "input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": use_cache, } # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel._reorder_cache def _reorder_cache(self, past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past @add_start_docstrings("""Ernie Model with a `language modeling` head on top.""", ERNIE_START_DOCSTRING) class ErnieForMaskedLM(ErniePreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.__init__ with Bert->Ernie,bert->ernie def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `ErnieForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.ernie = ErnieModel(config, add_pooling_layer=False) self.cls = ErnieOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.get_output_embeddings def get_output_embeddings(self): return self.cls.predictions.decoder # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.set_output_embeddings def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, expected_output="'paris'", expected_loss=0.88, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.prepare_inputs_for_generation def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs): input_shape = input_ids.shape effective_batch_size = input_shape[0] # add a dummy token if self.config.pad_token_id is None: raise ValueError("The PAD token should be defined for generation") attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1) dummy_token = torch.full( (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device ) input_ids = torch.cat([input_ids, dummy_token], dim=1) return {"input_ids": input_ids, "attention_mask": attention_mask} @add_start_docstrings( """Ernie Model with a `next sentence prediction (classification)` head on top.""", ERNIE_START_DOCSTRING, ) class ErnieForNextSentencePrediction(ErniePreTrainedModel): # Copied from transformers.models.bert.modeling_bert.BertForNextSentencePrediction.__init__ with Bert->Ernie,bert->ernie def __init__(self, config): super().__init__(config) self.ernie = ErnieModel(config) self.cls = ErnieOnlyNSPHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring). Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Returns: Example: ```python >>> from transformers import AutoTokenizer, ErnieForNextSentencePrediction >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh") >>> model = ErnieForNextSentencePrediction.from_pretrained("nghuyong/ernie-1.0-base-zh") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> logits = outputs.logits >>> assert logits[0, 0] < logits[0, 1] # next sentence was random ``` """ if "next_sentence_label" in kwargs: warnings.warn( "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" " `labels` instead.", FutureWarning, ) labels = kwargs.pop("next_sentence_label") return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] seq_relationship_scores = self.cls(pooled_output) next_sentence_loss = None if labels is not None: loss_fct = CrossEntropyLoss() next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) if not return_dict: output = (seq_relationship_scores,) + outputs[2:] return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output return NextSentencePredictorOutput( loss=next_sentence_loss, logits=seq_relationship_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Ernie Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ERNIE_START_DOCSTRING, ) class ErnieForSequenceClassification(ErniePreTrainedModel): # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->Ernie,bert->ernie def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.ernie = ErnieModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Ernie Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ERNIE_START_DOCSTRING, ) class ErnieForMultipleChoice(ErniePreTrainedModel): # Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->Ernie,bert->ernie def __init__(self, config): super().__init__(config) self.ernie = ErnieModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Ernie Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, ERNIE_START_DOCSTRING, ) class ErnieForTokenClassification(ErniePreTrainedModel): # Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->Ernie,bert->ernie def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ernie = ErnieModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Ernie Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ERNIE_START_DOCSTRING, ) class ErnieForQuestionAnswering(ErniePreTrainedModel): # Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->Ernie,bert->ernie def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ernie = ErnieModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, task_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/ernie/modeling_ernie.py/0

# Copyright 2021 AlQuraishi Laboratory # Copyright 2021 DeepMind Technologies Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Protein data type.""" import dataclasses import re import string from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple import numpy as np from . import residue_constants FeatureDict = Mapping[str, np.ndarray] ModelOutput = Mapping[str, Any] # Is a nested dict. PICO_TO_ANGSTROM = 0.01 @dataclasses.dataclass(frozen=True) class Protein: """Protein structure representation.""" # Cartesian coordinates of atoms in angstroms. The atom types correspond to # residue_constants.atom_types, i.e. the first three are N, CA, CB. atom_positions: np.ndarray # [num_res, num_atom_type, 3] # Amino-acid type for each residue represented as an integer between 0 and # 20, where 20 is 'X'. aatype: np.ndarray # [num_res] # Binary float mask to indicate presence of a particular atom. 1.0 if an atom # is present and 0.0 if not. This should be used for loss masking. atom_mask: np.ndarray # [num_res, num_atom_type] # Residue index as used in PDB. It is not necessarily continuous or 0-indexed. residue_index: np.ndarray # [num_res] # B-factors, or temperature factors, of each residue (in sq. angstroms units), # representing the displacement of the residue from its ground truth mean # value. b_factors: np.ndarray # [num_res, num_atom_type] # Chain indices for multi-chain predictions chain_index: Optional[np.ndarray] = None # Optional remark about the protein. Included as a comment in output PDB # files remark: Optional[str] = None # Templates used to generate this protein (prediction-only) parents: Optional[Sequence[str]] = None # Chain corresponding to each parent parents_chain_index: Optional[Sequence[int]] = None def from_proteinnet_string(proteinnet_str: str) -> Protein: tag_re = r"(\[[A-Z]+\]\n)" tags: List[str] = [tag.strip() for tag in re.split(tag_re, proteinnet_str) if len(tag) > 0] groups: Iterator[Tuple[str, List[str]]] = zip(tags[0::2], [l.split("\n") for l in tags[1::2]]) atoms: List[str] = ["N", "CA", "C"] aatype = None atom_positions = None atom_mask = None for g in groups: if "[PRIMARY]" == g[0]: seq = g[1][0].strip() for i in range(len(seq)): if seq[i] not in residue_constants.restypes: seq[i] = "X" # FIXME: strings are immutable aatype = np.array( [residue_constants.restype_order.get(res_symbol, residue_constants.restype_num) for res_symbol in seq] ) elif "[TERTIARY]" == g[0]: tertiary: List[List[float]] = [] for axis in range(3): tertiary.append(list(map(float, g[1][axis].split()))) tertiary_np = np.array(tertiary) atom_positions = np.zeros((len(tertiary[0]) // 3, residue_constants.atom_type_num, 3)).astype(np.float32) for i, atom in enumerate(atoms): atom_positions[:, residue_constants.atom_order[atom], :] = np.transpose(tertiary_np[:, i::3]) atom_positions *= PICO_TO_ANGSTROM elif "[MASK]" == g[0]: mask = np.array(list(map({"-": 0, "+": 1}.get, g[1][0].strip()))) atom_mask = np.zeros( ( len(mask), residue_constants.atom_type_num, ) ).astype(np.float32) for i, atom in enumerate(atoms): atom_mask[:, residue_constants.atom_order[atom]] = 1 atom_mask *= mask[..., None] assert aatype is not None return Protein( atom_positions=atom_positions, atom_mask=atom_mask, aatype=aatype, residue_index=np.arange(len(aatype)), b_factors=None, ) def get_pdb_headers(prot: Protein, chain_id: int = 0) -> List[str]: pdb_headers: List[str] = [] remark = prot.remark if remark is not None: pdb_headers.append(f"REMARK {remark}") parents = prot.parents parents_chain_index = prot.parents_chain_index if parents is not None and parents_chain_index is not None: parents = [p for i, p in zip(parents_chain_index, parents) if i == chain_id] if parents is None or len(parents) == 0: parents = ["N/A"] pdb_headers.append(f"PARENT {' '.join(parents)}") return pdb_headers def add_pdb_headers(prot: Protein, pdb_str: str) -> str: """Add pdb headers to an existing PDB string. Useful during multi-chain recycling """ out_pdb_lines: List[str] = [] lines = pdb_str.split("\n") remark = prot.remark if remark is not None: out_pdb_lines.append(f"REMARK {remark}") parents_per_chain: List[List[str]] if prot.parents is not None and len(prot.parents) > 0: parents_per_chain = [] if prot.parents_chain_index is not None: parent_dict: Dict[str, List[str]] = {} for p, i in zip(prot.parents, prot.parents_chain_index): parent_dict.setdefault(str(i), []) parent_dict[str(i)].append(p) max_idx = max([int(chain_idx) for chain_idx in parent_dict]) for i in range(max_idx + 1): chain_parents = parent_dict.get(str(i), ["N/A"]) parents_per_chain.append(chain_parents) else: parents_per_chain.append(list(prot.parents)) else: parents_per_chain = [["N/A"]] def make_parent_line(p: Sequence[str]) -> str: return f"PARENT {' '.join(p)}" out_pdb_lines.append(make_parent_line(parents_per_chain[0])) chain_counter = 0 for i, l in enumerate(lines): if "PARENT" not in l and "REMARK" not in l: out_pdb_lines.append(l) if "TER" in l and "END" not in lines[i + 1]: chain_counter += 1 if not chain_counter >= len(parents_per_chain): chain_parents = parents_per_chain[chain_counter] else: chain_parents = ["N/A"] out_pdb_lines.append(make_parent_line(chain_parents)) return "\n".join(out_pdb_lines) def to_pdb(prot: Protein) -> str: """Converts a `Protein` instance to a PDB string. Args: prot: The protein to convert to PDB. Returns: PDB string. """ restypes = residue_constants.restypes + ["X"] def res_1to3(r: int) -> str: return residue_constants.restype_1to3.get(restypes[r], "UNK") atom_types = residue_constants.atom_types pdb_lines: List[str] = [] atom_mask = prot.atom_mask aatype = prot.aatype atom_positions = prot.atom_positions residue_index = prot.residue_index.astype(np.int32) b_factors = prot.b_factors chain_index = prot.chain_index if np.any(aatype > residue_constants.restype_num): raise ValueError("Invalid aatypes.") headers = get_pdb_headers(prot) if len(headers) > 0: pdb_lines.extend(headers) n = aatype.shape[0] atom_index = 1 prev_chain_index = 0 chain_tags = string.ascii_uppercase chain_tag = None # Add all atom sites. for i in range(n): res_name_3 = res_1to3(aatype[i]) for atom_name, pos, mask, b_factor in zip(atom_types, atom_positions[i], atom_mask[i], b_factors[i]): if mask < 0.5: continue record_type = "ATOM" name = atom_name if len(atom_name) == 4 else f" {atom_name}" alt_loc = "" insertion_code = "" occupancy = 1.00 element = atom_name[0] # Protein supports only C, N, O, S, this works. charge = "" chain_tag = "A" if chain_index is not None: chain_tag = chain_tags[chain_index[i]] # PDB is a columnar format, every space matters here! atom_line = ( f"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}" f"{res_name_3:>3} {chain_tag:>1}" f"{residue_index[i]:>4}{insertion_code:>1} " f"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}" f"{occupancy:>6.2f}{b_factor:>6.2f} " f"{element:>2}{charge:>2}" ) pdb_lines.append(atom_line) atom_index += 1 should_terminate = i == n - 1 if chain_index is not None: if i != n - 1 and chain_index[i + 1] != prev_chain_index: should_terminate = True prev_chain_index = chain_index[i + 1] if should_terminate: # Close the chain. chain_end = "TER" chain_termination_line = ( f"{chain_end:<6}{atom_index:>5} {res_1to3(aatype[i]):>3} {chain_tag:>1}{residue_index[i]:>4}" ) pdb_lines.append(chain_termination_line) atom_index += 1 if i != n - 1: # "prev" is a misnomer here. This happens at the beginning of # each new chain. pdb_lines.extend(get_pdb_headers(prot, prev_chain_index)) pdb_lines.append("END") pdb_lines.append("") return "\n".join(pdb_lines) def ideal_atom_mask(prot: Protein) -> np.ndarray: """Computes an ideal atom mask. `Protein.atom_mask` typically is defined according to the atoms that are reported in the PDB. This function computes a mask according to heavy atoms that should be present in the given sequence of amino acids. Args: prot: `Protein` whose fields are `numpy.ndarray` objects. Returns: An ideal atom mask. """ return residue_constants.STANDARD_ATOM_MASK[prot.aatype] def from_prediction( features: FeatureDict, result: ModelOutput, b_factors: Optional[np.ndarray] = None, chain_index: Optional[np.ndarray] = None, remark: Optional[str] = None, parents: Optional[Sequence[str]] = None, parents_chain_index: Optional[Sequence[int]] = None, ) -> Protein: """Assembles a protein from a prediction. Args: features: Dictionary holding model inputs. result: Dictionary holding model outputs. b_factors: (Optional) B-factors to use for the protein. chain_index: (Optional) Chain indices for multi-chain predictions remark: (Optional) Remark about the prediction parents: (Optional) List of template names Returns: A protein instance. """ return Protein( aatype=features["aatype"], atom_positions=result["final_atom_positions"], atom_mask=result["final_atom_mask"], residue_index=features["residue_index"] + 1, b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"]), chain_index=chain_index, remark=remark, parents=parents, parents_chain_index=parents_chain_index, )

transformers/src/transformers/models/esm/openfold_utils/protein.py/0

# coding=utf-8 # Copyright 2021 Google Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch FNet model.""" import warnings from dataclasses import dataclass from functools import partial from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...utils import is_scipy_available if is_scipy_available(): from scipy import linalg from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, ModelOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_fnet import FNetConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/fnet-base" _CONFIG_FOR_DOC = "FNetConfig" FNET_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/fnet-base", "google/fnet-large", # See all FNet models at https://huggingface.co/models?filter=fnet ] # Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py def _two_dim_matmul(x, matrix_dim_one, matrix_dim_two): """Applies 2D matrix multiplication to 3D input arrays.""" seq_length = x.shape[1] matrix_dim_one = matrix_dim_one[:seq_length, :seq_length] x = x.type(torch.complex64) return torch.einsum("bij,jk,ni->bnk", x, matrix_dim_two, matrix_dim_one) # # Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py def two_dim_matmul(x, matrix_dim_one, matrix_dim_two): return _two_dim_matmul(x, matrix_dim_one, matrix_dim_two) # Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py def fftn(x): """ Applies n-dimensional Fast Fourier Transform (FFT) to input array. Args: x: Input n-dimensional array. Returns: n-dimensional Fourier transform of input n-dimensional array. """ out = x for axis in reversed(range(x.ndim)[1:]): # We don't need to apply FFT to last axis out = torch.fft.fft(out, axis=axis) return out class FNetEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # NOTE: This is the project layer and will be needed. The original code allows for different embedding and different model dimensions. self.projection = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.projection(embeddings) embeddings = self.dropout(embeddings) return embeddings class FNetBasicFourierTransform(nn.Module): def __init__(self, config): super().__init__() self._init_fourier_transform(config) def _init_fourier_transform(self, config): if not config.use_tpu_fourier_optimizations: self.fourier_transform = partial(torch.fft.fftn, dim=(1, 2)) elif config.max_position_embeddings <= 4096: if is_scipy_available(): self.register_buffer( "dft_mat_hidden", torch.tensor(linalg.dft(config.hidden_size), dtype=torch.complex64) ) self.register_buffer( "dft_mat_seq", torch.tensor(linalg.dft(config.tpu_short_seq_length), dtype=torch.complex64) ) self.fourier_transform = partial( two_dim_matmul, matrix_dim_one=self.dft_mat_seq, matrix_dim_two=self.dft_mat_hidden ) else: logging.warning( "SciPy is needed for DFT matrix calculation and is not found. Using TPU optimized fast fourier" " transform instead." ) self.fourier_transform = fftn else: self.fourier_transform = fftn def forward(self, hidden_states): # NOTE: We do not use torch.vmap as it is not integrated into PyTorch stable versions. # Interested users can modify the code to use vmap from the nightly versions, getting the vmap from here: # https://pytorch.org/docs/master/generated/torch.vmap.html. Note that fourier transform methods will need # change accordingly. outputs = self.fourier_transform(hidden_states).real return (outputs,) class FNetBasicOutput(nn.Module): def __init__(self, config): super().__init__() self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states, input_tensor): hidden_states = self.LayerNorm(input_tensor + hidden_states) return hidden_states class FNetFourierTransform(nn.Module): def __init__(self, config): super().__init__() self.self = FNetBasicFourierTransform(config) self.output = FNetBasicOutput(config) def forward(self, hidden_states): self_outputs = self.self(hidden_states) fourier_output = self.output(self_outputs[0], hidden_states) outputs = (fourier_output,) return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->FNet class FNetIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->FNet class FNetOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class FNetLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 # The dimension which has the sequence length self.fourier = FNetFourierTransform(config) self.intermediate = FNetIntermediate(config) self.output = FNetOutput(config) def forward(self, hidden_states): self_fourier_outputs = self.fourier(hidden_states) fourier_output = self_fourier_outputs[0] layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, fourier_output ) outputs = (layer_output,) return outputs def feed_forward_chunk(self, fourier_output): intermediate_output = self.intermediate(fourier_output) layer_output = self.output(intermediate_output, fourier_output) return layer_output class FNetEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([FNetLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward(self, hidden_states, output_hidden_states=False, return_dict=True): all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states) else: layer_outputs = layer_module(hidden_states) hidden_states = layer_outputs[0] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None) return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states) # Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->FNet class FNetPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output # Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->FNet class FNetPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class FNetLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = FNetPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) self.bias = self.decoder.bias class FNetOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = FNetLMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores # Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->FNet class FNetOnlyNSPHead(nn.Module): def __init__(self, config): super().__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output): seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score # Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->FNet class FNetPreTrainingHeads(nn.Module): def __init__(self, config): super().__init__() self.predictions = FNetLMPredictionHead(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, sequence_output, pooled_output): prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class FNetPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FNetConfig base_model_prefix = "fnet" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) # NOTE: Original code uses same initialization as weights for biases as well. if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) @dataclass class FNetForPreTrainingOutput(ModelOutput): """ Output type of [`FNetForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. """ loss: Optional[torch.FloatTensor] = None prediction_logits: torch.FloatTensor = None seq_relationship_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None FNET_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`FNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ FNET_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare FNet Model transformer outputting raw hidden-states without any specific head on top.", FNET_START_DOCSTRING, ) class FNetModel(FNetPreTrainedModel): """ The model can behave as an encoder, following the architecture described in [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon. """ def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = FNetEmbeddings(config) self.encoder = FNetEncoder(config) self.pooler = FNetPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutput]: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() batch_size, seq_length = input_shape elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size, seq_length = input_shape else: raise ValueError("You have to specify either input_ids or inputs_embeds") if ( self.config.use_tpu_fourier_optimizations and seq_length <= 4096 and self.config.tpu_short_seq_length != seq_length ): raise ValueError( "The `tpu_short_seq_length` in FNetConfig should be set equal to the sequence length being passed to" " the model when using TPU optimizations." ) device = input_ids.device if input_ids is not None else inputs_embeds.device if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooler_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooler_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooler_output, hidden_states=encoder_outputs.hidden_states, ) @add_start_docstrings( """ FNet Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. """, FNET_START_DOCSTRING, ) class FNetForPreTraining(FNetPreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] def __init__(self, config): super().__init__(config) self.fnet = FNetModel(config) self.cls = FNetPreTrainingHeads(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=FNetForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, next_sentence_label: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, FNetForPreTrainingOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. Returns: Example: ```python >>> from transformers import AutoTokenizer, FNetForPreTraining >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google/fnet-base") >>> model = FNetForPreTraining.from_pretrained("google/fnet-base") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.fnet( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) total_loss = None if labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss if not return_dict: output = (prediction_scores, seq_relationship_score) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return FNetForPreTrainingOutput( loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, ) @add_start_docstrings("""FNet Model with a `language modeling` head on top.""", FNET_START_DOCSTRING) class FNetForMaskedLM(FNetPreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] def __init__(self, config): super().__init__(config) self.fnet = FNetModel(config) self.cls = FNetOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.fnet( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput(loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states) @add_start_docstrings( """FNet Model with a `next sentence prediction (classification)` head on top.""", FNET_START_DOCSTRING, ) class FNetForNextSentencePrediction(FNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.fnet = FNetModel(config) self.cls = FNetOnlyNSPHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple, NextSentencePredictorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring). Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Returns: Example: ```python >>> from transformers import AutoTokenizer, FNetForNextSentencePrediction >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google/fnet-base") >>> model = FNetForNextSentencePrediction.from_pretrained("google/fnet-base") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> logits = outputs.logits >>> assert logits[0, 0] < logits[0, 1] # next sentence was random ```""" if "next_sentence_label" in kwargs: warnings.warn( "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" " `labels` instead.", FutureWarning, ) labels = kwargs.pop("next_sentence_label") return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.fnet( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] seq_relationship_scores = self.cls(pooled_output) next_sentence_loss = None if labels is not None: loss_fct = CrossEntropyLoss() next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) if not return_dict: output = (seq_relationship_scores,) + outputs[2:] return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output return NextSentencePredictorOutput( loss=next_sentence_loss, logits=seq_relationship_scores, hidden_states=outputs.hidden_states, ) @add_start_docstrings( """ FNet Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, FNET_START_DOCSTRING, ) class FNetForSequenceClassification(FNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.fnet = FNetModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.fnet( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states) @add_start_docstrings( """ FNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, FNET_START_DOCSTRING, ) class FNetForMultipleChoice(FNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.fnet = FNetModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.fnet( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states) @add_start_docstrings( """ FNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, FNET_START_DOCSTRING, ) class FNetForTokenClassification(FNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.fnet = FNetModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.fnet( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states) @add_start_docstrings( """ FNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, FNET_START_DOCSTRING, ) class FNetForQuestionAnswering(FNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.fnet = FNetModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.fnet( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states )

transformers/src/transformers/models/fnet/modeling_fnet.py/0

# coding=utf-8 # Copyright 2020-present Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 Funnel model.""" from __future__ import annotations import warnings from dataclasses import dataclass from typing import Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutput, TFMaskedLMOutput, TFMultipleChoiceModelOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFMaskedLanguageModelingLoss, TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFTokenClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_funnel import FunnelConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "FunnelConfig" TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = [ "funnel-transformer/small", # B4-4-4H768 "funnel-transformer/small-base", # B4-4-4H768, no decoder "funnel-transformer/medium", # B6-3x2-3x2H768 "funnel-transformer/medium-base", # B6-3x2-3x2H768, no decoder "funnel-transformer/intermediate", # B6-6-6H768 "funnel-transformer/intermediate-base", # B6-6-6H768, no decoder "funnel-transformer/large", # B8-8-8H1024 "funnel-transformer/large-base", # B8-8-8H1024, no decoder "funnel-transformer/xlarge-base", # B10-10-10H1024 "funnel-transformer/xlarge", # B10-10-10H1024, no decoder ] INF = 1e6 class TFFunnelEmbeddings(keras.layers.Layer): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.config = config self.hidden_size = config.hidden_size self.initializer_std = 1.0 if config.initializer_std is None else config.initializer_std self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout) def build(self, input_shape=None): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.config.vocab_size, self.hidden_size], initializer=get_initializer(initializer_range=self.initializer_std), ) if self.built: return self.built = True if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.d_model]) def call(self, input_ids=None, inputs_embeds=None, training=False): """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) assert not (input_ids is not None and inputs_embeds is not None) if input_ids is not None: check_embeddings_within_bounds(input_ids, self.config.vocab_size) inputs_embeds = tf.gather(self.weight, input_ids) final_embeddings = self.LayerNorm(inputs=inputs_embeds) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings class TFFunnelAttentionStructure: """ Contains helpers for `TFFunnelRelMultiheadAttention `. """ cls_token_type_id: int = 2 def __init__(self, config): self.d_model = config.d_model self.attention_type = config.attention_type self.num_blocks = config.num_blocks self.separate_cls = config.separate_cls self.truncate_seq = config.truncate_seq self.pool_q_only = config.pool_q_only self.pooling_type = config.pooling_type self.sin_dropout = keras.layers.Dropout(config.hidden_dropout) self.cos_dropout = keras.layers.Dropout(config.hidden_dropout) # Track where we are at in terms of pooling from the original input, e.g., by how much the sequence length was # divided. self.pooling_mult = None def init_attention_inputs(self, inputs_embeds, attention_mask=None, token_type_ids=None, training=False): """Returns the attention inputs associated to the inputs of the model.""" # inputs_embeds has shape batch_size x seq_len x d_model # attention_mask and token_type_ids have shape batch_size x seq_len self.pooling_mult = 1 self.seq_len = seq_len = shape_list(inputs_embeds)[1] position_embeds = self.get_position_embeds(seq_len, training=training) token_type_mat = self.token_type_ids_to_mat(token_type_ids) if token_type_ids is not None else None cls_mask = ( tf.pad(tf.ones([seq_len - 1, seq_len - 1], dtype=inputs_embeds.dtype), [[1, 0], [1, 0]]) if self.separate_cls else None ) return (position_embeds, token_type_mat, attention_mask, cls_mask) def token_type_ids_to_mat(self, token_type_ids): """Convert `token_type_ids` to `token_type_mat`.""" token_type_mat = tf.equal(tf.expand_dims(token_type_ids, -1), tf.expand_dims(token_type_ids, -2)) # Treat <cls> as in the same segment as both A & B cls_ids = tf.equal(token_type_ids, tf.constant([self.cls_token_type_id], dtype=token_type_ids.dtype)) cls_mat = tf.logical_or(tf.expand_dims(cls_ids, -1), tf.expand_dims(cls_ids, -2)) return tf.logical_or(cls_mat, token_type_mat) def get_position_embeds(self, seq_len, training=False): """ Create and cache inputs related to relative position encoding. Those are very different depending on whether we are using the factorized or the relative shift attention: For the factorized attention, it returns the matrices (phi, pi, psi, omega) used in the paper, appendix A.2.2, final formula. For the relative shift attention, it returns all possible vectors R used in the paper, appendix A.2.1, final formula. Paper link: https://arxiv.org/abs/2006.03236 """ if self.attention_type == "factorized": # Notations from the paper, appending A.2.2, final formula. # We need to create and return the matrices phi, psi, pi and omega. pos_seq = tf.range(0, seq_len, 1.0) freq_seq = tf.range(0, self.d_model // 2, 1.0) inv_freq = 1 / (10000 ** (freq_seq / (self.d_model // 2))) sinusoid = tf.einsum("i,d->id", pos_seq, inv_freq) sin_embed = tf.sin(sinusoid) sin_embed_d = self.sin_dropout(sin_embed, training=training) cos_embed = tf.cos(sinusoid) cos_embed_d = self.cos_dropout(cos_embed, training=training) # This is different from the formula on the paper... phi = tf.concat([sin_embed_d, sin_embed_d], axis=-1) psi = tf.concat([cos_embed, sin_embed], axis=-1) pi = tf.concat([cos_embed_d, cos_embed_d], axis=-1) omega = tf.concat([-sin_embed, cos_embed], axis=-1) return (phi, pi, psi, omega) else: # Notations from the paper, appending A.2.1, final formula. # We need to create and return all the possible vectors R for all blocks and shifts. freq_seq = tf.range(0, self.d_model // 2, 1.0) inv_freq = 1 / (10000 ** (freq_seq / (self.d_model // 2))) # Maximum relative positions for the first input rel_pos_id = tf.range(-seq_len * 2, seq_len * 2, 1.0) zero_offset = seq_len * tf.constant(2) sinusoid = tf.einsum("i,d->id", rel_pos_id, inv_freq) sin_embed = self.sin_dropout(tf.sin(sinusoid), training=training) cos_embed = self.cos_dropout(tf.cos(sinusoid), training=training) pos_embed = tf.concat([sin_embed, cos_embed], axis=-1) pos = tf.range(0, seq_len) pooled_pos = pos position_embeds_list = [] for block_index in range(0, self.num_blocks): # For each block with block_index > 0, we need two types position embeddings: # - Attention(pooled-q, unpooled-kv) # - Attention(pooled-q, pooled-kv) # For block_index = 0 we only need the second one and leave the first one as None. # First type position_embeds_pooling = tf.fill([1], value=-1.0) if block_index != 0: pooled_pos = self.stride_pool_pos(pos, block_index) # construct rel_pos_id stride = 2 ** (block_index - 1) rel_pos = self.relative_pos(pos, stride, pooled_pos, shift=2) # rel_pos = tf.expand_dims(rel_pos,1) + zero_offset # rel_pos = tf.broadcast_to(rel_pos, (rel_pos.shape[0], self.d_model)) rel_pos = tf.cast(rel_pos, dtype=zero_offset.dtype) rel_pos = rel_pos + zero_offset position_embeds_pooling = tf.gather(pos_embed, rel_pos, axis=0) # Second type pos = pooled_pos stride = 2**block_index rel_pos = self.relative_pos(pos, stride) # rel_pos = tf.expand_dims(rel_pos,1) + zero_offset # rel_pos = tf.broadcast_to(rel_pos, (rel_pos.shape[0], self.d_model)) rel_pos = tf.cast(rel_pos, dtype=zero_offset.dtype) rel_pos = rel_pos + zero_offset tf.debugging.assert_less(rel_pos, tf.shape(pos_embed)[0]) position_embeds_no_pooling = tf.gather(pos_embed, rel_pos, axis=0) position_embeds_list.append([position_embeds_no_pooling, position_embeds_pooling]) return position_embeds_list def stride_pool_pos(self, pos_id, block_index): """ Pool `pos_id` while keeping the cls token separate (if `self.separate_cls=True`). """ if self.separate_cls: # Under separate <cls>, we treat the <cls> as the first token in # the previous block of the 1st real block. Since the 1st real # block always has position 1, the position of the previous block # will be at `1 - 2 ** block_index`. cls_pos = tf.constant([-(2**block_index) + 1], dtype=pos_id.dtype) pooled_pos_id = pos_id[1:-1] if self.truncate_seq else pos_id[1:] return tf.concat([cls_pos, pooled_pos_id[::2]], 0) else: return pos_id[::2] def relative_pos(self, pos, stride, pooled_pos=None, shift=1): """ Build the relative positional vector between `pos` and `pooled_pos`. """ if pooled_pos is None: pooled_pos = pos ref_point = pooled_pos[0] - pos[0] num_remove = shift * shape_list(pooled_pos)[0] max_dist = ref_point + num_remove * stride min_dist = pooled_pos[0] - pos[-1] return tf.range(max_dist, min_dist - 1, -stride) def stride_pool(self, tensor, axis): """ Perform pooling by stride slicing the tensor along the given axis. """ if tensor is None: return None # Do the stride pool recursively if axis is a list or a tuple of ints. if isinstance(axis, (list, tuple)): for ax in axis: tensor = self.stride_pool(tensor, ax) return tensor # Do the stride pool recursively if tensor is a list or tuple of tensors. if isinstance(tensor, (tuple, list)): return type(tensor)(self.stride_pool(x, axis) for x in tensor) # Deal with negative axis axis %= len(shape_list(tensor)) axis_slice = slice(None, -1, 2) if self.separate_cls and self.truncate_seq else slice(None, None, 2) enc_slice = [slice(None)] * axis + [axis_slice] if self.separate_cls: cls_slice = [slice(None)] * axis + [slice(None, 1)] tensor = tf.concat([tensor[cls_slice], tensor], axis) return tensor[enc_slice] def pool_tensor(self, tensor, mode="mean", stride=2): """Apply 1D pooling to a tensor of size [B x T (x H)].""" if tensor is None: return None # Do the pool recursively if tensor is a list or tuple of tensors. if isinstance(tensor, (tuple, list)): return type(tensor)(self.pool_tensor(tensor, mode=mode, stride=stride) for x in tensor) if self.separate_cls: suffix = tensor[:, :-1] if self.truncate_seq else tensor tensor = tf.concat([tensor[:, :1], suffix], axis=1) ndim = len(shape_list(tensor)) if ndim == 2: tensor = tensor[:, :, None] if mode == "mean": tensor = tf.nn.avg_pool1d(tensor, stride, strides=stride, data_format="NWC", padding="SAME") elif mode == "max": tensor = tf.nn.max_pool1d(tensor, stride, strides=stride, data_format="NWC", padding="SAME") elif mode == "min": tensor = -tf.nn.max_pool1d(-tensor, stride, strides=stride, data_format="NWC", padding="SAME") else: raise NotImplementedError("The supported modes are 'mean', 'max' and 'min'.") return tf.squeeze(tensor, 2) if ndim == 2 else tensor def pre_attention_pooling(self, output, attention_inputs): """Pool `output` and the proper parts of `attention_inputs` before the attention layer.""" position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs if self.pool_q_only: if self.attention_type == "factorized": position_embeds = self.stride_pool(position_embeds[:2], 0) + position_embeds[2:] token_type_mat = self.stride_pool(token_type_mat, 1) cls_mask = self.stride_pool(cls_mask, 0) output = self.pool_tensor(output, mode=self.pooling_type) else: self.pooling_mult *= 2 if self.attention_type == "factorized": position_embeds = self.stride_pool(position_embeds, 0) token_type_mat = self.stride_pool(token_type_mat, [1, 2]) cls_mask = self.stride_pool(cls_mask, [1, 2]) attention_mask = self.pool_tensor(attention_mask, mode="min") output = self.pool_tensor(output, mode=self.pooling_type) attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask) return output, attention_inputs def post_attention_pooling(self, attention_inputs): """Pool the proper parts of `attention_inputs` after the attention layer.""" position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs if self.pool_q_only: self.pooling_mult *= 2 if self.attention_type == "factorized": position_embeds = position_embeds[:2] + self.stride_pool(position_embeds[2:], 0) token_type_mat = self.stride_pool(token_type_mat, 2) cls_mask = self.stride_pool(cls_mask, 1) attention_mask = self.pool_tensor(attention_mask, mode="min") attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask) return attention_inputs def _relative_shift_gather(positional_attn, context_len, shift): batch_size, n_head, seq_len, max_rel_len = shape_list(positional_attn) # max_rel_len = 2 * context_len + shift -1 is the numbers of possible relative positions i-j # What's next is the same as doing the following gather in PyTorch, which might be clearer code but less efficient. # idxs = context_len + torch.arange(0, context_len).unsqueeze(0) - torch.arange(0, seq_len).unsqueeze(1) # # matrix of context_len + i-j # return positional_attn.gather(3, idxs.expand([batch_size, n_head, context_len, context_len])) positional_attn = tf.reshape(positional_attn, [batch_size, n_head, max_rel_len, seq_len]) positional_attn = positional_attn[:, :, shift:, :] positional_attn = tf.reshape(positional_attn, [batch_size, n_head, seq_len, max_rel_len - shift]) positional_attn = positional_attn[..., :context_len] return positional_attn class TFFunnelRelMultiheadAttention(keras.layers.Layer): def __init__(self, config, block_index, **kwargs): super().__init__(**kwargs) self.attention_type = config.attention_type self.n_head = n_head = config.n_head self.d_head = d_head = config.d_head self.d_model = d_model = config.d_model self.initializer_range = config.initializer_range self.block_index = block_index self.hidden_dropout = keras.layers.Dropout(config.hidden_dropout) self.attention_dropout = keras.layers.Dropout(config.attention_dropout) initializer = get_initializer(config.initializer_range) self.q_head = keras.layers.Dense( n_head * d_head, use_bias=False, kernel_initializer=initializer, name="q_head" ) self.k_head = keras.layers.Dense(n_head * d_head, kernel_initializer=initializer, name="k_head") self.v_head = keras.layers.Dense(n_head * d_head, kernel_initializer=initializer, name="v_head") self.post_proj = keras.layers.Dense(d_model, kernel_initializer=initializer, name="post_proj") self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") self.scale = 1.0 / (d_head**0.5) def build(self, input_shape=None): n_head, d_head, d_model = self.n_head, self.d_head, self.d_model initializer = get_initializer(self.initializer_range) self.r_w_bias = self.add_weight( shape=(n_head, d_head), initializer=initializer, trainable=True, name="r_w_bias" ) self.r_r_bias = self.add_weight( shape=(n_head, d_head), initializer=initializer, trainable=True, name="r_r_bias" ) self.r_kernel = self.add_weight( shape=(d_model, n_head, d_head), initializer=initializer, trainable=True, name="r_kernel" ) self.r_s_bias = self.add_weight( shape=(n_head, d_head), initializer=initializer, trainable=True, name="r_s_bias" ) self.seg_embed = self.add_weight( shape=(2, n_head, d_head), initializer=initializer, trainable=True, name="seg_embed" ) if self.built: return self.built = True if getattr(self, "q_head", None) is not None: with tf.name_scope(self.q_head.name): self.q_head.build([None, None, d_model]) if getattr(self, "k_head", None) is not None: with tf.name_scope(self.k_head.name): self.k_head.build([None, None, d_model]) if getattr(self, "v_head", None) is not None: with tf.name_scope(self.v_head.name): self.v_head.build([None, None, d_model]) if getattr(self, "post_proj", None) is not None: with tf.name_scope(self.post_proj.name): self.post_proj.build([None, None, n_head * d_head]) if getattr(self, "layer_norm", None) is not None: with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, d_model]) def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None): """Relative attention score for the positional encodings""" # q_head has shape batch_size x sea_len x n_head x d_head if self.attention_type == "factorized": # Notations from the paper, appending A.2.2, final formula (https://arxiv.org/abs/2006.03236) # phi and pi have shape seq_len x d_model, psi and omega have shape context_len x d_model phi, pi, psi, omega = position_embeds # Shape n_head x d_head u = self.r_r_bias * self.scale # Shape d_model x n_head x d_head w_r = self.r_kernel # Shape batch_size x sea_len x n_head x d_model q_r_attention = tf.einsum("binh,dnh->bind", q_head + u, w_r) q_r_attention_1 = q_r_attention * phi[:, None] q_r_attention_2 = q_r_attention * pi[:, None] # Shape batch_size x n_head x seq_len x context_len positional_attn = tf.einsum("bind,jd->bnij", q_r_attention_1, psi) + tf.einsum( "bind,jd->bnij", q_r_attention_2, omega ) else: # Notations from the paper, appending A.2.1, final formula (https://arxiv.org/abs/2006.03236) # Grab the proper positional encoding, shape max_rel_len x d_model if shape_list(q_head)[1] != context_len: shift = 2 r = position_embeds[self.block_index][1] else: shift = 1 r = position_embeds[self.block_index][0] # Shape n_head x d_head v = self.r_r_bias * self.scale # Shape d_model x n_head x d_head w_r = self.r_kernel # Shape max_rel_len x n_head x d_model r_head = tf.einsum("td,dnh->tnh", r, w_r) # Shape batch_size x n_head x seq_len x max_rel_len positional_attn = tf.einsum("binh,tnh->bnit", q_head + v, r_head) # Shape batch_size x n_head x seq_len x context_len positional_attn = _relative_shift_gather(positional_attn, context_len, shift) if cls_mask is not None: positional_attn *= cls_mask return positional_attn def relative_token_type_attention(self, token_type_mat, q_head, cls_mask=None): """Relative attention score for the token_type_ids""" if token_type_mat is None: return 0 batch_size, seq_len, context_len = shape_list(token_type_mat) # q_head has shape batch_size x seq_len x n_head x d_head # Shape n_head x d_head r_s_bias = self.r_s_bias * self.scale # Shape batch_size x n_head x seq_len x 2 token_type_bias = tf.einsum("bind,snd->bnis", q_head + r_s_bias, self.seg_embed) # Shape batch_size x n_head x seq_len x context_len token_type_mat = tf.tile(token_type_mat[:, None], [1, shape_list(q_head)[2], 1, 1]) # token_type_mat = tf.broadcast_to(token_type_mat[:, None], new_shape) # Shapes batch_size x n_head x seq_len diff_token_type, same_token_type = tf.split(token_type_bias, 2, axis=-1) # Shape batch_size x n_head x seq_len x context_len token_type_attn = tf.where( token_type_mat, tf.tile(same_token_type, [1, 1, 1, context_len]), tf.tile(diff_token_type, [1, 1, 1, context_len]), ) if cls_mask is not None: token_type_attn *= cls_mask return token_type_attn def call(self, query, key, value, attention_inputs, output_attentions=False, training=False): # query has shape batch_size x seq_len x d_model # key and value have shapes batch_size x context_len x d_model position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs batch_size, seq_len, _ = shape_list(query) context_len = shape_list(key)[1] n_head, d_head = self.n_head, self.d_head # Shape batch_size x seq_len x n_head x d_head q_head = tf.reshape(self.q_head(query), [batch_size, seq_len, n_head, d_head]) # Shapes batch_size x context_len x n_head x d_head k_head = tf.reshape(self.k_head(key), [batch_size, context_len, n_head, d_head]) v_head = tf.reshape(self.v_head(value), [batch_size, context_len, n_head, d_head]) q_head = q_head * self.scale # Shape n_head x d_head r_w_bias = self.r_w_bias * self.scale # Shapes batch_size x n_head x seq_len x context_len content_score = tf.einsum("bind,bjnd->bnij", q_head + r_w_bias, k_head) positional_attn = self.relative_positional_attention(position_embeds, q_head, context_len, cls_mask) token_type_attn = self.relative_token_type_attention(token_type_mat, q_head, cls_mask) # merge attention scores attn_score = content_score + positional_attn + token_type_attn # perform masking if attention_mask is not None: attention_mask = tf.cast(attention_mask, dtype=attn_score.dtype) attn_score = attn_score - (INF * (1 - attention_mask[:, None, None])) # attention probability attn_prob = stable_softmax(attn_score, axis=-1) attn_prob = self.attention_dropout(attn_prob, training=training) # attention output, shape batch_size x seq_len x n_head x d_head attn_vec = tf.einsum("bnij,bjnd->bind", attn_prob, v_head) # Shape shape batch_size x seq_len x d_model attn_out = self.post_proj(tf.reshape(attn_vec, [batch_size, seq_len, n_head * d_head])) attn_out = self.hidden_dropout(attn_out, training=training) output = self.layer_norm(query + attn_out) return (output, attn_prob) if output_attentions else (output,) class TFFunnelPositionwiseFFN(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) initializer = get_initializer(config.initializer_range) self.linear_1 = keras.layers.Dense(config.d_inner, kernel_initializer=initializer, name="linear_1") self.activation_function = get_tf_activation(config.hidden_act) self.activation_dropout = keras.layers.Dropout(config.activation_dropout) self.linear_2 = keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="linear_2") self.dropout = keras.layers.Dropout(config.hidden_dropout) self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") self.config = config def call(self, hidden, training=False): h = self.linear_1(hidden) h = self.activation_function(h) h = self.activation_dropout(h, training=training) h = self.linear_2(h) h = self.dropout(h, training=training) return self.layer_norm(hidden + h) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "linear_1", None) is not None: with tf.name_scope(self.linear_1.name): self.linear_1.build([None, None, self.config.d_model]) if getattr(self, "linear_2", None) is not None: with tf.name_scope(self.linear_2.name): self.linear_2.build([None, None, self.config.d_inner]) if getattr(self, "layer_norm", None) is not None: with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.config.d_model]) class TFFunnelLayer(keras.layers.Layer): def __init__(self, config, block_index, **kwargs): super().__init__(**kwargs) self.attention = TFFunnelRelMultiheadAttention(config, block_index, name="attention") self.ffn = TFFunnelPositionwiseFFN(config, name="ffn") def call(self, query, key, value, attention_inputs, output_attentions=False, training=False): attn = self.attention( query, key, value, attention_inputs, output_attentions=output_attentions, training=training ) output = self.ffn(attn[0], training=training) return (output, attn[1]) if output_attentions else (output,) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "ffn", None) is not None: with tf.name_scope(self.ffn.name): self.ffn.build(None) class TFFunnelEncoder(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.separate_cls = config.separate_cls self.pool_q_only = config.pool_q_only self.block_repeats = config.block_repeats self.attention_structure = TFFunnelAttentionStructure(config) self.blocks = [ [TFFunnelLayer(config, block_index, name=f"blocks_._{block_index}_._{i}") for i in range(block_size)] for block_index, block_size in enumerate(config.block_sizes) ] def call( self, inputs_embeds, attention_mask=None, token_type_ids=None, output_attentions=False, output_hidden_states=False, return_dict=True, training=False, ): # The pooling is not implemented on long tensors, so we convert this mask. # attention_mask = tf.cast(attention_mask, inputs_embeds.dtype) attention_inputs = self.attention_structure.init_attention_inputs( inputs_embeds, attention_mask=attention_mask, token_type_ids=token_type_ids, training=training, ) hidden = inputs_embeds all_hidden_states = (inputs_embeds,) if output_hidden_states else None all_attentions = () if output_attentions else None for block_index, block in enumerate(self.blocks): pooling_flag = shape_list(hidden)[1] > (2 if self.separate_cls else 1) pooling_flag = pooling_flag and block_index > 0 pooled_hidden = tf.zeros(shape_list(hidden)) if pooling_flag: pooled_hidden, attention_inputs = self.attention_structure.pre_attention_pooling( hidden, attention_inputs ) for layer_index, layer in enumerate(block): for repeat_index in range(self.block_repeats[block_index]): do_pooling = (repeat_index == 0) and (layer_index == 0) and pooling_flag if do_pooling: query = pooled_hidden key = value = hidden if self.pool_q_only else pooled_hidden else: query = key = value = hidden layer_output = layer( query, key, value, attention_inputs, output_attentions=output_attentions, training=training ) hidden = layer_output[0] if do_pooling: attention_inputs = self.attention_structure.post_attention_pooling(attention_inputs) if output_attentions: all_attentions = all_attentions + layer_output[1:] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden,) if not return_dict: return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None) return TFBaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions) def build(self, input_shape=None): if self.built: return self.built = True for block in self.blocks: for layer in block: with tf.name_scope(layer.name): layer.build(None) def upsample(x, stride, target_len, separate_cls=True, truncate_seq=False): """ Upsample tensor `x` to match `target_len` by repeating the tokens `stride` time on the sequence length dimension. """ if stride == 1: return x if separate_cls: cls = x[:, :1] x = x[:, 1:] output = tf.repeat(x, repeats=stride, axis=1) if separate_cls: if truncate_seq: output = tf.pad(output, [[0, 0], [0, stride - 1], [0, 0]]) output = output[:, : target_len - 1] output = tf.concat([cls, output], axis=1) else: output = output[:, :target_len] return output class TFFunnelDecoder(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.separate_cls = config.separate_cls self.truncate_seq = config.truncate_seq self.stride = 2 ** (len(config.block_sizes) - 1) self.attention_structure = TFFunnelAttentionStructure(config) self.layers = [TFFunnelLayer(config, 0, name=f"layers_._{i}") for i in range(config.num_decoder_layers)] def call( self, final_hidden, first_block_hidden, attention_mask=None, token_type_ids=None, output_attentions=False, output_hidden_states=False, return_dict=True, training=False, ): upsampled_hidden = upsample( final_hidden, stride=self.stride, target_len=shape_list(first_block_hidden)[1], separate_cls=self.separate_cls, truncate_seq=self.truncate_seq, ) hidden = upsampled_hidden + first_block_hidden all_hidden_states = (hidden,) if output_hidden_states else None all_attentions = () if output_attentions else None attention_inputs = self.attention_structure.init_attention_inputs( hidden, attention_mask=attention_mask, token_type_ids=token_type_ids, training=training, ) for layer in self.layers: layer_output = layer( hidden, hidden, hidden, attention_inputs, output_attentions=output_attentions, training=training ) hidden = layer_output[0] if output_attentions: all_attentions = all_attentions + layer_output[1:] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden,) if not return_dict: return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None) return TFBaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layers", None) is not None: for layer in self.layers: with tf.name_scope(layer.name): layer.build(None) @keras_serializable class TFFunnelBaseLayer(keras.layers.Layer): """Base model without decoder""" config_class = FunnelConfig def __init__(self, config, **kwargs): super().__init__(**kwargs) self.config = config self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.embeddings = TFFunnelEmbeddings(config, name="embeddings") self.encoder = TFFunnelEncoder(config, name="encoder") def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, value): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models @unpack_inputs def call( self, input_ids=None, attention_mask=None, token_type_ids=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if attention_mask is None: attention_mask = tf.fill(input_shape, 1) if token_type_ids is None: token_type_ids = tf.fill(input_shape, 0) if inputs_embeds is None: inputs_embeds = self.embeddings(input_ids, training=training) encoder_outputs = self.encoder( inputs_embeds, attention_mask=attention_mask, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return encoder_outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) @keras_serializable class TFFunnelMainLayer(keras.layers.Layer): """Base model with decoder""" config_class = FunnelConfig def __init__(self, config, **kwargs): super().__init__(**kwargs) self.config = config self.block_sizes = config.block_sizes self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.embeddings = TFFunnelEmbeddings(config, name="embeddings") self.encoder = TFFunnelEncoder(config, name="encoder") self.decoder = TFFunnelDecoder(config, name="decoder") def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, value): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models @unpack_inputs def call( self, input_ids=None, attention_mask=None, token_type_ids=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if attention_mask is None: attention_mask = tf.fill(input_shape, 1) if token_type_ids is None: token_type_ids = tf.fill(input_shape, 0) if inputs_embeds is None: inputs_embeds = self.embeddings(input_ids, training=training) encoder_outputs = self.encoder( inputs_embeds, attention_mask=attention_mask, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=True, return_dict=return_dict, training=training, ) decoder_outputs = self.decoder( final_hidden=encoder_outputs[0], first_block_hidden=encoder_outputs[1][self.block_sizes[0]], attention_mask=attention_mask, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) if not return_dict: idx = 0 outputs = (decoder_outputs[0],) if output_hidden_states: idx += 1 outputs = outputs + (encoder_outputs[1] + decoder_outputs[idx],) if output_attentions: idx += 1 outputs = outputs + (encoder_outputs[2] + decoder_outputs[idx],) return outputs return TFBaseModelOutput( last_hidden_state=decoder_outputs[0], hidden_states=(encoder_outputs.hidden_states + decoder_outputs.hidden_states) if output_hidden_states else None, attentions=(encoder_outputs.attentions + decoder_outputs.attentions) if output_attentions else None, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "decoder", None) is not None: with tf.name_scope(self.decoder.name): self.decoder.build(None) class TFFunnelDiscriminatorPredictions(keras.layers.Layer): """Prediction module for the discriminator, made up of two dense layers.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) initializer = get_initializer(config.initializer_range) self.dense = keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="dense") self.activation_function = get_tf_activation(config.hidden_act) self.dense_prediction = keras.layers.Dense(1, kernel_initializer=initializer, name="dense_prediction") self.config = config def call(self, discriminator_hidden_states): hidden_states = self.dense(discriminator_hidden_states) hidden_states = self.activation_function(hidden_states) logits = tf.squeeze(self.dense_prediction(hidden_states)) return logits def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.d_model]) if getattr(self, "dense_prediction", None) is not None: with tf.name_scope(self.dense_prediction.name): self.dense_prediction.build([None, None, self.config.d_model]) class TFFunnelMaskedLMHead(keras.layers.Layer): def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.config = config self.hidden_size = config.hidden_size self.input_embeddings = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def get_output_embeddings(self): return self.input_embeddings def set_output_embeddings(self, value): self.input_embeddings.weight = value self.input_embeddings.vocab_size = shape_list(value)[0] def get_bias(self): return {"bias": self.bias} def set_bias(self, value): self.bias = value["bias"] self.config.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states, training=False): seq_length = shape_list(tensor=hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states class TFFunnelClassificationHead(keras.layers.Layer): def __init__(self, config, n_labels, **kwargs): super().__init__(**kwargs) initializer = get_initializer(config.initializer_range) self.linear_hidden = keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="linear_hidden") self.dropout = keras.layers.Dropout(config.hidden_dropout) self.linear_out = keras.layers.Dense(n_labels, kernel_initializer=initializer, name="linear_out") self.config = config def call(self, hidden, training=False): hidden = self.linear_hidden(hidden) hidden = keras.activations.tanh(hidden) hidden = self.dropout(hidden, training=training) return self.linear_out(hidden) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "linear_hidden", None) is not None: with tf.name_scope(self.linear_hidden.name): self.linear_hidden.build([None, None, self.config.d_model]) if getattr(self, "linear_out", None) is not None: with tf.name_scope(self.linear_out.name): self.linear_out.build([None, None, self.config.d_model]) class TFFunnelPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FunnelConfig base_model_prefix = "funnel" @property def dummy_inputs(self): # Funnel misbehaves with very small inputs, so we override and make them a bit bigger return {"input_ids": tf.ones((1, 3), dtype=tf.int32)} @dataclass class TFFunnelForPreTrainingOutput(ModelOutput): """ Output type of [`FunnelForPreTraining`]. Args: logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Prediction scores of the head (scores for each token before SoftMax). hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None FUNNEL_START_DOCSTRING = r""" The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le. This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`XxxConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ FUNNEL_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( """ The base Funnel Transformer Model transformer outputting raw hidden-states without upsampling head (also called decoder) or any task-specific head on top. """, FUNNEL_START_DOCSTRING, ) class TFFunnelBaseModel(TFFunnelPreTrainedModel): def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.funnel = TFFunnelBaseLayer(config, name="funnel") @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="funnel-transformer/small-base", output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC, ) @unpack_inputs def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFBaseModelOutput]: return self.funnel( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) def serving_output(self, output): # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of # different dimensions return TFBaseModelOutput( last_hidden_state=output.last_hidden_state, hidden_states=output.hidden_states, attentions=output.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "funnel", None) is not None: with tf.name_scope(self.funnel.name): self.funnel.build(None) @add_start_docstrings( "The bare Funnel Transformer Model transformer outputting raw hidden-states without any specific head on top.", FUNNEL_START_DOCSTRING, ) class TFFunnelModel(TFFunnelPreTrainedModel): def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.funnel = TFFunnelMainLayer(config, name="funnel") @unpack_inputs @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="funnel-transformer/small", output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFBaseModelOutput]: return self.funnel( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) def serving_output(self, output): # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of # different dimensions return TFBaseModelOutput( last_hidden_state=output.last_hidden_state, hidden_states=output.hidden_states, attentions=output.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "funnel", None) is not None: with tf.name_scope(self.funnel.name): self.funnel.build(None) @add_start_docstrings( """ Funnel model with a binary classification head on top as used during pretraining for identifying generated tokens. """, FUNNEL_START_DOCSTRING, ) class TFFunnelForPreTraining(TFFunnelPreTrainedModel): def __init__(self, config: FunnelConfig, **kwargs) -> None: super().__init__(config, **kwargs) self.funnel = TFFunnelMainLayer(config, name="funnel") self.discriminator_predictions = TFFunnelDiscriminatorPredictions(config, name="discriminator_predictions") @unpack_inputs @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFFunnelForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, **kwargs, ) -> Union[Tuple[tf.Tensor], TFFunnelForPreTrainingOutput]: r""" Returns: Examples: ```python >>> from transformers import AutoTokenizer, TFFunnelForPreTraining >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/small") >>> model = TFFunnelForPreTraining.from_pretrained("funnel-transformer/small") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> logits = model(inputs).logits ```""" discriminator_hidden_states = self.funnel( input_ids, attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) discriminator_sequence_output = discriminator_hidden_states[0] logits = self.discriminator_predictions(discriminator_sequence_output) if not return_dict: return (logits,) + discriminator_hidden_states[1:] return TFFunnelForPreTrainingOutput( logits=logits, hidden_states=discriminator_hidden_states.hidden_states, attentions=discriminator_hidden_states.attentions, ) def serving_output(self, output): # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of # different dimensions return TFFunnelForPreTrainingOutput( logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "funnel", None) is not None: with tf.name_scope(self.funnel.name): self.funnel.build(None) if getattr(self, "discriminator_predictions", None) is not None: with tf.name_scope(self.discriminator_predictions.name): self.discriminator_predictions.build(None) @add_start_docstrings("""Funnel Model with a `language modeling` head on top.""", FUNNEL_START_DOCSTRING) class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss): def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.funnel = TFFunnelMainLayer(config, name="funnel") self.lm_head = TFFunnelMaskedLMHead(config, self.funnel.embeddings, name="lm_head") def get_lm_head(self) -> TFFunnelMaskedLMHead: return self.lm_head def get_prefix_bias_name(self) -> str: warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name @unpack_inputs @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="funnel-transformer/small", output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFMaskedLMOutput]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ outputs = self.funnel( input_ids, attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output, training=training) loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores) if not return_dict: output = (prediction_scores,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput: # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of # different dimensions return TFMaskedLMOutput(logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "funnel", None) is not None: with tf.name_scope(self.funnel.name): self.funnel.build(None) if getattr(self, "lm_head", None) is not None: with tf.name_scope(self.lm_head.name): self.lm_head.build(None) @add_start_docstrings( """ Funnel Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, FUNNEL_START_DOCSTRING, ) class TFFunnelForSequenceClassification(TFFunnelPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.funnel = TFFunnelBaseLayer(config, name="funnel") self.classifier = TFFunnelClassificationHead(config, config.num_labels, name="classifier") @unpack_inputs @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="funnel-transformer/small-base", output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFSequenceClassifierOutput]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ outputs = self.funnel( input_ids, attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) last_hidden_state = outputs[0] pooled_output = last_hidden_state[:, 0] logits = self.classifier(pooled_output, training=training) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput: # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of # different dimensions return TFSequenceClassifierOutput( logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "funnel", None) is not None: with tf.name_scope(self.funnel.name): self.funnel.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build(None) @add_start_docstrings( """ Funnel Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, FUNNEL_START_DOCSTRING, ) class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss): def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.funnel = TFFunnelBaseLayer(config, name="funnel") self.classifier = TFFunnelClassificationHead(config, 1, name="classifier") @property def dummy_inputs(self): return {"input_ids": tf.ones((3, 3, 4), dtype=tf.int32)} @unpack_inputs @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint="funnel-transformer/small-base", output_type=TFMultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFMultipleChoiceModelOutput]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ if input_ids is not None: num_choices = shape_list(input_ids)[1] seq_length = shape_list(input_ids)[2] else: num_choices = shape_list(inputs_embeds)[1] seq_length = shape_list(inputs_embeds)[2] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None flat_inputs_embeds = ( tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3])) if inputs_embeds is not None else None ) outputs = self.funnel( flat_input_ids, attention_mask=flat_attention_mask, token_type_ids=flat_token_type_ids, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) last_hidden_state = outputs[0] pooled_output = last_hidden_state[:, 0] logits = self.classifier(pooled_output, training=training) reshaped_logits = tf.reshape(logits, (-1, num_choices)) loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits) if not return_dict: output = (reshaped_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFMultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput: # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of # different dimensions return TFMultipleChoiceModelOutput( logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "funnel", None) is not None: with tf.name_scope(self.funnel.name): self.funnel.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build(None) @add_start_docstrings( """ Funnel Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, FUNNEL_START_DOCSTRING, ) class TFFunnelForTokenClassification(TFFunnelPreTrainedModel, TFTokenClassificationLoss): def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.funnel = TFFunnelMainLayer(config, name="funnel") self.dropout = keras.layers.Dropout(config.hidden_dropout) self.classifier = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="funnel-transformer/small", output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFTokenClassifierOutput]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ outputs = self.funnel( input_ids, attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output, training=training) logits = self.classifier(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput: # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of # different dimensions return TFTokenClassifierOutput( logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "funnel", None) is not None: with tf.name_scope(self.funnel.name): self.funnel.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ Funnel Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, FUNNEL_START_DOCSTRING, ) class TFFunnelForQuestionAnswering(TFFunnelPreTrainedModel, TFQuestionAnsweringLoss): def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.funnel = TFFunnelMainLayer(config, name="funnel") self.qa_outputs = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="funnel-transformer/small", output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFQuestionAnsweringModelOutput]: r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ outputs = self.funnel( input_ids, attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = tf.split(logits, 2, axis=-1) start_logits = tf.squeeze(start_logits, axis=-1) end_logits = tf.squeeze(end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions, "end_position": end_positions} loss = self.hf_compute_loss(labels, (start_logits, end_logits)) if not return_dict: output = (start_logits, end_logits) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput: # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of # different dimensions return TFQuestionAnsweringModelOutput( start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=output.hidden_states, attentions=output.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "funnel", None) is not None: with tf.name_scope(self.funnel.name): self.funnel.build(None) if getattr(self, "qa_outputs", None) is not None: with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])

transformers/src/transformers/models/funnel/modeling_tf_funnel.py/0

# coding=utf-8 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch OpenAI GPT-2 model.""" import math import os import warnings from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.cuda.amp import autocast from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel, SequenceSummary from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ...utils.model_parallel_utils import assert_device_map, get_device_map from .configuration_gpt2 import GPT2Config logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "openai-community/gpt2" _CONFIG_FOR_DOC = "GPT2Config" GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "openai-community/gpt2", "openai-community/gpt2-medium", "openai-community/gpt2-large", "openai-community/gpt2-xl", "distilbert/distilgpt2", # See all GPT-2 models at https://huggingface.co/models?filter=gpt2 ] def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path): """Load tf checkpoints in a pytorch model""" try: import re import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(gpt2_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array.squeeze()) for name, array in zip(names, arrays): name = name[6:] # skip "model/" name = name.split("/") pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+\d+", m_name): scope_names = re.split(r"(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "w" or scope_names[0] == "g": pointer = getattr(pointer, "weight") elif scope_names[0] == "b": pointer = getattr(pointer, "bias") elif scope_names[0] == "wpe" or scope_names[0] == "wte": pointer = getattr(pointer, scope_names[0]) pointer = getattr(pointer, "weight") else: pointer = getattr(pointer, scope_names[0]) if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] try: if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") except ValueError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class GPT2Attention(nn.Module): def __init__(self, config, is_cross_attention=False, layer_idx=None): super().__init__() max_positions = config.max_position_embeddings self.register_buffer( "bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( 1, 1, max_positions, max_positions ), persistent=False, ) self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False) self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads self.split_size = self.embed_dim if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale_attn_weights = config.scale_attn_weights self.is_cross_attention = is_cross_attention # Layer-wise attention scaling, reordering, and upcasting self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx self.layer_idx = layer_idx self.reorder_and_upcast_attn = config.reorder_and_upcast_attn if self.is_cross_attention: self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim) self.q_attn = Conv1D(self.embed_dim, self.embed_dim) else: self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim) self.c_proj = Conv1D(self.embed_dim, self.embed_dim) self.attn_dropout = nn.Dropout(config.attn_pdrop) self.resid_dropout = nn.Dropout(config.resid_pdrop) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads) index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]) # Prune conv1d layers self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1) self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0) # Update hyper params self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads)) self.num_heads = self.num_heads - len(heads) self.pruned_heads = self.pruned_heads.union(heads) def _attn(self, query, key, value, attention_mask=None, head_mask=None): attn_weights = torch.matmul(query, key.transpose(-1, -2)) if self.scale_attn_weights: attn_weights = attn_weights / torch.full( [], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device ) # Layer-wise attention scaling if self.scale_attn_by_inverse_layer_idx: attn_weights = attn_weights / float(self.layer_idx + 1) if not self.is_cross_attention: # if only "normal" attention layer implements causal mask query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] mask_value = torch.finfo(attn_weights.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.full([], mask_value, dtype=attn_weights.dtype, device=attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value) if attention_mask is not None: # Apply the attention mask attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise attn_weights = attn_weights.type(value.dtype) attn_weights = self.attn_dropout(attn_weights) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None): # Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM) bsz, num_heads, q_seq_len, dk = query.size() _, _, k_seq_len, _ = key.size() # Preallocate attn_weights for `baddbmm` attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device) # Compute Scale Factor scale_factor = 1.0 if self.scale_attn_weights: scale_factor /= float(value.size(-1)) ** 0.5 if self.scale_attn_by_inverse_layer_idx: scale_factor /= float(self.layer_idx + 1) # Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk)) with autocast(enabled=False): q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len) attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor) attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) if not self.is_cross_attention: # if only "normal" attention layer implements causal mask query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] mask_value = torch.finfo(attn_weights.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights, mask_value) if attention_mask is not None: # Apply the attention mask attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise if attn_weights.dtype != torch.float32: raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32") attn_weights = attn_weights.type(value.dtype) attn_weights = self.attn_dropout(attn_weights) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def _split_heads(self, tensor, num_heads, attn_head_size): """ Splits hidden_size dim into attn_head_size and num_heads """ new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) tensor = tensor.view(new_shape) return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) def _merge_heads(self, tensor, num_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden_size """ tensor = tensor.permute(0, 2, 1, 3).contiguous() new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) return tensor.view(new_shape) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: if encoder_hidden_states is not None: if not hasattr(self, "q_attn"): raise ValueError( "If class is used as cross attention, the weights `q_attn` have to be defined. " "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." ) query = self.q_attn(hidden_states) key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) attention_mask = encoder_attention_mask else: query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: past_key, past_value = layer_past key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) if use_cache is True: present = (key, value) else: present = None if self.reorder_and_upcast_attn: attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask) else: attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) attn_output = self.c_proj(attn_output) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights,) return outputs # a, present, (attentions) class GPT2MLP(nn.Module): def __init__(self, intermediate_size, config): super().__init__() embed_dim = config.hidden_size self.c_fc = Conv1D(intermediate_size, embed_dim) self.c_proj = Conv1D(embed_dim, intermediate_size) self.act = ACT2FN[config.activation_function] self.dropout = nn.Dropout(config.resid_pdrop) def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor: hidden_states = self.c_fc(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.c_proj(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class GPT2Block(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() hidden_size = config.hidden_size inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.attn = GPT2Attention(config, layer_idx=layer_idx) self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) if config.add_cross_attention: self.crossattention = GPT2Attention(config, is_cross_attention=True, layer_idx=layer_idx) self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.mlp = GPT2MLP(inner_dim, config) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_outputs = self.attn( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] # residual connection hidden_states = attn_output + residual if encoder_hidden_states is not None: # add one self-attention block for cross-attention if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with " "cross-attention layers by setting `config.add_cross_attention=True`" ) residual = hidden_states hidden_states = self.ln_cross_attn(hidden_states) cross_attn_outputs = self.crossattention( hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, ) attn_output = cross_attn_outputs[0] # residual connection hidden_states = residual + attn_output outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights residual = hidden_states hidden_states = self.ln_2(hidden_states) feed_forward_hidden_states = self.mlp(hidden_states) # residual connection hidden_states = residual + feed_forward_hidden_states if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs # hidden_states, present, (attentions, cross_attentions) class GPT2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPT2Config load_tf_weights = load_tf_weights_in_gpt2 base_model_prefix = "transformer" is_parallelizable = True supports_gradient_checkpointing = True _no_split_modules = ["GPT2Block"] _skip_keys_device_placement = "past_key_values" def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (nn.Linear, Conv1D)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. # > -- GPT-2 :: https://openai.com/blog/better-language-models/ # # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py for name, p in module.named_parameters(): if name == "c_proj.weight": # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))) @dataclass class GPT2DoubleHeadsModelOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss. mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mc_labels` is provided): Multiple choice classification loss. logits (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). mc_logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): Prediction scores of the multiple choice classification head (scores for each choice before SoftMax). past_key_values (`Tuple[Tuple[torch.Tensor]]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of length `config.n_layers`, containing tuples of tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. GPT2Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None mc_loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None mc_logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None GPT2_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`GPT2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ GPT2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`): Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have their past given to this model should not be passed as `input_ids` as they have already been computed. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for `past_key_values`. In other words, the `attention_mask` always has to have the length: `len(past_key_values) + len(input_ids)` [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see `past_key_values`). use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ PARALLELIZE_DOCSTRING = r""" This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks across all devices. Args: device_map (`Dict[int, list]`, optional, defaults to None): A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always automatically mapped to the first device (for esoteric reasons). That means that the first device should have fewer attention modules mapped to it than other devices. For reference, the gpt2 models have the following number of attention modules: - openai-community/gpt2: 12 - openai-community/gpt2-medium: 24 - openai-community/gpt2-large: 36 - openai-community/gpt2-xl: 48 Example: ```python # Here is an example of a device map on a machine with 4 GPUs using gpt2-xl, which has a total of 48 attention modules: model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2-xl") device_map = { 0: [0, 1, 2, 3, 4, 5, 6, 7, 8], 1: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], 2: [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], 3: [35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47], } model.parallelize(device_map) ``` """ DEPARALLELIZE_DOCSTRING = r""" Moves the model to cpu from a model parallel state. Example: ```python # On a 4 GPU machine with openai-community/gpt2-large: model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2-large") device_map = { 0: [0, 1, 2, 3, 4, 5, 6, 7], 1: [8, 9, 10, 11, 12, 13, 14, 15], 2: [16, 17, 18, 19, 20, 21, 22, 23], 3: [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35], } model.parallelize(device_map) # Splits the model across several devices model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache() ``` """ @add_start_docstrings( "The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.", GPT2_START_DOCSTRING, ) class GPT2Model(GPT2PreTrainedModel): def __init__(self, config): super().__init__(config) self.embed_dim = config.hidden_size self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) self.drop = nn.Dropout(config.embd_pdrop) self.h = nn.ModuleList([GPT2Block(config, layer_idx=i) for i in range(config.num_hidden_layers)]) self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): # Check validity of device_map warnings.warn( "`GPT2Model.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your" " model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own" " `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1," " ...}", FutureWarning, ) self.device_map = ( get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.h)) self.model_parallel = True self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys())) self.last_device = "cuda:" + str(max(self.device_map.keys())) self.wte = self.wte.to(self.first_device) self.wpe = self.wpe.to(self.first_device) # Load onto devices for k, v in self.device_map.items(): for block in v: cuda_device = "cuda:" + str(k) self.h[block] = self.h[block].to(cuda_device) # ln_f to last self.ln_f = self.ln_f.to(self.last_device) @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): warnings.warn( "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.", FutureWarning, ) self.model_parallel = False self.device_map = None self.first_device = "cpu" self.last_device = "cpu" self.wte = self.wte.to("cpu") self.wpe = self.wpe.to("cpu") for index in range(len(self.h)): self.h[index] = self.h[index].to("cpu") self.ln_f = self.ln_f.to("cpu") torch.cuda.empty_cache() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} """ for layer, heads in heads_to_prune.items(): self.h[layer].attn.prune_heads(heads) @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) batch_size = input_ids.shape[0] elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size = inputs_embeds.shape[0] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, input_shape[-1]) if past_key_values is None: past_length = 0 past_key_values = tuple([None] * len(self.h)) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) position_ids = position_ids.unsqueeze(0) # GPT2Attention mask. if attention_mask is not None: if batch_size <= 0: raise ValueError("batch_size has to be defined and > 0") attention_mask = attention_mask.view(batch_size, -1) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask = attention_mask[:, None, None, :] # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.add_cross_attention and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # head_mask has shape n_layer x batch x n_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.n_layer) if inputs_embeds is None: inputs_embeds = self.wte(input_ids) position_embeds = self.wpe(position_ids) hidden_states = inputs_embeds + position_embeds if token_type_ids is not None: token_type_embeds = self.wte(token_type_ids) hidden_states = hidden_states + token_type_embeds hidden_states = self.drop(hidden_states) output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): # Model parallel if self.model_parallel: torch.cuda.set_device(hidden_states.device) # Ensure layer_past is on same device as hidden_states (might not be correct) if layer_past is not None: layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) # Ensure that attention_mask is always on the same device as hidden_states if attention_mask is not None: attention_mask = attention_mask.to(hidden_states.device) if isinstance(head_mask, torch.Tensor): head_mask = head_mask.to(hidden_states.device) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func( block.__call__, hidden_states, None, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, ) else: outputs = block( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (outputs[1],) if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) # Model Parallel: If it's the last layer for that device, put things on the next device if self.model_parallel: for k, v in self.device_map.items(): if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1)) hidden_states = self.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) @add_start_docstrings( """ The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, GPT2_START_DOCSTRING, ) class GPT2LMHeadModel(GPT2PreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = GPT2Model(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): warnings.warn( "`GPT2LMHeadModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load" " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own" " `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':" " 0, 'transformer.h.1': 1, ...}", FutureWarning, ) self.device_map = ( get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.transformer.h)) self.transformer.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.transformer.first_device) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): warnings.warn( "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.", FutureWarning, ) self.transformer.deparallelize() self.transformer = self.transformer.to("cpu") self.lm_head = self.lm_head.to("cpu") self.model_parallel = False torch.cuda.empty_cache() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # Omit tokens covered by past_key_values if past_key_values: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :] attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] else: position_ids = None # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } ) return model_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.transformer.first_device) hidden_states = hidden_states.to(self.lm_head.weight.device) lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, cross_attentions=transformer_outputs.cross_attentions, ) @staticmethod def _reorder_cache( past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor ) -> Tuple[Tuple[torch.Tensor]]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. """ return tuple( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past_key_values ) @add_start_docstrings( """ The GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the input embeddings, the classification head takes as input the input of a specified classification token index in the input sequence). """, GPT2_START_DOCSTRING, ) class GPT2DoubleHeadsModel(GPT2PreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) config.num_labels = 1 self.transformer = GPT2Model(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) self.multiple_choice_head = SequenceSummary(config) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): warnings.warn( "`GPT2DoubleHeadsModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should" " load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your" " own `device_map` but it needs to be a dictionary module_name to device, so for instance" " {'transformer.h.0': 0, 'transformer.h.1': 1, ...}", FutureWarning, ) self.device_map = ( get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.transformer.h)) self.transformer.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.transformer.first_device) self.multiple_choice_head = self.multiple_choice_head.to(self.transformer.first_device) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): warnings.warn( "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.", FutureWarning, ) self.transformer.deparallelize() self.transformer = self.transformer.to("cpu") self.lm_head = self.lm_head.to("cpu") self.multiple_choice_head = self.multiple_choice_head.to("cpu") self.model_parallel = False torch.cuda.empty_cache() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # Omit tokens covered by past_key_values if past_key_values: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :] attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] else: position_ids = None return { "input_ids": input_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=GPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, mc_token_ids: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, mc_labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple, GPT2DoubleHeadsModelOutput]: r""" mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input): Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) - 1]`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids`. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]` mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above) Return: Example: ```python >>> import torch >>> from transformers import AutoTokenizer, GPT2DoubleHeadsModel >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2") >>> model = GPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2") >>> # Add a [CLS] to the vocabulary (we should train it also!) >>> num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"}) >>> # Update the model embeddings with the new vocabulary size >>> embedding_layer = model.resize_token_embeddings(len(tokenizer)) >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"] >>> encoded_choices = [tokenizer.encode(s) for s in choices] >>> cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices] >>> input_ids = torch.tensor(encoded_choices).unsqueeze(0) # Batch size: 1, number of choices: 2 >>> mc_token_ids = torch.tensor([cls_token_location]) # Batch size: 1 >>> outputs = model(input_ids, mc_token_ids=mc_token_ids) >>> lm_logits = outputs.logits >>> mc_logits = outputs.mc_logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.transformer.first_device) hidden_states = hidden_states.to(self.lm_head.weight.device) lm_logits = self.lm_head(hidden_states) mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1) mc_loss = None if mc_labels is not None: loss_fct = CrossEntropyLoss() mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)) lm_loss = None if labels is not None: labels = labels.to(lm_logits.device) shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (lm_logits, mc_logits) + transformer_outputs[1:] if mc_loss is not None: output = (mc_loss,) + output return ((lm_loss,) + output) if lm_loss is not None else output return GPT2DoubleHeadsModelOutput( loss=lm_loss, mc_loss=mc_loss, logits=lm_logits, mc_logits=mc_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @staticmethod def _reorder_cache( past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor ) -> Tuple[Tuple[torch.Tensor]]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. """ return tuple( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past_key_values ) @add_start_docstrings( """ The GPT2 Model transformer with a sequence classification head on top (linear layer). [`GPT2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, GPT2_START_DOCSTRING, ) class GPT2ForSequenceClassification(GPT2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPT2Model(config) self.score = nn.Linear(config.n_embd, self.num_labels, bias=False) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint="microsoft/DialogRPT-updown", output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2] else: batch_size, sequence_length = inputs_embeds.shape[:2] assert ( self.config.pad_token_id is not None or batch_size == 1 ), "Cannot handle batch sizes > 1 if no padding token is defined." if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: sequence_lengths = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ GPT2 Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, GPT2_START_DOCSTRING, ) class GPT2ForTokenClassification(GPT2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPT2Model(config) if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: classifier_dropout = config.classifier_dropout elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: classifier_dropout = config.hidden_dropout else: classifier_dropout = 0.1 self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) # fmt: off @add_code_sample_docstrings( checkpoint="brad1141/gpt2-finetuned-comp2", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_loss=0.25, expected_output=[ "Lead", "Lead", "Lead", "Position", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", ], ) # fmt: on def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: labels = labels.to(logits.device) loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The GPT-2 Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, GPT2_START_DOCSTRING, ) class GPT2ForQuestionAnswering(GPT2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPT2Model(config) self.qa_outputs = nn.Linear(config.hidden_size, 2) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, real_checkpoint=_CHECKPOINT_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1).to(start_logits.device) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1).to(end_logits.device) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/gpt2/modeling_gpt2.py/0

# coding=utf-8 # Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for GPTNeoX.""" import json from typing import Optional, Tuple from tokenizers import pre_tokenizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "tokenizer_file": { "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "gpt-neox-20b": 2048, } class GPTNeoXTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" GPT-NeoX-20B tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ```python >>> from transformers import GPTNeoXTokenizerFast >>> tokenizer = GPTNeoXTokenizerFast.from_pretrained("openai-community/gpt2") >>> tokenizer("Hello world")["input_ids"] [15496, 995] >>> tokenizer(" Hello world")["input_ids"] [18435, 995] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. unk_token (`str`, *optional*, defaults to `<|endoftext|>`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (`str`, *optional*, defaults to `<|endoftext|>`): The beginning of sequence token. eos_token (`str`, *optional*, defaults to `<|endoftext|>`): The end of sequence token. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (GPTNeoX tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether or not the post-processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, unk_token="<|endoftext|>", bos_token="<|endoftext|>", eos_token="<|endoftext|>", add_prefix_space=False, **kwargs, ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, add_prefix_space=add_prefix_space, **kwargs, ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) @property # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.default_chat_template def default_chat_template(self): """ A simple chat template that ignores role information and just concatenates messages with EOS tokens. """ logger.warning_once( "\nNo chat template is defined for this tokenizer - using the default template " f"for the {self.__class__.__name__} class. If the default is not appropriate for " "your model, please set `tokenizer.chat_template` to an appropriate template. " "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n" ) return "{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}"

transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py/0

# coding=utf-8 # Copyright 2023 Toshiyuki Sakamoto(tanreinama) and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch GPTSANJapanese model.""" import copy from typing import List, Optional, Tuple, Union import torch import torch.nn as nn from ...activations import ACT2FN from ...modeling_outputs import MoECausalLMOutputWithPast, MoEModelOutputWithPastAndCrossAttentions from ...modeling_utils import PreTrainedModel from ...utils import ( DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging, ) from .configuration_gptsan_japanese import GPTSanJapaneseConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "GPTSanJapaneseConfig" _CHECKPOINT_FOR_DOC = "Tanrei/GPTSAN-japanese" #################################################### # This dict contains ids and associated url # for the pretrained weights provided with the models #################################################### GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "Tanrei/GPTSAN-japanese", # See all GPTSAN-japanese models at https://huggingface.co/models?filter=gptsan-japanese ] # Copied from transformers.models.switch_transformers.modeling_switch_transformers.router_z_loss_func def router_z_loss_func(router_logits: torch.Tensor) -> float: r""" Compute the router z-loss implemented in PyTorch. The router z-loss was introduced in [Designing Effective Sparse Expert Models](https://arxiv.org/abs/2202.08906). It encourages router logits to remain small in an effort to improve stability. Args: router_logits (`float`): Input logits of shape [batch_size, sequence_length, num_experts] Returns: Scalar router z-loss. """ num_groups, tokens_per_group, _ = router_logits.shape log_z = torch.logsumexp(router_logits, dim=-1) z_loss = log_z**2 return torch.sum(z_loss) / (num_groups * tokens_per_group) # Copied from transformers.models.switch_transformers.modeling_switch_transformers.load_balancing_loss_func def load_balancing_loss_func(router_probs: torch.Tensor, expert_indices: torch.Tensor) -> float: r""" Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between experts is too unbalanced. Args: router_probs (`torch.Tensor`): Probability assigned to each expert per token. Shape: [batch_size, seqeunce_length, num_experts]. expert_indices (`torch.Tensor`): Indices tensor of shape [batch_size, seqeunce_length] identifying the selected expert for a given token. Returns: The auxiliary loss. """ num_experts = router_probs.shape[-1] # cast the expert indices to int64, otherwise one-hot encoding will fail if expert_indices.dtype != torch.int64: expert_indices = expert_indices.to(torch.int64) if len(expert_indices.shape) == 2: expert_indices = expert_indices.unsqueeze(2) expert_mask = torch.nn.functional.one_hot(expert_indices, num_experts) # For a given token, determine if it was routed to a given expert. expert_mask = torch.max(expert_mask, axis=-2).values # cast to float32 otherwise mean will fail expert_mask = expert_mask.to(torch.float32) tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2) router_prob_per_group_and_expert = torch.mean(router_probs, axis=-2) return torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert) * (num_experts**2) class GPTSanJapaneseDenseActDense(nn.Module): """ FFN Layer for Switch Transformer and Extra layers GPTSAN can mix Switch Transformer layers and normal Transformer layers This class is used as Expert in Switch Transformer layers and as FFN in regular Transformer layers. RELU is used in the Switch Transformer layer, and Swish is used in the normal Transformer layer, so there is a choice of which is used in the argument. """ def __init__(self, config: GPTSanJapaneseConfig, ext_layer=False): super().__init__() d_inter = config.d_ext if ext_layer else config.d_ff self.wi = nn.Linear(config.d_model, d_inter, bias=ext_layer) self.wo = nn.Linear(d_inter, config.d_model, bias=ext_layer) self.dropout = nn.Identity() if ext_layer else nn.Dropout(config.dropout_rate) self.act = ACT2FN["swish" if ext_layer else "relu"] def forward(self, hidden_states): r""" Args: hidden_states (`torch.Tensor`) : [num_groups, tokens_per_group, hidden_dim] inputs to send to experts. Returns: torch.Tensor[num_groups, tokens_per_group, hidden_dim] """ hidden_states = self.wi(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersTop1Router with SwitchTransformers->GPTSanJapanese class GPTSanJapaneseTop1Router(nn.Module): """ Router using tokens choose top-1 experts assignment. This router uses the same mechanism as in Switch Transformer (https://arxiv.org/abs/2101.03961) and V-MoE (https://arxiv.org/abs/2106.05974): tokens choose their top experts. Items are sorted by router_probs and then routed to their choice of expert until the expert's expert_capacity is reached. **There is no guarantee that each token is processed by an expert**, or that each expert receives at least one token. """ def __init__(self, config: GPTSanJapaneseConfig): super().__init__() self.num_experts = config.num_experts self.expert_capacity = config.expert_capacity self.classifier = nn.Linear(config.hidden_size, self.num_experts, bias=config.router_bias) self.jitter_noise = config.router_jitter_noise self.ignore_padding_tokens = config.router_ignore_padding_tokens self.dtype = getattr(torch, config.router_dtype) def _compute_router_probabilities(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: r""" Computes router probabilities from input hidden states. Args: hidden_states (`torch.Tensor`): (batch_size, sequence_length, hidden_dim) from which router probabilities are computed. Returns: router_probabilities (`torch.Tensor`): Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each token and expert. Used for routing tokens to experts. router_logits (`torch.Tensor`): Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits. This is used later for computing router z-loss. """ # float32 is used to ensure stability. See the discussion of "selective precision" in # https://arxiv.org/abs/2101.03961. # We also store the previous dtype to cast back the output to the previous dtype self.input_dtype = hidden_states.dtype hidden_states = hidden_states.to(self.dtype) if self.training and self.jitter_noise > 0: # Multiply the token inputs by the uniform distribution - adding some noise hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise) # Shape: [num_groups, tokens_per_group, num_experts] self._cast_classifier() router_logits = self.classifier(hidden_states) # Apply Softmax and cast back to the original `dtype` router_probabilities = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype) return router_probabilities, router_logits def _cast_classifier(self): r""" `bitsandbytes` `Linear8bitLt` layers does not support manual casting Therefore we need to check if they are an instance of the `Linear8bitLt` class by checking special attributes. """ if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")): self.classifier = self.classifier.to(self.dtype) def forward(self, hidden_states: torch.Tensor) -> Tuple: r""" Generic forward function for every Router class. Each Router expects to have the same input hidden states (`hidden_states`) corresponding to the hidden states for each token, the `expert_capacity` corresponding to the number of tokens the Router will send to each expert, some Routers can send up to few tokens to each expert. Each Router works as the following: it expects the hidden states for each token, gets the `router_probs` and `router_logits` from the `router_weights`. This will assign for each token, the raw probability to be assigned to an expert. Then each Router class will have to define its own `_compute_routing_instructions`. Args: hidden_states (`torch.Tensor`) : [num_groups, tokens_per_group, hidden_dim] inputs to send to experts. Returns: Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`] Tuple containing the expert index, the router probs and the router logits. The router probabilities and logits are required to compute the loss. """ router_probs, router_logits = self._compute_router_probabilities(hidden_states) expert_index = torch.argmax(router_probs, dim=-1) expert_index = torch.nn.functional.one_hot(expert_index, num_classes=self.num_experts) # Mask tokens outside expert capacity. Sum over each sequence token_priority = torch.cumsum(expert_index, dim=-2) # mask if the token routed to to the expert will overflow expert_capacity_mask = token_priority <= self.expert_capacity expert_index = expert_index * expert_capacity_mask router_probs = torch.max(router_probs, dim=-1).values.unsqueeze(-1) return expert_index, router_probs, router_logits # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersSparseMLP with SwitchTransformers->GPTSanJapanese class GPTSanJapaneseSparseMLP(nn.Module): r""" Implementation of the Switch Transformers Sparse MLP module. """ def __init__(self, config: GPTSanJapaneseConfig, expert_class: nn.Module = GPTSanJapaneseDenseActDense): super().__init__() # Step 1: Get the correct router according to its class self.router = GPTSanJapaneseTop1Router(config) # Step 2: Get the experts self.experts = nn.ModuleDict() for idx in range(config.num_experts): self.experts[f"expert_{idx}"] = expert_class(config) def forward(self, hidden_states): r""" Hold on, this will be slightly tricky to understand In the correct order, a MoE layer does the following: 1- Gets the `router_mask` from the router. The shape of the mask is `(batch_size, sequence_length, num_expert)` and corresponds to the argmax of the `router_probs`. The probabilities are needed in the computation of the hidden states : they are broadcasted to the hidden states values (can be interpreted as a scaling factor). 2- Dispatch the tokens to its associated experts. We do a classic for loop over the experts and assign for each expert the corresponding hidden states. """ # Step 1: Get the router_mask from the router as wel as the probabilities router_mask, router_probs, router_logits = self.router(hidden_states) expert_index = torch.argmax(router_mask, dim=-1) # The routers introduced might not always map all the tokens, to a router, which means that some hidden states # can be unchanged from one layer to another. That is why the hidden states are cloned before updating only the seleced ones. next_states = hidden_states.clone() for idx, expert in enumerate(self.experts.values()): token_indices = router_mask[:, :, idx].bool() next_states[token_indices] = expert(hidden_states[token_indices]).to(next_states.dtype) hidden_states = router_probs * next_states return hidden_states, (router_logits, expert_index) class GPTSanJapaneseLayerSparseFF(nn.Module): r""" Switch Transformers Feed Forward layer module. This is a wrapper around the Mixture of Experts module. Parameters: config : ([`GPTSanJapaneseConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ def __init__(self, config: GPTSanJapaneseConfig): super().__init__() self.mlp = GPTSanJapaneseSparseMLP(config) self.soft_bypass_mlp = nn.Linear(config.d_model, config.d_model, bias=False) self.norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_epsilon) def forward(self, hidden_states, output_router_logits): r""" Args: hidden_states (`torch.Tensor`) : [num_groups, tokens_per_group, hidden_dim] inputs to send to experts. output_router_logits (`bool`) : output experts router output. Returns: torch.Tensor[num_groups, tokens_per_group, hidden_dim] """ forwarded_states, router_tuple = self.mlp(hidden_states) forwarded_states += torch.tanh(self.soft_bypass_mlp(hidden_states)) output = hidden_states + self.norm(forwarded_states) if output_router_logits and router_tuple is not None: return output, router_tuple else: return output class GPTSanJapaneseLayerDenseFF(nn.Module): r""" Extra Transformers Feed Forward layer module. Parameters: config : ([`GPTSanJapaneseConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ def __init__(self, config: GPTSanJapaneseConfig): super().__init__() # Check if it is a sparse layer, if not then it is a dense layer self.mlp = GPTSanJapaneseDenseActDense(config, ext_layer=True) self.norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_epsilon) def forward(self, hidden_states): r""" Args: hidden_states (`torch.Tensor`) : [num_groups, tokens_per_group, hidden_dim] inputs to send to experts. Returns: torch.Tensor[num_groups, tokens_per_group, hidden_dim] """ forwarded_states = self.mlp(hidden_states) output = hidden_states + self.norm(forwarded_states) return output # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->GPTSanJapanese class GPTSanJapaneseAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[GPTSanJapaneseConfig] = None, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads self.config = config if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.is_causal = is_causal self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.reshape(*proj_shape) value_states = value_states.reshape(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class GPTSanJapaneseLayerSelfAttention(nn.Module): """ Self Attention and Normalization Unit """ def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.self_attn = GPTSanJapaneseAttention( embed_dim=config.d_model, num_heads=config.num_heads, is_decoder=True, bias=has_relative_attention_bias, ) self.norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_epsilon) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: r""" Self-attention and normalize block. Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. Returns: Tuple[torch.Tensor[num_groups, tokens_per_group, hidden_dim],...] """ # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple atten_out = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=(1 - attention_mask) * torch.finfo(hidden_states.dtype).min, layer_head_mask=head_mask, output_attentions=output_attentions, ) if output_attentions: attn_weights = (atten_out[1],) else: attn_weights = () attention_output = atten_out[0] hidden = hidden_states + self.norm(attention_output) if use_cache: outputs = (hidden, atten_out[2]) # hidden, present, (attentions) else: outputs = (hidden,) # hidden, (attentions) return outputs + attn_weights class GPTSanJapaneseBlock(nn.Module): """ Self Attention and FFN Unit """ def __init__(self, config, ext_layer=False): super().__init__() self.self_attn = GPTSanJapaneseLayerSelfAttention(config) self.feed_forward = GPTSanJapaneseLayerDenseFF(config) if ext_layer else GPTSanJapaneseLayerSparseFF(config) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, output_router_tuple: Optional[bool] = False, ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: r""" GPTSAN transformer block. Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`) : output attention probabirities. output_router_tuple: output experts router logits and expert id. Returns: Tuple[torch.Tensor[num_groups, tokens_per_group, hidden_dim],...] """ atten_out = self.self_attn( hidden_states=hidden_states, past_key_value=past_key_value, attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attention_output = atten_out[0] if isinstance(self.feed_forward, GPTSanJapaneseLayerSparseFF): sparse_out = self.feed_forward(attention_output, output_router_tuple) if output_router_tuple: hidden, router_tuple = sparse_out else: hidden = sparse_out else: hidden = self.feed_forward(attention_output) outputs = (hidden,) + atten_out[1:] if isinstance(self.feed_forward, GPTSanJapaneseLayerSparseFF) and output_router_tuple: outputs += (router_tuple,) return outputs class GPTSanJapanesePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTSanJapaneseConfig base_model_prefix = "gptsan_japanese" supports_gradient_checkpointing = False _no_split_modules = ["GPTSanJapaneseBlock"] _skip_keys_device_placement = "past_key_values" @property def dummy_inputs(self): input_ids = torch.tensor(DUMMY_INPUTS) input_mask = torch.tensor(DUMMY_MASK) dummy_inputs = { "input_ids": input_ids, "attention_mask": input_mask, } return dummy_inputs def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor # Used for testing weights initialization if isinstance(module, nn.LayerNorm): module.weight.data.fill_(factor * 1.0) module.bias.data.zero_() elif isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module, "bias") and module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, GPTSanJapaneseModel): # Mesh TensorFlow embeddings initialization # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 module.embed_tokens.weight.data.normal_(mean=0.0, std=factor * 1.0) module.position_embeddings.weight.data.normal_(mean=0.0, std=factor * 1.0) if hasattr(module, "extra_position_embeddings") and module.extra_position_embeddings is not None: module.extra_position_embeddings.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, (GPTSanJapaneseModel, GPTSanJapaneseForConditionalGeneration)): # Mesh TensorFlow embeddings initialization # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 module.final_logits_bias.data.normal_(mean=0.0, std=factor * 1.0) if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, GPTSanJapaneseDenseActDense): # Mesh TensorFlow FF initialization # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, GPTSanJapaneseAttention): # Multi-headed attention d_model = self.config.d_model key_value_proj_dim = self.config.d_model n_heads = self.config.num_heads module.k_proj.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) module.v_proj.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) module.q_proj.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) module.out_proj.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)) elif isinstance(module, GPTSanJapaneseSparseMLP): # Mesh TensorFlow attention initialization to avoid scaling before softmax # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 d_model = self.config.d_model key_value_proj_dim = self.config.d_model n_heads = self.config.num_heads module.router.classifier.weight.data.normal_(mean=0.0, std=factor * 1) for idx in range(self.config.num_experts): module.experts[f"expert_{idx}"].wi.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.experts[f"expert_{idx}"].wo.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) # Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._shift_right def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id pad_token_id = self.config.pad_token_id if decoder_start_token_id is None: raise ValueError( "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id. " "See T5 docs for more information." ) # shift inputs to the right if is_torch_fx_proxy(input_ids): # Item assignment is not supported natively for proxies. shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id) shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1) else: shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids GPTSAN_JAPANESE_START_DOCSTRING = r""" The [GPTSAN-japanese](https://github.com/tanreinama/GPTSAN) model was proposed in General-purpose Swich transformer based Japanese language model This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`GPTSanJapaneseConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ GPTSAN_JAPANESE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. GPTSAN-japanese is a model that generates sentence continuations or predicts tokens at mask positions. Special tokens required for inputs to the model are automatically appended. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): An input that masks the Prefix part in the Prefix-LM input. Mask values selected in `[0, 1]`: - 1 for tokens that are **prefix** input, - 0 for tokens that are **not-prefix** input. spout (`torch.Tensor` of shape `(batch_size, config.d_spout)`): This vector is transformed through an 8-layer FFN and can be used instead of `past_key_values`. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models. """ @add_start_docstrings( "The bare GPTSAN-japanese Model transformer outputting raw hidden-states without any specific head on top.", GPTSAN_JAPANESE_START_DOCSTRING, ) class GPTSanJapaneseModel(GPTSanJapanesePreTrainedModel): def __init__(self, config: GPTSanJapaneseConfig): super().__init__(config) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.d_model) self.config = copy.deepcopy(config) self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model) self.last_project = nn.Linear(config.d_model, config.d_model, bias=True) self.act = ACT2FN["swish"] self.blocks = torch.nn.ModuleList([]) for _ in range(config.num_switch_layers): self.blocks.append(GPTSanJapaneseBlock(config)) for _ in range(config.num_ext_layers): self.blocks.append(GPTSanJapaneseBlock(config, ext_layer=True)) if config.num_ext_layers > 0: self.extra_position_embeddings = nn.Embedding(config.max_position_embeddings, config.d_model) if config.d_spout: spouts = [] for _ in range(8): spouts.append(nn.Linear(config.d_spout, config.d_spout, bias=False)) spouts.append(nn.Tanh()) spouts.append(nn.Linear(config.d_spout, config.num_layers * 2 * config.d_model, bias=False)) self.spout = nn.Sequential(*spouts) self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings @add_start_docstrings_to_model_forward(GPTSAN_JAPANESE_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.FloatTensor] = None, spout: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, output_router_logits: Optional[bool] = None, num_precontext: Optional[torch.LongTensor] = None, ) -> Union[MoEModelOutputWithPastAndCrossAttentions, Tuple[torch.FloatTensor]]: r""" num_precontext (`torch.LongTensor` of shape `(batch_size,1)`): length of `hybrid` input tokens in the input. Tokens up to this length refer to both front and back like BERT, tokens after that refer only to front like GPT. see also: https://github.com/tanreinama/GPTSAN/blob/main/report/model.md Returns: `MoEModelOutputWithPastAndCrossAttentions` or `tuple` if `return_dict` returns MoEModelOutputWithPastAndCrossAttentions insted of tuple """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict device = self.position_embeddings.weight.device if input_ids is None: input_ids = torch.zeros([1, 1]).int().to(device) # dummy for input_ids was None num_pasts_contexts = 0 num_batch = input_ids.shape[0] pasts_or_spout_value = None if past_key_values is not None: num_pasts_contexts = past_key_values[0][0].shape[2] elif self.config.d_spout and spout is not None: # `spout` is a special input vector specific to GPTSAN # This controls the output by projecting embedded information such as the class of sentences during learning. # It should passed instead of the first past_key_value. # See the original GPTSAN repository for details num_pasts_contexts += 1 # If there is an attention_mask, increase first one for spout if self.config.d_spout and spout is not None and attention_mask is not None: attention_mask_with_spout = torch.ones(num_batch, attention_mask.shape[1] + 1, device=device) attention_mask_with_spout[:, 1:] -= 1 - attention_mask # 1st token should be spout attention_mask = attention_mask_with_spout # update attention_mask if num_precontext is not None: # `num_precontext` is the number of tokens that refer to each other in prefix-lm # created per batch, so dimension of num_precontext should be [batch, 1] if not ( len(num_precontext.shape) == 2 and num_precontext.shape[1] == 1 ): # num_precontext Should be [batch,1] raise ValueError("num_precontext should be [batch, 1] size.") num_precontext = torch.reshape(num_precontext, [-1]) else: num_precontext = torch.zeros([num_batch]).int().to(device) num_input_contexts = input_ids.shape[1] num_output_contexts = num_input_contexts + num_pasts_contexts hidden_states = self.embed_tokens(input_ids) if past_key_values is not None: pasts_or_spout_value = past_key_values elif self.config.d_spout and spout is not None: # Make vector from `spout` of GPTSAN to the same shape as past_key_values pasts_or_spout_value = self.spout(spout) # projecting `spout` vector pasts_or_spout_value = torch.reshape( pasts_or_spout_value, [ num_batch, self.config.num_layers, 2, self.config.num_heads, num_pasts_contexts, self.config.d_model // self.config.num_heads, ], ) pasts_or_spout_value = torch.split(pasts_or_spout_value, [1] * self.config.num_layers, dim=1) # make same shape as past_key_values pasts_or_spout_value = tuple( tuple([b.squeeze(1) for b in torch.split(a.squeeze(1), [1, 1], dim=1)]) for a in pasts_or_spout_value ) else: pasts_or_spout_value = [None] * self.config.num_layers # Token position considering spout and pasts token_position = torch.arange(num_input_contexts).to(device) + num_pasts_contexts if attention_mask is None: attention_mask = torch.ones(num_batch, num_input_contexts, device=device) # positions for get position_embeddings gather_position = ( ( torch.zeros((num_batch, self.config.d_model, num_input_contexts)).to(device) + token_position.unsqueeze(0) ) .transpose(1, 2) .long() ) # When padding with padding_side="left", zeros line up on the left side of attention_mask, so position_embeddings is shifted accordingly gather_position -= (1 - attention_mask).argmin(dim=-1).unsqueeze(1).unsqueeze(2) gather_position = torch.clip(gather_position, num_pasts_contexts, self.config.max_position_embeddings - 1) # attention_mask is applied per batch for i in range(num_batch): hidden_states[i] += torch.gather(self.position_embeddings.weight, dim=0, index=gather_position[i]) # Create a mask to be used when making the prefix Input length of Prefix-LM variable causal_mask = ( torch.tril(torch.ones((num_output_contexts, num_output_contexts), dtype=torch.uint8)) .view(1, 1, num_output_contexts, num_output_contexts) .to(device) ) prefix_lm_mask = causal_mask[:, :, -num_input_contexts:, :] if token_type_ids is not None: token_type_ids = token_type_ids.unsqueeze(1).unsqueeze(2) prefix_lm_mask = ((prefix_lm_mask + token_type_ids) > 0).float() # Marge prefix_lm_mask and attention_mask extended_attention_mask = prefix_lm_mask * attention_mask.unsqueeze(1).unsqueeze(2) # Prepare head mask if needed if head_mask is not None: head_mask = self.get_head_mask( head_mask, self.config.num_switch_layers + self.config.num_ext_layers ) # n_layer x batch x n_heads x N x N # outputs present_key_value_states = () if self.config.use_cache or use_cache else None all_hidden_states = () if self.config.output_hidden_states or output_hidden_states else None all_attentions = () if self.config.output_attentions or output_attentions else None all_router_probs = () if self.config.output_router_logits or output_router_logits else None for layer, past in enumerate(pasts_or_spout_value): if layer == self.config.num_switch_layers: if self.config.num_ext_layers > 0: # extra_position_embeddings are extra position embeddings that are only created when extending the model with code from the original GPTSAN repository. Not used in the default model. # However, it is created when you create an additional layer and partially train only that location. # Therefore, convert_gptsan_tf_checkpoint_to_pytorch.py is used when converting and loading models created in the original GPTSAN repository. for i in range(num_batch): hidden_states[i] += torch.gather( self.extra_position_embeddings.weight, dim=0, index=gather_position[i] ) output_router_tuple = ( self.config.output_router_logits or output_router_logits ) and layer < self.config.num_switch_layers block_output = self.blocks[layer]( hidden_states=hidden_states, past_key_value=past, attention_mask=extended_attention_mask, head_mask=head_mask, use_cache=self.config.use_cache or use_cache, output_attentions=self.config.output_attentions or output_attentions, output_router_tuple=output_router_tuple, ) outpos = 0 hidden_states = block_output[outpos] if self.config.output_hidden_states or output_hidden_states: all_hidden_states += (hidden_states,) if self.config.use_cache or use_cache: outpos += 1 present = block_output[outpos] present_key_value_states += (present,) if self.config.output_attentions or output_attentions: outpos += 1 attention_probs = block_output[outpos] all_attentions += (attention_probs,) if output_router_tuple: outpos += 1 router_tuple = block_output[outpos] all_router_probs.append(router_tuple[0]) hidden_states = self.last_project(hidden_states) hidden_states = self.act(hidden_states) if self.config.output_hidden_states or output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, present_key_value_states, all_hidden_states, all_attentions, all_router_probs, ] if v is not None ) return MoEModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_attentions, router_probs=all_router_probs, ) @add_start_docstrings( "The bare GPTSAN-japanese Model with a language modeling head.", GPTSAN_JAPANESE_START_DOCSTRING, ) class GPTSanJapaneseForConditionalGeneration(GPTSanJapanesePreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: GPTSanJapaneseConfig): super().__init__(config) self.model = GPTSanJapaneseModel(config) self.register_buffer("final_logits_bias", torch.zeros([1, config.vocab_size])) self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) if not self.config.torchscript: self.lm_head.weight = self.model.embed_tokens.weight @add_start_docstrings_to_model_forward(GPTSAN_JAPANESE_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.FloatTensor] = None, spout: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, output_router_logits: Optional[bool] = None, labels: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.FloatTensor], MoECausalLMOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: `MoECausalLMOutputWithPast` or `tuple` if `return_dict` returns MoECausalLMOutputWithPast insted of tuple Example: Text Generation with regular LM Model ```python >>> from transformers import AutoModel, AutoTokenizer, trainer_utils >>> device = "cuda" >>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device) >>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese") >>> x_token = tokenizer("織田信長は、", return_tensors="pt") >>> trainer_utils.set_seed(30) >>> input_ids = x_token.input_ids.to(device) >>> gen_token = model.generate(input_ids, max_new_tokens=50) >>> tokenizer.decode(gen_token[0]) "織田信長は、政治・軍事の中枢まで掌握した政治家であり、日本史上類を見ない驚異的な軍事侵攻を続け..." ``` Text Generation with Prefix-LM Model ```python >>> from transformers import AutoModel, AutoTokenizer, trainer_utils >>> device = "cuda" >>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device) >>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese") >>> x_token = tokenizer("", prefix_text="織田信長は、", return_tensors="pt") >>> trainer_utils.set_seed(30) >>> input_ids = x_token.input_ids.to(device) >>> token_type_ids = x_token.token_type_ids.to(device) >>> gen_token = model.generate(input_ids, token_type_ids=token_type_ids, max_new_tokens=50) >>> tokenizer.decode(gen_token[0]) "織田信長は、政治・外交で数々の戦果を上げるが、1568年からは、いわゆる本能寺の変で細川晴元に暗殺される..." ``` Simultaneously Text Generation And Masked Language Model ```python >>> from transformers import AutoModel, AutoTokenizer, trainer_utils >>> device = "cuda" >>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device) >>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese") >>> masked_sentence = "武田信玄は、<|inputmask|>時代ファンならぜひ押さえ<|inputmask|>きたい名将の一人。" >>> x_token = tokenizer("", prefix_text=masked_sentence, return_tensors="pt") >>> trainer_utils.set_seed(30) >>> input_ids = x_token.input_ids.to(device) >>> token_type_ids = x_token.token_type_ids.to(device) >>> out_lm_token = model.generate(input_ids, token_type_ids=token_type_ids, max_new_tokens=50) >>> out_mlm_token = model(input_ids, token_type_ids=token_type_ids).logits.argmax(axis=-1) >>> tokenizer.decode(out_mlm_token[0]) "武田信玄は、戦国時代ファンならぜひ押さえておきたい名将の一人。" >>> tokenizer.decode(out_lm_token[0][input_ids.shape[1] :]) "武田氏の三代に渡った武田家のひとり\n甲斐市に住む、日本史上最大の戦国大名。..." ```""" SEG_TOKEN = self.config.separator_token_id use_cache = use_cache or self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict model_return_dict = True num_precontext = None if input_ids is not None: num_batch = input_ids.shape[0] num_precontext = torch.zeros([num_batch]).int().to(input_ids.device) where_separators = torch.where(input_ids == SEG_TOKEN) num_precontext[where_separators[0]] += where_separators[1] num_precontext = num_precontext.unsqueeze(1) outputs = self.model( input_ids, attention_mask, token_type_ids, spout, past_key_values, head_mask, use_cache, inputs_embeds, decoder_inputs_embeds, output_attentions, output_hidden_states, model_return_dict, output_router_logits, num_precontext, ) lm_logits = self.lm_head(outputs[0]) if lm_logits.shape[-1] == self.final_logits_bias.shape[-1]: lm_logits = lm_logits + self.final_logits_bias loss = None z_loss = None router_probs = None aux_loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) loss_fct = nn.CrossEntropyLoss(ignore_index=-100) if output_router_logits: # Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder router_logits, expert_indexes = self._unpack_router_logits(outputs.router_probs) z_loss = router_z_loss_func(router_logits) router_probs = nn.Softmax(dim=-1)(router_logits) aux_loss = load_balancing_loss_func(router_probs, expert_indexes) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) if not return_dict: return tuple( v for v in [ loss, lm_logits, outputs.past_key_values, outputs.hidden_states, outputs.router_probs, z_loss, aux_loss, ] if v is not None ) return MoECausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_probs, z_loss=z_loss, aux_loss=aux_loss, ) def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, attention_mask: torch.FloatTensor, token_type_ids: Optional[torch.FloatTensor] = None, spout: Optional[Union[List, torch.FloatTensor]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, **kwargs, ): if isinstance(spout, list): spout = torch.tensor(spout).float() if input_ids is not None: spout = spout.to(input_ids.device) if past_key_values is not None: return { "input_ids": input_ids[:, -1:] if input_ids is not None else None, "attention_mask": attention_mask, "token_type_ids": token_type_ids[:, -1:] if token_type_ids is not None else None, "spout": spout, "past_key_values": past_key_values, } return { "input_ids": input_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, "spout": spout, "past_key_values": None, } # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersForConditionalGeneration.prepare_decoder_input_ids_from_labels with SwitchTransformers->GPTSanJapanese def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) # Copied from transformers.models.mbart.modeling_mbart.MBartForConditionalGeneration.resize_token_embeddings with MBart->GPTSanJapanese def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding: new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of) self._resize_final_logits_bias(new_embeddings.weight.shape[0]) return new_embeddings # Copied from transformers.models.mbart.modeling_mbart.MBartForConditionalGeneration._resize_final_logits_bias with MBart->GPTSanJapanese def _resize_final_logits_bias(self, new_num_tokens: int) -> None: old_num_tokens = self.final_logits_bias.shape[-1] if new_num_tokens <= old_num_tokens: new_bias = self.final_logits_bias[:, :new_num_tokens] else: extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) self.register_buffer("final_logits_bias", new_bias) def get_input_embeddings(self): return self.model.get_input_embeddings() def set_input_embeddings(self, new_embeddings): self.model.set_input_embeddings(new_embeddings) # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersForConditionalGeneration.set_output_embeddings with SwitchTransformers->GPTSanJapanese def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersForConditionalGeneration.get_output_embeddings with SwitchTransformers->GPTSanJapanese def get_output_embeddings(self): return self.lm_head # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersForConditionalGeneration._unpack_router_logits with SwitchTransformers->GPTSanJapanese def _unpack_router_logits(self, router_outputs): total_router_logits = [] total_expert_indexes = [] for router_output in router_outputs: if len(router_output[0].shape) > 1: router_logits, expert_indexes = router_output total_router_logits.append(router_logits) total_expert_indexes.append(expert_indexes) return torch.cat(total_router_logits, dim=1), torch.cat(total_expert_indexes, dim=1)

transformers/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py/0

# coding=utf-8 # Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Hubert model configuration""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/hubert-base-ls960": "https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json", # See all Hubert models at https://huggingface.co/models?filter=hubert } class HubertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an Hubert model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Hubert [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32): Vocabulary size of the Hubert model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`HubertModel`]. Vocabulary size of the model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`HubertModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout(`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. activation_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for activations inside the fully connected layer. attention_dropout(`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`Wav2Vec2ForCTC`]. layerdrop (`float`, *optional*, defaults to 0.1): The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. feat_extract_norm (`str`, *optional*, defaults to `"group"`): The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D convolutional layers. feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the feature encoder. feat_proj_layer_norm (`bool`, *optional*, defaults to `True`): Whether to apply LayerNorm to the output of the feature encoder. feat_extract_activation (`str, `optional`, defaults to `"gelu"`): The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. conv_dim (`Tuple[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`): A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. conv_stride (`Tuple[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`): A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_kernel (`Tuple[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The length of *conv_kernel* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_bias (`bool`, *optional*, defaults to `False`): Whether the 1D convolutional layers have a bias. num_conv_pos_embeddings (`int`, *optional*, defaults to 128): Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer. num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): Number of groups of 1D convolutional positional embeddings layer. do_stable_layer_norm (`bool`, *optional*, defaults to `False`): Whether do apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is False` corresponds to applying layer norm after the attention layer. apply_spec_augment (`bool`, *optional*, defaults to `True`): Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://arxiv.org/abs/1904.08779). mask_time_prob (`float`, *optional*, defaults to 0.05): Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector span to be masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_time_length (`int`, *optional*, defaults to 10): Length of vector span along the time axis. mask_time_min_masks (`int`, *optional*, defaults to 2),: The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < mask_time_min_masks'' mask_feature_prob (`float`, *optional*, defaults to 0.0): Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_feature_length (`int`, *optional*, defaults to 10): Length of vector span along the feature axis. mask_feature_min_masks (`int`, *optional*, defaults to 0),: The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`): Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an instance of [`HubertForCTC`]. ctc_zero_infinity (`bool`, *optional*, defaults to `False`): Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of [`HubertForCTC`]. use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an instance of [`HubertForSequenceClassification`]. classifier_proj_size (`int`, *optional*, defaults to 256): Dimensionality of the projection before token mean-pooling for classification. Example: ```python >>> from transformers import HubertModel, HubertConfig >>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration >>> configuration = HubertConfig() >>> # Initializing a model from the facebook/hubert-base-ls960 style configuration >>> model = HubertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "hubert" def __init__( self, vocab_size=32, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout=0.1, activation_dropout=0.1, attention_dropout=0.1, feat_proj_layer_norm=True, feat_proj_dropout=0.0, final_dropout=0.1, layerdrop=0.1, initializer_range=0.02, layer_norm_eps=1e-5, feat_extract_norm="group", feat_extract_activation="gelu", conv_dim=(512, 512, 512, 512, 512, 512, 512), conv_stride=(5, 2, 2, 2, 2, 2, 2), conv_kernel=(10, 3, 3, 3, 3, 2, 2), conv_bias=False, num_conv_pos_embeddings=128, num_conv_pos_embedding_groups=16, do_stable_layer_norm=False, apply_spec_augment=True, mask_time_prob=0.05, mask_time_length=10, mask_time_min_masks=2, mask_feature_prob=0.0, mask_feature_length=10, mask_feature_min_masks=0, ctc_loss_reduction="sum", ctc_zero_infinity=False, use_weighted_layer_sum=False, classifier_proj_size=256, pad_token_id=0, bos_token_id=1, eos_token_id=2, **kwargs, ): super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_activation = feat_extract_activation self.conv_dim = list(conv_dim) self.conv_stride = list(conv_stride) self.conv_kernel = list(conv_kernel) self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_feat_extract_layers = len(self.conv_dim) self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.num_attention_heads = num_attention_heads self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.feat_proj_layer_norm = feat_proj_layer_norm self.feat_proj_dropout = feat_proj_dropout self.final_dropout = final_dropout self.layerdrop = layerdrop self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.use_weighted_layer_sum = use_weighted_layer_sum self.classifier_proj_size = classifier_proj_size if ( (len(self.conv_stride) != self.num_feat_extract_layers) or (len(self.conv_kernel) != self.num_feat_extract_layers) or (len(self.conv_dim) != self.num_feat_extract_layers) ): raise ValueError( "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==" " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =" f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`," f" `len(config.conv_kernel) = {len(self.conv_kernel)}`." ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 self.apply_spec_augment = apply_spec_augment self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.mask_time_min_masks = mask_time_min_masks self.mask_feature_prob = mask_feature_prob self.mask_feature_length = mask_feature_length self.mask_feature_min_masks = mask_feature_min_masks # ctc loss self.ctc_loss_reduction = ctc_loss_reduction self.ctc_zero_infinity = ctc_zero_infinity @property def inputs_to_logits_ratio(self): return functools.reduce(operator.mul, self.conv_stride, 1)

transformers/src/transformers/models/hubert/configuration_hubert.py/0

# coding=utf-8 # Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch IdeficsVision model: a copy of CLIPVisionModel using a simpler config object""" import math from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...utils import ModelOutput, logging from .configuration_idefics import IdeficsVisionConfig logger = logging.get_logger(__name__) @dataclass class IdeficsVisionModelOutput(ModelOutput): """ Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. Args: image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): The image embeddings obtained by applying the projection layer to the pooler_output. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ image_embeds: Optional[torch.FloatTensor] = None last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None # Adapted from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings class IdeficsVisionEmbeddings(nn.Module): def __init__(self, config: IdeficsVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.class_embedding = nn.Parameter(torch.randn(self.embed_dim)) self.patch_embedding = nn.Conv2d( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False, ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) # Heavily inspired from https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/vit/modeling_vit.py#L82 def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 """ num_patches = embeddings.shape[1] - 1 pos_embed = self.position_embedding(self.position_ids) num_positions = pos_embed.shape[1] - 1 if num_patches == num_positions and height == width: return pos_embed class_pos_embed = pos_embed[:, 0] patch_pos_embed = pos_embed[:, 1:] embed_dim = embeddings.shape[-1] num_h_patches = height // self.config.patch_size num_w_patches = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1 sqrt_num_positions = math.sqrt(num_positions) patch_pos_embed = patch_pos_embed.reshape(1, int(sqrt_num_positions), int(sqrt_num_positions), embed_dim) patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) fp32_upcasting = patch_pos_embed.dtype == torch.bfloat16 if fp32_upcasting: logger.warning_once( "Upcasting patch_pos_embed to fp32 for interpolation since `upsample_bicubic2d_out_frame` in nn.functional.interpolate " "is not implemented for 'torch.bfloat16' dtype. This will result in a slight overhead." ) patch_pos_embed = patch_pos_embed.to(torch.float) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, scale_factor=(num_h_patches / sqrt_num_positions, num_w_patches / sqrt_num_positions), mode="bicubic", align_corners=False, ) if fp32_upcasting: patch_pos_embed = patch_pos_embed.to(torch.bfloat16) if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]: raise ValueError( f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the " f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})" ) patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, embed_dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if not interpolate_pos_encoding: if height != self.image_size or width != self.image_size: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size}*{self.image_size}). You should try to set `interpolate_pos_encoding=True`" ) target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings # Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->IdeficsVision class IdeficsVisionAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" bsz, tgt_len, embed_dim = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scale key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) # apply the causal_attention_mask first if causal_attention_mask is not None: if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {causal_attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit akward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision class IdeficsVisionMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->IdeficsVision class IdeficsVisionEncoderLayer(nn.Module): def __init__(self, config: IdeficsVisionConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = IdeficsVisionAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = IdeficsVisionMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->IdeficsVision class IdeficsVisionEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`IdeficsVisionEncoderLayer`]. Args: config: IdeficsVisionConfig """ def __init__(self, config: IdeficsVisionConfig): super().__init__() self.config = config self.layers = nn.ModuleList([IdeficsVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Causal mask for the text model. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, attention_mask, causal_attention_mask, output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, causal_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) # Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer class IdeficsVisionTransformer(nn.Module): def __init__(self, config: IdeficsVisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = IdeficsVisionEmbeddings(config) self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.encoder = IdeficsVisionEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) # Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = False, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) hidden_states = self.pre_layrnorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, )

transformers/src/transformers/models/idefics/vision.py/0

# coding=utf-8 # Copyright 2022 The OpenAI Team Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Jukebox configuration""" import os from typing import List, Union from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP = { "openai/jukebox-5b-lyrics": "https://huggingface.co/openai/jukebox-5b-lyrics/blob/main/config.json", "openai/jukebox-1b-lyrics": "https://huggingface.co/openai/jukebox-1b-lyrics/blob/main/config.json", } _LARGE_ATTENTION = [ "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "cross_attention", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "cross_attention", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "cross_attention", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "cross_attention", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "cross_attention", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "cross_attention", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "block_attn", "transpose_block_attn", "prev_block_attn", "cross_attention", ] _RawColumnPreviousRowAttention = ["block_attn", "transpose_block_attn", "prev_block_attn"] _FullDenseAttention = ["dense_attention"] _PrimePrimeDenseAttention = ["prime_attn", "prime_attn", "dense_attn"] def full_dense_attention(layer): return _FullDenseAttention[0] def raw_column_previous_row_attention(layer): return _RawColumnPreviousRowAttention[layer % 3] def large_separated_enc_dec_w_lyrics(layer): return _LARGE_ATTENTION[layer % 79] def enc_dec_with_lyrics(layer): if layer % 16 == 15: return _PrimePrimeDenseAttention[layer % 3] return _RawColumnPreviousRowAttention[layer % 3] ATTENTION_PATTERNS = { "full_dense_attention": full_dense_attention, "raw_column_previous_row_attention": raw_column_previous_row_attention, # Alternate row, column and previous row attn "large_separated_enc_dec_w_lyrics": large_separated_enc_dec_w_lyrics, # Used by large separated_enc_dec model with lyrics "enc_dec_with_lyrics": enc_dec_with_lyrics, # Used by encoder_decoder model with lyrics } class JukeboxPriorConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`JukeboxPrior`]. It is used to instantiate a `JukeboxPrior` according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the top level prior from the [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox -1b-lyrics) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: act_fn (`str`, *optional*, defaults to `"quick_gelu"`): Activation function. alignment_head (`int`, *optional*, defaults to 2): Head that is responsible of the alignment between lyrics and music. Only used to compute the lyric to audio alignment alignment_layer (`int`, *optional*, defaults to 68): Index of the layer that is responsible of the alignment between lyrics and music. Only used to compute the lyric to audio alignment attention_multiplier (`float`, *optional*, defaults to 0.25): Multiplier coefficient used to define the hidden dimension of the attention layers. 0.25 means that 0.25*width of the model will be used. attention_pattern (`str`, *optional*, defaults to `"enc_dec_with_lyrics"`): Which attention pattern to use for the decoder/ attn_dropout (`int`, *optional*, defaults to 0): Dropout probability for the post-attention layer dropout in the decoder. attn_res_scale (`bool`, *optional*, defaults to `False`): Whether or not to scale the residuals in the attention conditioner block. blocks (`int`, *optional*, defaults to 64): Number of blocks used in the `block_attn`. A sequence of length seq_len is factored as `[blocks, seq_len // blocks]` in the `JukeboxAttention` layer. conv_res_scale (`int`, *optional*): Whether or not to scale the residuals in the conditioner block. Since the top level prior does not have a conditioner, the default value is to None and should not be modified. num_layers (`int`, *optional*, defaults to 72): Number of layers of the transformer architecture. emb_dropout (`int`, *optional*, defaults to 0): Embedding dropout used in the lyric decoder. encoder_config (`JukeboxPriorConfig`, *optional*) : Configuration of the encoder which models the prior on the lyrics. encoder_loss_fraction (`float`, *optional*, defaults to 0.4): Multiplication factor used in front of the lyric encoder loss. hidden_size (`int`, *optional*, defaults to 2048): Hidden dimension of the attention layers. init_scale (`float`, *optional*, defaults to 0.2): Initialization scales for the prior modules. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether or not the prior is an encoder-decoder model. In case it is not, and `nb_relevant_lyric_tokens` is greater than 0, the `encoder` args should be specified for the lyric encoding. mask (`bool`, *optional*, defaults to `False`): Whether or not to mask the previous positions in the attention. max_duration (`int`, *optional*, defaults to 600): Maximum supported duration of the generated song in seconds. max_nb_genres (`int`, *optional*, defaults to 1): Maximum number of genres that can be used to condition the model. merged_decoder (`bool`, *optional*, defaults to `True`): Whether or not the decoder and the encoder inputs are merged. This is used for the separated encoder-decoder architecture metadata_conditioning (`bool`, *optional*, defaults to `True)`: Whether or not to condition on the artist and genre metadata. metadata_dims (`List[int]`, *optional*, defaults to `[604, 7898]`): Number of genres and the number of artists that were used to train the embedding layers of the prior models. min_duration (`int`, *optional*, defaults to 0): Minimum duration of the generated audio on which the model was trained. mlp_multiplier (`float`, *optional*, defaults to 1.0): Multiplier coefficient used to define the hidden dimension of the MLP layers. 0.25 means that 0.25*width of the model will be used. music_vocab_size (`int`, *optional*, defaults to 2048): Number of different music tokens. Should be similar to the `JukeboxVQVAEConfig.nb_discrete_codes`. n_ctx (`int`, *optional*, defaults to 6144): Number of context tokens for each prior. The context tokens are the music tokens that are attended to when generating music tokens. n_heads (`int`, *optional*, defaults to 2): Number of attention heads. nb_relevant_lyric_tokens (`int`, *optional*, defaults to 384): Number of lyric tokens that are used when sampling a single window of length `n_ctx` res_conv_depth (`int`, *optional*, defaults to 3): Depth of the `JukeboxDecoderConvBock` used to upsample the previously sampled audio in the `JukeboxMusicTokenConditioner`. res_conv_width (`int`, *optional*, defaults to 128): Width of the `JukeboxDecoderConvBock` used to upsample the previously sampled audio in the `JukeboxMusicTokenConditioner`. res_convolution_multiplier (`int`, *optional*, defaults to 1): Multiplier used to scale the `hidden_dim` of the `JukeboxResConv1DBlock`. res_dilation_cycle (`int`, *optional*): Dilation cycle used to define the `JukeboxMusicTokenConditioner`. Usually similar to the ones used in the corresponding level of the VQVAE. The first prior does not use it as it is not conditioned on upper level tokens. res_dilation_growth_rate (`int`, *optional*, defaults to 1): Dilation grow rate used between each convolutionnal block of the `JukeboxMusicTokenConditioner` res_downs_t (`List[int]`, *optional*, defaults to `[3, 2, 2]`): Downsampling rates used in the audio conditioning network res_strides_t (`List[int]`, *optional*, defaults to `[2, 2, 2]`): Striding used in the audio conditioning network resid_dropout (`int`, *optional*, defaults to 0): Residual dropout used in the attention pattern. sampling_rate (`int`, *optional*, defaults to 44100): Sampling rate used for training. spread (`int`, *optional*): Spread used in the `summary_spread_attention` pattern timing_dims (`int`, *optional*, defaults to 64): Dimension of the timing embedding. zero_out (`bool`, *optional*, defaults to `False`): Whether or not to zero out convolution weights when initializing. """ model_type = "jukebox_prior" attribute_map = { "max_position_embeddings": "n_positions", "num_attention_heads": "n_head", } def __init__( self, act_fn="quick_gelu", level=0, alignment_head=2, alignment_layer=68, attention_multiplier=0.25, attention_pattern="enc_dec_with_lyrics", attn_dropout=0, attn_res_scale=False, blocks=64, conv_res_scale=None, num_layers=72, emb_dropout=0, encoder_config=None, encoder_loss_fraction=0.4, hidden_size=2048, init_scale=0.2, is_encoder_decoder=True, lyric_vocab_size=80, mask=False, max_duration=600, max_nb_genres=1, merged_decoder=True, metadata_conditioning=True, metadata_dims=[604, 7898], min_duration=0, mlp_multiplier=1.0, music_vocab_size=2048, n_ctx=6144, n_heads=2, nb_relevant_lyric_tokens=384, res_conv_depth=3, res_conv_width=128, res_convolution_multiplier=1, res_dilation_cycle=None, res_dilation_growth_rate=1, res_downs_t=[3, 2, 2], res_strides_t=[2, 2, 2], resid_dropout=0, sampling_rate=44100, spread=None, timing_dims=64, zero_out=False, **kwargs, ): self.act_fn = act_fn self.alignment_head = alignment_head self.alignment_layer = alignment_layer self.attention_multiplier = attention_multiplier self.attention_pattern = attention_pattern self.attn_dropout = attn_dropout self.attn_res_scale = attn_res_scale self.blocks = blocks self.conv_res_scale = conv_res_scale self.num_layers = num_layers self.emb_dropout = emb_dropout self.music_vocab_size = music_vocab_size if encoder_config is not None: self.encoder_config = JukeboxPriorConfig(**encoder_config) else: self.encoder_config = None self.encoder_loss_fraction = encoder_loss_fraction self.init_scale = init_scale self.is_encoder_decoder = is_encoder_decoder self.lyric_vocab_size = lyric_vocab_size self.level = level self.mask = mask self.max_duration = max_duration self.max_nb_genres = max_nb_genres self.merged_decoder = merged_decoder self.metadata_conditioning = metadata_conditioning self.metadata_dims = metadata_dims self.min_duration = min_duration self.mlp_multiplier = mlp_multiplier self.n_ctx = n_ctx self.n_heads = n_heads self.nb_relevant_lyric_tokens = nb_relevant_lyric_tokens self.res_conv_depth = res_conv_depth self.res_conv_width = res_conv_width self.res_convolution_multiplier = res_convolution_multiplier self.res_dilation_cycle = res_dilation_cycle self.res_dilation_growth_rate = res_dilation_growth_rate self.res_downs_t = res_downs_t self.res_strides_t = res_strides_t self.resid_dropout = resid_dropout self.sampling_rate = sampling_rate self.spread = spread self.timing_dims = timing_dims self.hidden_size = hidden_size self.zero_out = zero_out @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], level=0, **kwargs ) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the prior config dict if we are loading from JukeboxConfig if config_dict.get("model_type") == "jukebox": config_dict = config_dict[f"prior_{level}"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class JukeboxVQVAEConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`JukeboxVQVAE`]. It is used to instantiate a `JukeboxVQVAE` according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the VQVAE from [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox-1b-lyrics) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: act_fn (`str`, *optional*, defaults to `"relu"`): Activation function of the model. nb_discrete_codes (`int`, *optional*, defaults to 2048): Number of codes of the VQVAE. commit (`float`, *optional*, defaults to 0.02): Commit loss multiplier. conv_input_shape (`int`, *optional*, defaults to 1): Number of audio channels. conv_res_scale (`bool`, *optional*, defaults to `False`): Whether or not to scale the residuals of the `JukeboxResConv1DBlock`. embed_dim (`int`, *optional*, defaults to 64): Embedding dimension of the codebook vectors. hop_fraction (`List[int]`, *optional*, defaults to `[0.125, 0.5, 0.5]`): Fraction of non-intersecting window used when continuing the sampling process. levels (`int`, *optional*, defaults to 3): Number of hierarchical levels that used in the VQVAE. lmu (`float`, *optional*, defaults to 0.99): Used in the codebook update, exponential moving average coefficient. For more detail refer to Appendix A.1 of the original [VQVAE paper](https://arxiv.org/pdf/1711.00937v2.pdf) multipliers (`List[int]`, *optional*, defaults to `[2, 1, 1]`): Depth and width multipliers used for each level. Used on the `res_conv_width` and `res_conv_depth` res_conv_depth (`int`, *optional*, defaults to 4): Depth of the encoder and decoder block. If no `multipliers` are used, this is the same for each level. res_conv_width (`int`, *optional*, defaults to 32): Width of the encoder and decoder block. If no `multipliers` are used, this is the same for each level. res_convolution_multiplier (`int`, *optional*, defaults to 1): Scaling factor of the hidden dimension used in the `JukeboxResConv1DBlock`. res_dilation_cycle (`int`, *optional*): Dilation cycle value used in the `JukeboxResnet`. If an int is used, each new Conv1 block will have a depth reduced by a power of `res_dilation_cycle`. res_dilation_growth_rate (`int`, *optional*, defaults to 3): Resnet dilation growth rate used in the VQVAE (dilation_growth_rate ** depth) res_downs_t (`List[int]`, *optional*, defaults to `[3, 2, 2]`): Downsampling rate for each level of the hierarchical VQ-VAE. res_strides_t (`List[int]`, *optional*, defaults to `[2, 2, 2]`): Stride used for each level of the hierarchical VQ-VAE. sample_length (`int`, *optional*, defaults to 1058304): Provides the max input shape of the VQVAE. Is used to compute the input shape of each level. init_scale (`float`, *optional*, defaults to 0.2): Initialization scale. zero_out (`bool`, *optional*, defaults to `False`): Whether or not to zero out convolution weights when initializing. """ model_type = "jukebox_vqvae" def __init__( self, act_fn="relu", nb_discrete_codes=2048, commit=0.02, conv_input_shape=1, conv_res_scale=False, embed_dim=64, hop_fraction=[0.125, 0.5, 0.5], levels=3, lmu=0.99, multipliers=[2, 1, 1], res_conv_depth=4, res_conv_width=32, res_convolution_multiplier=1, res_dilation_cycle=None, res_dilation_growth_rate=3, res_downs_t=[3, 2, 2], res_strides_t=[2, 2, 2], sample_length=1058304, init_scale=0.2, zero_out=False, **kwargs, ): self.hop_fraction = hop_fraction self.conv_input_shape = conv_input_shape self.sample_length = sample_length # VQVAE parameters (all used) self.levels = levels self.embed_dim = embed_dim self.nb_discrete_codes = nb_discrete_codes self.res_conv_width = res_conv_width self.res_conv_depth = res_conv_depth self.res_convolution_multiplier = res_convolution_multiplier self.res_dilation_growth_rate = res_dilation_growth_rate self.res_dilation_cycle = res_dilation_cycle self.multipliers = multipliers self.res_downs_t = res_downs_t self.res_strides_t = res_strides_t self.lmu = lmu self.commit = commit self.conv_res_scale = conv_res_scale self.act_fn = act_fn self.init_scale = init_scale self.zero_out = zero_out @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the text config dict if we are loading from CLIPConfig if config_dict.get("model_type") == "jukebox": config_dict = config_dict["vqvae_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class JukeboxConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`JukeboxModel`]. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Instantiating a configuration with the defaults will yield a similar configuration to that of [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox-1b-lyrics) architecture. The downsampling and stride are used to determine downsampling of the input sequence. For example, downsampling = (5,3), and strides = (2, 2) will downsample the audio by 2^5 = 32 to get the first level of codes, and 2**8 = 256 to get the second level codes. This is mostly true for training the top level prior and the upsamplers. Args: vqvae_config (`JukeboxVQVAEConfig`, *optional*): Configuration for the `JukeboxVQVAE` model. prior_config_list (`List[JukeboxPriorConfig]`, *optional*): List of the configs for each of the `JukeboxPrior` of the model. The original architecture uses 3 priors. nb_priors (`int`, *optional*, defaults to 3): Number of prior models that will sequentially sample tokens. Each prior is conditional auto regressive (decoder) model, apart from the top prior, which can include a lyric encoder. The available models were trained using a top prior and 2 upsampler priors. sampling_rate (`int`, *optional*, defaults to 44100): Sampling rate of the raw audio. timing_dims (`int`, *optional*, defaults to 64): Dimensions of the JukeboxRangeEmbedding layer which is equivalent to traditional positional embedding layer. The timing embedding layer converts the absolute and relative position in the currently sampled audio to a tensor of length `timing_dims` that will be added to the music tokens. min_duration (`int`, *optional*, defaults to 0): Minimum duration of the audios to generate max_duration (`float`, *optional*, defaults to 600.0): Maximum duration of the audios to generate max_nb_genres (`int`, *optional*, defaults to 5): Maximum number of genres that can be used to condition a single sample. metadata_conditioning (`bool`, *optional*, defaults to `True`): Whether or not to use metadata conditioning, corresponding to the artist, the genre and the min/maximum duration. Example: ```python >>> from transformers import JukeboxModel, JukeboxConfig >>> # Initializing a Jukebox configuration >>> configuration = JukeboxConfig() >>> # Initializing a model from the configuration >>> model = JukeboxModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "jukebox" def __init__( self, vqvae_config=None, prior_config_list=None, nb_priors=3, sampling_rate=44100, timing_dims=64, min_duration=0, max_duration=600.0, max_nb_genres=5, metadata_conditioning=True, **kwargs, ): if vqvae_config is None: vqvae_config = {} logger.info("vqvae_config is None. initializing the JukeboxVQVAE with default values.") self.vqvae_config = JukeboxVQVAEConfig(**vqvae_config) if prior_config_list is not None: self.prior_configs = [JukeboxPriorConfig(**prior_config) for prior_config in prior_config_list] else: self.prior_configs = [] for prior_idx in range(nb_priors): prior_config = kwargs.pop(f"prior_{prior_idx}", None) if prior_config is None: prior_config = {} logger.info( f"prior_{prior_idx}'s config is None. Initializing the JukeboxPriorConfig list with default" " values." ) self.prior_configs.append(JukeboxPriorConfig(**prior_config)) self.hop_fraction = self.vqvae_config.hop_fraction self.nb_priors = nb_priors # Metadata conditioning self.max_nb_genres = max_nb_genres self.sampling_rate = sampling_rate self.timing_dims = timing_dims self.min_duration = min_duration self.max_duration = max_duration self.metadata_conditioning = metadata_conditioning super().__init__(**kwargs) @classmethod def from_configs(cls, prior_configs: List[JukeboxPriorConfig], vqvae_config: JukeboxVQVAEConfig, **kwargs): r""" Instantiate a [`JukeboxConfig`] (or a derived class) from clip text model configuration and clip vision model configuration. Returns: [`JukeboxConfig`]: An instance of a configuration object """ prior_config_list = [config.to_dict() for config in prior_configs] return cls(prior_config_list=prior_config_list, vqvae_config_dict=vqvae_config.to_dict(), **kwargs) def to_dict(self): # Override the default to_dict to apply to_dict to the list of prior configs. result = super().to_dict() result["prior_config_list"] = [config.to_dict() for config in result.pop("prior_configs")] return result

transformers/src/transformers/models/jukebox/configuration_jukebox.py/0

# coding=utf-8 # Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ LayoutLMv2 model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import is_detectron2_available, logging logger = logging.get_logger(__name__) LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP = { "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json", "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json", # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2 } # soft dependency if is_detectron2_available(): import detectron2 class LayoutLMv2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an LayoutLMv2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LayoutLMv2 [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`]. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. max_2d_position_embeddings (`int`, *optional*, defaults to 1024): The maximum value that the 2D position embedding might ever be used with. Typically set this to something large just in case (e.g., 1024). max_rel_pos (`int`, *optional*, defaults to 128): The maximum number of relative positions to be used in the self-attention mechanism. rel_pos_bins (`int`, *optional*, defaults to 32): The number of relative position bins to be used in the self-attention mechanism. fast_qkv (`bool`, *optional*, defaults to `True`): Whether or not to use a single matrix for the queries, keys, values in the self-attention layers. max_rel_2d_pos (`int`, *optional*, defaults to 256): The maximum number of relative 2D positions in the self-attention mechanism. rel_2d_pos_bins (`int`, *optional*, defaults to 64): The number of 2D relative position bins in the self-attention mechanism. image_feature_pool_shape (`List[int]`, *optional*, defaults to [7, 7, 256]): The shape of the average-pooled feature map. coordinate_size (`int`, *optional*, defaults to 128): Dimension of the coordinate embeddings. shape_size (`int`, *optional*, defaults to 128): Dimension of the width and height embeddings. has_relative_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a relative attention bias in the self-attention mechanism. has_spatial_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a spatial attention bias in the self-attention mechanism. has_visual_segment_embedding (`bool`, *optional*, defaults to `False`): Whether or not to add visual segment embeddings. detectron2_config_args (`dict`, *optional*): Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py) for details regarding default values. Example: ```python >>> from transformers import LayoutLMv2Config, LayoutLMv2Model >>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration >>> configuration = LayoutLMv2Config() >>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration >>> model = LayoutLMv2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "layoutlmv2" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, max_2d_position_embeddings=1024, max_rel_pos=128, rel_pos_bins=32, fast_qkv=True, max_rel_2d_pos=256, rel_2d_pos_bins=64, convert_sync_batchnorm=True, image_feature_pool_shape=[7, 7, 256], coordinate_size=128, shape_size=128, has_relative_attention_bias=True, has_spatial_attention_bias=True, has_visual_segment_embedding=False, detectron2_config_args=None, **kwargs, ): super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, intermediate_size=intermediate_size, hidden_act=hidden_act, hidden_dropout_prob=hidden_dropout_prob, attention_probs_dropout_prob=attention_probs_dropout_prob, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, initializer_range=initializer_range, layer_norm_eps=layer_norm_eps, pad_token_id=pad_token_id, **kwargs, ) self.max_2d_position_embeddings = max_2d_position_embeddings self.max_rel_pos = max_rel_pos self.rel_pos_bins = rel_pos_bins self.fast_qkv = fast_qkv self.max_rel_2d_pos = max_rel_2d_pos self.rel_2d_pos_bins = rel_2d_pos_bins self.convert_sync_batchnorm = convert_sync_batchnorm self.image_feature_pool_shape = image_feature_pool_shape self.coordinate_size = coordinate_size self.shape_size = shape_size self.has_relative_attention_bias = has_relative_attention_bias self.has_spatial_attention_bias = has_spatial_attention_bias self.has_visual_segment_embedding = has_visual_segment_embedding self.detectron2_config_args = ( detectron2_config_args if detectron2_config_args is not None else self.get_default_detectron2_config() ) @classmethod def get_default_detectron2_config(self): return { "MODEL.MASK_ON": True, "MODEL.PIXEL_STD": [57.375, 57.120, 58.395], "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone", "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"], "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]], "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"], "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000, "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000, "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000, "MODEL.POST_NMS_TOPK_TEST": 1000, "MODEL.ROI_HEADS.NAME": "StandardROIHeads", "MODEL.ROI_HEADS.NUM_CLASSES": 5, "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"], "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead", "MODEL.ROI_BOX_HEAD.NUM_FC": 2, "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14, "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead", "MODEL.ROI_MASK_HEAD.NUM_CONV": 4, "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7, "MODEL.RESNETS.DEPTH": 101, "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]], "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]], "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"], "MODEL.RESNETS.NUM_GROUPS": 32, "MODEL.RESNETS.WIDTH_PER_GROUP": 8, "MODEL.RESNETS.STRIDE_IN_1X1": False, } def get_detectron2_config(self): detectron2_config = detectron2.config.get_cfg() for k, v in self.detectron2_config_args.items(): attributes = k.split(".") to_set = detectron2_config for attribute in attributes[:-1]: to_set = getattr(to_set, attribute) setattr(to_set, attributes[-1], v) return detectron2_config

transformers/src/transformers/models/layoutlmv2/configuration_layoutlmv2.py/0

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available, is_vision_available, ) _import_structure = {"processing_layoutxlm": ["LayoutXLMProcessor"]} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_layoutxlm"] = ["LayoutXLMTokenizer"] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_layoutxlm_fast"] = ["LayoutXLMTokenizerFast"] if TYPE_CHECKING: from .processing_layoutxlm import LayoutXLMProcessor try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_layoutxlm import LayoutXLMTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/layoutxlm/__init__.py/0

# coding=utf-8 # Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Longformer configuration""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import TensorType, logging if TYPE_CHECKING: from ...onnx.config import PatchingSpec from ...tokenization_utils_base import PreTrainedTokenizerBase logger = logging.get_logger(__name__) LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json", "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json", "allenai/longformer-large-4096-finetuned-triviaqa": ( "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json" ), "allenai/longformer-base-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json" ), "allenai/longformer-large-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json" ), } class LongformerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LongformerModel`] or a [`TFLongformerModel`]. It is used to instantiate a Longformer model according to the specified arguments, defining the model architecture. This is the configuration class to store the configuration of a [`LongformerModel`]. It is used to instantiate an Longformer model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LongFormer [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) architecture with a sequence length 4,096. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the Longformer model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LongformerModel`] or [`TFLongformerModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`LongformerModel`] or [`TFLongformerModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. attention_window (`int` or `List[int]`, *optional*, defaults to 512): Size of an attention window around each token. If an `int`, use the same size for all layers. To specify a different window size for each layer, use a `List[int]` where `len(attention_window) == num_hidden_layers`. Example: ```python >>> from transformers import LongformerConfig, LongformerModel >>> # Initializing a Longformer configuration >>> configuration = LongformerConfig() >>> # Initializing a model from the configuration >>> model = LongformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "longformer" def __init__( self, attention_window: Union[List[int], int] = 512, sep_token_id: int = 2, pad_token_id: int = 1, bos_token_id: int = 0, eos_token_id: int = 2, vocab_size: int = 30522, hidden_size: int = 768, num_hidden_layers: int = 12, num_attention_heads: int = 12, intermediate_size: int = 3072, hidden_act: str = "gelu", hidden_dropout_prob: float = 0.1, attention_probs_dropout_prob: float = 0.1, max_position_embeddings: int = 512, type_vocab_size: int = 2, initializer_range: float = 0.02, layer_norm_eps: float = 1e-12, onnx_export: bool = False, **kwargs, ): """Constructs LongformerConfig.""" super().__init__(pad_token_id=pad_token_id, **kwargs) self.attention_window = attention_window self.sep_token_id = sep_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.onnx_export = onnx_export class LongformerOnnxConfig(OnnxConfig): def __init__(self, config: "PretrainedConfig", task: str = "default", patching_specs: "List[PatchingSpec]" = None): super().__init__(config, task, patching_specs) config.onnx_export = True @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} else: dynamic_axis = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("global_attention_mask", dynamic_axis), ] ) @property def outputs(self) -> Mapping[str, Mapping[int, str]]: outputs = super().outputs if self.task == "default": outputs["pooler_output"] = {0: "batch"} return outputs @property def atol_for_validation(self) -> float: """ What absolute tolerance value to use during model conversion validation. Returns: Float absolute tolerance value. """ return 1e-4 @property def default_onnx_opset(self) -> int: # needs to be >= 14 to support tril operator return max(super().default_onnx_opset, 14) def generate_dummy_inputs( self, tokenizer: "PreTrainedTokenizerBase", batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: inputs = super().generate_dummy_inputs( preprocessor=tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) import torch # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64) # makes the export fail randomly inputs["global_attention_mask"] = torch.zeros_like(inputs["input_ids"]) # make every second token global inputs["global_attention_mask"][:, ::2] = 1 return inputs

transformers/src/transformers/models/longformer/configuration_longformer.py/0

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _import_structure = { "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"], "configuration_maskformer_swin": ["MaskFormerSwinConfig"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_maskformer"] = ["MaskFormerFeatureExtractor"] _import_structure["image_processing_maskformer"] = ["MaskFormerImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_maskformer"] = [ "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "MaskFormerForInstanceSegmentation", "MaskFormerModel", "MaskFormerPreTrainedModel", ] _import_structure["modeling_maskformer_swin"] = [ "MaskFormerSwinBackbone", "MaskFormerSwinModel", "MaskFormerSwinPreTrainedModel", ] if TYPE_CHECKING: from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig from .configuration_maskformer_swin import MaskFormerSwinConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_maskformer import MaskFormerFeatureExtractor from .image_processing_maskformer import MaskFormerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_maskformer import ( MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskFormerForInstanceSegmentation, MaskFormerModel, MaskFormerPreTrainedModel, ) from .modeling_maskformer_swin import ( MaskFormerSwinBackbone, MaskFormerSwinModel, MaskFormerSwinPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

transformers/src/transformers/models/maskformer/__init__.py/0

# coding=utf-8 # Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/mbart-large-en-ro": ( "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model" ), "facebook/mbart-large-cc25": ( "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model" ), } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/mbart-large-en-ro": 1024, "facebook/mbart-large-cc25": 1024, } FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] # fmt: skip class MBartTokenizer(PreTrainedTokenizer): """ Construct an MBART tokenizer. Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code> <tokens> <eos>` for target language documents. Examples: ```python >>> from transformers import MBartTokenizer >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO") >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria" >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria" >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt") ```""" vocab_files_names = VOCAB_FILES_NAMES max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", tokenizer_file=None, src_lang=None, tgt_lang=None, sp_model_kwargs: Optional[Dict[str, Any]] = None, additional_special_tokens=None, **kwargs, ): # Mask token behave like a normal word, i.e. include the space before it mask_token = ( AddedToken(mask_token, lstrip=True, normalized=False) if isinstance(mask_token, str) else mask_token ) self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.fairseq_offset = 1 self.sp_model_size = len(self.sp_model) self.lang_code_to_id = { code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES) } self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()} self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset self.fairseq_tokens_to_ids.update(self.lang_code_to_id) self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} _additional_special_tokens = list(self.lang_code_to_id.keys()) if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, tokenizer_file=None, src_lang=src_lang, tgt_lang=tgt_lang, additional_special_tokens=_additional_special_tokens, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) self._src_lang = src_lang if src_lang is not None else "en_XX" self.cur_lang_code_id = self.lang_code_to_id[self._src_lang] self.tgt_lang = tgt_lang self.set_src_lang_special_tokens(self._src_lang) def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) @property def vocab_size(self): return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token @property def src_lang(self) -> str: return self._src_lang @src_lang.setter def src_lang(self, new_src_lang: str) -> None: self._src_lang = new_src_lang self.set_src_lang_special_tokens(self._src_lang) def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) prefix_ones = [1] * len(self.prefix_tokens) suffix_ones = [1] * len(self.suffix_tokens) if token_ids_1 is None: return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An MBART sequence has the following format, where `X` represents the sequence: - `input_ids` (for encoder) `X [eos, src_lang_code]` - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]` BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return self.prefix_tokens + token_ids_0 + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. mBART does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] def _build_translation_inputs( self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs ): """Used by translation pipeline, to prepare inputs for the generate function""" if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") self.src_lang = src_lang inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs) tgt_lang_id = self.convert_tokens_to_ids(tgt_lang) inputs["forced_bos_token_id"] = tgt_lang_id return inputs def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,) def prepare_seq2seq_batch( self, src_texts: List[str], src_lang: str = "en_XX", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "ro_RO", **kwargs, ) -> BatchEncoding: self.src_lang = src_lang self.tgt_lang = tgt_lang return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs) def _switch_to_input_mode(self): return self.set_src_lang_special_tokens(self.src_lang) def _switch_to_target_mode(self): return self.set_tgt_lang_special_tokens(self.tgt_lang) def set_src_lang_special_tokens(self, src_lang) -> None: """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].""" self.cur_lang_code = self.lang_code_to_id[src_lang] self.prefix_tokens = [] self.suffix_tokens = [self.eos_token_id, self.cur_lang_code] def set_tgt_lang_special_tokens(self, lang: str) -> None: """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code].""" self.cur_lang_code = self.lang_code_to_id[lang] self.prefix_tokens = [] self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]

transformers/src/transformers/models/mbart/tokenization_mbart.py/0

# coding=utf-8 # Copyright 2021 Studio Ousia and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License """ Tokenization classes for mLUKE.""" import itertools import json import os from collections.abc import Mapping from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...tokenization_utils_base import ( ENCODE_KWARGS_DOCSTRING, AddedToken, BatchEncoding, EncodedInput, PaddingStrategy, TensorType, TextInput, TextInputPair, TruncationStrategy, to_py_obj, ) from ...utils import add_end_docstrings, is_tf_tensor, is_torch_tensor, logging logger = logging.get_logger(__name__) EntitySpan = Tuple[int, int] EntitySpanInput = List[EntitySpan] Entity = str EntityInput = List[Entity] SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "entity_vocab_file": "entity_vocab.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/vocab.json", }, "merges_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/merges.txt", }, "entity_vocab_file": { "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/entity_vocab.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "studio-ousia/mluke-base": 512, } ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" return_token_type_ids (`bool`, *optional*): Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are token type IDs?](../glossary#token-type-ids) return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) return_overflowing_tokens (`bool`, *optional*, defaults to `False`): Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead of returning overflowing tokens. return_special_tokens_mask (`bool`, *optional*, defaults to `False`): Whether or not to return special tokens mask information. return_offsets_mapping (`bool`, *optional*, defaults to `False`): Whether or not to return `(char_start, char_end)` for each token. This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using Python's tokenizer, this method will raise `NotImplementedError`. return_length (`bool`, *optional*, defaults to `False`): Whether or not to return the lengths of the encoded inputs. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. **kwargs: passed to the `self.tokenize()` method Return: [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or if *"token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **entity_ids** -- List of entity ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **entity_position_ids** -- List of entity positions in the input sequence to be fed to a model. - **entity_token_type_ids** -- List of entity token type ids to be fed to a model (when `return_token_type_ids=True` or if *"entity_token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **entity_attention_mask** -- List of indices specifying which entities should be attended to by the model (when `return_attention_mask=True` or if *"entity_attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **entity_start_positions** -- List of the start positions of entities in the word token sequence (when `task="entity_span_classification"`). - **entity_end_positions** -- List of the end positions of entities in the word token sequence (when `task="entity_span_classification"`). - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and `return_overflowing_tokens=True`). - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and `return_overflowing_tokens=True`). - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). - **length** -- The length of the inputs (when `return_length=True`) """ class MLukeTokenizer(PreTrainedTokenizer): """ Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. entity_vocab_file (`str`): Path to the entity vocabulary file. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. task (`str`, *optional*): Task for which you want to prepare sequences. One of `"entity_classification"`, `"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity sequence is automatically created based on the given entity span(s). max_entity_length (`int`, *optional*, defaults to 32): The maximum length of `entity_ids`. max_mention_length (`int`, *optional*, defaults to 30): The maximum number of tokens inside an entity span. entity_token_1 (`str`, *optional*, defaults to `<ent>`): The special token used to represent an entity span in a word token sequence. This token is only used when `task` is set to `"entity_classification"` or `"entity_pair_classification"`. entity_token_2 (`str`, *optional*, defaults to `<ent2>`): The special token used to represent an entity span in a word token sequence. This token is only used when `task` is set to `"entity_pair_classification"`. additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`): Additional special tokens used by the tokenizer. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Attributes: sp_model (`SentencePieceProcessor`): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, entity_vocab_file, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", task=None, max_entity_length=32, max_mention_length=30, entity_token_1="<ent>", entity_token_2="<ent2>", entity_unk_token="[UNK]", entity_pad_token="[PAD]", entity_mask_token="[MASK]", entity_mask2_token="[MASK2]", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token # we add 2 special tokens for downstream tasks # for more information about lstrip and rstrip, see https://github.com/huggingface/transformers/pull/2778 entity_token_1 = ( AddedToken(entity_token_1, lstrip=False, rstrip=False) if isinstance(entity_token_1, str) else entity_token_1 ) entity_token_2 = ( AddedToken(entity_token_2, lstrip=False, rstrip=False) if isinstance(entity_token_2, str) else entity_token_2 ) additional_special_tokens = kwargs.pop("additional_special_tokens", []) additional_special_tokens += [entity_token_1, entity_token_2] self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.fairseq_offset = 1 self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + self.fairseq_offset self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle: self.entity_vocab = json.load(entity_vocab_handle) for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]: if entity_special_token not in self.entity_vocab: raise ValueError( f"Specified entity special token ``{entity_special_token}`` is not found in entity_vocab. " f"Probably an incorrect entity vocab file is loaded: {entity_vocab_file}." ) self.entity_unk_token_id = self.entity_vocab[entity_unk_token] self.entity_pad_token_id = self.entity_vocab[entity_pad_token] self.entity_mask_token_id = self.entity_vocab[entity_mask_token] self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token] self.task = task if task is None or task == "entity_span_classification": self.max_entity_length = max_entity_length elif task == "entity_classification": self.max_entity_length = 1 elif task == "entity_pair_classification": self.max_entity_length = 2 else: raise ValueError( f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification'," " 'entity_span_classification'] only." ) self.max_mention_length = max_mention_length super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, sp_model_kwargs=self.sp_model_kwargs, task=task, max_entity_length=max_entity_length, max_mention_length=max_mention_length, entity_token_1=entity_token_1, entity_token_2=entity_token_2, entity_unk_token=entity_unk_token, entity_pad_token=entity_pad_token, entity_mask_token=entity_mask_token, entity_mask2_token=entity_mask2_token, additional_special_tokens=additional_special_tokens, **kwargs, ) @property # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.vocab_size def vocab_size(self): return len(self.sp_model) + self.fairseq_offset + 1 # Add the <mask> token # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_vocab def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._tokenize def _tokenize(self, text: str) -> List[str]: # TODO check if the t5/llama PR also applies here return self.sp_model.encode(text, out_type=str) # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._convert_token_to_id def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.__call__ def __call__( self, text: Union[TextInput, List[TextInput]], text_pair: Optional[Union[TextInput, List[TextInput]]] = None, entity_spans: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None, entity_spans_pair: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None, entities: Optional[Union[EntityInput, List[EntityInput]]] = None, entities_pair: Optional[Union[EntityInput, List[EntityInput]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs, ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences, depending on the task you want to prepare them for. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this tokenizer does not support tokenization based on pretokenized strings. text_pair (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this tokenizer does not support tokenization based on pretokenized strings. entity_spans (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*): The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each with two integers denoting character-based start and end positions of entities. If you specify `"entity_classification"` or `"entity_pair_classification"` as the `task` argument in the constructor, the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each sequence must be equal to the length of each sequence of `entities`. entity_spans_pair (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*): The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each with two integers denoting character-based start and end positions of entities. If you specify the `task` argument in the constructor, this argument is ignored. If you specify `entities_pair`, the length of each sequence must be equal to the length of each sequence of `entities_pair`. entities (`List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of each sequence must be equal to the length of each sequence of `entity_spans`. If you specify `entity_spans` without specifying this argument, the entity sequence or the batch of entity sequences is automatically constructed by filling it with the [MASK] entity. entities_pair (`List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify `entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity sequences is automatically constructed by filling it with the [MASK] entity. max_entity_length (`int`, *optional*): The maximum length of `entity_ids`. """ # Input type checking for clearer error is_valid_single_text = isinstance(text, str) is_valid_batch_text = isinstance(text, (list, tuple)) and (len(text) == 0 or (isinstance(text[0], str))) if not (is_valid_single_text or is_valid_batch_text): raise ValueError("text input must be of type `str` (single example) or `List[str]` (batch).") is_valid_single_text_pair = isinstance(text_pair, str) is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and ( len(text_pair) == 0 or isinstance(text_pair[0], str) ) if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair): raise ValueError("text_pair input must be of type `str` (single example) or `List[str]` (batch).") is_batched = bool(isinstance(text, (list, tuple))) if is_batched: batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text if entities is None: batch_entities_or_entities_pairs = None else: batch_entities_or_entities_pairs = ( list(zip(entities, entities_pair)) if entities_pair is not None else entities ) if entity_spans is None: batch_entity_spans_or_entity_spans_pairs = None else: batch_entity_spans_or_entity_spans_pairs = ( list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans ) return self.batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs, batch_entities_or_entities_pairs=batch_entities_or_entities_pairs, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, max_entity_length=max_entity_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( text=text, text_pair=text_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, entities=entities, entities_pair=entities_pair, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, max_entity_length=max_entity_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._encode_plus def _encode_plus( self, text: Union[TextInput], text_pair: Optional[Union[TextInput]] = None, entity_spans: Optional[EntitySpanInput] = None, entity_spans_pair: Optional[EntitySpanInput] = None, entities: Optional[EntityInput] = None, entities_pair: Optional[EntityInput] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs, ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) if is_split_into_words: raise NotImplementedError("is_split_into_words is not supported in this tokenizer.") ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) = self._create_input_sequence( text=text, text_pair=text_pair, entities=entities, entities_pair=entities_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, **kwargs, ) # prepare_for_model will create the attention_mask and token_type_ids return self.prepare_for_model( first_ids, pair_ids=second_ids, entity_ids=first_entity_ids, pair_entity_ids=second_entity_ids, entity_token_spans=first_entity_token_spans, pair_entity_token_spans=second_entity_token_spans, add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_encode_plus def _batch_encode_plus( self, batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair]], batch_entity_spans_or_entity_spans_pairs: Optional[ Union[List[EntitySpanInput], List[Tuple[EntitySpanInput, EntitySpanInput]]] ] = None, batch_entities_or_entities_pairs: Optional[ Union[List[EntityInput], List[Tuple[EntityInput, EntityInput]]] ] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, is_split_into_words: Optional[bool] = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs, ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast." ) if is_split_into_words: raise NotImplementedError("is_split_into_words is not supported in this tokenizer.") # input_ids is a list of tuples (one for each example in the batch) input_ids = [] entity_ids = [] entity_token_spans = [] for index, text_or_text_pair in enumerate(batch_text_or_text_pairs): if not isinstance(text_or_text_pair, (list, tuple)): text, text_pair = text_or_text_pair, None else: text, text_pair = text_or_text_pair entities, entities_pair = None, None if batch_entities_or_entities_pairs is not None: entities_or_entities_pairs = batch_entities_or_entities_pairs[index] if entities_or_entities_pairs: if isinstance(entities_or_entities_pairs[0], str): entities, entities_pair = entities_or_entities_pairs, None else: entities, entities_pair = entities_or_entities_pairs entity_spans, entity_spans_pair = None, None if batch_entity_spans_or_entity_spans_pairs is not None: entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index] if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance( entity_spans_or_entity_spans_pairs[0], list ): entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs else: entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) = self._create_input_sequence( text=text, text_pair=text_pair, entities=entities, entities_pair=entities_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, **kwargs, ) input_ids.append((first_ids, second_ids)) entity_ids.append((first_entity_ids, second_entity_ids)) entity_token_spans.append((first_entity_token_spans, second_entity_token_spans)) batch_outputs = self._batch_prepare_for_model( input_ids, batch_entity_ids_pairs=entity_ids, batch_entity_token_spans_pairs=entity_token_spans, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=return_tensors, verbose=verbose, ) return BatchEncoding(batch_outputs) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._check_entity_input_format def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]): if not isinstance(entity_spans, list): raise ValueError("entity_spans should be given as a list") elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple): raise ValueError( "entity_spans should be given as a list of tuples containing the start and end character indices" ) if entities is not None: if not isinstance(entities, list): raise ValueError("If you specify entities, they should be given as a list") if len(entities) > 0 and not isinstance(entities[0], str): raise ValueError("If you specify entities, they should be given as a list of entity names") if len(entities) != len(entity_spans): raise ValueError("If you specify entities, entities and entity_spans must be the same length") # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._create_input_sequence def _create_input_sequence( self, text: Union[TextInput], text_pair: Optional[Union[TextInput]] = None, entities: Optional[EntityInput] = None, entities_pair: Optional[EntityInput] = None, entity_spans: Optional[EntitySpanInput] = None, entity_spans_pair: Optional[EntitySpanInput] = None, **kwargs, ) -> Tuple[list, list, list, list, list, list]: def get_input_ids(text): tokens = self.tokenize(text, **kwargs) return self.convert_tokens_to_ids(tokens) def get_input_ids_and_entity_token_spans(text, entity_spans): if entity_spans is None: return get_input_ids(text), None cur = 0 input_ids = [] entity_token_spans = [None] * len(entity_spans) split_char_positions = sorted(frozenset(itertools.chain(*entity_spans))) char_pos2token_pos = {} for split_char_position in split_char_positions: orig_split_char_position = split_char_position if ( split_char_position > 0 and text[split_char_position - 1] == " " ): # whitespace should be prepended to the following token split_char_position -= 1 if cur != split_char_position: input_ids += get_input_ids(text[cur:split_char_position]) cur = split_char_position char_pos2token_pos[orig_split_char_position] = len(input_ids) input_ids += get_input_ids(text[cur:]) entity_token_spans = [ (char_pos2token_pos[char_start], char_pos2token_pos[char_end]) for char_start, char_end in entity_spans ] return input_ids, entity_token_spans first_ids, second_ids = None, None first_entity_ids, second_entity_ids = None, None first_entity_token_spans, second_entity_token_spans = None, None if self.task is None: if entity_spans is None: first_ids = get_input_ids(text) else: self._check_entity_input_format(entities, entity_spans) first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) if entities is None: first_entity_ids = [self.entity_mask_token_id] * len(entity_spans) else: first_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities] if text_pair is not None: if entity_spans_pair is None: second_ids = get_input_ids(text_pair) else: self._check_entity_input_format(entities_pair, entity_spans_pair) second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans( text_pair, entity_spans_pair ) if entities_pair is None: second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair) else: second_entity_ids = [ self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities_pair ] elif self.task == "entity_classification": if not (isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)): raise ValueError( "Entity spans should be a list containing a single tuple " "containing the start and end character indices of an entity" ) first_entity_ids = [self.entity_mask_token_id] first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) # add special tokens to input ids entity_token_start, entity_token_end = first_entity_token_spans[0] first_ids = ( first_ids[:entity_token_end] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_end:] ) first_ids = ( first_ids[:entity_token_start] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_start:] ) first_entity_token_spans = [(entity_token_start, entity_token_end + 2)] elif self.task == "entity_pair_classification": if not ( isinstance(entity_spans, list) and len(entity_spans) == 2 and isinstance(entity_spans[0], tuple) and isinstance(entity_spans[1], tuple) ): raise ValueError( "Entity spans should be provided as a list of two tuples, " "each tuple containing the start and end character indices of an entity" ) head_span, tail_span = entity_spans first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id] first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) head_token_span, tail_token_span = first_entity_token_spans token_span_with_special_token_ids = [ (head_token_span, self.additional_special_tokens_ids[0]), (tail_token_span, self.additional_special_tokens_ids[1]), ] if head_token_span[0] < tail_token_span[0]: first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2) first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4) token_span_with_special_token_ids = reversed(token_span_with_special_token_ids) else: first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4) first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2) for (entity_token_start, entity_token_end), special_token_id in token_span_with_special_token_ids: first_ids = first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:] first_ids = first_ids[:entity_token_start] + [special_token_id] + first_ids[entity_token_start:] elif self.task == "entity_span_classification": if not (isinstance(entity_spans, list) and len(entity_spans) > 0 and isinstance(entity_spans[0], tuple)): raise ValueError( "Entity spans should be provided as a list of tuples, " "each tuple containing the start and end character indices of an entity" ) first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans) first_entity_ids = [self.entity_mask_token_id] * len(entity_spans) else: raise ValueError(f"Task {self.task} not supported") return ( first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_prepare_for_model def _batch_prepare_for_model( self, batch_ids_pairs: List[Tuple[List[int], None]], batch_entity_ids_pairs: List[Tuple[Optional[List[int]], Optional[List[int]]]], batch_entity_token_spans_pairs: List[Tuple[Optional[List[Tuple[int, int]]], Optional[List[Tuple[int, int]]]]], add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens Args: batch_ids_pairs: list of tokenized input ids or input ids pairs batch_entity_ids_pairs: list of entity ids or entity ids pairs batch_entity_token_spans_pairs: list of entity spans or entity spans pairs max_entity_length: The maximum length of the entity sequence. """ batch_outputs = {} for input_ids, entity_ids, entity_token_span_pairs in zip( batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs ): first_ids, second_ids = input_ids first_entity_ids, second_entity_ids = entity_ids first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs outputs = self.prepare_for_model( first_ids, second_ids, entity_ids=first_entity_ids, pair_entity_ids=second_entity_ids, entity_token_spans=first_entity_token_spans, pair_entity_token_spans=second_entity_token_spans, add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward truncation=truncation_strategy.value, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterward return_attention_mask=False, # we pad in batch afterward return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return batch_outputs @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.prepare_for_model def prepare_for_model( self, ids: List[int], pair_ids: Optional[List[int]] = None, entity_ids: Optional[List[int]] = None, pair_entity_ids: Optional[List[int]] = None, entity_token_spans: Optional[List[Tuple[int, int]]] = None, pair_entity_token_spans: Optional[List[Tuple[int, int]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, prepend_batch_axis: bool = False, **kwargs, ) -> BatchEncoding: """ Prepares a sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids, entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error. Args: ids (`List[int]`): Tokenized input ids of the first sequence. pair_ids (`List[int]`, *optional*): Tokenized input ids of the second sequence. entity_ids (`List[int]`, *optional*): Entity ids of the first sequence. pair_entity_ids (`List[int]`, *optional*): Entity ids of the second sequence. entity_token_spans (`List[Tuple[int, int]]`, *optional*): Entity spans of the first sequence. pair_entity_token_spans (`List[Tuple[int, int]]`, *optional*): Entity spans of the second sequence. max_entity_length (`int`, *optional*): The maximum length of the entity sequence. """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) # Compute lengths pair = bool(pair_ids is not None) len_ids = len(ids) len_pair_ids = len(pair_ids) if pair else 0 if return_token_type_ids and not add_special_tokens: raise ValueError( "Asking to return token_type_ids while setting add_special_tokens to False " "results in an undefined behavior. Please set add_special_tokens to True or " "set return_token_type_ids to None." ) if ( return_overflowing_tokens and truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is not None ): raise ValueError( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) # Load from model defaults if return_token_type_ids is None: return_token_type_ids = "token_type_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names encoded_inputs = {} # Compute the total size of the returned word encodings total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) # Truncation: Handle max sequence length and max_entity_length overflowing_tokens = [] if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: # truncate words up to max_length ids, pair_ids, overflowing_tokens = self.truncate_sequences( ids, pair_ids=pair_ids, num_tokens_to_remove=total_len - max_length, truncation_strategy=truncation_strategy, stride=stride, ) if return_overflowing_tokens: encoded_inputs["overflowing_tokens"] = overflowing_tokens encoded_inputs["num_truncated_tokens"] = total_len - max_length # Add special tokens if add_special_tokens: sequence = self.build_inputs_with_special_tokens(ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) entity_token_offset = 1 # 1 * <s> token pair_entity_token_offset = len(ids) + 3 # 1 * <s> token & 2 * <sep> tokens else: sequence = ids + pair_ids if pair else ids token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else []) entity_token_offset = 0 pair_entity_token_offset = len(ids) # Build output dictionary encoded_inputs["input_ids"] = sequence if return_token_type_ids: encoded_inputs["token_type_ids"] = token_type_ids if return_special_tokens_mask: if add_special_tokens: encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) else: encoded_inputs["special_tokens_mask"] = [0] * len(sequence) # Set max entity length if not max_entity_length: max_entity_length = self.max_entity_length if entity_ids is not None: total_entity_len = 0 num_invalid_entities = 0 valid_entity_ids = [ent_id for ent_id, span in zip(entity_ids, entity_token_spans) if span[1] <= len(ids)] valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)] total_entity_len += len(valid_entity_ids) num_invalid_entities += len(entity_ids) - len(valid_entity_ids) valid_pair_entity_ids, valid_pair_entity_token_spans = None, None if pair_entity_ids is not None: valid_pair_entity_ids = [ ent_id for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans) if span[1] <= len(pair_ids) ] valid_pair_entity_token_spans = [span for span in pair_entity_token_spans if span[1] <= len(pair_ids)] total_entity_len += len(valid_pair_entity_ids) num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids) if num_invalid_entities != 0: logger.warning( f"{num_invalid_entities} entities are ignored because their entity spans are invalid due to the" " truncation of input tokens" ) if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length: # truncate entities up to max_entity_length valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences( valid_entity_ids, pair_ids=valid_pair_entity_ids, num_tokens_to_remove=total_entity_len - max_entity_length, truncation_strategy=truncation_strategy, stride=stride, ) valid_entity_token_spans = valid_entity_token_spans[: len(valid_entity_ids)] if valid_pair_entity_token_spans is not None: valid_pair_entity_token_spans = valid_pair_entity_token_spans[: len(valid_pair_entity_ids)] if return_overflowing_tokens: encoded_inputs["overflowing_entities"] = overflowing_entities encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length final_entity_ids = valid_entity_ids + valid_pair_entity_ids if valid_pair_entity_ids else valid_entity_ids encoded_inputs["entity_ids"] = list(final_entity_ids) entity_position_ids = [] entity_start_positions = [] entity_end_positions = [] for token_spans, offset in ( (valid_entity_token_spans, entity_token_offset), (valid_pair_entity_token_spans, pair_entity_token_offset), ): if token_spans is not None: for start, end in token_spans: start += offset end += offset position_ids = list(range(start, end))[: self.max_mention_length] position_ids += [-1] * (self.max_mention_length - end + start) entity_position_ids.append(position_ids) entity_start_positions.append(start) entity_end_positions.append(end - 1) encoded_inputs["entity_position_ids"] = entity_position_ids if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = entity_start_positions encoded_inputs["entity_end_positions"] = entity_end_positions if return_token_type_ids: encoded_inputs["entity_token_type_ids"] = [0] * len(encoded_inputs["entity_ids"]) # Check lengths self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) # Padding if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: encoded_inputs = self.pad( encoded_inputs, max_length=max_length, max_entity_length=max_entity_length, padding=padding_strategy.value, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) if return_length: encoded_inputs["length"] = len(encoded_inputs["input_ids"]) batch_outputs = BatchEncoding( encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis ) return batch_outputs # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.pad def pad( self, encoded_inputs: Union[ BatchEncoding, List[BatchEncoding], Dict[str, EncodedInput], Dict[str, List[EncodedInput]], List[Dict[str, EncodedInput]], ], padding: Union[bool, str, PaddingStrategy] = True, max_length: Optional[int] = None, max_entity_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, ) -> BatchEncoding: """ Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the specific device of your tensors however. Args: encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]`): Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of tokenized inputs (list of [`BatchEncoding`], *Dict[str, List[List[int]]]* or *List[Dict[str, List[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader collate function. Instead of `List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see the note above for the return type. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). max_entity_length (`int`, *optional*): The maximum length of the entity sequence. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ # If we have a list of dicts, let's convert it in a dict of lists # We do this to allow using this method as a collate_fn function in PyTorch Dataloader if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping): encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()} # The model's main input name, usually `input_ids`, has be passed for padding if self.model_input_names[0] not in encoded_inputs: raise ValueError( "You should supply an encoding or a list of encodings to this method " f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}" ) required_input = encoded_inputs[self.model_input_names[0]] if not required_input: if return_attention_mask: encoded_inputs["attention_mask"] = [] return encoded_inputs # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch first_element = required_input[0] if isinstance(first_element, (list, tuple)): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. index = 0 while len(required_input[index]) == 0: index += 1 if index < len(required_input): first_element = required_input[index][0] # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do. if not isinstance(first_element, (int, list, tuple)): if is_tf_tensor(first_element): return_tensors = "tf" if return_tensors is None else return_tensors elif is_torch_tensor(first_element): return_tensors = "pt" if return_tensors is None else return_tensors elif isinstance(first_element, np.ndarray): return_tensors = "np" if return_tensors is None else return_tensors else: raise ValueError( f"type of {first_element} unknown: {type(first_element)}. " "Should be one of a python, numpy, pytorch or tensorflow object." ) for key, value in encoded_inputs.items(): encoded_inputs[key] = to_py_obj(value) # Convert padding_strategy in PaddingStrategy padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies( padding=padding, max_length=max_length, verbose=verbose ) if max_entity_length is None: max_entity_length = self.max_entity_length required_input = encoded_inputs[self.model_input_names[0]] if required_input and not isinstance(required_input[0], (list, tuple)): encoded_inputs = self._pad( encoded_inputs, max_length=max_length, max_entity_length=max_entity_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) return BatchEncoding(encoded_inputs, tensor_type=return_tensors) batch_size = len(required_input) if any(len(v) != batch_size for v in encoded_inputs.values()): raise ValueError("Some items in the output dictionary have a different batch size than others.") if padding_strategy == PaddingStrategy.LONGEST: max_length = max(len(inputs) for inputs in required_input) max_entity_length = ( max(len(inputs) for inputs in encoded_inputs["entity_ids"]) if "entity_ids" in encoded_inputs else 0 ) padding_strategy = PaddingStrategy.MAX_LENGTH batch_outputs = {} for i in range(batch_size): inputs = {k: v[i] for k, v in encoded_inputs.items()} outputs = self._pad( inputs, max_length=max_length, max_entity_length=max_entity_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) return BatchEncoding(batch_outputs, tensor_type=return_tensors) # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._pad def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, max_entity_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Pad encoded inputs (on left/right and up to predefined length or max length in the batch) Args: encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. max_entity_length: The maximum length of the entity sequence. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability `>= 7.5` (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ entities_provided = bool("entity_ids" in encoded_inputs) # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names if padding_strategy == PaddingStrategy.LONGEST: max_length = len(encoded_inputs["input_ids"]) if entities_provided: max_entity_length = len(encoded_inputs["entity_ids"]) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of if ( entities_provided and max_entity_length is not None and pad_to_multiple_of is not None and (max_entity_length % pad_to_multiple_of != 0) ): max_entity_length = ((max_entity_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and ( len(encoded_inputs["input_ids"]) != max_length or (entities_provided and len(encoded_inputs["entity_ids"]) != max_entity_length) ) # Initialize attention mask if not present. if return_attention_mask and "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) if entities_provided and return_attention_mask and "entity_attention_mask" not in encoded_inputs: encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"]) if needs_to_be_padded: difference = max_length - len(encoded_inputs["input_ids"]) if entities_provided: entity_difference = max_entity_length - len(encoded_inputs["entity_ids"]) if self.padding_side == "right": if return_attention_mask: encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference if entities_provided: encoded_inputs["entity_attention_mask"] = ( encoded_inputs["entity_attention_mask"] + [0] * entity_difference ) if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"] + [0] * difference if entities_provided: encoded_inputs["entity_token_type_ids"] = ( encoded_inputs["entity_token_type_ids"] + [0] * entity_difference ) if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference if entities_provided: encoded_inputs["entity_ids"] = ( encoded_inputs["entity_ids"] + [self.entity_pad_token_id] * entity_difference ) encoded_inputs["entity_position_ids"] = ( encoded_inputs["entity_position_ids"] + [[-1] * self.max_mention_length] * entity_difference ) if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = ( encoded_inputs["entity_start_positions"] + [0] * entity_difference ) encoded_inputs["entity_end_positions"] = ( encoded_inputs["entity_end_positions"] + [0] * entity_difference ) elif self.padding_side == "left": if return_attention_mask: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if entities_provided: encoded_inputs["entity_attention_mask"] = [0] * entity_difference + encoded_inputs[ "entity_attention_mask" ] if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = [0] * difference + encoded_inputs["token_type_ids"] if entities_provided: encoded_inputs["entity_token_type_ids"] = [0] * entity_difference + encoded_inputs[ "entity_token_type_ids" ] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"] if entities_provided: encoded_inputs["entity_ids"] = [self.entity_pad_token_id] * entity_difference + encoded_inputs[ "entity_ids" ] encoded_inputs["entity_position_ids"] = [ [-1] * self.max_mention_length ] * entity_difference + encoded_inputs["entity_position_ids"] if self.task == "entity_span_classification": encoded_inputs["entity_start_positions"] = [0] * entity_difference + encoded_inputs[ "entity_start_positions" ] encoded_inputs["entity_end_positions"] = [0] * entity_difference + encoded_inputs[ "entity_end_positions" ] else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str, str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) entity_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["entity_vocab_file"] ) with open(entity_vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.entity_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return out_vocab_file, entity_vocab_file # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.build_inputs_with_special_tokens def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM-RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_special_tokens_mask def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.create_token_type_ids_from_sequences def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

transformers/src/transformers/models/mluke/tokenization_mluke.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert MobileNetV2 checkpoints from the tensorflow/models library.""" import argparse import json import re from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( MobileNetV2Config, MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation, MobileNetV2ImageProcessor, load_tf_weights_in_mobilenet_v2, ) from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def get_mobilenet_v2_config(model_name): config = MobileNetV2Config(layer_norm_eps=0.001) if "quant" in model_name: raise ValueError("Quantized models are not supported.") matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name) if matches: config.depth_multiplier = float(matches[1]) config.image_size = int(matches[2]) if model_name.startswith("deeplabv3_"): config.output_stride = 8 config.num_labels = 21 filename = "pascal-voc-id2label.json" else: # The TensorFlow version of MobileNetV2 predicts 1001 classes instead # of the usual 1000. The first class (index 0) is "background". config.num_labels = 1001 filename = "imagenet-1k-id2label.json" repo_id = "huggingface/label-files" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) if config.num_labels == 1001: id2label = {int(k) + 1: v for k, v in id2label.items()} id2label[0] = "background" else: id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} return config # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False): """ Copy/paste/tweak model's weights to our MobileNetV2 structure. """ config = get_mobilenet_v2_config(model_name) # Load 🤗 model if model_name.startswith("deeplabv3_"): model = MobileNetV2ForSemanticSegmentation(config).eval() else: model = MobileNetV2ForImageClassification(config).eval() # Load weights from TensorFlow checkpoint load_tf_weights_in_mobilenet_v2(model, config, checkpoint_path) # Check outputs on an image, prepared by MobileNetV2ImageProcessor image_processor = MobileNetV2ImageProcessor( crop_size={"width": config.image_size, "height": config.image_size}, size={"shortest_edge": config.image_size + 32}, ) encoding = image_processor(images=prepare_img(), return_tensors="pt") outputs = model(**encoding) logits = outputs.logits if model_name.startswith("deeplabv3_"): assert logits.shape == (1, 21, 65, 65) if model_name == "deeplabv3_mobilenet_v2_1.0_513": expected_logits = torch.tensor( [ [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]], [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]], [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]], ] ) else: raise ValueError(f"Unknown model name: {model_name}") assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4) else: assert logits.shape == (1, 1001) if model_name == "mobilenet_v2_1.4_224": expected_logits = torch.tensor([0.0181, -1.0015, 0.4688]) elif model_name == "mobilenet_v2_1.0_224": expected_logits = torch.tensor([0.2445, -1.1993, 0.1905]) elif model_name == "mobilenet_v2_0.75_160": expected_logits = torch.tensor([0.2482, 0.4136, 0.6669]) elif model_name == "mobilenet_v2_0.35_96": expected_logits = torch.tensor([0.1451, -0.4624, 0.7192]) else: expected_logits = None if expected_logits is not None: assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4) Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving image processor to {pytorch_dump_folder_path}") image_processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print("Pushing to the hub...") repo_id = "google/" + model_name image_processor.push_to_hub(repo_id) model.push_to_hub(repo_id) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="mobilenet_v2_1.0_224", type=str, help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.", ) parser.add_argument( "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)." ) parser.add_argument( "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_movilevit_checkpoint( args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub )

transformers/src/transformers/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py/0

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ MPNet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json", } class MPNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MPNetModel`] or a [`TFMPNetModel`]. It is used to instantiate a MPNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the MPNet [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30527): Vocabulary size of the MPNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MPNetModel`] or [`TFMPNetModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. relative_attention_num_buckets (`int`, *optional*, defaults to 32): The number of buckets to use for each attention layer. Examples: ```python >>> from transformers import MPNetModel, MPNetConfig >>> # Initializing a MPNet mpnet-base style configuration >>> configuration = MPNetConfig() >>> # Initializing a model from the mpnet-base style configuration >>> model = MPNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "mpnet" def __init__( self, vocab_size=30527, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, initializer_range=0.02, layer_norm_eps=1e-12, relative_attention_num_buckets=32, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.relative_attention_num_buckets = relative_attention_num_buckets

transformers/src/transformers/models/mpnet/configuration_mpnet.py/0

# coding=utf-8 # Copyright 2020 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tensorflow mT5 model.""" from ...utils import logging from ..t5.modeling_tf_t5 import TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model from .configuration_mt5 import MT5Config logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "T5Config" class TFMT5Model(TFT5Model): r""" This class overrides [`TFT5Model`]. Please check the superclass for the appropriate documentation alongside usage examples. Examples: ```python >>> from transformers import TFMT5Model, AutoTokenizer >>> model = TFMT5Model.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, return_tensors="tf") >>> labels = tokenizer(text_target=summary, return_tensors="tf") >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```""" model_type = "mt5" config_class = MT5Config class TFMT5ForConditionalGeneration(TFT5ForConditionalGeneration): r""" This class overrides [`TFT5ForConditionalGeneration`]. Please check the superclass for the appropriate documentation alongside usage examples. Examples: ```python >>> from transformers import TFMT5ForConditionalGeneration, AutoTokenizer >>> model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, text_target=summary, return_tensors="tf") >>> outputs = model(**inputs) >>> loss = outputs.loss ```""" model_type = "mt5" config_class = MT5Config class TFMT5EncoderModel(TFT5EncoderModel): r""" This class overrides [`TFT5EncoderModel`]. Please check the superclass for the appropriate documentation alongside usage examples. Examples: ```python >>> from transformers import TFMT5EncoderModel, AutoTokenizer >>> model = TFMT5EncoderModel.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> input_ids = tokenizer(article, return_tensors="tf").input_ids >>> outputs = model(input_ids) >>> hidden_state = outputs.last_hidden_state ```""" model_type = "mt5" config_class = MT5Config

transformers/src/transformers/models/mt5/modeling_tf_mt5.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch Nezha model.""" import math import os import warnings from dataclasses import dataclass from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_nezha import NezhaConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "sijunhe/nezha-cn-base" _CONFIG_FOR_DOC = "NezhaConfig" NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "sijunhe/nezha-cn-base", "sijunhe/nezha-cn-large", "sijunhe/nezha-base-wwm", "sijunhe/nezha-large-wwm", # See all Nezha models at https://huggingface.co/models?filter=nezha ] def load_tf_weights_in_nezha(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) try: if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class NezhaRelativePositionsEncoding(nn.Module): """Implement the Functional Relative Position Encoding""" def __init__(self, length, depth, max_relative_position=127): super().__init__() vocab_size = max_relative_position * 2 + 1 range_vec = torch.arange(length) range_mat = range_vec.repeat(length).view(length, length) distance_mat = range_mat - torch.t(range_mat) distance_mat_clipped = torch.clamp(distance_mat, -max_relative_position, max_relative_position) final_mat = distance_mat_clipped + max_relative_position embeddings_table = torch.zeros(vocab_size, depth) position = torch.arange(0, vocab_size, dtype=torch.int64).float().unsqueeze(1) div_term = torch.exp(torch.arange(0, depth, 2).float() * (-math.log(10000.0) / depth)) embeddings_table[:, 0::2] = torch.sin(position * div_term) embeddings_table[:, 1::2] = torch.cos(position * div_term) flat_relative_positions_matrix = final_mat.view(-1) one_hot_relative_positions_matrix = torch.nn.functional.one_hot( flat_relative_positions_matrix, num_classes=vocab_size ).float() positions_encoding = torch.matmul(one_hot_relative_positions_matrix, embeddings_table) my_shape = list(final_mat.size()) my_shape.append(depth) positions_encoding = positions_encoding.view(my_shape) self.register_buffer("positions_encoding", positions_encoding, persistent=False) def forward(self, length): return self.positions_encoding[:length, :length, :] class NezhaEmbeddings(nn.Module): """Construct the embeddings from word and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.register_buffer( "token_type_ids", torch.zeros((1, config.max_position_embeddings), dtype=torch.long), persistent=False ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=inputs_embeds.device) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class NezhaSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.relative_positions_encoding = NezhaRelativePositionsEncoding( length=config.max_position_embeddings, depth=self.attention_head_size, max_relative_position=config.max_relative_position, ) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) batch_size, num_attention_heads, from_seq_length, to_seq_length = attention_scores.size() relations_keys = self.relative_positions_encoding(to_seq_length) query_layer_t = query_layer.permute(2, 0, 1, 3) query_layer_r = query_layer_t.contiguous().view( from_seq_length, batch_size * num_attention_heads, self.attention_head_size ) key_position_scores = torch.matmul(query_layer_r, relations_keys.permute(0, 2, 1)) key_position_scores_r = key_position_scores.view( from_seq_length, batch_size, num_attention_heads, from_seq_length ) key_position_scores_r_t = key_position_scores_r.permute(1, 2, 0, 3) attention_scores = attention_scores + key_position_scores_r_t attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in NezhaModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) relations_values = self.relative_positions_encoding(to_seq_length) attention_probs_t = attention_probs.permute(2, 0, 1, 3) attentions_probs_r = attention_probs_t.contiguous().view( from_seq_length, batch_size * num_attention_heads, to_seq_length ) value_position_scores = torch.matmul(attentions_probs_r, relations_values) value_position_scores_r = value_position_scores.view( from_seq_length, batch_size, num_attention_heads, self.attention_head_size ) value_position_scores_r_t = value_position_scores_r.permute(1, 2, 0, 3) context_layer = context_layer + value_position_scores_r_t context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Nezha class NezhaSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class NezhaAttention(nn.Module): def __init__(self, config): super().__init__() self.self = NezhaSelfAttention(config) self.output = NezhaSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Nezha class NezhaIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Nezha class NezhaOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class NezhaLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = NezhaAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = NezhaAttention(config) self.intermediate = NezhaIntermediate(config) self.output = NezhaOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Nezha class NezhaEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([NezhaLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Nezha class NezhaPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output # Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Nezha class NezhaPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Nezha class NezhaLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = NezhaPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Nezha class NezhaOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = NezhaLMPredictionHead(config) def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: prediction_scores = self.predictions(sequence_output) return prediction_scores # Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->Nezha class NezhaOnlyNSPHead(nn.Module): def __init__(self, config): super().__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output): seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score # Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->Nezha class NezhaPreTrainingHeads(nn.Module): def __init__(self, config): super().__init__() self.predictions = NezhaLMPredictionHead(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, sequence_output, pooled_output): prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class NezhaPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = NezhaConfig load_tf_weights = load_tf_weights_in_nezha base_model_prefix = "nezha" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) @dataclass class NezhaForPreTrainingOutput(ModelOutput): """ Output type of [`NezhaForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None prediction_logits: torch.FloatTensor = None seq_relationship_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None NEZHA_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`NezhaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ NEZHA_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare Nezha Model transformer outputting raw hidden-states without any specific head on top.", NEZHA_START_DOCSTRING, ) class NezhaModel(NezhaPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. """ def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = NezhaEmbeddings(config) self.encoder = NezhaEncoder(config) self.pooler = NezhaPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """ Nezha Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. """, NEZHA_START_DOCSTRING, ) class NezhaForPreTraining(NezhaPreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder"] def __init__(self, config): super().__init__(config) self.nezha = NezhaModel(config) self.cls = NezhaPreTrainingHeads(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=NezhaForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, next_sentence_label: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], NezhaForPreTrainingOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. Returns: Example: ```python >>> from transformers import AutoTokenizer, NezhaForPreTraining >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("sijunhe/nezha-cn-base") >>> model = NezhaForPreTraining.from_pretrained("sijunhe/nezha-cn-base") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nezha( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) total_loss = None if labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss if not return_dict: output = (prediction_scores, seq_relationship_score) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return NezhaForPreTrainingOutput( loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""Nezha Model with a `language modeling` head on top.""", NEZHA_START_DOCSTRING) class NezhaForMaskedLM(NezhaPreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `NezhaForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.nezha = NezhaModel(config, add_pooling_layer=False) self.cls = NezhaOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nezha( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs): input_shape = input_ids.shape effective_batch_size = input_shape[0] # add a dummy token if self.config.pad_token_id is None: raise ValueError("The PAD token should be defined for generation") attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1) dummy_token = torch.full( (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device ) input_ids = torch.cat([input_ids, dummy_token], dim=1) return {"input_ids": input_ids, "attention_mask": attention_mask} @add_start_docstrings( """Nezha Model with a `next sentence prediction (classification)` head on top.""", NEZHA_START_DOCSTRING, ) class NezhaForNextSentencePrediction(NezhaPreTrainedModel): def __init__(self, config): super().__init__(config) self.nezha = NezhaModel(config) self.cls = NezhaOnlyNSPHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring). Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Returns: Example: ```python >>> from transformers import AutoTokenizer, NezhaForNextSentencePrediction >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("sijunhe/nezha-cn-base") >>> model = NezhaForNextSentencePrediction.from_pretrained("sijunhe/nezha-cn-base") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> logits = outputs.logits >>> assert logits[0, 0] < logits[0, 1] # next sentence was random ``` """ if "next_sentence_label" in kwargs: warnings.warn( "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" " `labels` instead.", FutureWarning, ) labels = kwargs.pop("next_sentence_label") return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nezha( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] seq_relationship_scores = self.cls(pooled_output) next_sentence_loss = None if labels is not None: loss_fct = CrossEntropyLoss() next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) if not return_dict: output = (seq_relationship_scores,) + outputs[2:] return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output return NextSentencePredictorOutput( loss=next_sentence_loss, logits=seq_relationship_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Nezha Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, NEZHA_START_DOCSTRING, ) class NezhaForSequenceClassification(NezhaPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.nezha = NezhaModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nezha( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Nezha Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, NEZHA_START_DOCSTRING, ) class NezhaForMultipleChoice(NezhaPreTrainedModel): def __init__(self, config): super().__init__(config) self.nezha = NezhaModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.nezha( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] print(pooled_output.shape) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) print(logits.shape) print(num_choices) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Nezha Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, NEZHA_START_DOCSTRING, ) class NezhaForTokenClassification(NezhaPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.nezha = NezhaModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nezha( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Nezha Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, NEZHA_START_DOCSTRING, ) class NezhaForQuestionAnswering(NezhaPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.nezha = NezhaModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nezha( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/nezha/modeling_nezha.py/0

# coding=utf-8 # Copyright 2022 UW-Madison The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Nystromformer model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_nystromformer import NystromformerConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "uw-madison/nystromformer-512" _CONFIG_FOR_DOC = "NystromformerConfig" NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "uw-madison/nystromformer-512", # See all Nyströmformer models at https://huggingface.co/models?filter=nystromformer ] class NystromformerEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings + 2, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + 2, persistent=False ) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device), persistent=False, ) def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class NystromformerSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.num_landmarks = config.num_landmarks self.seq_len = config.segment_means_seq_len self.conv_kernel_size = config.conv_kernel_size if config.inv_coeff_init_option: self.init_option = config["inv_init_coeff_option"] else: self.init_option = "original" self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.conv_kernel_size is not None: self.conv = nn.Conv2d( in_channels=self.num_attention_heads, out_channels=self.num_attention_heads, kernel_size=(self.conv_kernel_size, 1), padding=(self.conv_kernel_size // 2, 0), bias=False, groups=self.num_attention_heads, ) # Function to approximate Moore-Penrose inverse via the iterative method def iterative_inv(self, mat, n_iter=6): identity = torch.eye(mat.size(-1), device=mat.device) key = mat # The entries of key are positive and ||key||_{\infty} = 1 due to softmax if self.init_option == "original": # This original implementation is more conservative to compute coefficient of Z_0. value = 1 / torch.max(torch.sum(key, dim=-2)) * key.transpose(-1, -2) else: # This is the exact coefficient computation, 1 / ||key||_1, of initialization of Z_0, leading to faster convergence. value = 1 / torch.max(torch.sum(key, dim=-2), dim=-1).values[:, :, None, None] * key.transpose(-1, -2) for _ in range(n_iter): key_value = torch.matmul(key, value) value = torch.matmul( 0.25 * value, 13 * identity - torch.matmul(key_value, 15 * identity - torch.matmul(key_value, 7 * identity - key_value)), ) return value def transpose_for_scores(self, layer): new_layer_shape = layer.size()[:-1] + (self.num_attention_heads, self.attention_head_size) layer = layer.view(*new_layer_shape) return layer.permute(0, 2, 1, 3) def forward(self, hidden_states, attention_mask=None, output_attentions=False): mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) query_layer = query_layer / math.sqrt(math.sqrt(self.attention_head_size)) key_layer = key_layer / math.sqrt(math.sqrt(self.attention_head_size)) if self.num_landmarks == self.seq_len: attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in NystromformerModel forward() function) attention_scores = attention_scores + attention_mask attention_probs = nn.functional.softmax(attention_scores, dim=-1) context_layer = torch.matmul(attention_probs, value_layer) else: q_landmarks = query_layer.reshape( -1, self.num_attention_heads, self.num_landmarks, self.seq_len // self.num_landmarks, self.attention_head_size, ).mean(dim=-2) k_landmarks = key_layer.reshape( -1, self.num_attention_heads, self.num_landmarks, self.seq_len // self.num_landmarks, self.attention_head_size, ).mean(dim=-2) kernel_1 = torch.nn.functional.softmax(torch.matmul(query_layer, k_landmarks.transpose(-1, -2)), dim=-1) kernel_2 = torch.nn.functional.softmax(torch.matmul(q_landmarks, k_landmarks.transpose(-1, -2)), dim=-1) attention_scores = torch.matmul(q_landmarks, key_layer.transpose(-1, -2)) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in NystromformerModel forward() function) attention_scores = attention_scores + attention_mask kernel_3 = nn.functional.softmax(attention_scores, dim=-1) attention_probs = torch.matmul(kernel_1, self.iterative_inv(kernel_2)) new_value_layer = torch.matmul(kernel_3, value_layer) context_layer = torch.matmul(attention_probs, new_value_layer) if self.conv_kernel_size is not None: context_layer += self.conv(value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.bert.modeling_bert.BertSelfOutput class NystromformerSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class NystromformerAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = NystromformerSelfAttention(config, position_embedding_type=position_embedding_type) self.output = NystromformerSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward(self, hidden_states, attention_mask=None, output_attentions=False): self_outputs = self.self(hidden_states, attention_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Nystromformer class NystromformerIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Nystromformer class NystromformerOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class NystromformerLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = NystromformerAttention(config) self.add_cross_attention = config.add_cross_attention self.intermediate = NystromformerIntermediate(config) self.output = NystromformerOutput(config) def forward(self, hidden_states, attention_mask=None, output_attentions=False): self_attention_outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class NystromformerEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([NystromformerLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, output_attentions, ) else: layer_outputs = layer_module(hidden_states, attention_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Nystromformer class NystromformerPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Nystromformer class NystromformerLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = NystromformerPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Nystromformer class NystromformerOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = NystromformerLMPredictionHead(config) def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: prediction_scores = self.predictions(sequence_output) return prediction_scores class NystromformerPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = NystromformerConfig base_model_prefix = "nystromformer" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) NYSTROMFORMER_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`NystromformerConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ NYSTROMFORMER_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare Nyströmformer Model transformer outputting raw hidden-states without any specific head on top.", NYSTROMFORMER_START_DOCSTRING, ) class NystromformerModel(NystromformerPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embeddings = NystromformerEmbeddings(config) self.encoder = NystromformerEncoder(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] if not return_dict: return (sequence_output,) + encoder_outputs[1:] return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings("""Nyströmformer Model with a `language modeling` head on top.""", NYSTROMFORMER_START_DOCSTRING) class NystromformerForMaskedLM(NystromformerPreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder"] def __init__(self, config): super().__init__(config) self.nystromformer = NystromformerModel(config) self.cls = NystromformerOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nystromformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class NystromformerClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) self.config = config def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = ACT2FN[self.config.hidden_act](x) x = self.dropout(x) x = self.out_proj(x) return x @add_start_docstrings( """ Nyströmformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, NYSTROMFORMER_START_DOCSTRING, ) class NystromformerForSequenceClassification(NystromformerPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.nystromformer = NystromformerModel(config) self.classifier = NystromformerClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nystromformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Nyströmformer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, NYSTROMFORMER_START_DOCSTRING, ) class NystromformerForMultipleChoice(NystromformerPreTrainedModel): def __init__(self, config): super().__init__(config) self.nystromformer = NystromformerModel(config) self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.nystromformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_state = outputs[0] # (bs * num_choices, seq_len, dim) pooled_output = hidden_state[:, 0] # (bs * num_choices, dim) pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim) pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Nyströmformer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, NYSTROMFORMER_START_DOCSTRING, ) class NystromformerForTokenClassification(NystromformerPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.nystromformer = NystromformerModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nystromformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Nyströmformer Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, NYSTROMFORMER_START_DOCSTRING, ) class NystromformerForQuestionAnswering(NystromformerPreTrainedModel): def __init__(self, config): super().__init__(config) config.num_labels = 2 self.num_labels = config.num_labels self.nystromformer = NystromformerModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.nystromformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/nystromformer/modeling_nystromformer.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert OPT checkpoint.""" import argparse from pathlib import Path import torch from transformers import OPTConfig, OPTModel from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def load_checkpoint(checkpoint_path): """Checkpoint path should end in model.pt""" sd = torch.load(checkpoint_path, map_location="cpu") if "model" in sd.keys(): sd = torch.load(checkpoint_path, map_location="cpu")["model"] # pop unnecessary weights keys_to_delete = [ "decoder.version", "decoder.output_projection.weight", ] for key in keys_to_delete: if key in sd: sd.pop(key) keys_to_rename = { "decoder.project_in_dim.weight": "decoder.project_in.weight", "decoder.project_out_dim.weight": "decoder.project_out.weight", "decoder.layer_norm.weight": "decoder.final_layer_norm.weight", "decoder.layer_norm.bias": "decoder.final_layer_norm.bias", } for old_key, new_key in keys_to_rename.items(): if old_key in sd: sd[new_key] = sd.pop(old_key) keys = list(sd.keys()) for key in keys: if ".qkv_proj." in key: value = sd[key] # We split QKV in separate Q,K,V q_name = key.replace(".qkv_proj.", ".q_proj.") k_name = key.replace(".qkv_proj.", ".k_proj.") v_name = key.replace(".qkv_proj.", ".v_proj.") depth = value.shape[0] assert depth % 3 == 0 # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming: # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97 k, v, q = torch.split(value, depth // 3, dim=0) sd[q_name] = q sd[k_name] = k sd[v_name] = v del sd[key] return sd @torch.no_grad() def convert_opt_checkpoint(checkpoint_path, pytorch_dump_folder_path, config=None): """ Copy/paste/tweak model's weights to our BERT structure. """ state_dict = load_checkpoint(checkpoint_path) if config is not None: config = OPTConfig.from_pretrained(config) else: config = OPTConfig() model = OPTModel(config).half().eval() model.load_state_dict(state_dict) # Check results Path(pytorch_dump_folder_path).mkdir(exist_ok=True) model.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--fairseq_path", type=str, help=( "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:" " https://huggingface.co/models?other=opt_metasq" ), ) parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.") args = parser.parse_args() convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)

transformers/src/transformers/models/opt/convert_opt_original_pytorch_checkpoint_to_pytorch.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Image/Text processor class for OWL-ViT """ import warnings from typing import List import numpy as np from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding from ...utils import is_flax_available, is_tf_available, is_torch_available class OwlViTProcessor(ProcessorMixin): r""" Constructs an OWL-ViT processor which wraps [`OwlViTImageProcessor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`] into a single processor that interits both the image processor and tokenizer functionalities. See the [`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information. Args: image_processor ([`OwlViTImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`], *optional*): The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "OwlViTImageProcessor" tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast") def __init__(self, image_processor=None, tokenizer=None, **kwargs): feature_extractor = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead.", FutureWarning, ) feature_extractor = kwargs.pop("feature_extractor") image_processor = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`.") if tokenizer is None: raise ValueError("You need to specify a `tokenizer`.") super().__init__(image_processor, tokenizer) def __call__(self, text=None, images=None, query_images=None, padding="max_length", return_tensors="np", **kwargs): """ Main method to prepare for the model one or several text(s) and image(s). This method forwards the `text` and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode: the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring of the above two methods for more information. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. query_images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The query image to be prepared, one query image is expected per target image to be queried. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. """ if text is None and query_images is None and images is None: raise ValueError( "You have to specify at least one text or query image or image. All three cannot be none." ) if text is not None: if isinstance(text, str) or (isinstance(text, List) and not isinstance(text[0], List)): encodings = [self.tokenizer(text, padding=padding, return_tensors=return_tensors, **kwargs)] elif isinstance(text, List) and isinstance(text[0], List): encodings = [] # Maximum number of queries across batch max_num_queries = max([len(t) for t in text]) # Pad all batch samples to max number of text queries for t in text: if len(t) != max_num_queries: t = t + [" "] * (max_num_queries - len(t)) encoding = self.tokenizer(t, padding=padding, return_tensors=return_tensors, **kwargs) encodings.append(encoding) else: raise TypeError("Input text should be a string, a list of strings or a nested list of strings") if return_tensors == "np": input_ids = np.concatenate([encoding["input_ids"] for encoding in encodings], axis=0) attention_mask = np.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0) elif return_tensors == "jax" and is_flax_available(): import jax.numpy as jnp input_ids = jnp.concatenate([encoding["input_ids"] for encoding in encodings], axis=0) attention_mask = jnp.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0) elif return_tensors == "pt" and is_torch_available(): import torch input_ids = torch.cat([encoding["input_ids"] for encoding in encodings], dim=0) attention_mask = torch.cat([encoding["attention_mask"] for encoding in encodings], dim=0) elif return_tensors == "tf" and is_tf_available(): import tensorflow as tf input_ids = tf.stack([encoding["input_ids"] for encoding in encodings], axis=0) attention_mask = tf.stack([encoding["attention_mask"] for encoding in encodings], axis=0) else: raise ValueError("Target return tensor type could not be returned") encoding = BatchEncoding() encoding["input_ids"] = input_ids encoding["attention_mask"] = attention_mask if query_images is not None: encoding = BatchEncoding() query_pixel_values = self.image_processor( query_images, return_tensors=return_tensors, **kwargs ).pixel_values encoding["query_pixel_values"] = query_pixel_values if images is not None: image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs) if text is not None and images is not None: encoding["pixel_values"] = image_features.pixel_values return encoding elif query_images is not None and images is not None: encoding["pixel_values"] = image_features.pixel_values return encoding elif text is not None or query_images is not None: return encoding else: return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors) def post_process(self, *args, **kwargs): """ This method forwards all its arguments to [`OwlViTImageProcessor.post_process`]. Please refer to the docstring of this method for more information. """ return self.image_processor.post_process(*args, **kwargs) def post_process_object_detection(self, *args, **kwargs): """ This method forwards all its arguments to [`OwlViTImageProcessor.post_process_object_detection`]. Please refer to the docstring of this method for more information. """ return self.image_processor.post_process_object_detection(*args, **kwargs) def post_process_image_guided_detection(self, *args, **kwargs): """ This method forwards all its arguments to [`OwlViTImageProcessor.post_process_one_shot_object_detection`]. Please refer to the docstring of this method for more information. """ return self.image_processor.post_process_image_guided_detection(*args, **kwargs) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @property def feature_extractor_class(self): warnings.warn( "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.", FutureWarning, ) return self.image_processor_class @property def feature_extractor(self): warnings.warn( "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.", FutureWarning, ) return self.image_processor

transformers/src/transformers/models/owlvit/processing_owlvit.py/0

# coding=utf-8 # Copyright 2022, Google and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PEGASUS-X model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json", "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json", # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x } class PegasusXConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`PegasusXModel`]. It is used to instantiate a PEGASUS-X model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the PEGASUS-X [google/pegasus-x-large](https://huggingface.co/google/pegasus-x-large) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 96103): Vocabulary size of the PEGASUS-X model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PegasusXModel`]. d_model (`int`, *optional*, defaults to 1024): Dimension of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 16): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 16): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. max_position_embeddings (`int`, *optional*, defaults to 16384): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models) forced_eos_token_id (`int`, *optional*, defaults to 1): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. num_global_tokens (`int`, *optional*, defaults to 128): Number of global tokens to use for the encoder block_size (`int`, *optional*, defaults to 512): Block size for encoder local attention. Sequence length should be an exact multiple of block size. block_size must be a multiple of 2 if stagger_local_block is True stagger_local_block (`bool`, *optional*, defaults to `True`): Whether to stagger every other local attention by half a block Example: ```python >>> from transformers import PegasusXConfig, PegasusXModel >>> # Initializing a PEGASUS google/pegasus-x-large style configuration >>> configuration = PegasusXConfig() >>> # Initializing a model (with random weights) from the google/pegasus-x-large style configuration >>> model = PegasusXModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "pegasus_x" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=96103, max_position_embeddings=16384, encoder_layers=16, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=16, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="gelu", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=0, scale_embedding=True, pad_token_id=0, eos_token_id=1, forced_eos_token_id=1, num_global_tokens=32, block_size=512, stagger_local_blocks=True, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.num_global_tokens = num_global_tokens self.block_size = block_size self.stagger_local_blocks = stagger_local_blocks super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) @property def num_attention_heads(self) -> int: return self.encoder_attention_heads @property def hidden_size(self) -> int: return self.d_model

transformers/src/transformers/models/pegasus_x/configuration_pegasus_x.py/0

# coding=utf-8 # Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Phi model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from packaging import version from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache from ...modeling_attn_mask_utils import ( _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa, ) from ...modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, get_torch_version, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings, ) from .configuration_phi import PhiConfig if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "microsoft/phi-1" _CONFIG_FOR_DOC = "PhiConfig" PHI_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/phi-1", "microsoft/phi-1_5", "microsoft/phi-2", # See all Phi models at https://huggingface.co/models?filter=phi ] # Copied from transformers.models.llama.modeling_llama._get_unpad_data def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) # Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Phi class PhiRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) # Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->Phi class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding): """PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) t = t / self.scaling_factor freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) # Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->Phi class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding): """PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) # Copied from transformers.models.llama.modeling_llama.rotate_half def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) # Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos[position_ids].unsqueeze(unsqueeze_dim) sin = sin[position_ids].unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi class PhiMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states # Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class PhiAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.attention_dropout = config.attention_dropout self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.partial_rotary_factor = config.partial_rotary_factor self.is_causal = True if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads})." ) self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True) self.qk_layernorm = config.qk_layernorm if self.qk_layernorm: self.q_layernorm = nn.LayerNorm( config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True ) self.k_layernorm = nn.LayerNorm( config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True ) self._init_rope() def _init_rope(self): if self.config.rope_scaling is None: self.rotary_emb = PhiRotaryEmbedding( int(self.partial_rotary_factor * self.head_dim), max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) else: scaling_type = self.config.rope_scaling["type"] scaling_factor = self.config.rope_scaling["factor"] if scaling_type == "linear": self.rotary_emb = PhiLinearScalingRotaryEmbedding( int(self.partial_rotary_factor * self.head_dim), max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "dynamic": self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding( int(self.partial_rotary_factor * self.head_dim), max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) if self.qk_layernorm: query_states = self.q_layernorm(query_states) key_states = self.k_layernorm(key_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: if self.layer_idx is None: raise ValueError( f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " "with a layer index." ) kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) # Partial rotary embedding query_rot, query_pass = ( query_states[..., : self.rotary_emb.dim], query_states[..., self.rotary_emb.dim :], ) key_rot, key_pass = ( key_states[..., : self.rotary_emb.dim], key_states[..., self.rotary_emb.dim :], ) # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids) # [batch_size, seq_length, num_heads, head_dim] query_states = torch.cat((query_rot, query_pass), dim=-1) key_states = torch.cat((key_rot, key_pass), dim=-1) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow attn_weights = torch.matmul( query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3) ) / math.sqrt(self.head_dim) if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.dense(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class PhiFlashAttention2(PhiAttention): """ Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: # PhiFlashAttention2 attention does not support output_attentions output_attentions = False bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) if self.qk_layernorm: query_states = self.q_layernorm(query_states) key_states = self.k_layernorm(key_states) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) # Partial rotary embedding query_rot, query_pass = ( query_states[..., : self.rotary_emb.dim], query_states[..., self.rotary_emb.dim :], ) key_rot, key_pass = ( key_states[..., : self.rotary_emb.dim], key_states[..., self.rotary_emb.dim :], ) # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids) # [batch_size, seq_length, num_heads, head_dim] query_states = torch.cat((query_rot, query_pass), dim=-1) key_states = torch.cat((key_rot, key_pass), dim=-1) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) attn_dropout = self.attention_dropout if self.training else 0.0 # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. if query_states.dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) attn_output = self._flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=attn_dropout, softmax_scale=None ) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.dense(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`int`, *optional*): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) """ if not self._flash_attn_uses_top_left_mask: causal = self.is_causal else: # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. causal = self.is_causal and query_length != 1 # Contains at least one padding token in the sequence if attention_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query_states, key_states, value_states, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=causal, ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal ) return attn_output # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) class PhiSdpaAttention(PhiAttention): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.require_contiguous_qkv = version.parse(get_torch_version()) < version.parse("2.2.0") """ SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `PhiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ # Adapted from PhiAttention.forward def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if output_attentions: # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. logger.warning_once( "PhiModel is using PhiSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not " "support `output_attentions=True`. Falling back to the manual attention implementation, but specifying " "the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can " 'be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) if self.qk_layernorm: query_states = self.q_layernorm(query_states) key_states = self.k_layernorm(key_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: if self.layer_idx is None: raise ValueError( f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " "with a layer index." ) kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) # Partial rotary embedding query_rot, query_pass = ( query_states[..., : self.rotary_emb.dim], query_states[..., self.rotary_emb.dim :], ) key_rot, key_pass = ( key_states[..., : self.rotary_emb.dim], key_states[..., self.rotary_emb.dim :], ) # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids) # [batch_size, seq_length, num_heads, head_dim] query_states = torch.cat((query_rot, query_pass), dim=-1) key_states = torch.cat((key_rot, key_pass), dim=-1) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0. # Reference: https://github.com/pytorch/pytorch/issues/112577 if self.require_contiguous_qkv and query_states.device.type == "cuda" and attention_mask is not None: query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attention_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=self.is_causal and attention_mask is None and q_len > 1, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.dense(attn_output) return attn_output, None, past_key_value PHI_ATTENTION_CLASSES = { "eager": PhiAttention, "flash_attention_2": PhiFlashAttention2, "sdpa": PhiSdpaAttention, } class PhiDecoderLayer(nn.Module): def __init__(self, config: PhiConfig, layer_idx: int): super().__init__() self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx) self.mlp = PhiMLP(config) self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.resid_dropout = nn.Dropout(config.resid_pdrop) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, past_key_value: Optional[Tuple[torch.Tensor]] = None, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention attn_outputs, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) attn_outputs = self.resid_dropout(attn_outputs) feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states)) hidden_states = attn_outputs + feed_forward_hidden_states + residual outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs PHI_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`PhiConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( "The bare Phi Model outputting raw hidden-states without any specific head on top.", PHI_START_DOCSTRING, ) class PhiPreTrainedModel(PreTrainedModel): config_class = PhiConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["PhiDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() PHI_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance; - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare Phi Model outputting raw hidden-states without any specific head on top.", PHI_START_DOCSTRING, ) class PhiModel(PhiPreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`] Args: config: PhiConfig """ def __init__(self, config: PhiConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.embed_dropout = nn.Dropout(config.embd_pdrop) self.layers = nn.ModuleList( [PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self._use_sdpa = config._attn_implementation == "sdpa" self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: batch_size, seq_length = input_ids.shape[:2] elif inputs_embeds is not None: batch_size, seq_length = inputs_embeds.shape[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") past_key_values_length = 0 if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if use_cache: use_legacy_cache = not isinstance(past_key_values, Cache) if use_legacy_cache: past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_key_values_length = past_key_values.get_usable_length(seq_length) if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device ) position_ids = position_ids.unsqueeze(0) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_dropout(inputs_embeds) # Attention mask. if self._use_flash_attention_2: # 2d mask is passed through the layers attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None elif self._use_sdpa and not output_attentions: attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length, ) else: # 4d mask is passed through the layers attention_mask = _prepare_4d_causal_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length ) hidden_states = inputs_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, past_key_values, output_attentions, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.final_layernorm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = None if use_cache: next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) class PhiForCausalLM(PhiPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True def __init__(self, config): super().__init__(config) self.model = PhiModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True) # Initialize weights and apply final processing self.post_init() # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings def get_input_embeddings(self): return self.model.embed_tokens # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings def set_input_embeddings(self, value): self.model.embed_tokens = value # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings def get_output_embeddings(self): return self.lm_head # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder def set_decoder(self, decoder): self.model = decoder # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder def get_decoder(self): return self.model @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, PhiForCausalLM >>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1") >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1") >>> prompt = "This is an example script ." >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] 'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str' ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) logits = logits.float() loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM.prepare_inputs_for_generation def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs ): if past_key_values is not None: if isinstance(past_key_values, Cache): cache_length = past_key_values.get_seq_length() past_length = past_key_values.seen_tokens max_cache_length = past_key_values.get_max_length() else: cache_length = past_length = past_key_values[0][0].shape[2] max_cache_length = None # Keep only the unprocessed tokens: # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as # input) if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard # input_ids based on the past_length. elif past_length < input_ids.shape[1]: input_ids = input_ids[:, past_length:] # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. if ( max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length ): attention_mask = attention_mask[:, -max_cache_length:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } ) return model_inputs @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past @add_start_docstrings( """ The PhiModel with a sequence classification head on top (linear layer). [`PhiForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, PHI_START_DOCSTRING, ) # Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->PHI,Llama->Phi with self.transformer->self.model, transformer_outputs->model_outputs class PhiForSequenceClassification(PhiPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.model = PhiModel(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict model_outputs = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = model_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size = input_ids.shape[0] else: batch_size = inputs_embeds.shape[0] if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: sequence_lengths = -1 pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None if labels is not None: labels = labels.to(logits.device) if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + model_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=model_outputs.past_key_values, hidden_states=model_outputs.hidden_states, attentions=model_outputs.attentions, ) @add_start_docstrings( """ PhiModel with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, PHI_START_DOCSTRING, ) # Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with MPT->PHI,Mpt->Phi,self.transformer->self.model,transformer_outputs->model_outputs class PhiForTokenClassification(PhiPreTrainedModel): def __init__(self, config: PhiConfig): super().__init__(config) self.num_labels = config.num_labels self.model = PhiModel(config) if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: classifier_dropout = config.classifier_dropout elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: classifier_dropout = config.hidden_dropout else: classifier_dropout = 0.1 self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, attention_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **deprecated_arguments, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict model_outputs = self.model( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = model_outputs[0] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(logits.device) batch_size, seq_length = labels.shape loss_fct = CrossEntropyLoss() loss = loss_fct( logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length) ) if not return_dict: output = (logits,) + model_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=model_outputs.hidden_states, attentions=model_outputs.attentions, )

transformers/src/transformers/models/phi/modeling_phi.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert PoolFormer checkpoints from the original repository. URL: https://github.com/sail-sg/poolformer""" import argparse import json from collections import OrderedDict from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def replace_key_with_offset(key, offset, original_name, new_name): """ Replaces the key by subtracting the offset from the original layer number """ to_find = original_name.split(".")[0] key_list = key.split(".") orig_block_num = int(key_list[key_list.index(to_find) - 2]) layer_num = int(key_list[key_list.index(to_find) - 1]) new_block_num = orig_block_num - offset key = key.replace(f"{orig_block_num}.{layer_num}.{original_name}", f"block.{new_block_num}.{layer_num}.{new_name}") return key def rename_keys(state_dict): new_state_dict = OrderedDict() total_embed_found, patch_emb_offset = 0, 0 for key, value in state_dict.items(): if key.startswith("network"): key = key.replace("network", "poolformer.encoder") if "proj" in key: # Works for the first embedding as well as the internal embedding layers if key.endswith("bias") and "patch_embed" not in key: patch_emb_offset += 1 to_replace = key[: key.find("proj")] key = key.replace(to_replace, f"patch_embeddings.{total_embed_found}.") key = key.replace("proj", "projection") if key.endswith("bias"): total_embed_found += 1 if "patch_embeddings" in key: key = "poolformer.encoder." + key if "mlp.fc1" in key: key = replace_key_with_offset(key, patch_emb_offset, "mlp.fc1", "output.conv1") if "mlp.fc2" in key: key = replace_key_with_offset(key, patch_emb_offset, "mlp.fc2", "output.conv2") if "norm1" in key: key = replace_key_with_offset(key, patch_emb_offset, "norm1", "before_norm") if "norm2" in key: key = replace_key_with_offset(key, patch_emb_offset, "norm2", "after_norm") if "layer_scale_1" in key: key = replace_key_with_offset(key, patch_emb_offset, "layer_scale_1", "layer_scale_1") if "layer_scale_2" in key: key = replace_key_with_offset(key, patch_emb_offset, "layer_scale_2", "layer_scale_2") if "head" in key: key = key.replace("head", "classifier") new_state_dict[key] = value return new_state_dict # We will verify our results on a COCO image def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) return image @torch.no_grad() def convert_poolformer_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path): """ Copy/paste/tweak model's weights to our PoolFormer structure. """ # load default PoolFormer configuration config = PoolFormerConfig() # set attributes based on model_name repo_id = "huggingface/label-files" size = model_name[-3:] config.num_labels = 1000 filename = "imagenet-1k-id2label.json" expected_shape = (1, 1000) # set config attributes id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} if size == "s12": config.depths = [2, 2, 6, 2] config.hidden_sizes = [64, 128, 320, 512] config.mlp_ratio = 4.0 crop_pct = 0.9 elif size == "s24": config.depths = [4, 4, 12, 4] config.hidden_sizes = [64, 128, 320, 512] config.mlp_ratio = 4.0 crop_pct = 0.9 elif size == "s36": config.depths = [6, 6, 18, 6] config.hidden_sizes = [64, 128, 320, 512] config.mlp_ratio = 4.0 config.layer_scale_init_value = 1e-6 crop_pct = 0.9 elif size == "m36": config.depths = [6, 6, 18, 6] config.hidden_sizes = [96, 192, 384, 768] config.mlp_ratio = 4.0 config.layer_scale_init_value = 1e-6 crop_pct = 0.95 elif size == "m48": config.depths = [8, 8, 24, 8] config.hidden_sizes = [96, 192, 384, 768] config.mlp_ratio = 4.0 config.layer_scale_init_value = 1e-6 crop_pct = 0.95 else: raise ValueError(f"Size {size} not supported") # load image processor image_processor = PoolFormerImageProcessor(crop_pct=crop_pct) # Prepare image image = prepare_img() pixel_values = image_processor(images=image, return_tensors="pt").pixel_values logger.info(f"Converting model {model_name}...") # load original state dict state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu")) # rename keys state_dict = rename_keys(state_dict) # create HuggingFace model and load state dict model = PoolFormerForImageClassification(config) model.load_state_dict(state_dict) model.eval() # Define image processor image_processor = PoolFormerImageProcessor(crop_pct=crop_pct) pixel_values = image_processor(images=prepare_img(), return_tensors="pt").pixel_values # forward pass outputs = model(pixel_values) logits = outputs.logits # define expected logit slices for different models if size == "s12": expected_slice = torch.tensor([-0.3045, -0.6758, -0.4869]) elif size == "s24": expected_slice = torch.tensor([0.4402, -0.1374, -0.8045]) elif size == "s36": expected_slice = torch.tensor([-0.6080, -0.5133, -0.5898]) elif size == "m36": expected_slice = torch.tensor([0.3952, 0.2263, -1.2668]) elif size == "m48": expected_slice = torch.tensor([0.1167, -0.0656, -0.3423]) else: raise ValueError(f"Size {size} not supported") # verify logits assert logits.shape == expected_shape assert torch.allclose(logits[0, :3], expected_slice, atol=1e-2) # finally, save model and image processor logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...") Path(pytorch_dump_folder_path).mkdir(exist_ok=True) model.save_pretrained(pytorch_dump_folder_path) print(f"Saving image processor to {pytorch_dump_folder_path}") image_processor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model_name", default="poolformer_s12", type=str, help="Name of the model you'd like to convert.", ) parser.add_argument( "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model." ) args = parser.parse_args() convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

transformers/src/transformers/models/poolformer/convert_poolformer_original_to_pytorch.py/0

# coding=utf-8 # Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, # Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team. # All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available, ) _import_structure = { "configuration_pvt": ["PVT_PRETRAINED_CONFIG_ARCHIVE_MAP", "PvtConfig", "PvtOnnxConfig"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["image_processing_pvt"] = ["PvtImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_pvt"] = [ "PVT_PRETRAINED_MODEL_ARCHIVE_LIST", "PvtForImageClassification", "PvtModel", "PvtPreTrainedModel", ] if TYPE_CHECKING: from .configuration_pvt import PVT_PRETRAINED_CONFIG_ARCHIVE_MAP, PvtConfig, PvtOnnxConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_pvt import PvtImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_pvt import ( PVT_PRETRAINED_MODEL_ARCHIVE_LIST, PvtForImageClassification, PvtModel, PvtPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/pvt/__init__.py/0

# coding=utf-8 # Copyright 2020, The RAG Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TFRAG model implementation.""" from __future__ import annotations import copy from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import tensorflow as tf from ...configuration_utils import PretrainedConfig from ...generation import TFLogitsProcessorList from ...modeling_tf_utils import ( TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, keras, shape_list, unpack_inputs, ) from ...utils import ModelOutput, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_rag import RagConfig from .retrieval_rag import RagRetriever logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "RagConfig" @dataclass class TFRetrievAugLMMarginOutput(ModelOutput): """ Base class for retriever augmented marginalized models outputs. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss. logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token. past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding. doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`): Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. retrieved_doc_embeds (`tf.Tensor` of shape `(batch_size, config.n_docs, hidden_size)`, *optional*, returned when *output_retrieved=True*): Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`. retrieved_doc_ids (`tf.Tensor` (int32) of shape `(batch_size, config.n_docs)`, *optional*, returned when *output_retrieved=True*): The indexes of the embedded documents retrieved by the retriever. context_input_ids (`tf.Tensor`(int32) of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever. context_attention_mask (`tf.Tensor` (int32) of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. question_encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model. question_enc_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden states of the question encoder at the output of each layer plus the initial embedding outputs. question_enc_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. generator_enc_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the generator encoder of the model. generator_enc_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs. generator_enc_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. generator_dec_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs. generator_dec_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None doc_scores: tf.Tensor | None = None retrieved_doc_embeds: tf.Tensor | None = None retrieved_doc_ids: tf.Tensor | None = None context_input_ids: tf.Tensor | None = None context_attention_mask: tf.Tensor | None = None question_encoder_last_hidden_state: tf.Tensor | None = None question_enc_hidden_states: Tuple[tf.Tensor, ...] | None = None question_enc_attentions: Tuple[tf.Tensor, ...] | None = None generator_enc_last_hidden_state: tf.Tensor | None = None generator_enc_hidden_states: Tuple[tf.Tensor, ...] | None = None generator_enc_attentions: Tuple[tf.Tensor, ...] | None = None generator_dec_hidden_states: Tuple[tf.Tensor, ...] | None = None generator_dec_attentions: Tuple[tf.Tensor, ...] | None = None @dataclass class TFRetrievAugLMOutput(ModelOutput): """ Args: logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token. past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding. doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`): Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. retrieved_doc_embeds (`tf.Tensor` of shape `(batch_size, config.n_docs, hidden_size)`, *optional*, returned when *output_retrieved=True*): Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`. retrieved_doc_ids (`tf.Tensor` of shape `(batch_size, config.n_docs)`, *optional*, returned when *output_retrieved=True*): The indexes of the embedded documents retrieved by the retriever. context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever. context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. question_encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model. question_enc_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden states of the question encoder at the output of each layer plus the initial embedding outputs. question_enc_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. generator_enc_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the generator encoder of the model. generator_enc_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs. generator_enc_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. generator_dec_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs. generator_dec_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None doc_scores: tf.Tensor | None = None retrieved_doc_embeds: tf.Tensor | None = None retrieved_doc_ids: tf.Tensor | None = None context_input_ids: tf.Tensor | None = None context_attention_mask: tf.Tensor | None = None question_encoder_last_hidden_state: tf.Tensor | None = None question_enc_hidden_states: Tuple[tf.Tensor, ...] | None = None question_enc_attentions: Tuple[tf.Tensor, ...] | None = None generator_enc_last_hidden_state: tf.Tensor | None = None generator_enc_hidden_states: Tuple[tf.Tensor, ...] | None = None generator_enc_attentions: Tuple[tf.Tensor, ...] | None = None generator_dec_hidden_states: Tuple[tf.Tensor, ...] | None = None generator_dec_attentions: Tuple[tf.Tensor, ...] | None = None class TFRagPreTrainedModel(TFPreTrainedModel): r""" RAG models were released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandra Piktus et al. RAG is a retriever augmented model and encapsulate three components: a question encoder, a dataset retriever and a generator, the encoder and generator are trainable while the retriever is just an indexed dataset. """ config_class = RagConfig base_model_prefix = "rag" _keys_to_ignore_on_load_missing = [r"position_ids"] @classmethod def from_pretrained_question_encoder_generator( cls, question_encoder_pretrained_model_name_or_path: str = None, generator_pretrained_model_name_or_path: str = None, retriever: RagRetriever = None, *model_args, **kwargs, ) -> TFPreTrainedModel: r""" Instantiates an question encoder and a generator from one or two base classes of the library from pretrained model checkpoints. Params: question_encoder_pretrained_model_name_or_path (`str`, *optional*): Information necessary to initiate the question encoder. Can be either: - A string with the *shortcut name* of a pretrained model to load from cache or download, e.g., `google-bert/bert-base-uncased`. - A string with the *identifier name* of a pretrained model that was user-uploaded to our S3, e.g., `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *pytorch index checkpoint file* (e.g, `./pt_model/`). In this case, `question_encoder_from_pt` should be set to `True`. generator_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`): Information necessary to initiate the generator. Can be either: - A string with the *shortcut name* of a pretrained model to load from cache or download, e.g., `google-t5/t5-small`. - A string with the *identifier name* of a pretrained model that was user-uploaded to our S3, e.g., `facebook/bart-base`. - A path to a *directory* containing model weights saved using [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *pytorch checkpoint file* (e.g, `./pt_model/`). In this case, `generator_from_pt` should be set to `True`. model_args (remaining positional arguments, *optional*): All remaining positional arguments will be passed to the underlying model's `__init__` method. retriever ([`RagRetriever`], *optional*): The retriever to use. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the question_encoder configuration, use the prefix *question_encoder_* for each configuration parameter. - To update the generator configuration, use the prefix *generator_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import RagRetriever, TFRagModel >>> # initialize a RAG from two pretrained models. >>> model = TFRagModel.from_pretrained_question_encoder_generator( ... "facebook/dpr-question_encoder-single-nq-base", "google-t5/t5-small" ... ) >>> # alternatively, initialize from pytorch pretrained models can also be done >>> model = TFRagModel.from_pretrained_question_encoder_generator( ... "facebook/dpr-question_encoder-single-nq-base", ... "facebook/bart-base", ... generator_from_pt=True, ... question_encoder_from_pt=True, ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./rag") >>> # load retriever >>> retriever = RagRetriever.from_pretrained( ... "facebook/rag-token-base", index_name="exact", use_dummy_dataset=True ... ) >>> # load fine-tuned model with retriever >>> model = TFRagModel.from_pretrained("./rag", retriever=retriever) ```""" kwargs_question_encoder = { argument[len("question_encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("question_encoder_") } kwargs_generator = { argument[len("generator_") :]: value for argument, value in kwargs.items() if argument.startswith("generator_") } # remove question_encoder, generator kwargs from kwargs for key in kwargs_question_encoder.keys(): del kwargs["question_encoder_" + key] for key in kwargs_generator.keys(): del kwargs["generator_" + key] # Load and initialize the question_encoder and generator # The distinction between question_encoder and generator at the model level is made # by the value of the flag `is_generator` that we need to set correctly. question_encoder = kwargs_question_encoder.pop("model", None) if question_encoder is None: assert question_encoder_pretrained_model_name_or_path is not None, ( "If `model` is not defined as an argument, a `question_encoder_pretrained_model_name_or_path` has to" " be defined" ) from ..auto.modeling_tf_auto import TFAutoModel if "config" not in kwargs_question_encoder: from ..auto.configuration_auto import AutoConfig question_encoder_config = AutoConfig.from_pretrained(question_encoder_pretrained_model_name_or_path) kwargs_question_encoder["config"] = question_encoder_config question_encoder = TFAutoModel.from_pretrained( question_encoder_pretrained_model_name_or_path, name="question_encoder", load_weight_prefix=cls.load_weight_prefix, *model_args, **kwargs_question_encoder, ) generator = kwargs_generator.pop("generator", None) if generator is None: assert generator_pretrained_model_name_or_path is not None, ( "If `generator_model` is not defined as an argument, a `generator_pretrained_model_name_or_path` has" " to be defined" ) from ..auto.modeling_tf_auto import TFAutoModelForSeq2SeqLM if "config" not in kwargs_generator: from ..auto.configuration_auto import AutoConfig generator_config = AutoConfig.from_pretrained(generator_pretrained_model_name_or_path) kwargs_generator["config"] = generator_config generator = TFAutoModelForSeq2SeqLM.from_pretrained( generator_pretrained_model_name_or_path, name="generator", load_weight_prefix=cls.load_weight_prefix, **kwargs_generator, ) # instantiate config with corresponding kwargs config = kwargs.get("config", None) if config is None: config = RagConfig.from_question_encoder_generator_configs( question_encoder.config, generator.config, **kwargs ) return cls(question_encoder=question_encoder, generator=generator, config=config, retriever=retriever) RAG_START_DOCSTRING = r""" RAG is a sequence-to-sequence model which encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator. The question encoder can be any *autoencoding* model, preferably [`TFDPRQuestionEncoder`], and the generator can be any *seq2seq* model, preferably [`TFBartForConditionalGeneration`]. The model can be initialized with a [`RagRetriever`] for end-to-end generation or used in combination with the outputs of a retriever in multiple steps---see examples for more details. The model is compatible any *autoencoding* model as the `question_encoder` and any *seq2seq* model with language model head as the `generator`. It has been tested with [`TFDPRQuestionEncoder`] as the `question_encoder` and [`TFBartForConditionalGeneration`] as the `generator`. This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Tensorflow [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. The model is in a developing state as it is now fully supports in eager-mode only, and may not be exported in SavedModel format. Args: config ([`RagConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. question_encoder ([`TFPreTrainedModel`]): An encoder model compatible with the faiss index encapsulated by the `retriever`. generator ([`TFPreTrainedModel`]): A seq2seq model used as the generator in the RAG architecture. retriever ([`RagRetriever`]): A retriever class encapsulating a faiss index queried to obtain context documents for current inputs. """ RAG_FORWARD_INPUTS_DOCSTRING = r""" Args: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. [`RagConfig`], used to initialize the model, specifies which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to obtain the indices. attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_outputs (`tuple(tuple(tf.Tensor)`, *optional*) Tuple consists of (`generator_enc_last_hidden_state`, *optional*: `generator_enc_hidden_states`, *optional*: `generator_enc_attentions`). `generator_enc_last_hidden_state` of shape `(batch_size, n_docs * sequence_length, hidden_size)` is a sequence of hidden-states at the output of the last layer of the generator's encoder. Used by the ([`TFRagModel`]) model during decoding. decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): Provide for generation tasks. `None` by default, construct as per instructions for the generator model you're using with your RAG instance. decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. past_key_values (`tuple(tuple(tf.Tensor))`): Tuple consists of two elements: `encoder_outputs` of the RAG model (see `encoder_outputs`) and `past_key_values` of the underlying generator. Can be used to speed up decoding. `past_key_values` are used in the ([`RagTokenForGeneration`]) model during decoding. doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`): Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` `doc_scores` has to be provided to the forward pass. `doc_scores` can be computed via `question_encoder_last_hidden_state` and `retrieved_doc_embeds`, see examples for more information. context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever` ``context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by [`~RagRetriever.__call__`]. context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever` `context_attention_mask` has to be provided to the forward pass. `context_attention_mask` are returned by [`~RagRetriever.__call__`]. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. output_retrieved(`bool`, *optional*): Whether or not to return the `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask`. See returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`TFRetrievAugLMOutput`] instead of a plain tuple. n_docs (`int`, *optional*, defaults to `config.n_docs``) Number of documents to retrieve and/or number of documents for which to generate an answer. """ @add_start_docstrings_to_model_forward(RAG_START_DOCSTRING) class TFRagModel(TFRagPreTrainedModel): load_weight_prefix = "tf_rag_model_1" def __init__( self, config: Optional[PretrainedConfig] = None, question_encoder: Optional[TFPreTrainedModel] = None, generator: Optional[TFPreTrainedModel] = None, retriever: Optional[RagRetriever] = None, load_weight_prefix: Optional[str] = None, **kwargs, ): assert config is not None or ( question_encoder is not None and generator is not None ), "Either a configuration or an question_encoder and a generator has to be provided." if config is None: config = RagConfig.from_question_encoder_generator_configs( question_encoder.config, generator.config, **kwargs ) else: assert isinstance(config, self.config_class), f"config: {config} has to be of type {self.config_class}" super().__init__(config, **kwargs) if question_encoder is None: from ..auto.modeling_tf_auto import TFAutoModel question_encoder = TFAutoModel.from_config(config.question_encoder, name="question_encoder") if generator is None: from ..auto.modeling_tf_auto import TFAutoModelForSeq2SeqLM load_weight_prefix = load_weight_prefix if load_weight_prefix is not None else self.load_weight_prefix generator = TFAutoModelForSeq2SeqLM.from_config( config.generator, name="generator", load_weight_prefix=load_weight_prefix + "/generator" ) self.retriever = retriever if self.retriever is not None: assert isinstance( retriever, RagRetriever ), f"`self.retriever` is of type {type(self.retriever)}, but should be of type `RagRetriever`" self.retriever = retriever self.question_encoder = question_encoder self.generator = generator def set_retriever(self, retriever: RagRetriever): self.retriever = retriever @unpack_inputs @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFRetrievAugLMOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, encoder_outputs: np.ndarray | tf.Tensor | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None, decoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Tuple[Tuple[Union[np.ndarray, tf.Tensor]]] | None = None, doc_scores: np.ndarray | tf.Tensor | None = None, context_input_ids: np.ndarray | tf.Tensor | None = None, context_attention_mask: np.ndarray | tf.Tensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, output_retrieved: bool | None = None, n_docs: int | None = None, return_dict: bool | None = None, training: bool = False, **kwargs, ) -> TFRetrievAugLMOutput: r""" Returns: Example: ```python >>> from transformers import AutoTokenizer, RagRetriever, TFRagModel >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-base") >>> retriever = RagRetriever.from_pretrained( ... "facebook/rag-token-base", index_name="exact", use_dummy_dataset=True ... ) >>> # initialize with RagRetriever to do everything in one forward call >>> model = TFRagModel.from_pretrained("facebook/rag-token-base", retriever=retriever, from_pt=True) >>> input_dict = tokenizer.prepare_seq2seq_batch( ... "How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="tf" ... ) >>> input_ids = input_dict["input_ids"] >>> outputs = model(input_ids) ```""" assert ( "decoder_cached_states" not in kwargs ), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py # aliasing to minimize code changing n_docs = n_docs if n_docs is not None else self.config.n_docs # whether retriever has to be used has_to_retrieve = ( self.retriever is not None and (context_input_ids is None or context_attention_mask is None or doc_scores is None) and encoder_outputs is None ) # encoder_outputs are pre-computed during RAG-token generation if encoder_outputs is None: if has_to_retrieve: question_enc_outputs = self.question_encoder( input_ids, attention_mask=attention_mask, return_dict=True, training=training ) # see https://github.com/huggingface/transformers/blob/main/src/transformers/models/dpr/modeling_tf_dpr.py#L91 question_encoder_last_hidden_state = question_enc_outputs[ 0 ] # hidden states of question encoder => pooler_output retriever_outputs = self.retriever( input_ids, question_encoder_last_hidden_state.numpy(), prefix=self.generator.config.prefix, n_docs=n_docs, return_tensors="tf", ) context_input_ids, context_attention_mask, retrieved_doc_embeds, retrieved_doc_ids = ( retriever_outputs["context_input_ids"], retriever_outputs["context_attention_mask"], retriever_outputs["retrieved_doc_embeds"], retriever_outputs["doc_ids"], ) context_input_ids = tf.cast(context_input_ids, tf.int32) context_attention_mask = tf.cast(context_attention_mask, tf.int32) retrieved_doc_embeds = tf.cast(retrieved_doc_embeds, tf.float32) retrieved_doc_ids = tf.cast(retrieved_doc_ids, tf.int32) # compute doc_scores doc_scores = tf.squeeze( tf.matmul( tf.expand_dims(question_encoder_last_hidden_state, axis=1), retrieved_doc_embeds, transpose_b=True, ), axis=1, ) else: assert context_input_ids is not None, ( "Make sure that `context_input_ids` are passed, if no `retriever` is set. Alternatively, you can" " set a retriever using the `set_retriever(...)` function." ) assert context_attention_mask is not None, ( "Make sure that `context_attention_mask` are passed, if no `retriever` is set. Alternatively, you" " can set a retriever using the `set_retriever(...)` function." ) assert doc_scores is not None, ( "Make sure that `doc_scores` are passed, if no `retriever` is set. Alternatively, you can set a" " retriever using the `set_retriever(...)` function." ) assert ( doc_scores is not None ), "Make sure that `doc_scores` are passed when passing `encoder_outputs` to the forward function." assert (doc_scores.shape[1] % n_docs) == 0, ( f" The first dimension of `context_input_ids` should be a multiple of `n_docs`={n_docs}, but is" f" {context_input_ids.shape[0]}." ) # Decoder input without context documents if decoder_input_ids is not None: decoder_input_ids = tf.repeat(decoder_input_ids, n_docs, axis=0) if decoder_attention_mask is not None: decoder_attention_mask = tf.repeat(decoder_attention_mask, n_docs, axis=0) gen_outputs = self.generator( context_input_ids, attention_mask=context_attention_mask, encoder_outputs=encoder_outputs, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, return_dict=True, training=training, ) if not has_to_retrieve: question_encoder_last_hidden_state = None question_enc_hidden_states = None question_enc_attentions = None retrieved_doc_embeds = None retrieved_doc_ids = None else: question_enc_hidden_states = question_enc_outputs.hidden_states question_enc_attentions = question_enc_outputs.attentions if not has_to_retrieve or not output_retrieved: # don't output retrieved docs context_input_ids = (None,) context_attention_mask = None retrieved_doc_embeds = None retrieved_doc_ids = None return TFRetrievAugLMOutput( logits=gen_outputs.logits, doc_scores=doc_scores, past_key_values=gen_outputs.past_key_values, context_input_ids=context_input_ids, context_attention_mask=context_attention_mask, retrieved_doc_embeds=retrieved_doc_embeds, retrieved_doc_ids=retrieved_doc_ids, question_encoder_last_hidden_state=question_encoder_last_hidden_state, question_enc_hidden_states=question_enc_hidden_states, question_enc_attentions=question_enc_attentions, generator_enc_last_hidden_state=gen_outputs.encoder_last_hidden_state, generator_enc_hidden_states=gen_outputs.encoder_hidden_states, generator_enc_attentions=gen_outputs.encoder_attentions, generator_dec_hidden_states=gen_outputs.decoder_hidden_states, generator_dec_attentions=gen_outputs.decoder_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True with tf.name_scope(self.generator.name): self.generator.build(None) with tf.name_scope(self.question_encoder.name): self.question_encoder.build(None) @add_start_docstrings_to_model_forward( """ A TF RAG-token model implementation. It performs RAG-token specific marginalization in the forward pass. """, RAG_START_DOCSTRING, ) class TFRagTokenForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss): load_weight_prefix = "tf_rag_token_for_generation_1/rag" def __init__( self, config: Optional[PretrainedConfig] = None, question_encoder: Optional[TFPreTrainedModel] = None, generator: Optional[TFPreTrainedModel] = None, retriever: Optional[RagRetriever] = None, **kwargs, ): assert config is not None or ( question_encoder is not None and generator is not None ), "Either a configuration or an encoder and a generator has to be provided." if config is None: config = RagConfig.from_question_encoder_generator_configs( question_encoder.config, generator.config, **kwargs ) super().__init__(config) # instantiate model self.rag = TFRagModel( config=config, question_encoder=question_encoder, generator=generator, retriever=retriever, load_weight_prefix=self.load_weight_prefix, name="rag", ) def set_retriever(self, retriever: RagRetriever): self.rag.retriever = retriever # Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_bart.py def prepare_inputs_for_generation( self, decoder_input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, doc_scores=None, n_docs=None, **kwargs, ): if past_key_values is not None: # if past is defined use only last decoder_input_ids decoder_input_ids = decoder_input_ids[:, -1:] return { "input_ids": None, "encoder_outputs": encoder_outputs, "doc_scores": doc_scores, "context_attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "past_key_values": past_key_values, "use_cache": use_cache, "do_marginalize": True, "n_docs": n_docs, } @property def retriever(self): return self.rag.retriever @property def generator(self): return self.rag.generator @property def question_encoder(self): return self.rag.question_encoder @staticmethod def _gather_beams(nested, beam_indices, batch_axis=0): """ RAG-specific `_gather_beams`: gathers the beam slices indexed by beam_indices into new beam array. If the nested tensor has a shape mismatch with the beam indices, then it means it is the cache. In that case, isolates and takes care of the extra dimension for ndocs. """ def gather_fn(tensor): is_rag_cache = tensor.shape[0] != beam_indices.shape[0] if is_rag_cache: n_docs = tensor.shape[0] // beam_indices.shape[0] batch_size = beam_indices.shape[0] # reshapes into (batch size, num beams, n_docs, ...), the cache format expected by RAG tensor = tf.reshape(tensor, (batch_size, -1, n_docs, *tensor.shape[2:])) gathered_tensor = tf.gather(params=tensor, indices=beam_indices, axis=1, batch_dims=1) if is_rag_cache: # reshapes back into the shape expected by beam search gathered_tensor = tf.reshape(gathered_tensor, (batch_size * n_docs, -1, *gathered_tensor.shape[3:])) return gathered_tensor return tf.nest.map_structure(gather_fn, nested) def marginalize(self, seq_logits, doc_scores, n_docs=None): n_docs = n_docs if n_docs is not None else self.config.n_docs # RAG-token marginalization seq_logprobs = tf.nn.log_softmax(seq_logits, axis=-1) seq_logprobs = tf.reshape(seq_logprobs, [seq_logits.shape[0] // n_docs, n_docs, -1, seq_logits.shape[-1]]) doc_logprobs = tf.nn.log_softmax(doc_scores, axis=1) doc_logprobs = tf.expand_dims(doc_logprobs, axis=-1) doc_logprobs = tf.expand_dims(doc_logprobs, axis=-1) # twice log_prob_sum = seq_logprobs + doc_logprobs return tf.reduce_logsumexp(log_prob_sum, axis=1) @unpack_inputs @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFRetrievAugLMMarginOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None, decoder_attention_mask: np.ndarray | tf.Tensor | None = None, encoder_outputs: np.ndarray | tf.Tensor | None = None, past_key_values: Tuple[Tuple[Union[np.ndarray, tf.Tensor]]] | None = None, doc_scores: np.ndarray | tf.Tensor | None = None, context_input_ids: np.ndarray | tf.Tensor | None = None, context_attention_mask: np.ndarray | tf.Tensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, output_retrieved: bool | None = None, n_docs: int | None = None, do_marginalize: bool | None = None, labels: np.ndarray | tf.Tensor | None = None, reduce_loss: bool | None = None, return_dict: bool | None = None, training: bool = False, **kwargs, # needs kwargs for generation ) -> TFRetrievAugLMMarginOutput: r""" do_marginalize (`bool`, *optional*): If `True`, the logits are marginalized over all documents by making use of `torch.nn.functional.log_softmax`. labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss according to Rag-Token model formulation See https://arxiv.org/pdf/2005.11401.pdf Section 2.1 for details about Rag-Token formulation. Indices should be in `[0, ..., config.vocab_size - 1]`. reduce_loss (`bool`, *optional*): Only relevant if `labels` is passed. If `True`, the NLL loss is reduced using the `tf.Tensor.sum` operation. kwargs (`Dict[str, any]`, optional, defaults to *{}*): Legacy dictionary, which is required so that model can use *generate()* function. Returns: Example: ```python >>> import tensorflow as tf >>> from transformers import AutoTokenizer, RagRetriever, TFRagTokenForGeneration >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-nq") >>> retriever = RagRetriever.from_pretrained( ... "facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True ... ) >>> # initialize with RagRetriever to do everything in one forward call >>> model = TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever, from_pt=True) >>> input_dict = tokenizer.prepare_seq2seq_batch( ... "How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="tf" ... ) >>> outputs = model(input_dict, output_retrieved=True) >>> # or use retriever separately >>> # 1. Encode >>> input_ids = input_dict["input_ids"] >>> question_hidden_states = model.question_encoder(input_ids)[0] >>> # 2. Retrieve >>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.numpy(), return_tensors="tf") >>> doc_scores = tf.squeeze( ... tf.matmul( ... tf.expand_dims(question_hidden_states, axis=1), docs_dict["retrieved_doc_embeds"], transpose_b=True ... ), ... axis=1, ... ) >>> # 3. Forward to generator >>> outputs = model( ... inputs=None, ... context_input_ids=docs_dict["context_input_ids"], ... context_attention_mask=docs_dict["context_attention_mask"], ... doc_scores=doc_scores, ... decoder_input_ids=input_dict["labels"], ... ) >>> # or directly generate >>> generated = model.generate( ... context_input_ids=docs_dict["context_input_ids"], ... context_attention_mask=docs_dict["context_attention_mask"], ... doc_scores=doc_scores, ... ) >>> generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True) ```""" assert ( "decoder_cached_states" not in kwargs ), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py do_marginalize = do_marginalize if do_marginalize else self.config.do_marginalize reduce_loss = reduce_loss if reduce_loss else self.config.reduce_loss if labels is not None: if decoder_input_ids is None: decoder_input_ids = labels use_cache = False outputs = self.rag( input_ids, attention_mask=attention_mask, encoder_outputs=encoder_outputs, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, context_input_ids=context_input_ids, context_attention_mask=context_attention_mask, doc_scores=doc_scores, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_retrieved=output_retrieved, n_docs=n_docs, training=training, ) loss = None logits = outputs.logits if labels is not None: assert decoder_input_ids is not None loss = self.get_nll( outputs.logits, outputs.doc_scores, labels, reduce_loss=reduce_loss, epsilon=self.config.label_smoothing, n_docs=n_docs, ) if do_marginalize: logits = self.marginalize(logits, outputs.doc_scores, n_docs) return TFRetrievAugLMMarginOutput( loss=loss, logits=logits, past_key_values=outputs.past_key_values, doc_scores=outputs.doc_scores, context_input_ids=outputs.context_input_ids, context_attention_mask=outputs.context_attention_mask, retrieved_doc_embeds=outputs.retrieved_doc_embeds, retrieved_doc_ids=outputs.retrieved_doc_ids, question_encoder_last_hidden_state=outputs.question_encoder_last_hidden_state, question_enc_hidden_states=outputs.question_enc_hidden_states, question_enc_attentions=outputs.question_enc_attentions, generator_enc_last_hidden_state=outputs.generator_enc_last_hidden_state, generator_enc_hidden_states=outputs.generator_enc_hidden_states, generator_enc_attentions=outputs.generator_enc_attentions, generator_dec_hidden_states=outputs.generator_dec_hidden_states, generator_dec_attentions=outputs.generator_dec_attentions, ) def generate( self, input_ids: TFModelInputType | None = None, attention_mask: tf.Tensor | None = None, context_input_ids=None, context_attention_mask=None, doc_scores=None, n_docs=None, generation_config=None, logits_processor=TFLogitsProcessorList(), **kwargs, ): """ Implements TFRAG token decoding. Args: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): The sequence used as a prompt for the generation. If `input_ids` is not passed, then `context_input_ids` has to be provided. attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by [`~RagRetriever.__call__`]. context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by [`~RagRetriever.__call__`]. doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`): Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by [`~RagRetriever.__call__`]. n_docs (`int`, *optional*, defaults to `config.n_docs`) Number of documents to retrieve and/or number of documents for which to generate an answer. generation_config (`~generation.GenerationConfig`, *optional*): The generation configuration to be used as base parametrization for the generation call. `**kwargs` passed to generate matching the attributes of `generation_config` will override them. If `generation_config` is not provided, the default will be used, which had the following loading priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s default values, whose documentation should be checked to parameterize generation. logits_processor (`TFLogitsProcessorList`, *optional*): Custom logits processors that complement the default logits processors built from arguments and a model's config. If a logit processor is passed that is already created with the arguments or a model's config an error is thrown. kwargs (`Dict[str, Any]`, *optional*): Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. Return: `tf.Tensor` of shape `(batch_size * num_return_sequences, sequence_length)`: The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`. """ # Handle `generation_config` and kwargs that might update it if generation_config is None: generation_config = self.generation_config generation_config = copy.deepcopy(generation_config) model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs # set default parameters n_docs = n_docs if n_docs is not None else self.config.n_docs # retrieve docs if self.retriever is not None and context_input_ids is None: question_hidden_states = self.question_encoder(input_ids, attention_mask=attention_mask)[0] out = self.retriever( input_ids, question_hidden_states.numpy().astype(np.float32), prefix=self.generator.config.prefix, n_docs=n_docs, return_tensors="tf", ) context_input_ids, context_attention_mask, retrieved_doc_embeds = ( out["context_input_ids"], out["context_attention_mask"], out["retrieved_doc_embeds"], ) context_input_ids = tf.cast(context_input_ids, tf.int32) context_attention_mask = tf.cast(context_attention_mask, tf.int32) retrieved_doc_embeds = tf.cast(retrieved_doc_embeds, tf.float32) # compute doc_scores doc_scores = tf.matmul( tf.expand_dims(question_hidden_states, axis=1), retrieved_doc_embeds, transpose_b=True ) doc_scores = tf.squeeze(doc_scores, axis=1) assert (context_input_ids.shape[0] % n_docs) == 0, ( f" The first dimension of `context_input_ids` should be a multiple of `n_docs`={n_docs}, but is" f" {context_input_ids.shape[0]}." ) batch_size = context_input_ids.shape[0] // n_docs encoder = self.rag.generator.get_encoder() encoder_outputs = encoder( input_ids=context_input_ids, attention_mask=context_attention_mask, output_attentions=generation_config.output_attentions, output_hidden_states=generation_config.output_hidden_states, return_dict=True, ) decoder_input_ids = tf.fill( (batch_size * generation_config.num_beams, 1), tf.cast(generation_config.decoder_start_token_id, tf.int32), ) last_hidden_state = encoder_outputs["last_hidden_state"] def extend_enc_output(tensor, num_beams=None): """ Broadcast tensor with `num_beams` replica, with correct order Input: tensor of shape (batch_size*n_docs , d) Output: tensor of shape (batch_size*num_beams*n_docs , d) """ # expand batch_size & num_beam dimensions d_shape_list = tensor.shape[1:] # split n_docs dimensions new_shape = (batch_size, 1, n_docs) + d_shape_list tensor = tf.reshape(tensor, new_shape) # repeat same last hidden states over `num_beams` dimension new_shape = (batch_size, num_beams, n_docs) + d_shape_list tensor = tf.broadcast_to(tensor, new_shape) # merge `batch_size`, `num_beams`, `num_docs` dims again new_shape = (batch_size * num_beams * n_docs,) + d_shape_list return tf.reshape(tensor, new_shape) # correctly extend last_hidden_state and attention mask context_attention_mask = extend_enc_output(context_attention_mask, num_beams=generation_config.num_beams) encoder_outputs["last_hidden_state"] = extend_enc_output( last_hidden_state, num_beams=generation_config.num_beams ) doc_scores = tf.repeat(doc_scores, generation_config.num_beams, axis=0) # define start_len & additional parameters model_kwargs["doc_scores"] = doc_scores model_kwargs["encoder_outputs"] = encoder_outputs model_kwargs["attention_mask"] = context_attention_mask model_kwargs["n_docs"] = n_docs pre_processor = self._get_logits_processor( generation_config=generation_config, input_ids_seq_length=tf.shape(decoder_input_ids)[-1], logits_processor=logits_processor, ) if generation_config.num_beams == 1: return self.greedy_search( input_ids=decoder_input_ids, max_length=generation_config.max_length, pad_token_id=generation_config.pad_token_id, eos_token_id=generation_config.eos_token_id, logits_processor=pre_processor, output_attentions=generation_config.output_attentions, output_hidden_states=generation_config.output_hidden_states, output_scores=generation_config.output_scores, return_dict_in_generate=generation_config.return_dict_in_generate, **model_kwargs, ) elif generation_config.num_beams > 1: if generation_config.num_beams < generation_config.num_return_sequences: raise ValueError( "Beam search decoding cannot return more sequences than it has beams. Please set num_beams >=" f" num_return_sequences, got {generation_config.num_beams} and" f" {generation_config.num_return_sequences} (respectivelly)" ) def unflatten_beam_dim(tensor): """Unflattens the first, flat batch*beam dimension of a non-scalar array.""" shape = shape_list(tensor) return tf.reshape(tensor, [-1, generation_config.num_beams] + shape[1:]) decoder_input_ids = unflatten_beam_dim(decoder_input_ids) model_kwargs["attention_mask"] = unflatten_beam_dim(model_kwargs["attention_mask"]) model_kwargs["encoder_outputs"]["last_hidden_state"] = unflatten_beam_dim( model_kwargs["encoder_outputs"]["last_hidden_state"] ) return self.beam_search( input_ids=decoder_input_ids, max_length=generation_config.max_length, pad_token_id=generation_config.pad_token_id, eos_token_id=generation_config.eos_token_id, logits_processor=pre_processor, output_attentions=generation_config.output_attentions, output_hidden_states=generation_config.output_hidden_states, output_scores=generation_config.output_scores, return_dict_in_generate=generation_config.return_dict_in_generate, **model_kwargs, ) else: raise ValueError( f"`num_beams` has to be an integer strictly superior to 0 (≥ 1), but is {generation_config.num_beams}" ) def get_input_embeddings(self): return self.rag.generator.get_input_embeddings() def get_output_embeddings(self): return self.rag.generator.get_output_embeddings() # Adapted from tf_t5's & tf_bart's _shift_right def shift_tokens_right(self, input_ids, start_token_id=None): """Shift input ids one token to the right, and pad with start_token_id""" if start_token_id is None: start_token_id = self.generator.config.decoder_start_token_id assert start_token_id is not None, ( "self.generator.config.decoder_start_token_id has to be defined. In Rag we commonly use Bart as" " generator, see Bart docs for more information" ) pad_token_id = self.generator.config.pad_token_id assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined." start_tokens = tf.fill((shape_list(input_ids)[0], 1), tf.cast(start_token_id, input_ids.dtype)) shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = tf.where( shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), tf.cast(pad_token_id, input_ids.dtype)), shifted_input_ids, ) # "Verify that `labels` has only positive values and -100" assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.cast(0, shifted_input_ids.dtype)) # Make sure the assertion op is called by wrapping the result in an identity no-op with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids) return shifted_input_ids # nll stands for 'negative log likelihood' def get_nll(self, seq_logits, doc_scores, target, reduce_loss=False, epsilon=0.0, n_docs=None): n_docs = n_docs if n_docs is not None else self.config.n_docs # shift tokens left (from original Pytorch's version) target = tf.concat( [target[:, 1:], tf.fill([target.shape[0], 1], tf.cast(self.config.generator.pad_token_id, target.dtype))], axis=1, ) rag_logprobs = self.marginalize(seq_logits, doc_scores, n_docs) loss = self.hf_compute_loss(target, rag_logprobs, from_logits=True, reduce_loss=reduce_loss) return loss # Adopted modeling_tf_bart + add smooth_loss to match with pytorch version def hf_compute_loss(self, labels, y_pred, smooth_epsilon=0.0, from_logits=True, reduce_loss=False): """CrossEntropyLoss that ignores pad tokens""" # Matt: As written, this loss is not XLA-compatible, but it's doing some very weird things # and I don't feel comfortable converting it. loss_fn = keras.losses.SparseCategoricalCrossentropy( from_logits=True, reduction=keras.losses.Reduction.SUM, ) if from_logits is False: # convert to logits eps = 1e-9 y_pred = tf.clip_by_value(y_pred, clip_value_min=eps, clip_value_max=1 - eps) y_pred = tf.math.log(y_pred) logits = y_pred melted_labels = tf.reshape(labels, (-1,)) active_loss = tf.not_equal(melted_labels, self.config.generator.pad_token_id) reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, logits.shape[2])), active_loss) labels = tf.boolean_mask(melted_labels, active_loss) nll_loss = loss_fn(labels, reduced_logits) smooth_loss = -tf.reduce_sum(reduced_logits, axis=-1) smooth_loss = tf.reduce_sum(smooth_loss) # sum and squeeze like torch eps_i = smooth_epsilon / reduced_logits.shape[-1] loss = (1.0 - smooth_epsilon) * nll_loss + eps_i * smooth_loss return loss def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "rag", None) is not None: with tf.name_scope(self.rag.name): self.rag.build(None) @add_start_docstrings_to_model_forward( """ A TF RAG-sequence model implementation. It performs RAG-sequence specific marginalization in the forward pass. """, RAG_START_DOCSTRING, ) class TFRagSequenceForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss): load_weight_prefix = "tf_rag_sequence_for_generation_1/rag" def __init__( self, config: Optional[PretrainedConfig] = None, question_encoder: Optional[TFPreTrainedModel] = None, generator: Optional[TFPreTrainedModel] = None, retriever: Optional[RagRetriever] = None, **kwargs, ): assert config is not None or ( question_encoder is not None and generator is not None ), "Either a configuration or an encoder and a generator has to be provided." if config is None: config = RagConfig.from_question_encoder_generator_configs( question_encoder.config, generator.config, **kwargs ) super().__init__(config) # instantiate model self.rag = TFRagModel( config=config, question_encoder=question_encoder, generator=generator, retriever=retriever, load_weight_prefix=self.load_weight_prefix, name="rag", ) def set_retriever(self, retriever: RagRetriever): self.rag.retriever = retriever @property def retriever(self): return self.rag.retriever @property def generator(self): return self.rag.generator @property def question_encoder(self): return self.rag.question_encoder @unpack_inputs @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFRetrievAugLMMarginOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None, decoder_attention_mask: np.ndarray | tf.Tensor | None = None, encoder_outputs: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, doc_scores: np.ndarray | tf.Tensor | None = None, context_input_ids: np.ndarray | tf.Tensor | None = None, context_attention_mask: np.ndarray | tf.Tensor | None = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_retrieved: Optional[bool] = None, n_docs: Optional[int] = None, exclude_bos_score: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, reduce_loss: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, **kwargs, # needs kwargs for generation ) -> Union[Tuple[tf.Tensor], TFRetrievAugLMMarginOutput]: r""" exclude_bos_score (`bool`, *optional*): Only relevant if `labels` is passed. If `True`, the score of the BOS token is disregarded when computing the loss. labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss according to Rag-Sequence model formulation See https://arxiv.org/pdf/2005.11401.pdf Section 2.1 for details about Rag-Sequence formulation. Indices should be in `[0, ..., config.vocab_size - 1]`. reduce_loss (`bool`, *optional*): Only relevant if `labels` is passed. If `True`, the NLL loss is reduced using the `tf.Tensor.sum` operation. kwargs (`Dict[str, any]`, optional, defaults to *{}*): Legacy dictionary, which is required so that model can use *generate()* function. Returns: Example: ```python >>> from transformers import AutoTokenizer, RagRetriever, TFRagSequenceForGeneration >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq") >>> retriever = RagRetriever.from_pretrained( ... "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True ... ) >>> # initialize with RagRetriever to do everything in one forward call >>> model = TFRagSequenceForGeneration.from_pretrained( ... "facebook/rag-sequence-nq", retriever=retriever, from_pt=True ... ) >>> input_dict = tokenizer.prepare_seq2seq_batch( ... "How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="tf" ... ) >>> outputs = model(input_dict, output_retrieved=True) >>> # or use retriever separately >>> # 1. Encode >>> input_ids = input_dict["input_ids"] >>> question_hidden_states = model.question_encoder(input_ids)[0] >>> # 2. Retrieve >>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.numpy(), return_tensors="tf") >>> doc_scores = tf.squeeze( ... tf.matmul( ... tf.expand_dims(question_hidden_states, axis=1), docs_dict["retrieved_doc_embeds"], transpose_b=True ... ), ... axis=1, ... ) >>> # 3. Forward to generator >>> outputs = model( ... inputs=None, ... context_input_ids=docs_dict["context_input_ids"], ... context_attention_mask=docs_dict["context_attention_mask"], ... doc_scores=doc_scores, ... decoder_input_ids=input_dict["labels"], ... ) >>> # or directly generate >>> generated = model.generate( ... context_input_ids=docs_dict["context_input_ids"], ... context_attention_mask=docs_dict["context_attention_mask"], ... doc_scores=doc_scores, ... ) >>> generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True) ```""" assert ( "decoder_cached_states" not in kwargs ), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py exclude_bos_score = exclude_bos_score if exclude_bos_score else self.config.exclude_bos_score reduce_loss = reduce_loss if reduce_loss else self.config.reduce_loss if labels is not None: if decoder_input_ids is None: decoder_input_ids = labels use_cache = False outputs = self.rag( input_ids, attention_mask=attention_mask, encoder_outputs=encoder_outputs, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, context_input_ids=context_input_ids, context_attention_mask=context_attention_mask, doc_scores=doc_scores, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_retrieved=output_retrieved, n_docs=n_docs, training=training, ) loss = None if labels is not None: loss = self.get_nll( outputs.logits, outputs.doc_scores, labels, reduce_loss=reduce_loss, epsilon=self.config.label_smoothing, n_docs=n_docs, ) return TFRetrievAugLMMarginOutput( loss=loss, logits=outputs.logits, doc_scores=outputs.doc_scores, past_key_values=outputs.past_key_values, context_input_ids=outputs.context_input_ids, context_attention_mask=outputs.context_attention_mask, retrieved_doc_embeds=outputs.retrieved_doc_embeds, retrieved_doc_ids=outputs.retrieved_doc_ids, question_encoder_last_hidden_state=outputs.question_encoder_last_hidden_state, question_enc_hidden_states=outputs.question_enc_hidden_states, question_enc_attentions=outputs.question_enc_attentions, generator_enc_last_hidden_state=outputs.generator_enc_last_hidden_state, generator_enc_hidden_states=outputs.generator_enc_hidden_states, generator_enc_attentions=outputs.generator_enc_attentions, generator_dec_hidden_states=outputs.generator_dec_hidden_states, generator_dec_attentions=outputs.generator_dec_attentions, ) def get_nll( self, seq_logits, doc_scores, target, reduce_loss=False, epsilon=0.0, exclude_bos_score=False, n_docs=None ): # shift tokens left target = tf.concat( [target[:, 1:], tf.fill([target.shape[0], 1], tf.cast(self.config.generator.pad_token_id, target.dtype))], axis=1, ) # bos_token_id is None for T5 bos_token_id = self.config.bos_token_id or self.config.generator.bos_token_id n_docs = n_docs if n_docs is not None else self.config.n_docs equal_bos_token_id_all = tf.reduce_all(tf.equal(target[:, 0], bos_token_id)) use_bos = bos_token_id is not None and equal_bos_token_id_all def _mask_pads(ll, smooth_obj): pad_mask = tf.equal(target, tf.cast(self.config.generator.pad_token_id, target.dtype)) if tf.reduce_any(pad_mask): ll = tf.where(pad_mask, 0.0, ll) smooth_obj = tf.where(pad_mask, 0.0, smooth_obj) return tf.squeeze(ll, axis=-1), tf.squeeze(smooth_obj, axis=-1) # seq_logits.shape = (batch*n_docs, tgt_len , vocabs) seq_logprobs = tf.nn.log_softmax(seq_logits, axis=-1) seq_logprobs = tf.reshape( seq_logprobs, (seq_logits.shape[0] // n_docs, n_docs, -1, seq_logits.shape[-1]) ) # (batch_size, n_docs, tgt_len, vocabs) doc_logprobs = tf.nn.log_softmax(doc_scores, axis=1) doc_logprobs = tf.expand_dims(doc_logprobs, axis=-1) doc_logprobs = tf.expand_dims(doc_logprobs, axis=-1) # done twice to get 4-D # RAG-sequence marginalization first_token_scores = seq_logprobs[:, :, :1, :] second_token_scores = seq_logprobs[:, :, 1:2, :] remainder = seq_logprobs[:, :, 2:, :] rag_logprobs = tf.concat([first_token_scores, second_token_scores + doc_logprobs, remainder], axis=2) # calculate loss target = tf.expand_dims(target, axis=1) # n_docs dimension target = tf.expand_dims(target, axis=-1) # logits dimension target = tf.repeat(target, n_docs, axis=1) assert len(target.shape) == len(rag_logprobs.shape) # last-axis gathering only - use 2D-reshape-trick for Torch's style nD gathering def torch_gather(param, id_tensor): # 2d-gather torch equivalent: https://stackoverflow.com/questions/52129909/tensorflow-equivalent-of-torch-gather def gather2d(target, id_tensor): idx = tf.stack([tf.range(tf.shape(id_tensor)[0], dtype=id_tensor.dtype), id_tensor[:, 0]], axis=-1) result = tf.gather_nd(target, idx) return tf.expand_dims(result, axis=-1) target = tf.reshape(param, (-1, param.shape[-1])) # reshape 2D target_shape = id_tensor.shape id_tensor = tf.reshape(id_tensor, (-1, 1)) # also 2D-index result = gather2d(target, id_tensor) return tf.reshape(result, target_shape) ll = torch_gather(rag_logprobs, id_tensor=target) smooth_obj = tf.reduce_sum(rag_logprobs, axis=-1, keepdims=True) # total sum of all (normalised) logits ll, smooth_obj = _mask_pads(ll, smooth_obj) # sum over tokens, exclude bos while scoring if exclude_bos_score and use_bos: ll = tf.reduce_sum(ll[:, :, 1:], axis=2) else: ll = tf.reduce_sum(ll, axis=2) smooth_obj = tf.reduce_sum(smooth_obj, axis=2) ll = tf.math.reduce_logsumexp(ll, axis=1) # logsumexp over docs smooth_obj = tf.math.reduce_logsumexp(smooth_obj, axis=1) nll_loss = -ll smooth_loss = -smooth_obj if reduce_loss: nll_loss = tf.reduce_sum(nll_loss) smooth_loss = tf.reduce_sum(smooth_loss) eps_i = epsilon / rag_logprobs.shape[-1] loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss return loss def generate( self, input_ids: TFModelInputType | None = None, attention_mask: tf.Tensor | None = None, context_input_ids=None, context_attention_mask=None, doc_scores=None, do_deduplication=None, # defaults to True num_return_sequences=None, # defaults to 1 num_beams=None, # defaults to 1 n_docs=None, **model_kwargs, ): """ Implements RAG sequence "thorough" decoding. Read the [`~generation.GenerationMixin.generate`]` documentation for more information on how to set other generate input parameters Args: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): The sequence used as a prompt for the generation. If `input_ids` is not passed, then `context_input_ids` has to be provided. attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Input IDs post-processed from the retrieved documents and the question encoder input_ids by the retriever. context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*): Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever` or `input_ids` is not given, `context_input_ids` and `context_attention_mask` have to be provided to the forward pass. They are returned by [`~RagRetriever.__call__`]. doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`): Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` or `input_ids` is not given, `doc_scores` has to be provided to the forward pass. `doc_scores` are returned by [`~RagRetriever.__call__`]. do_deduplication (`bool`, *optional*): Whether or not to deduplicate the generations from different context documents for a given input. Has to be set to `False` if used while training with distributed backend. num_return_sequences(`int`, *optional*, defaults to 1): The number of independently computed returned sequences for each element in the batch. Note that this is not the value we pass to the `generator`'s `[`~generation.GenerationMixin.generate`]` function, where we set `num_return_sequences` to `num_beams`. num_beams (`int`, *optional*, defaults to 1): Number of beams for beam search. 1 means no beam search. n_docs (`int`, *optional*, defaults to `config.n_docs`) Number of documents to retrieve and/or number of documents for which to generate an answer. kwargs (`Dict[str, Any]`, *optional*): Additional kwargs will be passed to [`~generation.GenerationMixin.generate`] Return: `tf.Tensor` of shape `(batch_size * num_return_sequences, sequence_length)`: The generated sequences. The second dimension (sequence length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`. """ n_docs = n_docs if n_docs is not None else self.config.n_docs do_deduplication = do_deduplication if do_deduplication is not None else self.config.do_deduplication num_doc_return_sequences = ( num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences ) num_beams = num_beams if num_beams is not None else self.config.num_beams assert ( input_ids is not None or context_input_ids is not None ), " At least one of input_ids or context_input_ids must be given" if self.retriever is not None and context_input_ids is None: question_hidden_states = self.question_encoder(input_ids, attention_mask=attention_mask)[0] context_input_ids = self.retriever( input_ids, question_hidden_states.numpy(), prefix=self.generator.config.prefix, n_docs=n_docs, return_tensors="tf", )["context_input_ids"] hypos = [] model_kwargs["num_beams"] = num_beams model_kwargs["num_return_sequences"] = num_beams # put here so that not confused with num_doc_return_sequences model_kwargs["attention_mask"] = None batch_size = input_ids.shape[0] if input_ids is not None else context_input_ids.shape[0] // n_docs for index in range(batch_size): # first, generate beams from documents: generator_input_ids = context_input_ids[index * n_docs : (index + 1) * n_docs] # (n_docs, max_len) output_sequences = self.generator.generate( generator_input_ids, **model_kwargs, ) # n_docs * n_beam, tgt_len if do_deduplication: # do_deduplication -- for TF, work on Eager mode only! output_sequences = tf.stack(list({str(k.numpy().tolist()): k for k in output_sequences}.values())) num_candidates = output_sequences.shape[ 0 ] # after deduplication, this number can be less than n_docs*n_beam # then, run model forwards to get nll scores: if input_ids is not None: new_input_ids = tf.tile(input_ids[index : index + 1], (num_candidates, 1)) outputs = self(new_input_ids, labels=output_sequences, exclude_bos_score=True) else: # input_ids is None, need context_input_ids/mask and doc_scores assert context_attention_mask is not None, ( "Make sure that `context_attention_mask` are passed, if no `input_ids` is set. Alternatively, you" " can set a retriever using the `set_retriever(...)` function." ) assert doc_scores is not None, ( "Make sure that `doc_scores` are passed, if no `input_ids` is set. Alternatively, you can set a" " retriever using the `set_retriever(...)` function." ) individual_input_ids = tf.tile( generator_input_ids, (num_candidates, 1) ) # (num_candidates*n_docs, max_len) individual_attention_mask = context_attention_mask[index * n_docs : (index + 1) * n_docs] individual_attention_mask = tf.tile(individual_attention_mask, (num_candidates, 1)) individual_doc_scores = doc_scores[index : (index + 1), :] # doc_scores.shape = [batch, n_docs] individual_doc_scores = tf.tile(individual_doc_scores, (num_candidates, 1)) # [num_candidates, n_docs] outputs = self( input_ids=None, context_input_ids=individual_input_ids, context_attention_mask=individual_attention_mask, doc_scores=individual_doc_scores, labels=output_sequences, exclude_bos_score=True, ) top_cand_inds = tf.math.top_k((-outputs["loss"]), k=num_doc_return_sequences)[1] # add hypothesis hypos.append(tf.gather(output_sequences, top_cand_inds)) return self._cat_and_pad(hypos, pad_token_id=self.config.generator.pad_token_id) @staticmethod def _cat_and_pad(tensors, pad_token_id): # used by generate(): tensors is a (batched) list of (candidates, len); len is varied across batch # Initialize padded tensor with shape ( all_candidates , max_candidate_length ), # where all_candidates counted from all inputs new_shape = sum([t.shape[0] for t in tensors]), max([t.shape[1] for t in tensors]) output = tf.fill(new_shape, pad_token_id) # Normal tensor doesn't support slice assignment, so we need tf.Variable output = tf.Variable(output) # Assign, and then convert back to tensor ind = 0 for t in tensors: output[ind : ind + t.shape[0], : t.shape[1]].assign(t) ind += t.shape[0] output = tf.convert_to_tensor(output) return tf.cast(output, tensors[0][0][0].dtype) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "rag", None) is not None: with tf.name_scope(self.rag.name): self.rag.build(None)

transformers/src/transformers/models/rag/modeling_tf_rag.py/0

# coding=utf-8 # Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ RegNet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/regnet-y-040": "https://huggingface.co/facebook/regnet-y-040/blob/main/config.json", } class RegNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`RegNetModel`]. It is used to instantiate a RegNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the RegNet [facebook/regnet-y-040](https://huggingface.co/facebook/regnet-y-040) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. embedding_size (`int`, *optional*, defaults to 64): Dimensionality (hidden size) for the embedding layer. hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`): Dimensionality (hidden size) at each stage. depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`): Depth (number of layers) for each stage. layer_type (`str`, *optional*, defaults to `"y"`): The layer to use, it can be either `"x" or `"y"`. An `x` layer is a ResNet's BottleNeck layer with `reduction` fixed to `1`. While a `y` layer is a `x` but with squeeze and excitation. Please refer to the paper for a detailed explanation of how these layers were constructed. hidden_act (`str`, *optional*, defaults to `"relu"`): The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. downsample_in_first_stage (`bool`, *optional*, defaults to `False`): If `True`, the first stage will downsample the inputs using a `stride` of 2. Example: ```python >>> from transformers import RegNetConfig, RegNetModel >>> # Initializing a RegNet regnet-y-40 style configuration >>> configuration = RegNetConfig() >>> # Initializing a model from the regnet-y-40 style configuration >>> model = RegNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "regnet" layer_types = ["x", "y"] def __init__( self, num_channels=3, embedding_size=32, hidden_sizes=[128, 192, 512, 1088], depths=[2, 6, 12, 2], groups_width=64, layer_type="y", hidden_act="relu", **kwargs, ): super().__init__(**kwargs) if layer_type not in self.layer_types: raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}") self.num_channels = num_channels self.embedding_size = embedding_size self.hidden_sizes = hidden_sizes self.depths = depths self.groups_width = groups_width self.layer_type = layer_type self.hidden_act = hidden_act # always downsample in the first stage self.downsample_in_first_stage = True

transformers/src/transformers/models/regnet/configuration_regnet.py/0

# coding=utf-8 # Copyright 2023 HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from functools import partial from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from ...modeling_flax_outputs import ( FlaxBaseModelOutputWithNoAttention, FlaxBaseModelOutputWithPoolingAndNoAttention, FlaxImageClassifierOutputWithNoAttention, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward from .configuration_resnet import ResNetConfig RESNET_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a [flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`ResNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ RESNET_INPUTS_DOCSTRING = r""" Args: pixel_values (`jax.numpy.float32` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`AutoImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class Identity(nn.Module): """Identity function.""" @nn.compact def __call__(self, x, **kwargs): return x class FlaxResNetConvLayer(nn.Module): out_channels: int kernel_size: int = 3 stride: int = 1 activation: Optional[str] = "relu" dtype: jnp.dtype = jnp.float32 def setup(self): self.convolution = nn.Conv( self.out_channels, kernel_size=(self.kernel_size, self.kernel_size), strides=self.stride, padding=self.kernel_size // 2, dtype=self.dtype, use_bias=False, kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="normal", dtype=self.dtype), ) self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype) self.activation_func = ACT2FN[self.activation] if self.activation is not None else Identity() def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: hidden_state = self.convolution(x) hidden_state = self.normalization(hidden_state, use_running_average=deterministic) hidden_state = self.activation_func(hidden_state) return hidden_state class FlaxResNetEmbeddings(nn.Module): """ ResNet Embeddings (stem) composed of a single aggressive convolution. """ config: ResNetConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.embedder = FlaxResNetConvLayer( self.config.embedding_size, kernel_size=7, stride=2, activation=self.config.hidden_act, dtype=self.dtype, ) self.max_pool = partial(nn.max_pool, window_shape=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1))) def __call__(self, pixel_values: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: num_channels = pixel_values.shape[-1] if num_channels != self.config.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) embedding = self.embedder(pixel_values, deterministic=deterministic) embedding = self.max_pool(embedding) return embedding class FlaxResNetShortCut(nn.Module): """ ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to downsample the input using `stride=2`. """ out_channels: int stride: int = 2 dtype: jnp.dtype = jnp.float32 def setup(self): self.convolution = nn.Conv( self.out_channels, kernel_size=(1, 1), strides=self.stride, use_bias=False, kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"), dtype=self.dtype, ) self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype) def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: hidden_state = self.convolution(x) hidden_state = self.normalization(hidden_state, use_running_average=deterministic) return hidden_state class FlaxResNetBasicLayerCollection(nn.Module): out_channels: int stride: int = 1 dtype: jnp.dtype = jnp.float32 def setup(self): self.layer = [ FlaxResNetConvLayer(self.out_channels, stride=self.stride, dtype=self.dtype), FlaxResNetConvLayer(self.out_channels, activation=None, dtype=self.dtype), ] def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: for layer in self.layer: hidden_state = layer(hidden_state, deterministic=deterministic) return hidden_state class FlaxResNetBasicLayer(nn.Module): """ A classic ResNet's residual layer composed by two `3x3` convolutions. """ in_channels: int out_channels: int stride: int = 1 activation: Optional[str] = "relu" dtype: jnp.dtype = jnp.float32 def setup(self): should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1 self.shortcut = ( FlaxResNetShortCut(self.out_channels, stride=self.stride, dtype=self.dtype) if should_apply_shortcut else None ) self.layer = FlaxResNetBasicLayerCollection( out_channels=self.out_channels, stride=self.stride, dtype=self.dtype, ) self.activation_func = ACT2FN[self.activation] def __call__(self, hidden_state, deterministic: bool = True): residual = hidden_state hidden_state = self.layer(hidden_state, deterministic=deterministic) if self.shortcut is not None: residual = self.shortcut(residual, deterministic=deterministic) hidden_state += residual hidden_state = self.activation_func(hidden_state) return hidden_state class FlaxResNetBottleNeckLayerCollection(nn.Module): out_channels: int stride: int = 1 activation: Optional[str] = "relu" reduction: int = 4 dtype: jnp.dtype = jnp.float32 def setup(self): reduces_channels = self.out_channels // self.reduction self.layer = [ FlaxResNetConvLayer(reduces_channels, kernel_size=1, dtype=self.dtype, name="0"), FlaxResNetConvLayer(reduces_channels, stride=self.stride, dtype=self.dtype, name="1"), FlaxResNetConvLayer(self.out_channels, kernel_size=1, activation=None, dtype=self.dtype, name="2"), ] def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: for layer in self.layer: hidden_state = layer(hidden_state, deterministic=deterministic) return hidden_state class FlaxResNetBottleNeckLayer(nn.Module): """ A classic ResNet's bottleneck layer composed by three `3x3` convolutions. The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3` convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. """ in_channels: int out_channels: int stride: int = 1 activation: Optional[str] = "relu" reduction: int = 4 dtype: jnp.dtype = jnp.float32 def setup(self): should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1 self.shortcut = ( FlaxResNetShortCut(self.out_channels, stride=self.stride, dtype=self.dtype) if should_apply_shortcut else None ) self.layer = FlaxResNetBottleNeckLayerCollection( self.out_channels, stride=self.stride, activation=self.activation, reduction=self.reduction, dtype=self.dtype, ) self.activation_func = ACT2FN[self.activation] def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: residual = hidden_state if self.shortcut is not None: residual = self.shortcut(residual, deterministic=deterministic) hidden_state = self.layer(hidden_state, deterministic) hidden_state += residual hidden_state = self.activation_func(hidden_state) return hidden_state class FlaxResNetStageLayersCollection(nn.Module): """ A ResNet stage composed by stacked layers. """ config: ResNetConfig in_channels: int out_channels: int stride: int = 2 depth: int = 2 dtype: jnp.dtype = jnp.float32 def setup(self): layer = FlaxResNetBottleNeckLayer if self.config.layer_type == "bottleneck" else FlaxResNetBasicLayer layers = [ # downsampling is done in the first layer with stride of 2 layer( self.in_channels, self.out_channels, stride=self.stride, activation=self.config.hidden_act, dtype=self.dtype, name="0", ), ] for i in range(self.depth - 1): layers.append( layer( self.out_channels, self.out_channels, activation=self.config.hidden_act, dtype=self.dtype, name=str(i + 1), ) ) self.layers = layers def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: hidden_state = x for layer in self.layers: hidden_state = layer(hidden_state, deterministic=deterministic) return hidden_state class FlaxResNetStage(nn.Module): """ A ResNet stage composed by stacked layers. """ config: ResNetConfig in_channels: int out_channels: int stride: int = 2 depth: int = 2 dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = FlaxResNetStageLayersCollection( self.config, in_channels=self.in_channels, out_channels=self.out_channels, stride=self.stride, depth=self.depth, dtype=self.dtype, ) def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: return self.layers(x, deterministic=deterministic) class FlaxResNetStageCollection(nn.Module): config: ResNetConfig dtype: jnp.dtype = jnp.float32 def setup(self): in_out_channels = zip(self.config.hidden_sizes, self.config.hidden_sizes[1:]) stages = [ FlaxResNetStage( self.config, self.config.embedding_size, self.config.hidden_sizes[0], stride=2 if self.config.downsample_in_first_stage else 1, depth=self.config.depths[0], dtype=self.dtype, name="0", ) ] for i, ((in_channels, out_channels), depth) in enumerate(zip(in_out_channels, self.config.depths[1:])): stages.append( FlaxResNetStage(self.config, in_channels, out_channels, depth=depth, dtype=self.dtype, name=str(i + 1)) ) self.stages = stages def __call__( self, hidden_state: jnp.ndarray, output_hidden_states: bool = False, deterministic: bool = True, ) -> FlaxBaseModelOutputWithNoAttention: hidden_states = () if output_hidden_states else None for stage_module in self.stages: if output_hidden_states: hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),) hidden_state = stage_module(hidden_state, deterministic=deterministic) return hidden_state, hidden_states class FlaxResNetEncoder(nn.Module): config: ResNetConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.stages = FlaxResNetStageCollection(self.config, dtype=self.dtype) def __call__( self, hidden_state: jnp.ndarray, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ) -> FlaxBaseModelOutputWithNoAttention: hidden_state, hidden_states = self.stages( hidden_state, output_hidden_states=output_hidden_states, deterministic=deterministic ) if output_hidden_states: hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),) if not return_dict: return tuple(v for v in [hidden_state, hidden_states] if v is not None) return FlaxBaseModelOutputWithNoAttention( last_hidden_state=hidden_state, hidden_states=hidden_states, ) class FlaxResNetPreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ResNetConfig base_model_prefix = "resnet" main_input_name = "pixel_values" module_class: nn.Module = None def __init__( self, config: ResNetConfig, input_shape=(1, 224, 224, 3), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) if input_shape is None: input_shape = (1, config.image_size, config.image_size, config.num_channels) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors pixel_values = jnp.zeros(input_shape, dtype=self.dtype) rngs = {"params": rng} random_params = self.module.init(rngs, pixel_values, return_dict=False) if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params @add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING) def __call__( self, pixel_values, params: dict = None, train: bool = False, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} return self.module.apply( { "params": params["params"] if params is not None else self.params["params"], "batch_stats": params["batch_stats"] if params is not None else self.params["batch_stats"], }, jnp.array(pixel_values, dtype=jnp.float32), not train, output_hidden_states, return_dict, rngs=rngs, mutable=["batch_stats"] if train else False, # Returing tuple with batch_stats only when train is True ) class FlaxResNetModule(nn.Module): config: ResNetConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.embedder = FlaxResNetEmbeddings(self.config, dtype=self.dtype) self.encoder = FlaxResNetEncoder(self.config, dtype=self.dtype) # Adaptive average pooling used in resnet self.pooler = partial( nn.avg_pool, padding=((0, 0), (0, 0)), ) def __call__( self, pixel_values, deterministic: bool = True, output_hidden_states: bool = False, return_dict: bool = True, ) -> FlaxBaseModelOutputWithPoolingAndNoAttention: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict embedding_output = self.embedder(pixel_values, deterministic=deterministic) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) last_hidden_state = encoder_outputs[0] pooled_output = self.pooler( last_hidden_state, window_shape=(last_hidden_state.shape[1], last_hidden_state.shape[2]), strides=(last_hidden_state.shape[1], last_hidden_state.shape[2]), ).transpose(0, 3, 1, 2) last_hidden_state = last_hidden_state.transpose(0, 3, 1, 2) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return FlaxBaseModelOutputWithPoolingAndNoAttention( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, ) @add_start_docstrings( "The bare ResNet model outputting raw features without any specific head on top.", RESNET_START_DOCSTRING, ) class FlaxResNetModel(FlaxResNetPreTrainedModel): module_class = FlaxResNetModule FLAX_VISION_MODEL_DOCSTRING = """ Returns: Examples: ```python >>> from transformers import AutoImageProcessor, FlaxResNetModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") >>> model = FlaxResNetModel.from_pretrained("microsoft/resnet-50") >>> inputs = image_processor(images=image, return_tensors="np") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state ``` """ overwrite_call_docstring(FlaxResNetModel, FLAX_VISION_MODEL_DOCSTRING) append_replace_return_docstrings( FlaxResNetModel, output_type=FlaxBaseModelOutputWithPoolingAndNoAttention, config_class=ResNetConfig ) class FlaxResNetClassifierCollection(nn.Module): config: ResNetConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, name="1") def __call__(self, x: jnp.ndarray) -> jnp.ndarray: return self.classifier(x) class FlaxResNetForImageClassificationModule(nn.Module): config: ResNetConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.resnet = FlaxResNetModule(config=self.config, dtype=self.dtype) if self.config.num_labels > 0: self.classifier = FlaxResNetClassifierCollection(self.config, dtype=self.dtype) else: self.classifier = Identity() def __call__( self, pixel_values=None, deterministic: bool = True, output_hidden_states=None, return_dict=None, ): return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.resnet( pixel_values, deterministic=deterministic, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(pooled_output[:, :, 0, 0]) if not return_dict: output = (logits,) + outputs[2:] return output return FlaxImageClassifierOutputWithNoAttention(logits=logits, hidden_states=outputs.hidden_states) @add_start_docstrings( """ ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, RESNET_START_DOCSTRING, ) class FlaxResNetForImageClassification(FlaxResNetPreTrainedModel): module_class = FlaxResNetForImageClassificationModule FLAX_VISION_CLASSIF_DOCSTRING = """ Returns: Example: ```python >>> from transformers import AutoImageProcessor, FlaxResNetForImageClassification >>> from PIL import Image >>> import jax >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") >>> model = FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50") >>> inputs = image_processor(images=image, return_tensors="np") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1) >>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()]) ``` """ overwrite_call_docstring(FlaxResNetForImageClassification, FLAX_VISION_CLASSIF_DOCSTRING) append_replace_return_docstrings( FlaxResNetForImageClassification, output_type=FlaxImageClassifierOutputWithNoAttention, config_class=ResNetConfig )

transformers/src/transformers/models/resnet/modeling_flax_resnet.py/0

# coding=utf-8 # Copyright 2022 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 RoBERTa-PreLayerNorm model.""" from __future__ import annotations import math import warnings from typing import Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFBaseModelOutputWithPoolingAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFMaskedLMOutput, TFMultipleChoiceModelOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFCausalLanguageModelingLoss, TFMaskedLanguageModelingLoss, TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFTokenClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_roberta_prelayernorm import RobertaPreLayerNormConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "andreasmadsen/efficient_mlm_m0.40" _CONFIG_FOR_DOC = "RobertaPreLayerNormConfig" TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST = [ "andreasmadsen/efficient_mlm_m0.15", "andreasmadsen/efficient_mlm_m0.20", "andreasmadsen/efficient_mlm_m0.30", "andreasmadsen/efficient_mlm_m0.40", "andreasmadsen/efficient_mlm_m0.50", "andreasmadsen/efficient_mlm_m0.60", "andreasmadsen/efficient_mlm_m0.70", "andreasmadsen/efficient_mlm_m0.80", # See all RoBERTaWithPreLayerNorm models at https://huggingface.co/models?filter=roberta_with_prelayernorm ] # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaEmbeddings with Roberta->RobertaPreLayerNorm class TFRobertaPreLayerNormEmbeddings(keras.layers.Layer): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config, **kwargs): super().__init__(**kwargs) self.padding_idx = 1 self.config = config self.hidden_size = config.hidden_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape=None): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.config.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.config.type_vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) if self.built: return self.built = True if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: input_ids: tf.Tensor Returns: tf.Tensor """ mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype) incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask return incremental_indices + self.padding_idx def call( self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, past_key_values_length=0, training=False, ): """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) if input_ids is not None: check_embeddings_within_bounds(input_ids, self.config.vocab_size) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = self.create_position_ids_from_input_ids( input_ids=input_ids, past_key_values_length=past_key_values_length ) else: position_ids = tf.expand_dims( tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0 ) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = inputs_embeds + position_embeds + token_type_embeds final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->RobertaPreLayerNorm class TFRobertaPreLayerNormPooler(keras.layers.Layer): def __init__(self, config: RobertaPreLayerNormConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(inputs=first_token_tensor) return pooled_output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->RobertaPreLayerNorm class TFRobertaPreLayerNormSelfAttention(keras.layers.Layer): def __init__(self, config: RobertaPreLayerNormConfig, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder self.config = config def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) key_layer = tf.concat([past_key_value[0], key_layer], axis=2) value_layer = tf.concat([past_key_value[1], value_layer], axis=2) else: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFRobertaPreLayerNormModel call() function) attention_scores = tf.add(attention_scores, attention_mask) # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "query", None) is not None: with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size]) class TFRobertaPreLayerNormSelfOutput(keras.layers.Layer): def __init__(self, config: RobertaPreLayerNormConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = hidden_states + input_tensor return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) class TFRobertaPreLayerNormAttention(keras.layers.Layer): def __init__(self, config: RobertaPreLayerNormConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFRobertaPreLayerNormSelfAttention(config, name="self") self.dense_output = TFRobertaPreLayerNormSelfOutput(config, name="output") self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.config = config # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention.prune_heads def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: hidden_states_pre_layer_norm = self.LayerNorm(inputs=input_tensor) self_outputs = self.self_attention( hidden_states=hidden_states_pre_layer_norm, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) # add attentions (possibly with past_key_value) if we output them outputs = (attention_output,) + self_outputs[1:] return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self_attention", None) is not None: with tf.name_scope(self.self_attention.name): self.self_attention.build(None) if getattr(self, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) class TFRobertaPreLayerNormIntermediate(keras.layers.Layer): def __init__(self, config: RobertaPreLayerNormConfig, **kwargs): super().__init__(**kwargs) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dense = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.LayerNorm(inputs=hidden_states) hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) class TFRobertaPreLayerNormOutput(keras.layers.Layer): def __init__(self, config: RobertaPreLayerNormConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = hidden_states + input_tensor return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->RobertaPreLayerNorm class TFRobertaPreLayerNormLayer(keras.layers.Layer): def __init__(self, config: RobertaPreLayerNormConfig, **kwargs): super().__init__(**kwargs) self.attention = TFRobertaPreLayerNormAttention(config, name="attention") self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = TFRobertaPreLayerNormAttention(config, name="crossattention") self.intermediate = TFRobertaPreLayerNormIntermediate(config, name="intermediate") self.bert_output = TFRobertaPreLayerNormOutput(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_value: Tuple[tf.Tensor] | None, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( input_tensor=hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=self_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( input_tensor=attention_output, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value intermediate_output = self.intermediate(hidden_states=attention_output) layer_output = self.bert_output( hidden_states=intermediate_output, input_tensor=attention_output, training=training ) outputs = (layer_output,) + outputs # add attentions if we output them # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "intermediate", None) is not None: with tf.name_scope(self.intermediate.name): self.intermediate.build(None) if getattr(self, "bert_output", None) is not None: with tf.name_scope(self.bert_output.name): self.bert_output.build(None) if getattr(self, "crossattention", None) is not None: with tf.name_scope(self.crossattention.name): self.crossattention.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->RobertaPreLayerNorm class TFRobertaPreLayerNormEncoder(keras.layers.Layer): def __init__(self, config: RobertaPreLayerNormConfig, **kwargs): super().__init__(**kwargs) self.config = config self.layer = [TFRobertaPreLayerNormLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_values: Tuple[Tuple[tf.Tensor]] | None, use_cache: Optional[bool], output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) past_key_value = past_key_values[i] if past_key_values is not None else None layer_outputs = layer_module( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layer", None) is not None: for layer in self.layer: with tf.name_scope(layer.name): layer.build(None) @keras_serializable class TFRobertaPreLayerNormMainLayer(keras.layers.Layer): config_class = RobertaPreLayerNormConfig def __init__(self, config, add_pooling_layer=True, **kwargs): super().__init__(**kwargs) self.config = config self.is_decoder = config.is_decoder self.num_hidden_layers = config.num_hidden_layers self.initializer_range = config.initializer_range self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.encoder = TFRobertaPreLayerNormEncoder(config, name="encoder") self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.pooler = TFRobertaPreLayerNormPooler(config, name="pooler") if add_pooling_layer else None # The embeddings must be the last declaration in order to follow the weights order self.embeddings = TFRobertaPreLayerNormEmbeddings(config, name="embeddings") def get_input_embeddings(self) -> keras.layers.Layer: return self.embeddings def set_input_embeddings(self, value: tf.Variable): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError @unpack_inputs def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]: if not self.config.is_decoder: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape if past_key_values is None: past_key_values_length = 0 past_key_values = [None] * len(self.encoder.layer) else: past_key_values_length = shape_list(past_key_values[0][0])[-2] if attention_mask is None: attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1) if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, training=training, ) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask_shape = shape_list(attention_mask) mask_seq_length = seq_length + past_key_values_length # Provided a padding mask of dimensions [batch_size, mask_seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] if self.is_decoder: seq_ids = tf.range(mask_seq_length) causal_mask = tf.less_equal( tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)), seq_ids[None, :, None], ) causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype) extended_attention_mask = causal_mask * attention_mask[:, None, :] attention_mask_shape = shape_list(extended_attention_mask) extended_attention_mask = tf.reshape( extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2]) ) if past_key_values[0] is not None: # attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length] extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :] else: extended_attention_mask = tf.reshape( attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]) ) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) one_cst = tf.constant(1.0, dtype=embedding_output.dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) if self.is_decoder and encoder_attention_mask is not None: # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype) num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) if num_dims_encoder_attention_mask == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] if num_dims_encoder_attention_mask == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, # tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers encoder_outputs = self.encoder( hidden_states=embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] sequence_output = self.LayerNorm(inputs=sequence_output) pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None if not return_dict: return ( sequence_output, pooled_output, ) + encoder_outputs[1:] return TFBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) if getattr(self, "pooler", None) is not None: with tf.name_scope(self.pooler.name): self.pooler.build(None) if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaPreTrainedModel with Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm class TFRobertaPreLayerNormPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RobertaPreLayerNormConfig base_model_prefix = "roberta_prelayernorm" ROBERTA_PRELAYERNORM_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`RobertaPreLayerNormConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare RoBERTa-PreLayerNorm Model transformer outputting raw hidden-states without any specific head on top.", ROBERTA_PRELAYERNORM_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaModel with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm class TFRobertaPreLayerNormModel(TFRobertaPreLayerNormPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta_prelayernorm = TFRobertaPreLayerNormMainLayer(config, name="roberta_prelayernorm") @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[Tuple, TFBaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation """ outputs = self.roberta_prelayernorm( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta_prelayernorm", None) is not None: with tf.name_scope(self.roberta_prelayernorm.name): self.roberta_prelayernorm.build(None) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->RobertaPreLayerNorm class TFRobertaPreLayerNormLMHead(keras.layers.Layer): """RobertaPreLayerNorm Head for masked language modeling.""" def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.config = config self.hidden_size = config.hidden_size self.dense = keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") self.act = get_tf_activation("gelu") # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = input_embeddings def build(self, input_shape=None): self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "layer_norm", None) is not None: with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.config.hidden_size]) def get_output_embeddings(self): return self.decoder def set_output_embeddings(self, value): self.decoder.weight = value self.decoder.vocab_size = shape_list(value)[0] def get_bias(self): return {"bias": self.bias} def set_bias(self, value): self.bias = value["bias"] self.config.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.layer_norm(hidden_states) # project back to size of vocabulary with bias seq_length = shape_list(tensor=hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states @add_start_docstrings( """RoBERTa-PreLayerNorm Model with a `language modeling` head on top.""", ROBERTA_PRELAYERNORM_START_DOCSTRING ) class TFRobertaPreLayerNormForMaskedLM(TFRobertaPreLayerNormPreTrainedModel, TFMaskedLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"] # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMaskedLM.__init__ with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta_prelayernorm = TFRobertaPreLayerNormMainLayer( config, add_pooling_layer=False, name="roberta_prelayernorm" ) self.lm_head = TFRobertaPreLayerNormLMHead(config, self.roberta_prelayernorm.embeddings, name="lm_head") def get_lm_head(self): return self.lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.69, ) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMaskedLM.call with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ outputs = self.roberta_prelayernorm( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta_prelayernorm", None) is not None: with tf.name_scope(self.roberta_prelayernorm.name): self.roberta_prelayernorm.build(None) if getattr(self, "lm_head", None) is not None: with tf.name_scope(self.lm_head.name): self.lm_head.build(None) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForCausalLM with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm class TFRobertaPreLayerNormForCausalLM(TFRobertaPreLayerNormPreTrainedModel, TFCausalLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"] def __init__(self, config: RobertaPreLayerNormConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) if not config.is_decoder: logger.warning( "If you want to use `TFRobertaPreLayerNormLMHeadModel` as a standalone, add `is_decoder=True.`" ) self.roberta_prelayernorm = TFRobertaPreLayerNormMainLayer( config, add_pooling_layer=False, name="roberta_prelayernorm" ) self.lm_head = TFRobertaPreLayerNormLMHead( config, input_embeddings=self.roberta_prelayernorm.embeddings, name="lm_head" ) def get_lm_head(self): return self.lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.prepare_inputs_for_generation def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = tf.ones(input_shape) # cut decoder_input_ids if past is used if past_key_values is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values} @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ outputs = self.roberta_prelayernorm( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.lm_head(hidden_states=sequence_output, training=training) loss = None if labels is not None: # shift labels to the left and cut last logit token shifted_logits = logits[:, :-1] labels = labels[:, 1:] loss = self.hf_compute_loss(labels=labels, logits=shifted_logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFCausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta_prelayernorm", None) is not None: with tf.name_scope(self.roberta_prelayernorm.name): self.roberta_prelayernorm.build(None) if getattr(self, "lm_head", None) is not None: with tf.name_scope(self.lm_head.name): self.lm_head.build(None) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaClassificationHead with Roberta->RobertaPreLayerNorm class TFRobertaPreLayerNormClassificationHead(keras.layers.Layer): """Head for sentence-level classification tasks.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = keras.layers.Dropout(classifier_dropout) self.out_proj = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj" ) self.config = config def call(self, features, training=False): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x, training=training) x = self.dense(x) x = self.dropout(x, training=training) x = self.out_proj(x) return x def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "out_proj", None) is not None: with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ RoBERTa-PreLayerNorm Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ROBERTA_PRELAYERNORM_START_DOCSTRING, ) class TFRobertaPreLayerNormForSequenceClassification( TFRobertaPreLayerNormPreTrainedModel, TFSequenceClassificationLoss ): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta_prelayernorm = TFRobertaPreLayerNormMainLayer( config, add_pooling_layer=False, name="roberta_prelayernorm" ) self.classifier = TFRobertaPreLayerNormClassificationHead(config, name="classifier") @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForSequenceClassification.call with roberta->roberta_prelayernorm def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ outputs = self.roberta_prelayernorm( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.classifier(sequence_output, training=training) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta_prelayernorm", None) is not None: with tf.name_scope(self.roberta_prelayernorm.name): self.roberta_prelayernorm.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build(None) @add_start_docstrings( """ RobertaPreLayerNorm Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ROBERTA_PRELAYERNORM_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMultipleChoice with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm class TFRobertaPreLayerNormForMultipleChoice(TFRobertaPreLayerNormPreTrainedModel, TFMultipleChoiceLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"lm_head"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta_prelayernorm = TFRobertaPreLayerNormMainLayer(config, name="roberta_prelayernorm") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.classifier = keras.layers.Dense( 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward( ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ if input_ids is not None: num_choices = shape_list(input_ids)[1] seq_length = shape_list(input_ids)[2] else: num_choices = shape_list(inputs_embeds)[1] seq_length = shape_list(inputs_embeds)[2] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None outputs = self.roberta_prelayernorm( flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, training=training) logits = self.classifier(pooled_output) reshaped_logits = tf.reshape(logits, (-1, num_choices)) loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta_prelayernorm", None) is not None: with tf.name_scope(self.roberta_prelayernorm.name): self.roberta_prelayernorm.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ RoBERTa-PreLayerNorm Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, ROBERTA_PRELAYERNORM_START_DOCSTRING, ) class TFRobertaPreLayerNormForTokenClassification(TFRobertaPreLayerNormPreTrainedModel, TFTokenClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta_prelayernorm = TFRobertaPreLayerNormMainLayer( config, add_pooling_layer=False, name="roberta_prelayernorm" ) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = keras.layers.Dropout(classifier_dropout) self.classifier = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForTokenClassification.call with roberta->roberta_prelayernorm def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ outputs = self.roberta_prelayernorm( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output, training=training) logits = self.classifier(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta_prelayernorm", None) is not None: with tf.name_scope(self.roberta_prelayernorm.name): self.roberta_prelayernorm.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ RoBERTa-PreLayerNorm Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ROBERTA_PRELAYERNORM_START_DOCSTRING, ) class TFRobertaPreLayerNormForQuestionAnswering(TFRobertaPreLayerNormPreTrainedModel, TFQuestionAnsweringLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta_prelayernorm = TFRobertaPreLayerNormMainLayer( config, add_pooling_layer=False, name="roberta_prelayernorm" ) self.qa_outputs = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForQuestionAnswering.call with roberta->roberta_prelayernorm def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ outputs = self.roberta_prelayernorm( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = tf.split(logits, 2, axis=-1) start_logits = tf.squeeze(start_logits, axis=-1) end_logits = tf.squeeze(end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions} labels["end_position"] = end_positions loss = self.hf_compute_loss(labels, (start_logits, end_logits)) if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta_prelayernorm", None) is not None: with tf.name_scope(self.roberta_prelayernorm.name): self.roberta_prelayernorm.build(None) if getattr(self, "qa_outputs", None) is not None: with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])

transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert a RWKV checkpoint from BlinkDL to the Hugging Face format.""" import argparse import gc import json import os import re import torch from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint NUM_HIDDEN_LAYERS_MAPPING = { "169M": 12, "430M": 24, "1B5": 24, "3B": 32, "7B": 32, "14B": 40, } HIDEN_SIZE_MAPPING = { "169M": 768, "430M": 1024, "1B5": 2048, "3B": 2560, "7B": 4096, "14B": 5120, } def convert_state_dict(state_dict): state_dict_keys = list(state_dict.keys()) for name in state_dict_keys: weight = state_dict.pop(name) # emb -> embedding if name.startswith("emb."): name = name.replace("emb.", "embeddings.") # ln_0 -> pre_ln (only present at block 0) if name.startswith("blocks.0.ln0"): name = name.replace("blocks.0.ln0", "blocks.0.pre_ln") # att -> attention name = re.sub(r"blocks\.(\d+)\.att", r"blocks.\1.attention", name) # ffn -> feed_forward name = re.sub(r"blocks\.(\d+)\.ffn", r"blocks.\1.feed_forward", name) # time_mix_k -> time_mix_key and reshape if name.endswith(".time_mix_k"): name = name.replace(".time_mix_k", ".time_mix_key") # time_mix_v -> time_mix_value and reshape if name.endswith(".time_mix_v"): name = name.replace(".time_mix_v", ".time_mix_value") # time_mix_r -> time_mix_key and reshape if name.endswith(".time_mix_r"): name = name.replace(".time_mix_r", ".time_mix_receptance") if name != "head.weight": name = "rwkv." + name state_dict[name] = weight return state_dict def convert_rmkv_checkpoint_to_hf_format( repo_id, checkpoint_file, output_dir, size=None, tokenizer_file=None, push_to_hub=False, model_name=None ): # 1. If possible, build the tokenizer. if tokenizer_file is None: print("No `--tokenizer_file` provided, we will use the default tokenizer.") vocab_size = 50277 tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") else: tokenizer = PreTrainedTokenizerFast(tokenizer_file=tokenizer_file) vocab_size = len(tokenizer) tokenizer.save_pretrained(output_dir) # 2. Build the config possible_sizes = list(NUM_HIDDEN_LAYERS_MAPPING.keys()) if size is None: # Try to infer size from the checkpoint name for candidate in possible_sizes: if candidate in checkpoint_file: size = candidate break if size is None: raise ValueError("Could not infer the size, please provide it with the `--size` argument.") if size not in possible_sizes: raise ValueError(f"`size` should be one of {possible_sizes}, got {size}.") config = RwkvConfig( vocab_size=vocab_size, num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size], hidden_size=HIDEN_SIZE_MAPPING[size], ) config.save_pretrained(output_dir) # 3. Download model file then convert state_dict model_file = hf_hub_download(repo_id, checkpoint_file) state_dict = torch.load(model_file, map_location="cpu") state_dict = convert_state_dict(state_dict) # 4. Split in shards and save shards, index = shard_checkpoint(state_dict) for shard_file, shard in shards.items(): torch.save(shard, os.path.join(output_dir, shard_file)) if index is not None: save_index_file = os.path.join(output_dir, WEIGHTS_INDEX_NAME) # Save the index as well with open(save_index_file, "w", encoding="utf-8") as f: content = json.dumps(index, indent=2, sort_keys=True) + "\n" f.write(content) # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict print( "Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model." ) shard_files = list(shards.keys()) del state_dict del shards gc.collect() for shard_file in shard_files: state_dict = torch.load(os.path.join(output_dir, shard_file)) torch.save({k: v.cpu().clone() for k, v in state_dict.items()}, os.path.join(output_dir, shard_file)) del state_dict gc.collect() if push_to_hub: if model_name is None: raise ValueError("Please provide a `model_name` to push the model to the Hub.") model = AutoModelForCausalLM.from_pretrained(output_dir) model.push_to_hub(model_name, max_shard_size="2GB") tokenizer.push_to_hub(model_name) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--repo_id", default=None, type=str, required=True, help="Repo ID from which to pull the checkpoint." ) parser.add_argument( "--checkpoint_file", default=None, type=str, required=True, help="Name of the checkpoint file in the repo." ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="Where to save the converted model." ) parser.add_argument( "--tokenizer_file", default=None, type=str, help="Path to the tokenizer file to use (if not provided, only the model is converted).", ) parser.add_argument( "--size", default=None, type=str, help="Size of the model. Will be inferred from the `checkpoint_file` if not passed.", ) parser.add_argument( "--push_to_hub", action="store_true", help="Push to the Hub the converted model.", ) parser.add_argument( "--model_name", default=None, type=str, help="Name of the pushed model on the Hub, including the username / organization.", ) args = parser.parse_args() convert_rmkv_checkpoint_to_hf_format( args.repo_id, args.checkpoint_file, args.output_dir, size=args.size, tokenizer_file=args.tokenizer_file, push_to_hub=args.push_to_hub, model_name=args.model_name, )

transformers/src/transformers/models/rwkv/convert_rwkv_checkpoint_to_hf.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fast Tokenization class for SeamlessM4T.""" import os from shutil import copyfile from typing import List, Optional, Tuple, Union from tokenizers import processors from ...tokenization_utils import ( BatchEncoding, PreTokenizedInput, TextInput, ) from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import PaddingStrategy, is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_seamless_m4t import SeamlessM4TTokenizer else: SeamlessM4TTokenizer = None logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/hf-seamless-m4t-medium": "https://huggingface.co/facebook/hf-seamless-m4t-medium/resolve/main/vocab.txt", }, "tokenizer_file": { "facebook/hf-seamless-m4t-medium": "https://huggingface.co/facebook/hf-seamless-m4t-medium/resolve/main/tokenizer.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/hf-seamless-m4t-medium": 2048, } class SeamlessM4TTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" SeamlessM4T tokenizer (backed by HuggingFace's *tokenizers* library). Based on [BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models). This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. The tokenization method is `<language code> <tokens> <eos>` for source language documents, and `<eos> <language code> <tokens> <eos>` for target language documents. Examples: ```python >>> from transformers import SeamlessM4TTokenizerFast >>> tokenizer = SeamlessM4TTokenizerFast.from_pretrained( ... "facebook/hf-seamless-m4t-medium", src_lang="eng", tgt_lang="fra" ... ) >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria" >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie." >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt") ``` Args: vocab_file (`str`, *optional*): Path to the vocabulary file. tokenizer_file (`str`, *optional*): The path to a tokenizer file to use instead of the vocab file. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. src_lang (`str`, *optional*, defaults to `"eng"`): The language to use as source language for translation. tgt_lang (`str`, *optional*, defaults to `"fra"`): The language to use as target language for translation. additional_special_tokens (tuple or list of `str` or `tokenizers.AddedToken`, *optional*): A tuple or a list of additional special tokens. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = SeamlessM4TTokenizer model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, vocab_file=None, tokenizer_file=None, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", src_lang="eng", tgt_lang="fra", additional_special_tokens=None, **kwargs, ): super().__init__( vocab_file=vocab_file, tokenizer_file=tokenizer_file, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, src_lang=src_lang, tgt_lang=tgt_lang, additional_special_tokens=additional_special_tokens, **kwargs, ) self.vocab_file = vocab_file self._src_lang = f"__{src_lang}__" if "__" not in src_lang else src_lang self._tgt_lang = f"__{tgt_lang}__" if "__" not in tgt_lang else tgt_lang self.set_src_lang_special_tokens(self._src_lang) self.set_tgt_lang_special_tokens(self._tgt_lang) @property def can_save_slow_tokenizer(self) -> bool: return os.path.isfile(self.vocab_file) if self.vocab_file else False @property # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.src_lang def src_lang(self) -> str: return self._src_lang @src_lang.setter def src_lang(self, new_src_lang: str) -> None: if "__" not in new_src_lang: self._src_lang = f"__{new_src_lang}__" else: self._src_lang = new_src_lang self.set_src_lang_special_tokens(self._src_lang) @property def tgt_lang(self) -> str: return self._tgt_lang @tgt_lang.setter def tgt_lang(self, new_tgt_lang: str) -> None: if "__" not in new_tgt_lang: self._tgt_lang = f"__{new_tgt_lang}__" else: self._tgt_lang = new_tgt_lang self.set_tgt_lang_special_tokens(self._tgt_lang) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. The special tokens depend on calling set_lang. An SeamlessM4T sequence has the following format, where `X` represents the sequence: - `input_ids` (for encoder) `[src_lang_code] X [eos]` - `decoder_input_ids`: (for decoder) `[eos, tgt_lang_code] X [eos]` BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return self.prefix_tokens + token_ids_0 + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens # Copied from transformers.models.nllb.tokenization_nllb_fast.NllbTokenizerFast.create_token_type_ids_from_sequences def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. nllb does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] def _build_translation_inputs( self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs ): """Used by translation pipeline, to prepare inputs for the generate function""" if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") self.src_lang = src_lang inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs) if "__" not in tgt_lang: tgt_lang = f"__{tgt_lang}__" tgt_lang_id = self.convert_tokens_to_ids(tgt_lang) inputs["forced_bos_token_id"] = tgt_lang_id return inputs # Copied from transformers.models.nllb.tokenization_nllb_fast.NllbTokenizerFast.prepare_seq2seq_batch with "fra_Latn"->"fra", "eng_Latn"->"eng" def prepare_seq2seq_batch( self, src_texts: List[str], src_lang: str = "eng", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "fra", **kwargs, ) -> BatchEncoding: self.src_lang = src_lang self.tgt_lang = tgt_lang return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs) # Copied from transformers.models.nllb.tokenization_nllb_fast.NllbTokenizerFast._switch_to_input_mode def _switch_to_input_mode(self): return self.set_src_lang_special_tokens(self.src_lang) # Copied from transformers.models.nllb.tokenization_nllb_fast.NllbTokenizerFast._switch_to_target_mode def _switch_to_target_mode(self): return self.set_tgt_lang_special_tokens(self.tgt_lang) def set_src_lang_special_tokens(self, src_lang) -> None: """Reset the special tokens to the source lang setting. Prefix=[src_lang_code], suffix = [eos] """ self.cur_lang_code = self.convert_tokens_to_ids(src_lang) if self.cur_lang_code == self.unk_token_id: logger.warning_once( f"`tgt_lang={src_lang}` has not be found in the `vocabulary`. Behaviour will probably be unexpected because the language token id will be replaced by the unknown token id." ) self.init_kwargs["src_lang"] = src_lang self.prefix_tokens = [self.cur_lang_code] self.suffix_tokens = [self.eos_token_id] prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens) suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens) self._tokenizer.post_processor = processors.TemplateProcessing( single=prefix_tokens_str + ["$A"] + suffix_tokens_str, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)), ) def set_tgt_lang_special_tokens(self, lang: str) -> None: """Reset the special tokens to the target lang setting. Prefix=[eos, tgt_lang_code] and suffix=[eos]. """ self.cur_lang_code = self.convert_tokens_to_ids(lang) if self.cur_lang_code == self.unk_token_id: logger.warning_once( f"`tgt_lang={lang}` has not be found in the `vocabulary`. Behaviour will probably be unexpected because the language token id will be replaced by the unknown token id." ) self.init_kwargs["tgt_lang"] = lang self.prefix_tokens = [self.eos_token_id, self.cur_lang_code] self.suffix_tokens = [self.eos_token_id] prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens) suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens) self._tokenizer.post_processor = processors.TemplateProcessing( single=prefix_tokens_str + ["$A"] + suffix_tokens_str, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)), ) # Copied from transformers.models.nllb.tokenization_nllb_fast.NllbTokenizerFast.save_vocabulary def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not self.can_save_slow_tokenizer: raise ValueError( "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " "tokenizer." ) if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory.") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) return (out_vocab_file,) @classmethod def _from_pretrained( cls, resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, token=None, cache_dir=None, local_files_only=False, _commit_hash=None, _is_local=False, **kwargs, ): tokenizer = super()._from_pretrained( resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, token=token, cache_dir=cache_dir, local_files_only=local_files_only, _commit_hash=_commit_hash, _is_local=_is_local, **kwargs, ) # ensure also set after from pretrained tokenizer.set_src_lang_special_tokens(tokenizer._src_lang) tokenizer.set_tgt_lang_special_tokens(tokenizer._tgt_lang) return tokenizer def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None, text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, text_pair_target: Optional[ Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] ] = None, padding: Union[bool, str, PaddingStrategy] = True, pad_to_multiple_of: Optional[int] = 2, src_lang: Optional[str] = None, tgt_lang: Optional[str] = None, **kwargs, ): """ Args: text (`str`, `List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). text_pair (`str`, `List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). text_target (`str`, `List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). text_pair_target (`str`, `List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). src_lang (`str`, *optional*): A string representing the source language. If not specified, the last `src_lang` specified (either during initialization or when calling this tokenizer) will be used. tgt_lang (`str`, *optional*): A string representing the target language. If not specified, the last `tgt_lang` specified (either during initialization or when calling this tokenizer) will be used. kwargs (*optional*): Remaining dictionary of keyword arguments that will be passed to [`PreTrainedTokenizerFast.__call__`]. """ if src_lang is not None: self.src_lang = src_lang if tgt_lang is not None: self.tgt_lang = tgt_lang output = super().__call__( text=text, text_pair=text_pair, text_target=text_target, text_pair_target=text_pair_target, padding=padding, pad_to_multiple_of=pad_to_multiple_of, **kwargs, ) return output

transformers/src/transformers/models/seamless_m4t/tokenization_seamless_m4t_fast.py/0

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Classes to support Speech-Encoder-Text-Decoder architectures""" from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import CrossEntropyLoss from ...configuration_utils import PretrainedConfig from ...modeling_outputs import BaseModelOutput, Seq2SeqLMOutput from ...modeling_utils import PreTrainedModel from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from ..auto.configuration_auto import AutoConfig from ..auto.modeling_auto import AutoModel, AutoModelForCausalLM from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "SpeechEncoderDecoderConfig" SPEECH_ENCODER_DECODER_START_DOCSTRING = r""" This class can be used to initialize a speech-sequence-to-text-sequence model with any pretrained speech autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`] function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like summarization. The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. Additionally, in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) it is shown how leveraging large pretrained speech models for speech translation yields a significant performance improvement. After such an Speech-Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`SpeechEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ SPEECH_ENCODER_DECODER_INPUTS_DOCSTRING = r""" Args: inputs (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, feature_dim)`, *optional*): Float values of input raw speech waveform or speech features. Values can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into `inputs`, either the [`Wav2Vec2Processor`] or [`Speech2TextProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). For training, `decoder_input_ids` are automatically created by the model by shifting the `labels` to the right, replacing -100 by the `pad_token_id` and prepending them with the `decoder_start_token_id`. decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. encoder_outputs (`tuple(torch.FloatTensor)`, *optional*): This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) is a tensor of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install soundfile*). To prepare the array into *input_values*, the [`Wav2Vec2Processor`] should be used for padding and conversion into a tensor of type *torch.FloatTensor*. See [`Wav2Vec2Processor.__call__`] for details. input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_size)`, *optional*): Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [`Speech2TextFeatureExtractor`] should be used for extracting the fbank features, padding and conversion into a tensor of type `torch.FloatTensor`. See [`~Speech2TextFeatureExtractor.__call__`] return_dict (`bool`, *optional*): If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple. kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors: - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function. - With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function. """ # Copied from transformers.models.encoder_decoder.modeling_encoder_decoder.shift_tokens_right def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): """ Shift input ids one token to the right. """ shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() if decoder_start_token_id is None: raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.") shifted_input_ids[:, 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids @add_start_docstrings(SPEECH_ENCODER_DECODER_START_DOCSTRING) class SpeechEncoderDecoderModel(PreTrainedModel): r""" [`SpeechEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with one of the base model classes of the library as encoder and another one as decoder when created with the :meth*~transformers.AutoModel.from_pretrained* class method for the encoder and :meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder. """ config_class = SpeechEncoderDecoderConfig base_model_prefix = "speech_encoder_decoder" main_input_name = "inputs" supports_gradient_checkpointing = True def __init__( self, config: Optional[PretrainedConfig] = None, encoder: Optional[PreTrainedModel] = None, decoder: Optional[PreTrainedModel] = None, ): if config is None and (encoder is None or decoder is None): raise ValueError("Either a configuration or an encoder and a decoder has to be provided.") if config is None: config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config) else: if not isinstance(config, self.config_class): raise ValueError(f"Config: {config} has to be of type {self.config_class}") if config.decoder.cross_attention_hidden_size is not None: if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: raise ValueError( "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" " `config.encoder.hidden_size`." ) # initialize with config # make sure input & output embeddings is not tied config.tie_word_embeddings = False super().__init__(config) if encoder is None: encoder = AutoModel.from_config(config.encoder) if decoder is None: decoder = AutoModelForCausalLM.from_config(config.decoder) self.encoder = encoder self.decoder = decoder if self.encoder.config.to_dict() != self.config.encoder.to_dict(): logger.warning( f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:" f" {self.config.encoder}" ) if self.decoder.config.to_dict() != self.config.decoder.to_dict(): logger.warning( f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:" f" {self.config.decoder}" ) # make sure that the individual model's config refers to the shared config # so that the updates to the config will be synced self.encoder.config = self.config.encoder self.decoder.config = self.config.decoder # get encoder output hidden size self.encoder_output_dim = getattr(config.encoder, "output_hidden_size", config.encoder.hidden_size) if ( self.encoder_output_dim != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): # encoder outputs might need to be projected to different dimension for decoder self.enc_to_dec_proj = nn.Linear(self.encoder.config.hidden_size, self.decoder.config.hidden_size) if self.encoder.get_output_embeddings() is not None: raise ValueError( f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head" ) def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def get_output_embeddings(self): return self.decoder.get_output_embeddings() def set_output_embeddings(self, new_embeddings): return self.decoder.set_output_embeddings(new_embeddings) def freeze_feature_encoder(self): """ Calling this function will disable the gradient computation for the feature encoder of the speech encoder so that its parameters will not be updated during training. """ self.encoder.freeze_feature_encoder() @classmethod def from_pretrained(cls, *args, **kwargs): # At the moment fast initialization is not supported for composite models if kwargs.get("_fast_init", False): logger.warning( "Fast initialization is currently not supported for SpeechEncoderDecoderModel. " "Falling back to slow initialization..." ) kwargs["_fast_init"] = False return super().from_pretrained(*args, **kwargs) @classmethod def from_encoder_decoder_pretrained( cls, encoder_pretrained_model_name_or_path: str = None, decoder_pretrained_model_name_or_path: str = None, *model_args, **kwargs, ) -> PreTrainedModel: r""" Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model checkpoints. The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train the model, you need to first set it back in training mode with `model.train()`. Params: encoder_pretrained_model_name_or_path (`str`, *optional*): Information necessary to initiate the encoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. - A path to a *directory* containing model weights saved using [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`): Information necessary to initiate the decoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. - A path to a *directory* containing model weights saved using [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import SpeechEncoderDecoderModel >>> # initialize a wav2vec2bert from a pretrained Wav2Vec2 and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized >>> model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained( ... "facebook/wav2vec2-base-960h", "google-bert/bert-base-uncased" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./wav2vec2bert") >>> # load fine-tuned model >>> model = SpeechEncoderDecoderModel.from_pretrained("./wav2vec2bert") ```""" kwargs_encoder = { argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") } kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } # remove encoder, decoder kwargs from kwargs for key in kwargs_encoder.keys(): del kwargs["encoder_" + key] for key in kwargs_decoder.keys(): del kwargs["decoder_" + key] # Load and initialize the encoder and decoder # The distinction between encoder and decoder at the model level is made # by the value of the flag `is_decoder` that we need to set correctly. encoder = kwargs_encoder.pop("model", None) if encoder is None: if encoder_pretrained_model_name_or_path is None: raise ValueError( "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_encoder: encoder_config, kwargs_encoder = AutoConfig.from_pretrained( encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True ) if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: logger.info( f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " "from a decoder model. Cross-attention and casual mask are disabled." ) encoder_config.is_decoder = False encoder_config.add_cross_attention = False kwargs_encoder["config"] = encoder_config encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder) decoder = kwargs_decoder.pop("model", None) if decoder is None: if decoder_pretrained_model_name_or_path is None: raise ValueError( "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_decoder: decoder_config, kwargs_decoder = AutoConfig.from_pretrained( decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True ) if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: logger.info( f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." ) decoder_config.is_decoder = True decoder_config.add_cross_attention = True kwargs_decoder["config"] = decoder_config if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: logger.warning( f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" ) decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) # instantiate config with corresponding kwargs config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) # make sure input & output embeddings is not tied config.tie_word_embeddings = False return cls(encoder=encoder, decoder=decoder, config=config) @add_start_docstrings_to_model_forward(SPEECH_ENCODER_DECODER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def forward( self, inputs: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, input_values: Optional[torch.FloatTensor] = None, input_features: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: r""" Returns: Examples: ```python >>> from transformers import SpeechEncoderDecoderModel, AutoProcessor >>> from datasets import load_dataset >>> import torch >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15") >>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values >>> # Inference: Translate English speech to German >>> generated = model.generate(input_values) >>> decoded = processor.batch_decode(generated, skip_special_tokens=True)[0] >>> decoded 'Mr. Quilter ist der Apostel der Mittelschicht und wir freuen uns, sein Evangelium willkommen heißen zu können.' >>> # Training: Train model on English transcription >>> labels = processor(text=ds[0]["text"], return_tensors="pt").input_ids >>> loss = model(input_values, labels=labels).loss >>> loss.backward() ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")} kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } if encoder_outputs is None: if inputs is None: if input_values is not None and input_features is not None: raise ValueError("You cannot specify both input_values and input_features at the same time") elif input_values is not None: inputs = input_values elif input_features is not None: inputs = input_features else: raise ValueError("You have to specify either input_values or input_features") encoder_outputs = self.encoder( inputs, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, **kwargs_encoder, ) elif isinstance(encoder_outputs, tuple): encoder_outputs = BaseModelOutput(*encoder_outputs) encoder_hidden_states = encoder_outputs[0] # optionally project encoder_hidden_states if ( self.encoder_output_dim != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) # compute correct encoder attention mask if attention_mask is not None: encoder_attention_mask = self.encoder._get_feature_vector_attention_mask( encoder_hidden_states.shape[1], attention_mask ) else: encoder_attention_mask = None if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None): decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, inputs_embeds=decoder_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache, past_key_values=past_key_values, return_dict=return_dict, **kwargs_decoder, ) # Compute loss independent from decoder (as some shift the logits inside them) loss = None if labels is not None: logits = decoder_outputs.logits if return_dict else decoder_outputs[0] loss_fct = CrossEntropyLoss() loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1)) if not return_dict: if loss is not None: return (loss,) + decoder_outputs + encoder_outputs else: return decoder_outputs + encoder_outputs return Seq2SeqLMOutput( loss=loss, logits=decoder_outputs.logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_hidden_states, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs ): decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values) decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None input_dict = { "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "decoder_input_ids": decoder_inputs["input_ids"], "encoder_outputs": encoder_outputs, "past_key_values": decoder_inputs["past_key_values"], "use_cache": use_cache, } return input_dict def resize_token_embeddings(self, *args, **kwargs): raise NotImplementedError( "Resizing the embedding layers via the SpeechEncoderDecoderModel directly is not supported. Please use the" " respective methods of the wrapped decoder object (model.decoder.resize_token_embeddings(...))" ) def _reorder_cache(self, past_key_values, beam_idx): # apply decoder cache reordering here return self.decoder._reorder_cache(past_key_values, beam_idx)

transformers/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert SpeechT5 HiFi-GAN checkpoint.""" import argparse import numpy as np import torch from transformers import SpeechT5HifiGan, SpeechT5HifiGanConfig, logging logging.set_verbosity_info() logger = logging.get_logger("transformers.models.speecht5") def load_weights(checkpoint, hf_model, config): hf_model.apply_weight_norm() hf_model.conv_pre.weight_g.data = checkpoint["input_conv.weight_g"] hf_model.conv_pre.weight_v.data = checkpoint["input_conv.weight_v"] hf_model.conv_pre.bias.data = checkpoint["input_conv.bias"] for i in range(len(config.upsample_rates)): hf_model.upsampler[i].weight_g.data = checkpoint[f"upsamples.{i}.1.weight_g"] hf_model.upsampler[i].weight_v.data = checkpoint[f"upsamples.{i}.1.weight_v"] hf_model.upsampler[i].bias.data = checkpoint[f"upsamples.{i}.1.bias"] for i in range(len(config.upsample_rates) * len(config.resblock_kernel_sizes)): for j in range(len(config.resblock_dilation_sizes)): hf_model.resblocks[i].convs1[j].weight_g.data = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"] hf_model.resblocks[i].convs1[j].weight_v.data = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"] hf_model.resblocks[i].convs1[j].bias.data = checkpoint[f"blocks.{i}.convs1.{j}.1.bias"] hf_model.resblocks[i].convs2[j].weight_g.data = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"] hf_model.resblocks[i].convs2[j].weight_v.data = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"] hf_model.resblocks[i].convs2[j].bias.data = checkpoint[f"blocks.{i}.convs2.{j}.1.bias"] hf_model.conv_post.weight_g.data = checkpoint["output_conv.1.weight_g"] hf_model.conv_post.weight_v.data = checkpoint["output_conv.1.weight_v"] hf_model.conv_post.bias.data = checkpoint["output_conv.1.bias"] hf_model.remove_weight_norm() @torch.no_grad() def convert_hifigan_checkpoint( checkpoint_path, stats_path, pytorch_dump_folder_path, config_path=None, repo_id=None, ): if config_path is not None: config = SpeechT5HifiGanConfig.from_pretrained(config_path) else: config = SpeechT5HifiGanConfig() model = SpeechT5HifiGan(config) orig_checkpoint = torch.load(checkpoint_path) load_weights(orig_checkpoint["model"]["generator"], model, config) stats = np.load(stats_path) mean = stats[0].reshape(-1) scale = stats[1].reshape(-1) model.mean = torch.from_numpy(mean).float() model.scale = torch.from_numpy(scale).float() model.save_pretrained(pytorch_dump_folder_path) if repo_id: print("Pushing to the hub...") model.push_to_hub(repo_id) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model." ) parser.add_argument( "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." ) args = parser.parse_args() convert_hifigan_checkpoint( args.checkpoint_path, args.stats_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub, )

transformers/src/transformers/models/speecht5/convert_hifigan.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Swin2SR checkpoints from the original repository. URL: https://github.com/mv-lab/swin2sr""" import argparse import requests import torch from PIL import Image from torchvision.transforms import Compose, Normalize, Resize, ToTensor from transformers import Swin2SRConfig, Swin2SRForImageSuperResolution, Swin2SRImageProcessor def get_config(checkpoint_url): config = Swin2SRConfig() if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url: config.upscale = 4 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url: config.upscale = 4 config.image_size = 48 config.upsampler = "pixelshuffle_aux" elif "Swin2SR_Lightweight_X2_64" in checkpoint_url: config.depths = [6, 6, 6, 6] config.embed_dim = 60 config.num_heads = [6, 6, 6, 6] config.upsampler = "pixelshuffledirect" elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url: config.upscale = 4 config.upsampler = "nearest+conv" elif "Swin2SR_Jpeg_dynamic" in checkpoint_url: config.num_channels = 1 config.upscale = 1 config.image_size = 126 config.window_size = 7 config.img_range = 255.0 config.upsampler = "" return config def rename_key(name, config): if "patch_embed.proj" in name and "layers" not in name: name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "embeddings.patch_embeddings.layernorm") if "layers" in name: name = name.replace("layers", "encoder.stages") if "residual_group.blocks" in name: name = name.replace("residual_group.blocks", "layers") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if "q_bias" in name: name = name.replace("q_bias", "query.bias") if "k_bias" in name: name = name.replace("k_bias", "key.bias") if "v_bias" in name: name = name.replace("v_bias", "value.bias") if "cpb_mlp" in name: name = name.replace("cpb_mlp", "continuous_position_bias_mlp") if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "patch_embed.projection") if name == "norm.weight": name = "layernorm.weight" if name == "norm.bias": name = "layernorm.bias" if "conv_first" in name: name = name.replace("conv_first", "first_convolution") if ( "upsample" in name or "conv_before_upsample" in name or "conv_bicubic" in name or "conv_up" in name or "conv_hr" in name or "conv_last" in name or "aux" in name ): # heads if "conv_last" in name: name = name.replace("conv_last", "final_convolution") if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]: if "conv_before_upsample.0" in name: name = name.replace("conv_before_upsample.0", "conv_before_upsample") if "upsample.0" in name: name = name.replace("upsample.0", "upsample.convolution_0") if "upsample.2" in name: name = name.replace("upsample.2", "upsample.convolution_1") name = "upsample." + name elif config.upsampler == "pixelshuffledirect": name = name.replace("upsample.0.weight", "upsample.conv.weight") name = name.replace("upsample.0.bias", "upsample.conv.bias") else: pass else: name = "swin2sr." + name return name def convert_state_dict(orig_state_dict, config): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "qkv" in key: key_split = key.split(".") stage_num = int(key_split[1]) block_num = int(key_split[4]) dim = config.embed_dim if "weight" in key: orig_state_dict[ f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[ f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.key.weight" ] = val[dim : dim * 2, :] orig_state_dict[ f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[ f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.query.bias" ] = val[:dim] orig_state_dict[ f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.key.bias" ] = val[dim : dim * 2] orig_state_dict[ f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.value.bias" ] = val[-dim:] pass else: orig_state_dict[rename_key(key, config)] = val return orig_state_dict def convert_swin2sr_checkpoint(checkpoint_url, pytorch_dump_folder_path, push_to_hub): config = get_config(checkpoint_url) model = Swin2SRForImageSuperResolution(config) model.eval() state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") new_state_dict = convert_state_dict(state_dict, config) missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False) if len(missing_keys) > 0: raise ValueError("Missing keys when converting: {}".format(missing_keys)) for key in unexpected_keys: if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key): raise ValueError(f"Unexpected key {key} in state_dict") # verify values url = "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = Swin2SRImageProcessor() # pixel_values = processor(image, return_tensors="pt").pixel_values image_size = 126 if "Jpeg" in checkpoint_url else 256 transforms = Compose( [ Resize((image_size, image_size)), ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ] ) pixel_values = transforms(image).unsqueeze(0) if config.num_channels == 1: pixel_values = pixel_values[:, 0, :, :].unsqueeze(1) outputs = model(pixel_values) # assert values if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url: expected_shape = torch.Size([1, 3, 512, 512]) expected_slice = torch.tensor( [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] ) elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url: expected_shape = torch.Size([1, 3, 1024, 1024]) expected_slice = torch.tensor( [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] ) elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url: # TODO values didn't match exactly here expected_shape = torch.Size([1, 3, 1024, 1024]) expected_slice = torch.tensor( [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] ) elif "Swin2SR_Lightweight_X2_64" in checkpoint_url: expected_shape = torch.Size([1, 3, 512, 512]) expected_slice = torch.tensor( [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] ) elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url: expected_shape = torch.Size([1, 3, 1024, 1024]) expected_slice = torch.tensor( [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] ) assert ( outputs.reconstruction.shape == expected_shape ), f"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}" assert torch.allclose(outputs.reconstruction[0, 0, :3, :3], expected_slice, atol=1e-3) print("Looks ok!") url_to_name = { "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": ( "swin2SR-classical-sr-x2-64" ), "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": ( "swin2SR-classical-sr-x4-64" ), "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": ( "swin2SR-compressed-sr-x4-48" ), "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": ( "swin2SR-lightweight-x2-64" ), "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": ( "swin2SR-realworld-sr-x4-64-bsrgan-psnr" ), } model_name = url_to_name[checkpoint_url] if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving image processor to {pytorch_dump_folder_path}") processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: model.push_to_hub(f"caidas/{model_name}") processor.push_to_hub(f"caidas/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth", type=str, help="URL of the original Swin2SR checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.") args = parser.parse_args() convert_swin2sr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

transformers/src/transformers/models/swin2sr/convert_swin2sr_original_to_pytorch.py/0

# coding=utf-8 # Copyright 2022 Google LLC and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Convert T5X checkpoint to PyTorch Steps: - Install gsutil according to https://cloud.google.com/storage/docs/gsutil_install - Get a T5X checkpoint at https://github.com/google-research/t5x/blob/main/docs/models.md#t5-11-checkpoints Example: `gsutil -m cp -r gs://t5-data/pretrained_models/t5x/t5_1_1_small $HOME/` - Create or download a corresponding config for the downloaded model. E.g. for T5 v1.1 small, you can use https://huggingface.co/google/t5-v1_1-small/blob/main/config.json - Convert: ``` python3 convert_t5x_checkpoint_to_pytorch.py --t5x_checkpoint_path=$HOME/t5_1_1_small --config_file=config.json\ --pytorch_dump_path=$HOME/t5_1_1_small_pt ``` """ import argparse import collections import torch from flax import traverse_util from t5x import checkpoints from transformers import T5Config, T5EncoderModel, T5ForConditionalGeneration from transformers.utils import logging logging.set_verbosity_info() def t5x_attention_lookup(params, i, prefix, layer_name="attention"): """Returns the KOQV parameters of (self-)attention. Does not transpose.""" k = params[f"{prefix}/layers_{i}/{layer_name}/key/kernel"] o = params[f"{prefix}/layers_{i}/{layer_name}/out/kernel"] q = params[f"{prefix}/layers_{i}/{layer_name}/query/kernel"] v = params[f"{prefix}/layers_{i}/{layer_name}/value/kernel"] return k, o, q, v def t5x_mlp_lookup(params, i, prefix, split_mlp_wi=False): """Returns the MLP parameters of a layer. Does not transpose.""" if split_mlp_wi: wi_0 = params[f"{prefix}/layers_{i}/mlp/wi_0/kernel"] wi_1 = params[f"{prefix}/layers_{i}/mlp/wi_1/kernel"] wi = (wi_0, wi_1) else: wi = params[f"{prefix}/layers_{i}/mlp/wi/kernel"] wo = params[f"{prefix}/layers_{i}/mlp/wo/kernel"] return wi, wo def t5x_layer_norm_lookup(params, i, prefix, layer_name): """Returns the layer norm param of a layer.""" return params[f"{prefix}/layers_{i}/{layer_name}/scale"] def convert_t5x_to_pytorch(variables: dict, *, num_layers: int, num_decoder_layers: int, is_encoder_only: bool): """Converts the parameters from T5X-Flax to Transformers-PyTorch.""" old = traverse_util.flatten_dict(variables["target"]) old = {"/".join(k): v for k, v in old.items()} # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi split_mlp_wi = "encoder/layers_0/mlp/wi_0/kernel" in old print("Split MLP:", split_mlp_wi) new = collections.OrderedDict() # Shared embeddings. new["shared.weight"] = old["token_embedder/embedding"] # Encoder. for i in range(num_layers): # Block i, layer 0 (Self Attention). layer_norm = t5x_layer_norm_lookup(old, i, "encoder", "pre_attention_layer_norm") k, o, q, v = t5x_attention_lookup(old, i, "encoder", "attention") new[f"encoder.block.{i}.layer.0.layer_norm.weight"] = layer_norm new[f"encoder.block.{i}.layer.0.SelfAttention.k.weight"] = k.T new[f"encoder.block.{i}.layer.0.SelfAttention.o.weight"] = o.T new[f"encoder.block.{i}.layer.0.SelfAttention.q.weight"] = q.T new[f"encoder.block.{i}.layer.0.SelfAttention.v.weight"] = v.T # Block i, layer 1 (MLP). layer_norm = t5x_layer_norm_lookup(old, i, "encoder", "pre_mlp_layer_norm") wi, wo = t5x_mlp_lookup(old, i, "encoder", split_mlp_wi) new[f"encoder.block.{i}.layer.1.layer_norm.weight"] = layer_norm if split_mlp_wi: new[f"encoder.block.{i}.layer.1.DenseReluDense.wi_0.weight"] = wi[0].T new[f"encoder.block.{i}.layer.1.DenseReluDense.wi_1.weight"] = wi[1].T else: new[f"encoder.block.{i}.layer.1.DenseReluDense.wi.weight"] = wi.T new[f"encoder.block.{i}.layer.1.DenseReluDense.wo.weight"] = wo.T new["encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"] = old[ "encoder/relpos_bias/rel_embedding" ].T new["encoder.final_layer_norm.weight"] = old["encoder/encoder_norm/scale"] if not is_encoder_only: # Decoder. for i in range(num_decoder_layers): # Block i, layer 0 (Self Attention). layer_norm = t5x_layer_norm_lookup(old, i, "decoder", "pre_self_attention_layer_norm") k, o, q, v = t5x_attention_lookup(old, i, "decoder", "self_attention") new[f"decoder.block.{i}.layer.0.layer_norm.weight"] = layer_norm new[f"decoder.block.{i}.layer.0.SelfAttention.k.weight"] = k.T new[f"decoder.block.{i}.layer.0.SelfAttention.o.weight"] = o.T new[f"decoder.block.{i}.layer.0.SelfAttention.q.weight"] = q.T new[f"decoder.block.{i}.layer.0.SelfAttention.v.weight"] = v.T # Block i, layer 1 (Cross Attention). layer_norm = t5x_layer_norm_lookup(old, i, "decoder", "pre_cross_attention_layer_norm") k, o, q, v = t5x_attention_lookup(old, i, "decoder", "encoder_decoder_attention") new[f"decoder.block.{i}.layer.1.layer_norm.weight"] = layer_norm new[f"decoder.block.{i}.layer.1.EncDecAttention.k.weight"] = k.T new[f"decoder.block.{i}.layer.1.EncDecAttention.o.weight"] = o.T new[f"decoder.block.{i}.layer.1.EncDecAttention.q.weight"] = q.T new[f"decoder.block.{i}.layer.1.EncDecAttention.v.weight"] = v.T # Block i, layer 2 (MLP). layer_norm = t5x_layer_norm_lookup(old, i, "decoder", "pre_mlp_layer_norm") wi, wo = t5x_mlp_lookup(old, i, "decoder", split_mlp_wi) new[f"decoder.block.{i}.layer.2.layer_norm.weight"] = layer_norm if split_mlp_wi: new[f"decoder.block.{i}.layer.2.DenseReluDense.wi_0.weight"] = wi[0].T new[f"decoder.block.{i}.layer.2.DenseReluDense.wi_1.weight"] = wi[1].T else: new[f"decoder.block.{i}.layer.2.DenseReluDense.wi.weight"] = wi.T new[f"decoder.block.{i}.layer.2.DenseReluDense.wo.weight"] = wo.T new["decoder.final_layer_norm.weight"] = old["decoder/decoder_norm/scale"] new["decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"] = old[ "decoder/relpos_bias/rel_embedding" ].T # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead) if "decoder/logits_dense/kernel" in old: new["lm_head.weight"] = old["decoder/logits_dense/kernel"].T return new def make_state_dict(converted_params, is_encoder_only: bool): """Prepares a state dict for the PyTorch model.""" # Make a state dict with torch tensors. state_dict = collections.OrderedDict([(k, torch.from_numpy(v.copy())) for (k, v) in converted_params.items()]) # Add what is missing. if "encoder.embed_tokens.weight" not in state_dict: state_dict["encoder.embed_tokens.weight"] = state_dict["shared.weight"] if not is_encoder_only: if "decoder.embed_tokens.weight" not in state_dict: state_dict["decoder.embed_tokens.weight"] = state_dict["shared.weight"] if "lm_head.weight" not in state_dict: # For old 1.0 models. print("Using shared word embeddings as lm_head.") state_dict["lm_head.weight"] = state_dict["shared.weight"] return state_dict def load_t5x_weights_in_t5(model, config, t5x_checkpoint_path, is_encoder_only): """Replaces the params in model witht the T5X converted params.""" variables = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path) converted = convert_t5x_to_pytorch( variables, num_layers=config.num_layers, num_decoder_layers=config.num_decoder_layers, is_encoder_only=is_encoder_only, ) state_dict = make_state_dict(converted, is_encoder_only) model.load_state_dict(state_dict, strict=True) def convert_t5x_checkpoint_to_pytorch( t5x_checkpoint_path, config_file, pytorch_dump_path, is_encoder_only: bool = False ): """Loads the config and model, converts the T5X checkpoint, and saves a PyTorch checkpoint.""" # Initialise PyTorch model config = T5Config.from_json_file(config_file) print(f"Building PyTorch model from configuration: {config}") # Non-v1.1 checkpoints could also use T5Model, but this works for all. # The v1.0 checkpoints will simply have an LM head that is the word embeddings. if is_encoder_only: model = T5EncoderModel(config) else: model = T5ForConditionalGeneration(config) # Load weights from tf checkpoint load_t5x_weights_in_t5(model, config, t5x_checkpoint_path, is_encoder_only) # Save pytorch-model print(f"Save PyTorch model to {pytorch_dump_path}") model.save_pretrained(pytorch_dump_path) # Verify that we can load the checkpoint. model.from_pretrained(pytorch_dump_path) print("Done") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.") # Required parameters parser.add_argument( "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint." ) parser.add_argument( "--config_file", default=None, type=str, required=True, help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False ) args = parser.parse_args() convert_t5x_checkpoint_to_pytorch( args.t5x_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only )

transformers/src/transformers/models/t5/convert_t5x_checkpoint_to_pytorch.py/0

# coding=utf-8 # Copyright 2021 Google Research and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TF 2.0 TAPAS model.""" from __future__ import annotations import enum import math from dataclasses import dataclass from typing import Dict, Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFBaseModelOutputWithPooling, TFMaskedLMOutput, TFSequenceClassifierOutput, ) from ...modeling_tf_utils import ( TFMaskedLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_tensorflow_probability_available, logging, replace_return_docstrings, requires_backends, ) from .configuration_tapas import TapasConfig logger = logging.get_logger(__name__) # soft dependency if is_tensorflow_probability_available(): try: import tensorflow_probability as tfp # On the first call, check whether a compatible version of TensorFlow is installed # TensorFlow Probability depends on a recent stable release of TensorFlow n = tfp.distributions.Normal(loc=0.0, scale=1.0) except ImportError: logger.error( "TAPAS models are not usable since `tensorflow_probability` can't be loaded. " "It seems you have `tensorflow_probability` installed with the wrong tensorflow version. " "Please try to reinstall it following the instructions here: https://github.com/tensorflow/probability." ) _CONFIG_FOR_DOC = "TapasConfig" _CHECKPOINT_FOR_DOC = "google/tapas-base" TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = [ # large models "google/tapas-large", "google/tapas-large-finetuned-sqa", "google/tapas-large-finetuned-wtq", "google/tapas-large-finetuned-wikisql-supervised", "google/tapas-large-finetuned-tabfact", # base models "google/tapas-base", "google/tapas-base-finetuned-sqa", "google/tapas-base-finetuned-wtq", "google/tapas-base-finetuned-wikisql-supervised", "google/tapas-base-finetuned-tabfact", # small models "google/tapas-small", "google/tapas-small-finetuned-sqa", "google/tapas-small-finetuned-wtq", "google/tapas-small-finetuned-wikisql-supervised", "google/tapas-small-finetuned-tabfact", # mini models "google/tapas-mini", "google/tapas-mini-finetuned-sqa", "google/tapas-mini-finetuned-wtq", "google/tapas-mini-finetuned-wikisql-supervised", "google/tapas-mini-finetuned-tabfact", # tiny models "google/tapas-tiny", "google/tapas-tiny-finetuned-sqa", "google/tapas-tiny-finetuned-wtq", "google/tapas-tiny-finetuned-wikisql-supervised", "google/tapas-tiny-finetuned-tabfact", # See all TAPAS models at https://huggingface.co/models?filter=tapas ] EPSILON_ZERO_DIVISION = 1e-10 CLOSE_ENOUGH_TO_LOG_ZERO = -10000.0 @dataclass class TFTableQuestionAnsweringOutput(ModelOutput): """ Output type of [`TFTapasForQuestionAnswering`]. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` (and possibly `answer`, `aggregation_labels`, `numeric_values` and `numeric_values_scale` are provided)): Total loss as the sum of the hierarchical cell selection log-likelihood loss and (optionally) the semi-supervised regression loss and (optionally) supervised loss for aggregations. logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Prediction scores of the cell selection head, for every token. logits_aggregation (`tf.Tensor`, *optional*, of shape `(batch_size, num_aggregation_labels)`): Prediction scores of the aggregation head, for every aggregation operator. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None logits: tf.Tensor = None logits_aggregation: tf.Tensor | None = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None class TFTapasEmbeddings(keras.layers.Layer): """ Construct the embeddings from word, position and token_type embeddings. Same as BertEmbeddings but with a number of additional token type embeddings to encode tabular structure. """ def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.config = config self.number_of_token_type_embeddings = len(config.type_vocab_sizes) self.reset_position_index_per_cell = config.reset_position_index_per_cell self.hidden_size = config.hidden_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape=None): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.config.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) for i, type_vocab_size in enumerate(self.config.type_vocab_sizes): with tf.name_scope(f"token_type_embeddings_{i}"): setattr( self, f"token_type_embeddings_{i}", self.add_weight( name="embeddings", shape=[type_vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ), ) if self.built: return self.built = True if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) def call( self, input_ids: tf.Tensor = None, position_ids: tf.Tensor = None, token_type_ids: tf.Tensor = None, inputs_embeds: tf.Tensor = None, training: bool = False, ) -> tf.Tensor: """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) if input_ids is not None: input_shape = shape_list(input_ids) else: input_shape = shape_list(inputs_embeds)[:-1] seq_length = input_shape[1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape + [self.number_of_token_type_embeddings], value=0) if position_ids is None: # create absolute position embeddings position_ids = tf.expand_dims(tf.range(start=0, limit=seq_length), axis=0) position_ids = tf.broadcast_to(position_ids, shape=input_shape) # when self.config.reset_position_index_per_cell is set to True, create relative position embeddings if self.reset_position_index_per_cell: # shape (batch_size, seq_len) col_index = IndexMap(token_type_ids[:, :, 1], self.config.type_vocab_sizes[1], batch_dims=1) # shape (batch_size, seq_len) row_index = IndexMap(token_type_ids[:, :, 2], self.config.type_vocab_sizes[2], batch_dims=1) # shape (batch_size, seq_len) full_index = ProductIndexMap(col_index, row_index) # shape (max_rows * max_columns,). First absolute position for every cell first_position_per_segment = reduce_min(position_ids, full_index)[0] # ? shape (batch_size, seq_len). First absolute position of the cell for every token first_position = gather(first_position_per_segment, full_index) # shape (1, seq_len) position = tf.expand_dims(tf.range(start=0, limit=seq_length), axis=0) position_ids = tf.math.minimum(self.max_position_embeddings - 1, position - first_position) if input_ids is not None: check_embeddings_within_bounds(input_ids, self.config.vocab_size) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) position_embeddings = tf.gather(self.position_embeddings, indices=position_ids) final_embeddings = inputs_embeds + position_embeddings for i in range(self.number_of_token_type_embeddings): name = f"token_type_embeddings_{i}" final_embeddings += tf.gather(params=getattr(self, name), indices=token_type_ids[:, :, i]) final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Tapas class TFTapasSelfAttention(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder self.config = config def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) key_layer = tf.concat([past_key_value[0], key_layer], axis=2) value_layer = tf.concat([past_key_value[1], value_layer], axis=2) else: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFTapasModel call() function) attention_scores = tf.add(attention_scores, attention_mask) # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "query", None) is not None: with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Tapas class TFTapasSelfOutput(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Tapas class TFTapasAttention(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFTapasSelfAttention(config, name="self") self.dense_output = TFTapasSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.self_attention( hidden_states=input_tensor, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) # add attentions (possibly with past_key_value) if we output them outputs = (attention_output,) + self_outputs[1:] return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self_attention", None) is not None: with tf.name_scope(self.self_attention.name): self.self_attention.build(None) if getattr(self, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Tapas class TFTapasIntermediate(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Tapas class TFTapasOutput(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Tapas class TFTapasLayer(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.attention = TFTapasAttention(config, name="attention") self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = TFTapasAttention(config, name="crossattention") self.intermediate = TFTapasIntermediate(config, name="intermediate") self.bert_output = TFTapasOutput(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_value: Tuple[tf.Tensor] | None, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( input_tensor=hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=self_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( input_tensor=attention_output, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value intermediate_output = self.intermediate(hidden_states=attention_output) layer_output = self.bert_output( hidden_states=intermediate_output, input_tensor=attention_output, training=training ) outputs = (layer_output,) + outputs # add attentions if we output them # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "intermediate", None) is not None: with tf.name_scope(self.intermediate.name): self.intermediate.build(None) if getattr(self, "bert_output", None) is not None: with tf.name_scope(self.bert_output.name): self.bert_output.build(None) if getattr(self, "crossattention", None) is not None: with tf.name_scope(self.crossattention.name): self.crossattention.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Tapas class TFTapasEncoder(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.config = config self.layer = [TFTapasLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_values: Tuple[Tuple[tf.Tensor]] | None, use_cache: Optional[bool], output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) past_key_value = past_key_values[i] if past_key_values is not None else None layer_outputs = layer_module( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layer", None) is not None: for layer in self.layer: with tf.name_scope(layer.name): layer.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Tapas class TFTapasPooler(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(inputs=first_token_tensor) return pooled_output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Tapas class TFTapasPredictionHeadTransform(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) if isinstance(config.hidden_act, str): self.transform_act_fn = get_tf_activation(config.hidden_act) else: self.transform_act_fn = config.hidden_act self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(inputs=hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Tapas class TFTapasLMPredictionHead(keras.layers.Layer): def __init__(self, config: TapasConfig, input_embeddings: keras.layers.Layer, **kwargs): super().__init__(**kwargs) self.config = config self.hidden_size = config.hidden_size self.transform = TFTapasPredictionHeadTransform(config, name="transform") # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.input_embeddings = input_embeddings def build(self, input_shape=None): self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") if self.built: return self.built = True if getattr(self, "transform", None) is not None: with tf.name_scope(self.transform.name): self.transform.build(None) def get_output_embeddings(self) -> keras.layers.Layer: return self.input_embeddings def set_output_embeddings(self, value: tf.Variable): self.input_embeddings.weight = value self.input_embeddings.vocab_size = shape_list(value)[0] def get_bias(self) -> Dict[str, tf.Variable]: return {"bias": self.bias} def set_bias(self, value: tf.Variable): self.bias = value["bias"] self.config.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.transform(hidden_states=hidden_states) seq_length = shape_list(hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->Tapas class TFTapasMLMHead(keras.layers.Layer): def __init__(self, config: TapasConfig, input_embeddings: keras.layers.Layer, **kwargs): super().__init__(**kwargs) self.predictions = TFTapasLMPredictionHead(config, input_embeddings, name="predictions") def call(self, sequence_output: tf.Tensor) -> tf.Tensor: prediction_scores = self.predictions(hidden_states=sequence_output) return prediction_scores def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "predictions", None) is not None: with tf.name_scope(self.predictions.name): self.predictions.build(None) @keras_serializable class TFTapasMainLayer(keras.layers.Layer): config_class = TapasConfig def __init__(self, config: TapasConfig, add_pooling_layer: bool = True, **kwargs): requires_backends(self, "tensorflow_probability") super().__init__(**kwargs) self.config = config self.embeddings = TFTapasEmbeddings(config, name="embeddings") self.encoder = TFTapasEncoder(config, name="encoder") self.pooler = TFTapasPooler(config, name="pooler") if add_pooling_layer else None def get_input_embeddings(self) -> keras.layers.Layer: return self.embeddings def set_input_embeddings(self, value: tf.Variable): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError @unpack_inputs def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if attention_mask is None: attention_mask = tf.fill(dims=input_shape, value=1) if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape + [len(self.config.type_vocab_sizes)], value=0) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, training=training, ) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1])) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) one_cst = tf.constant(1.0, dtype=embedding_output.dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers encoder_outputs = self.encoder( hidden_states=embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None if not return_dict: return ( sequence_output, pooled_output, ) + encoder_outputs[1:] return TFBaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "pooler", None) is not None: with tf.name_scope(self.pooler.name): self.pooler.build(None) class TFTapasPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = TapasConfig base_model_prefix = "tapas" @property def input_signature(self): return { "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.float32, name="attention_mask"), "token_type_ids": tf.TensorSpec((None, None, 7), tf.int32, name="token_type_ids"), } TAPAS_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`TapasConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ TAPAS_INPUTS_DOCSTRING = r""" Args: input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0}, 7)`, *optional*): Token indices that encode tabular structure. Indices can be obtained using [`AutoTokenizer`]. See this class for more info. [What are token type IDs?](../glossary#token-type-ids) position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. If `reset_position_index_per_cell` of [`TapasConfig`] is set to `True`, relative position embeddings will be used. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False``): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare Tapas Model transformer outputting raw hidden-states without any specific head on top.", TAPAS_START_DOCSTRING, ) class TFTapasModel(TFTapasPreTrainedModel): def __init__(self, config: TapasConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.tapas = TFTapasMainLayer(config, name="tapas") @unpack_inputs @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: r""" Returns: Examples: ```python >>> from transformers import AutoTokenizer, TapasModel >>> import pandas as pd >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base") >>> model = TapasModel.from_pretrained("google/tapas-base") >>> data = { ... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], ... "Age": ["56", "45", "59"], ... "Number of movies": ["87", "53", "69"], ... } >>> table = pd.DataFrame.from_dict(data) >>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"] >>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state ```""" outputs = self.tapas( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "tapas", None) is not None: with tf.name_scope(self.tapas.name): self.tapas.build(None) @add_start_docstrings("""Tapas Model with a `language modeling` head on top.""", TAPAS_START_DOCSTRING) class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss): def __init__(self, config: TapasConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) if config.is_decoder: logger.warning( "If you want to use `TFTapasForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.tapas = TFTapasMainLayer(config, add_pooling_layer=False, name="tapas") self.lm_head = TFTapasMLMHead(config, input_embeddings=self.tapas.embeddings, name="cls") def get_lm_head(self) -> keras.layers.Layer: return self.lm_head.predictions @unpack_inputs @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` Returns: Examples: ```python >>> from transformers import AutoTokenizer, TapasForMaskedLM >>> import pandas as pd >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base") >>> model = TapasForMaskedLM.from_pretrained("google/tapas-base") >>> data = { ... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], ... "Age": ["56", "45", "59"], ... "Number of movies": ["87", "53", "69"], ... } >>> table = pd.DataFrame.from_dict(data) >>> inputs = tokenizer( ... table=table, queries="How many [MASK] has George [MASK] played in?", return_tensors="tf" ... ) >>> labels = tokenizer( ... table=table, queries="How many movies has George Clooney played in?", return_tensors="tf" ... )["input_ids"] >>> outputs = model(**inputs, labels=labels) >>> logits = outputs.logits ```""" outputs = self.tapas( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "tapas", None) is not None: with tf.name_scope(self.tapas.name): self.tapas.build(None) if getattr(self, "lm_head", None) is not None: with tf.name_scope(self.lm_head.name): self.lm_head.build(None) class TFTapasComputeTokenLogits(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) self.temperature = config.temperature # cell selection heads with tf.name_scope("output"): self.output_weights = self.add_weight( name="output_weights", shape=(config.hidden_size,), dtype=tf.float32, trainable=True, initializer=tf.zeros_initializer() if config.init_cell_selection_weights_to_zero else keras.initializers.TruncatedNormal(stddev=config.initializer_range), ) self.output_bias = self.add_weight( name="output_bias", shape=(), trainable=True, initializer=tf.zeros_initializer() ) def call(self, sequence_output: tf.Tensor) -> tf.Tensor: """ Computes logits per token Args: sequence_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the model. Returns: logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Logits per token. """ logits = (tf.einsum("bsj,j->bs", sequence_output, self.output_weights) + self.output_bias) / self.temperature return logits class TFTapasComputeColumnLogits(keras.layers.Layer): def __init__(self, config: TapasConfig, **kwargs): super().__init__(**kwargs) with tf.name_scope("column_output"): self.column_output_weights = self.add_weight( name="column_output_weights", shape=[config.hidden_size], dtype=tf.float32, trainable=True, initializer=tf.zeros_initializer() if config.init_cell_selection_weights_to_zero else keras.initializers.TruncatedNormal(stddev=config.initializer_range), ) self.column_output_bias = self.add_weight( name="column_output_bias", shape=(), trainable=True, initializer=tf.zeros_initializer() ) def call(self, sequence_output, cell_index, cell_mask, allow_empty_column_selection) -> tf.Tensor: """ Computes the column logits. Args: sequence_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the model. cell_index (`ProductIndexMap`): Index that groups tokens into cells. cell_mask (`tf.Tensor` of shape `(batch_size, max_num_rows * max_num_cols)`): Mask for cells that exist in the table (i.e. that are not padding). allow_empty_column_selection (`bool`): Whether to allow not to select any column Returns: column_logits (`tf.Tensor`of shape `(batch_size, max_num_cols)`): Tensor containing the column logits for every example in the batch. """ # First, compute the token logits (batch_size, seq_len) - without temperature token_logits = tf.einsum("bsj,j->bs", sequence_output, self.column_output_weights) + self.column_output_bias # Next, average the logits per cell (batch_size, max_num_cols*max_num_rows) cell_logits, cell_logits_index = reduce_mean(token_logits, cell_index) # Finally, average the logits per column (batch_size, max_num_cols) column_index = cell_index.project_inner(cell_logits_index) column_logits, out_index = reduce_sum(cell_logits * cell_mask, column_index) cell_count, _ = reduce_sum(cell_mask, column_index) column_logits /= cell_count + EPSILON_ZERO_DIVISION # Mask columns that do not appear in the example. is_padding = tf.logical_and(cell_count < 0.5, tf.not_equal(out_index.indices, 0)) column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * tf.cast(is_padding, tf.float32) if not allow_empty_column_selection: column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * tf.cast(tf.equal(out_index.indices, 0), tf.float32) return column_logits @add_start_docstrings( """ Tapas Model with a cell selection head and optional aggregation head on top for question-answering tasks on tables (linear layers on top of the hidden-states output to compute `logits` and optional `logits_aggregation`), e.g. for SQA, WTQ or WikiSQL-supervised tasks. """, TAPAS_START_DOCSTRING, ) class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): def __init__(self, config: TapasConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) # base model self.tapas = TFTapasMainLayer(config, name="tapas") # dropout self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.compute_token_logits = TFTapasComputeTokenLogits(config, name="compute_token_logits") self.compute_column_logits = TFTapasComputeColumnLogits(config, name="compute_column_logits") if config.num_aggregation_labels > 0: self.aggregation_classifier = keras.layers.Dense( config.num_aggregation_labels, kernel_initializer=get_initializer(config.initializer_range), name="aggregation_classifier", ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFTableQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, table_mask: np.ndarray | tf.Tensor | None = None, aggregation_labels: np.ndarray | tf.Tensor | None = None, float_answer: np.ndarray | tf.Tensor | None = None, numeric_values: np.ndarray | tf.Tensor | None = None, numeric_values_scale: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFTableQuestionAnsweringOutput, Tuple[tf.Tensor]]: r""" table_mask (`tf.Tensor` of shape `(batch_size, seq_length)`, *optional*): Mask for the table. Indicates which tokens belong to the table (1). Question tokens, table headers and padding are 0. labels (`tf.Tensor` of shape `(batch_size, seq_length)`, *optional*): Labels per token for computing the hierarchical cell selection loss. This encodes the positions of the answer appearing in the table. Can be obtained using [`AutoTokenizer`]. - 1 for tokens that are **part of the answer**, - 0 for tokens that are **not part of the answer**. aggregation_labels (`tf.Tensor` of shape `(batch_size, )`, *optional*): Aggregation function index for every example in the batch for computing the aggregation loss. Indices should be in `[0, ..., config.num_aggregation_labels - 1]`. Only required in case of strong supervision for aggregation (WikiSQL-supervised). float_answer (`tf.Tensor` of shape `(batch_size, )`, *optional*): Float answer for every example in the batch. Set to *float('nan')* for cell selection questions. Only required in case of weak supervision (WTQ) to calculate the aggregate mask and regression loss. numeric_values (`tf.Tensor` of shape `(batch_size, seq_length)`, *optional*): Numeric values of every token, NaN for tokens which are not numeric values. Can be obtained using [`AutoTokenizer`]. Only required in case of weak supervision for aggregation (WTQ) to calculate the regression loss. numeric_values_scale (`tf.Tensor` of shape `(batch_size, seq_length)`, *optional*): Scale of the numeric values of every token. Can be obtained using [`AutoTokenizer`]. Only required in case of weak supervision for aggregation (WTQ) to calculate the regression loss. Returns: Examples: ```python >>> from transformers import AutoTokenizer, TapasForQuestionAnswering >>> import pandas as pd >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-wtq") >>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq") >>> data = { ... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], ... "Age": ["56", "45", "59"], ... "Number of movies": ["87", "53", "69"], ... } >>> table = pd.DataFrame.from_dict(data) >>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"] >>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> logits_aggregation = outputs.logits_aggregation ```""" outputs = self.tapas( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] pooled_output = outputs[1] sequence_output = self.dropout(sequence_output) if input_ids is not None: input_shape = shape_list(input_ids) else: input_shape = shape_list(inputs_embeds)[:-1] # Construct indices for the table. if token_type_ids is None: token_type_ids = tf.fill(input_shape + [len(self.config.type_vocab_sizes)], 0) token_types = [ "segment_ids", "column_ids", "row_ids", "prev_labels", "column_ranks", "inv_column_ranks", "numeric_relations", ] row_ids = token_type_ids[:, :, token_types.index("row_ids")] column_ids = token_type_ids[:, :, token_types.index("column_ids")] # Construct indices for the table. row_index = IndexMap( indices=tf.minimum(tf.cast(row_ids, tf.int32), self.config.max_num_rows - 1), num_segments=self.config.max_num_rows, batch_dims=1, ) col_index = IndexMap( indices=tf.minimum(tf.cast(column_ids, tf.int32), self.config.max_num_columns - 1), num_segments=self.config.max_num_columns, batch_dims=1, ) cell_index = ProductIndexMap(row_index, col_index) # Masks. input_shape = shape_list(input_ids) if input_ids is not None else shape_list(inputs_embeds)[:-1] if attention_mask is None: attention_mask = tf.ones(input_shape) # Table cells only, without question tokens and table headers. if table_mask is None: table_mask = tf.where(row_ids > 0, tf.ones_like(row_ids), tf.zeros_like(row_ids)) # <float32>[batch_size, seq_length] input_mask_float = tf.cast(attention_mask, tf.float32) table_mask_float = tf.cast(table_mask, tf.float32) # Mask for cells that exist in the table (i.e. that are not padding). cell_mask, _ = reduce_mean(input_mask_float, cell_index) # Compute logits per token. These are used to select individual cells. logits = self.compute_token_logits(sequence_output) # Compute logits per column. These are used to select a column. column_logits = None if self.config.select_one_column: column_logits = self.compute_column_logits( sequence_output, cell_index, cell_mask, self.config.allow_empty_column_selection ) # Aggregate logits. logits_aggregation = None if self.config.num_aggregation_labels > 0: logits_aggregation = self.aggregation_classifier(pooled_output) # Total loss calculation total_loss = tf.zeros(shape=(1,), dtype=tf.float32) calculate_loss = False if labels is not None: calculate_loss = True is_supervised = not self.config.num_aggregation_labels > 0 or not self.config.use_answer_as_supervision # Semi-supervised cell selection in case of no aggregation: # If the answer (the denotation) appears directly in the table we might # select the answer without applying any aggregation function. There are # some ambiguous cases, see utils._calculate_aggregate_mask for more info. # `aggregate_mask` is 1 for examples where we chose to aggregate and 0 # for examples where we chose to select the answer directly. # `labels` encodes the positions of the answer appearing in the table. if is_supervised: aggregate_mask = None else: if float_answer is not None: assert ( shape_list(labels)[0] == shape_list(float_answer)[0] ), "Make sure the answers are a FloatTensor of shape (batch_size,)" # <float32>[batch_size] aggregate_mask = _calculate_aggregate_mask( float_answer, pooled_output, self.config.cell_selection_preference, labels, self.aggregation_classifier, ) else: aggregate_mask = None raise ValueError("You have to specify float answers in order to calculate the aggregate mask") # Cell selection log-likelihood if self.config.average_logits_per_cell: logits_per_cell, _ = reduce_mean(logits, cell_index) logits = gather(logits_per_cell, cell_index) dist_per_token = tfp.distributions.Bernoulli(logits=logits) # Compute cell selection loss per example. selection_loss_per_example = None if not self.config.select_one_column: weight = tf.where( labels == 0, tf.ones_like(labels, dtype=tf.float32), self.config.positive_label_weight * tf.ones_like(labels, dtype=tf.float32), ) selection_loss_per_token = -dist_per_token.log_prob(labels) * weight selection_loss_per_example = tf.reduce_sum(selection_loss_per_token * input_mask_float, axis=1) / ( tf.reduce_sum(input_mask_float, axis=1) + EPSILON_ZERO_DIVISION ) else: selection_loss_per_example, logits = _single_column_cell_selection_loss( logits, column_logits, labels, cell_index, col_index, cell_mask ) dist_per_token = tfp.distributions.Bernoulli(logits=logits) # Supervised cell selection if self.config.disable_per_token_loss: pass elif is_supervised: total_loss += tf.reduce_mean(selection_loss_per_example) else: # For the not supervised case, do not assign loss for cell selection total_loss += tf.reduce_mean(selection_loss_per_example * (1.0 - aggregate_mask)) # Semi-supervised regression loss and supervised loss for aggregations if self.config.num_aggregation_labels > 0: if is_supervised: # Note that `aggregate_mask` is None if the setting is supervised. if aggregation_labels is not None: assert ( shape_list(labels)[0] == shape_list(aggregation_labels)[0] ), "Make sure the aggregation labels are a LongTensor of shape (batch_size,)" per_example_additional_loss = _calculate_aggregation_loss( logits_aggregation, aggregate_mask, aggregation_labels, self.config.use_answer_as_supervision, self.config.num_aggregation_labels, self.config.aggregation_loss_weight, ) else: raise ValueError( "You have to specify aggregation labels in order to calculate the aggregation loss" ) else: aggregation_labels = tf.zeros(shape_list(labels)[0], dtype=tf.int32) per_example_additional_loss = _calculate_aggregation_loss( logits_aggregation, aggregate_mask, aggregation_labels, self.config.use_answer_as_supervision, self.config.num_aggregation_labels, self.config.aggregation_loss_weight, ) if self.config.use_answer_as_supervision: if numeric_values is not None and numeric_values_scale is not None: assert shape_list(numeric_values) == shape_list(numeric_values_scale) # Add regression loss for numeric answers which require aggregation. answer_loss, large_answer_loss_mask = _calculate_regression_loss( float_answer, aggregate_mask, dist_per_token, numeric_values, numeric_values_scale, table_mask_float, logits_aggregation, self.config, ) per_example_additional_loss += answer_loss # Zero loss for examples with answer_loss > cutoff. per_example_additional_loss *= large_answer_loss_mask else: raise ValueError( "You have to specify numeric values and numeric values scale in order to calculate the" " regression loss" ) total_loss += tf.reduce_mean(per_example_additional_loss) else: # if no label ids are provided, set them to zeros in order to properly compute logits labels = tf.zeros_like(logits) _, logits = _single_column_cell_selection_loss( logits, column_logits, labels, cell_index, col_index, cell_mask ) if not return_dict: output = (logits, logits_aggregation) + outputs[2:] return ((total_loss,) + output) if calculate_loss else output return TFTableQuestionAnsweringOutput( loss=total_loss if calculate_loss else None, logits=logits, logits_aggregation=logits_aggregation, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "tapas", None) is not None: with tf.name_scope(self.tapas.name): self.tapas.build(None) if getattr(self, "compute_token_logits", None) is not None: with tf.name_scope(self.compute_token_logits.name): self.compute_token_logits.build(None) if getattr(self, "compute_column_logits", None) is not None: with tf.name_scope(self.compute_column_logits.name): self.compute_column_logits.build(None) if getattr(self, "aggregation_classifier", None) is not None: with tf.name_scope(self.aggregation_classifier.name): self.aggregation_classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ Tapas Model with a sequence classification head on top (a linear layer on top of the pooled output), e.g. for table entailment tasks, such as TabFact (Chen et al., 2020). """, TAPAS_START_DOCSTRING, ) class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: TapasConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.tapas = TFTapasMainLayer(config, name="tapas") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, name="dropout") self.classifier = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Note: this is called "classification_class_index" in the original implementation. Returns: Examples: ```python >>> from transformers import AutoTokenizer, TapasForSequenceClassification >>> import tensorflow as tf >>> import pandas as pd >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-tabfact") >>> model = TapasForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact") >>> data = { ... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], ... "Age": ["56", "45", "59"], ... "Number of movies": ["87", "53", "69"], ... } >>> table = pd.DataFrame.from_dict(data) >>> queries = [ ... "There is only one actor who is 45 years old", ... "There are 3 actors which played in more than 60 movies", ... ] >>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf") >>> labels = tf.convert_to_tensor([1, 0]) # 1 means entailed, 0 means refuted >>> outputs = model(**inputs, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits ```""" outputs = self.tapas( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs[1] pooled_output = self.dropout(inputs=pooled_output, training=training) logits = self.classifier(inputs=pooled_output) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "tapas", None) is not None: with tf.name_scope(self.tapas.name): self.tapas.build(None) if getattr(self, "dropout", None) is not None: with tf.name_scope(self.dropout.name): self.dropout.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) """ TAPAS utilities.""" class AverageApproximationFunction(str, enum.Enum): RATIO = "ratio" FIRST_ORDER = "first_order" SECOND_ORDER = "second_order" # Beginning of everything related to segmented tensors class IndexMap(object): """Index grouping entries within a tensor.""" def __init__(self, indices, num_segments, batch_dims=0): """ Creates an index. Args: indices: <int32> Tensor of indices, same shape as `values`. num_segments: <int32> Scalar tensor, the number of segments. All elements in a batched segmented tensor must have the same number of segments (although many segments can be empty). batch_dims: Python integer, the number of batch dimensions. The first `batch_dims` dimensions of a SegmentedTensor are treated as batch dimensions. Segments in different batch elements are always distinct even if they have the same index. """ self.indices = tf.convert_to_tensor(indices) self.num_segments = tf.convert_to_tensor(num_segments) self.batch_dims = batch_dims def batch_shape(self): return tf.shape(self.indices)[: self.batch_dims] class ProductIndexMap(IndexMap): """The product of two indices.""" def __init__(self, outer_index, inner_index): """ Combines indices i and j into pairs (i, j). The result is an index where each segment (i, j) is the intersection of segments i and j. For example if the inputs represent table cells indexed by respectively rows and columns the output will be a table indexed by (row, column) pairs, i.e. by cell. The implementation combines indices {0, .., n - 1} and {0, .., m - 1} into {0, .., nm - 1}. The output has `num_segments` equal to `outer_index.num_segements` * `inner_index.num_segments`. Args: outer_index: IndexMap. inner_index: IndexMap, must have the same shape as `outer_index`. """ if outer_index.batch_dims != inner_index.batch_dims: raise ValueError("outer_index.batch_dims and inner_index.batch_dims must be the same.") super(ProductIndexMap, self).__init__( indices=( inner_index.indices + outer_index.indices * tf.cast(inner_index.num_segments, inner_index.indices.dtype) ), num_segments=inner_index.num_segments * outer_index.num_segments, batch_dims=inner_index.batch_dims, ) self.outer_index = outer_index self.inner_index = inner_index def project_outer(self, index): """Projects an index with the same index set onto the outer components.""" return IndexMap( indices=tf.math.floordiv(index.indices, self.inner_index.num_segments), num_segments=self.outer_index.num_segments, batch_dims=index.batch_dims, ) def project_inner(self, index): """Projects an index with the same index set onto the inner components.""" return IndexMap( indices=tf.math.floormod(index.indices, self.inner_index.num_segments), num_segments=self.inner_index.num_segments, batch_dims=index.batch_dims, ) def gather(values, index, name="segmented_gather"): """ Gathers from `values` using the index map. For each element in the domain of the index map this operation looks up a value for that index in `values`. Two elements from the same segment always get assigned the same value. Args: values: [B1, ..., Bn, num_segments, V1, ...] Tensor with segment values. index: [B1, ..., Bn, I1, ..., Ik] IndexMap. name: Name for the TensorFlow operation. Returns: [B1, ..., Bn, I1, ..., Ik, V1, ...] Tensor with the gathered values. """ return tf.gather(values, index.indices, batch_dims=index.batch_dims, name=name) def flatten(index, name="segmented_flatten"): """ Flattens a batched index map to a 1d index map. This operation relabels the segments to keep batch elements distinct. The k-th batch element will have indices shifted by `num_segments` * (k - 1). The result is a tensor with `num_segments` multiplied by the number of elements in the batch. Args: index: IndexMap to flatten. name: Name for the TensorFlow operation. Returns: The flattened IndexMap. """ batch_size = tf.reduce_prod(index.batch_shape()) offset = tf.range(batch_size) * index.num_segments offset = tf.reshape(offset, index.batch_shape()) for _ in range(index.batch_dims, index.indices.shape.rank): offset = tf.expand_dims(offset, -1) indices = tf.cast(offset, index.indices.dtype) + index.indices return IndexMap(indices=tf.reshape(indices, [-1]), num_segments=index.num_segments * batch_size, batch_dims=0) def range_index_map(batch_shape, num_segments, name="range_index_map"): """ Constructs an index map equal to range(num_segments). Args: batch_shape (`tf.Tensor`): Batch shape num_segments (`int`): Number of segments name (`str`, *optional*, defaults to 'range_index_map'): Name for the operation. Currently not used Returns: (`IndexMap`): IndexMap of shape batch_shape with elements equal to range(num_segments). """ batch_shape = tf.convert_to_tensor(batch_shape) batch_shape.shape.assert_has_rank(1) num_segments = tf.convert_to_tensor(num_segments) num_segments.shape.assert_has_rank(0) indices = tf.range(num_segments) shape = tf.concat([tf.ones_like(batch_shape, dtype=tf.int32), tf.expand_dims(num_segments, axis=0)], axis=0) indices = tf.reshape(indices, shape) multiples = tf.concat([batch_shape, [1]], axis=0) indices = tf.tile(indices, multiples) return IndexMap(indices=indices, num_segments=num_segments, batch_dims=batch_shape.shape.as_list()[0]) def _segment_reduce(values, index, segment_reduce_fn, name): """ Applies a segment reduction segment-wise. Args: values (`tf.Tensor`): Tensor with segment values. index (`IndexMap`): IndexMap. segment_reduce_fn (`str`): Name for the reduce operation. One of "sum", "mean", "max" or "min". name (`str`): Name for the operation. Currently not used Returns: (`IndexMap`): IndexMap of shape batch_shape with elements equal to range(num_segments). """ # Flatten the batch dimensions, as segments ops do not support batching. # However if `values` has extra dimensions to the right keep them # unflattened. Segmented ops support vector-valued operations. flat_index = flatten(index) vector_shape = tf.shape(values)[index.indices.shape.rank :] flattened_shape = tf.concat([[-1], vector_shape], axis=0) flat_values = tf.reshape(values, flattened_shape) segment_means = segment_reduce_fn( data=flat_values, segment_ids=flat_index.indices, num_segments=flat_index.num_segments ) # Unflatten the values. new_shape = tf.concat([index.batch_shape(), [index.num_segments], vector_shape], axis=0) output_values = tf.reshape(segment_means, new_shape) output_index = range_index_map(index.batch_shape(), index.num_segments) return output_values, output_index def reduce_mean(values, index, name="segmented_reduce_mean"): """ Averages a tensor over its segments. Outputs 0 for empty segments. This operations computes the mean over segments, with support for: - Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices. - Vectorization using the last dimension [V1, V2, ...]. If they are present the output will be a mean of vectors rather than scalars. Only the middle dimensions [I1, ..., Ik] are reduced by the operation. Args: values: [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..] tensor of values to be averaged. index: IndexMap [B1, B2, ..., Bn, I1, .., Ik] index defining the segments. name: Name for the TensorFlow ops. Returns: A pair (output_values, output_index) where `output_values` is a tensor of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..] and `index` is an IndexMap with shape [B1, B2, ..., Bn, num_segments]. """ return _segment_reduce(values, index, tf.math.unsorted_segment_mean, name) def reduce_sum(values, index, name="segmented_reduce_sum"): """ Sums a tensor over its segments. Outputs 0 for empty segments. This operations computes the sum over segments, with support for: - Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices. - Vectorization using the last dimension [V1, V2, ...]. If they are present the output will be a sum of vectors rather than scalars. Only the middle dimensions [I1, ..., Ik] are reduced by the operation. Args: values: [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..] tensor of values to be averaged. index: IndexMap [B1, B2, ..., Bn, I1, .., Ik] index defining the segments. name: Name for the TensorFlow ops. Returns: A pair (output_values, output_index) where `output_values` is a tensor of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..] and `index` is an IndexMap with shape [B1, B2, ..., Bn, num_segments]. """ return _segment_reduce(values, index, tf.math.unsorted_segment_sum, name) def reduce_max(values, index, name="segmented_reduce_max"): """ Computes the maximum over segments. This operations computes the maximum over segments, with support for: - Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices. - Vectorization using the last dimension [V1, V2, ...]. If they are present the output will be an element-wise maximum of vectors rather than scalars. Only the middle dimensions [I1, ..., Ik] are reduced by the operation. Args: values: [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..] tensor of values to be averaged. index: IndexMap [B1, B2, ..., Bn, I1, .., Ik] index defining the segments. name: Name for the TensorFlow ops. Returns: A pair (output_values, output_index) where `output_values` is a tensor of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..] and `index` is an IndexMap with shape [B1, B2, ..., Bn, num_segments]. """ return _segment_reduce(values, index, tf.math.unsorted_segment_max, name) def reduce_min(values, index, name="segmented_reduce_min"): """Computes the minimum over segments.""" return _segment_reduce(values, index, tf.math.unsorted_segment_min, name) def _single_column_cell_selection_loss(token_logits, column_logits, labels, cell_index, col_index, cell_mask): """ Computes the loss for cell selection constrained to a single column. The loss is a hierarchical log-likelihood. The model first predicts a column and then selects cells within that column (conditioned on the column). Cells outside the selected column are never selected. Args: token_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Tensor containing the logits per token. column_logits (`tf.Tensor` of shape `(batch_size, max_num_cols)`): Tensor containing the logits per column. labels (`tf.Tensor` of shape `(batch_size, sequence_length)`): Labels per token. cell_index (`ProductIndexMap`): Index that groups tokens into cells. col_index (`IndexMap`): Index that groups tokens into columns. cell_mask (`tf.Tensor` of shape `(batch_size, max_num_rows * max_num_cols)`): Mask for cells that exist in the table (i.e. that are not padding). Returns: selection_loss_per_example (`tf.Tensor` of shape `(batch_size,)`): Loss for each example. logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): New logits which are only allowed to select cells in a single column. Logits outside of the most likely column according to *column_logits* will be set to a very low value (such that the probabilities are 0). """ # First find the column we should select. We use the column with maximum # number of selected cells. labels_per_column, _ = reduce_sum(tf.cast(labels, tf.float32), col_index) column_label = tf.argmax(labels_per_column, axis=-1, output_type=tf.int32) # Check if there are no selected cells in the column. In that case the model # should predict the special column id 0, which means "select nothing". no_cell_selected = tf.equal(tf.reduce_max(labels_per_column, axis=-1), 0) column_label = tf.where(no_cell_selected, tf.zeros_like(column_label), column_label) column_dist = tfp.distributions.Categorical(logits=column_logits) column_loss_per_example = -column_dist.log_prob(column_label) # Reduce the labels and logits to per-cell from per-token. logits_per_cell, _ = reduce_mean(token_logits, cell_index) labels_per_cell, labels_index = reduce_max(tf.cast(labels, tf.int32), cell_index) # Mask for the selected column. column_id_for_cells = cell_index.project_inner(labels_index).indices column_mask = tf.cast(tf.equal(column_id_for_cells, tf.expand_dims(column_label, axis=1)), tf.float32) # Compute the log-likelihood for cells, but only for the selected column. cell_dist = tfp.distributions.Bernoulli(logits=logits_per_cell) cell_log_prob = cell_dist.log_prob(labels_per_cell) cell_loss = -tf.reduce_sum(cell_log_prob * column_mask * cell_mask, axis=1) # We need to normalize the loss by the number of cells in the column. cell_loss /= tf.reduce_sum(column_mask * cell_mask, axis=1) + EPSILON_ZERO_DIVISION selection_loss_per_example = column_loss_per_example selection_loss_per_example += tf.where(no_cell_selected, tf.zeros_like(selection_loss_per_example), cell_loss) # Set the probs outside the selected column (selected by the *model*) # to 0. This ensures backwards compatibility with models that select # cells from multiple columns. selected_column_id = tf.argmax(column_logits, axis=-1, output_type=tf.int32) selected_column_mask = tf.cast( tf.equal(column_id_for_cells, tf.expand_dims(selected_column_id, axis=-1)), tf.float32 ) # Never select cells with the special column id 0. selected_column_mask = tf.where( tf.equal(column_id_for_cells, 0), tf.zeros_like(selected_column_mask), selected_column_mask ) logits_per_cell += CLOSE_ENOUGH_TO_LOG_ZERO * (1.0 - cell_mask * selected_column_mask) logits = gather(logits_per_cell, cell_index) return selection_loss_per_example, logits def _calculate_aggregate_mask(answer, pooled_output, cell_selection_preference, labels, aggregation_classifier): """ Finds examples where the model should select cells with no aggregation. Returns a mask that determines for which examples should the model select answers directly from the table, without any aggregation function. If the answer is a piece of text the case is unambiguous as aggregation functions only apply to numbers. If the answer is a number but does not appear in the table then we must use some aggregation case. The ambiguous case is when the answer is a number that also appears in the table. In this case we use the aggregation function probabilities predicted by the model to decide whether to select or aggregate. The threshold for this is a hyperparameter *cell_selection_preference* Args: answer (`tf.Tensor` of shape `(batch_size, )`): Answer for every example in the batch. Nan if there is no scalar answer. pooled_output (`tf.Tensor` of shape `(batch_size, hidden_size)`): Output of the pooler (BertPooler) on top of the encoder layer. cell_selection_preference (`float`): Preference for cell selection in ambiguous cases. labels (`tf.Tensor` of shape `(batch_size, sequence_length)`): Labels per token. aggregation_classifier (`torch.nn.Linear`): Aggregation head Returns: aggregate_mask (`tf.Tensor` of shape `(batch_size,)`): A mask set to 1 for examples that should use aggregation functions. """ # tf.Tensor(batch_size,) aggregate_mask_init = tf.cast(tf.logical_not(tf.math.is_nan(answer)), tf.float32) logits_aggregation = aggregation_classifier(pooled_output) dist_aggregation = tfp.distributions.Categorical(logits=logits_aggregation) # Index 0 corresponds to "no aggregation". aggregation_ops_total_mass = tf.reduce_sum(dist_aggregation.probs_parameter()[:, 1:], axis=1) # Cell selection examples according to current model. is_pred_cell_selection = aggregation_ops_total_mass <= cell_selection_preference # Examples with non-empty cell selection supervision. is_cell_supervision_available = tf.reduce_sum(labels, axis=1) > 0 aggregate_mask = tf.where( tf.logical_and(is_pred_cell_selection, is_cell_supervision_available), tf.zeros_like(aggregate_mask_init, dtype=tf.float32), aggregate_mask_init, ) aggregate_mask = tf.stop_gradient(aggregate_mask) return aggregate_mask def _calculate_aggregation_loss_known( logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels ): """ Calculates aggregation loss when its type is known during training. In the weakly supervised setting, the only known information is that for cell selection examples, "no aggregation" should be predicted. For other examples (those that require aggregation), no loss is accumulated. In the setting where aggregation type is always known, standard cross entropy loss is accumulated for all examples Args: logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`): Logits per aggregation operation. aggregate_mask (`tf.Tensor` of shape `(batch_size, )`): A mask set to 1 for examples that should use aggregation functions. aggregation_labels (`tf.Tensor` of shape `(batch_size, )`): Aggregation function id for every example in the batch. use_answer_as_supervision (`bool`, *optional*): Whether to use the answer as the only supervision for aggregation examples. num_aggregation_labels (`int`, *optional*, defaults to 0): The number of aggregation operators to predict. Returns: aggregation_loss_known (`tf.Tensor` of shape `(batch_size,)`): Aggregation loss (when its type is known during training) per example. """ if use_answer_as_supervision: # Prepare "no aggregation" targets for cell selection examples. target_aggregation = tf.zeros_like(aggregate_mask, dtype=tf.int32) else: # Use aggregation supervision as the target. target_aggregation = aggregation_labels one_hot_labels = tf.one_hot(target_aggregation, depth=num_aggregation_labels, dtype=tf.float32) log_probs = tf.nn.log_softmax(logits_aggregation, axis=-1) # <float32>[batch_size] per_example_aggregation_intermediate = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) if use_answer_as_supervision: # Accumulate loss only for examples requiring cell selection # (no aggregation). return per_example_aggregation_intermediate * (1 - aggregate_mask) else: return per_example_aggregation_intermediate def _calculate_aggregation_loss_unknown(logits_aggregation, aggregate_mask): """ Calculates aggregation loss in the case of answer supervision. Args: logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`): Logits per aggregation operation. aggregate_mask (`tf.Tensor` of shape `(batch_size, )`): A mask set to 1 for examples that should use aggregation functions Returns: aggregation_loss_unknown (`tf.Tensor` of shape `(batch_size,)`): Aggregation loss (in case of answer supervision) per example. """ dist_aggregation = tfp.distributions.Categorical(logits=logits_aggregation) # Index 0 corresponds to "no aggregation". aggregation_ops_total_mass = tf.reduce_sum(dist_aggregation.probs_parameter()[:, 1:], axis=1) # Predict some aggregation in case of an answer that needs aggregation. # This increases the probability of all aggregation functions, in a way # similar to MML, but without considering whether the function gives the # correct answer. return -tf.math.log(aggregation_ops_total_mass) * aggregate_mask def _calculate_aggregation_loss( logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels, aggregation_loss_weight, ): """ Calculates the aggregation loss per example. Args: logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`): Logits per aggregation operation. aggregate_mask (`tf.Tensor` of shape `(batch_size, )`): A mask set to 1 for examples that should use aggregation functions. aggregation_labels (`tf.Tensor` of shape `(batch_size, )`): Aggregation function id for every example in the batch. use_answer_as_supervision (`bool`, *optional*): Whether to use the answer as the only supervision for aggregation examples. num_aggregation_labels (`int`, *optional*, defaults to 0): The number of aggregation operators to predict. aggregation_loss_weight (`float`, *optional*, defaults to 1.0): Importance weight for the aggregation loss. Returns: aggregation_loss (`tf.Tensor` of shape `(batch_size,)`): Aggregation loss per example. """ per_example_aggregation_loss = _calculate_aggregation_loss_known( logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels ) if use_answer_as_supervision: # Add aggregation loss for numeric answers that need aggregation. per_example_aggregation_loss += _calculate_aggregation_loss_unknown(logits_aggregation, aggregate_mask) return aggregation_loss_weight * per_example_aggregation_loss def _calculate_expected_result( dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config ): """ Calculates the expected result given cell and aggregation probabilities. Args: dist_per_cell (`tfp.distributions.Bernoulli`): Cell selection distribution for each cell. numeric_values (`tf.Tensor` of shape `(batch_size, seq_length)`): Numeric values of every token. Nan for tokens which are not numeric values. numeric_values_scale (`tf.Tensor` of shape `(batch_size, seq_length)`): Scale of the numeric values of every token. input_mask_float (`tf.Tensor` of shape `(batch_size, seq_length)`): Mask for the table, without question tokens and table headers. logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`): Logits per aggregation operation. config ([`TapasConfig`]): Model configuration class with all the hyperparameters of the model Returns: expected_result (`tf.Tensor` of shape `(batch_size,)`): The expected result per example. """ if config.use_gumbel_for_cells: gumbel_dist = tfp.distributions.RelaxedBernoulli( # The token logits where already divided by the temperature and used for # computing cell selection errors so we need to multiply it again here config.temperature, logits=dist_per_cell.logits_parameter() * config.temperature, ) scaled_probability_per_cell = gumbel_dist.sample() else: scaled_probability_per_cell = dist_per_cell.probs_parameter() # <float32>[batch_size, seq_length] scaled_probability_per_cell = (scaled_probability_per_cell / numeric_values_scale) * input_mask_float count_result = tf.reduce_sum(scaled_probability_per_cell, axis=1) numeric_values_masked = tf.where( tf.math.is_nan(numeric_values), tf.zeros_like(numeric_values), numeric_values ) # Mask non-numeric table values to zero. sum_result = tf.reduce_sum(scaled_probability_per_cell * numeric_values_masked, axis=1) avg_approximation = config.average_approximation_function if avg_approximation == AverageApproximationFunction.RATIO: average_result = sum_result / (count_result + EPSILON_ZERO_DIVISION) elif avg_approximation == AverageApproximationFunction.FIRST_ORDER: # The sum of all probabilities exept that correspond to other cells ex = tf.reduce_sum(scaled_probability_per_cell, axis=1, keepdims=True) - scaled_probability_per_cell + 1 average_result = tf.reduce_sum(numeric_values_masked * scaled_probability_per_cell / ex, axis=1) elif avg_approximation == AverageApproximationFunction.SECOND_ORDER: # The sum of all probabilities exept that correspond to other cells ex = tf.reduce_sum(scaled_probability_per_cell, axis=1, keepdims=True) - scaled_probability_per_cell + 1 pointwise_var = scaled_probability_per_cell * (1 - scaled_probability_per_cell) var = tf.reduce_sum(pointwise_var, axis=1, keepdims=True) - pointwise_var multiplier = (var / tf.math.square(ex) + 1) / ex average_result = tf.reduce_sum(numeric_values_masked * scaled_probability_per_cell * multiplier, axis=1) else: raise ValueError("Invalid average_approximation_function: %s", config.average_approximation_function) if config.use_gumbel_for_aggregation: gumbel_dist = tfp.distributions.RelaxedOneHotCategorical( config.aggregation_temperature, logits=logits_aggregation[:, 1:] ) # <float32>[batch_size, num_aggregation_labels - 1] aggregation_op_only_probs = gumbel_dist.sample() else: # <float32>[batch_size, num_aggregation_labels - 1] aggregation_op_only_probs = stable_softmax(logits_aggregation[:, 1:] / config.aggregation_temperature, axis=-1) all_results = tf.concat( [ tf.expand_dims(sum_result, axis=1), tf.expand_dims(average_result, axis=1), tf.expand_dims(count_result, axis=1), ], axis=1, ) expected_result = tf.reduce_sum(all_results * aggregation_op_only_probs, axis=1) return expected_result def _calculate_regression_loss( answer, aggregate_mask, dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config, ): """ Calculates the regression loss per example. Args: answer (`tf.Tensor` of shape `(batch_size,)`): Answer for every example in the batch. Nan if there is no scalar answer. aggregate_mask (`tf.Tensor` of shape `(batch_size,)`): A mask set to 1 for examples that should use aggregation functions. dist_per_cell (`torch.distributions.Bernoulli`): Cell selection distribution for each cell. numeric_values (`tf.Tensor` of shape `(batch_size, seq_length)`): Numeric values of every token. Nan for tokens which are not numeric values. numeric_values_scale (`tf.Tensor` of shape `(batch_size, seq_length)`): Scale of the numeric values of every token. input_mask_float (`tf.Tensor` of shape `(batch_size, seq_length)`): Mask for the table, without question tokens and table headers. logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`): Logits per aggregation operation. config ([`TapasConfig`]): Model configuration class with all the parameters of the model Returns: per_example_answer_loss_scaled (`tf.Tensor` of shape `(batch_size,)`): Scales answer loss for each example in the batch. large_answer_loss_mask (`tf.Tensor` of shape `(batch_size,)`): A mask which is 1 for examples for which their answer loss is larger than the answer_loss_cutoff. """ # float32 (batch_size,) expected_result = _calculate_expected_result( dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config ) # <float32>[batch_size] answer_masked = tf.where(tf.math.is_nan(answer), tf.zeros_like(answer), answer) if config.use_normalized_answer_loss: normalizer = tf.stop_gradient( tf.math.maximum(tf.math.abs(expected_result), tf.math.abs(answer_masked)) + EPSILON_ZERO_DIVISION ) normalized_answer_masked = answer_masked / normalizer normalized_expected_result = expected_result / normalizer per_example_answer_loss = tf.compat.v1.losses.huber_loss( normalized_answer_masked * aggregate_mask, normalized_expected_result * aggregate_mask, delta=tf.cast(1.0, tf.float32), reduction=tf.losses.Reduction.NONE, ) else: per_example_answer_loss = tf.compat.v1.losses.huber_loss( answer_masked * aggregate_mask, expected_result * aggregate_mask, delta=tf.cast(config.huber_loss_delta, tf.float32), reduction=tf.losses.Reduction.NONE, ) if config.answer_loss_cutoff is None: large_answer_loss_mask = tf.ones_like(per_example_answer_loss, dtype=tf.float32) else: large_answer_loss_mask = tf.where( per_example_answer_loss > config.answer_loss_cutoff, tf.zeros_like(per_example_answer_loss, dtype=tf.float32), tf.ones_like(per_example_answer_loss, dtype=tf.float32), ) per_example_answer_loss_scaled = config.answer_loss_importance * (per_example_answer_loss * aggregate_mask) return per_example_answer_loss_scaled, large_answer_loss_mask

transformers/src/transformers/models/tapas/modeling_tf_tapas.py/0

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Processor class for TrOCR. """ import warnings from contextlib import contextmanager from ...processing_utils import ProcessorMixin class TrOCRProcessor(ProcessorMixin): r""" Constructs a TrOCR processor which wraps a vision image processor and a TrOCR tokenizer into a single processor. [`TrOCRProcessor`] offers all the functionalities of [`ViTImageProcessor`/`DeiTImageProcessor`] and [`RobertaTokenizer`/`XLMRobertaTokenizer`]. See the [`~TrOCRProcessor.__call__`] and [`~TrOCRProcessor.decode`] for more information. Args: image_processor ([`ViTImageProcessor`/`DeiTImageProcessor`], *optional*): An instance of [`ViTImageProcessor`/`DeiTImageProcessor`]. The image processor is a required input. tokenizer ([`RobertaTokenizer`/`XLMRobertaTokenizer`], *optional*): An instance of [`RobertaTokenizer`/`XLMRobertaTokenizer`]. The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" def __init__(self, image_processor=None, tokenizer=None, **kwargs): feature_extractor = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead.", FutureWarning, ) feature_extractor = kwargs.pop("feature_extractor") image_processor = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`.") if tokenizer is None: raise ValueError("You need to specify a `tokenizer`.") super().__init__(image_processor, tokenizer) self.current_processor = self.image_processor self._in_target_context_manager = False def __call__(self, *args, **kwargs): """ When used in normal mode, this method forwards all its arguments to AutoImageProcessor's [`~AutoImageProcessor.__call__`] and returns its output. If used in the context [`~TrOCRProcessor.as_target_processor`] this method forwards all its arguments to TrOCRTokenizer's [`~TrOCRTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information. """ # For backward compatibility if self._in_target_context_manager: return self.current_processor(*args, **kwargs) images = kwargs.pop("images", None) text = kwargs.pop("text", None) if len(args) > 0: images = args[0] args = args[1:] if images is None and text is None: raise ValueError("You need to specify either an `images` or `text` input to process.") if images is not None: inputs = self.image_processor(images, *args, **kwargs) if text is not None: encodings = self.tokenizer(text, **kwargs) if text is None: return inputs elif images is None: return encodings else: inputs["labels"] = encodings["input_ids"] return inputs def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @contextmanager def as_target_processor(self): """ Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning TrOCR. """ warnings.warn( "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your " "labels by using the argument `text` of the regular `__call__` method (either in the same call as " "your images inputs, or in a separate call." ) self._in_target_context_manager = True self.current_processor = self.tokenizer yield self.current_processor = self.image_processor self._in_target_context_manager = False @property def feature_extractor_class(self): warnings.warn( "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.", FutureWarning, ) return self.image_processor_class @property def feature_extractor(self): warnings.warn( "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.", FutureWarning, ) return self.image_processor

transformers/src/transformers/models/trocr/processing_trocr.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert ConvNext + UperNet checkpoints from mmsegmentation.""" import argparse import json import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation def get_upernet_config(model_name): auxiliary_in_channels = 384 if "tiny" in model_name: depths = [3, 3, 9, 3] hidden_sizes = [96, 192, 384, 768] if "small" in model_name: depths = [3, 3, 27, 3] hidden_sizes = [96, 192, 384, 768] if "base" in model_name: depths = [3, 3, 27, 3] hidden_sizes = [128, 256, 512, 1024] auxiliary_in_channels = 512 if "large" in model_name: depths = [3, 3, 27, 3] hidden_sizes = [192, 384, 768, 1536] auxiliary_in_channels = 768 if "xlarge" in model_name: depths = [3, 3, 27, 3] hidden_sizes = [256, 512, 1024, 2048] auxiliary_in_channels = 1024 # set label information num_labels = 150 repo_id = "huggingface/label-files" filename = "ade20k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} label2id = {v: k for k, v in id2label.items()} backbone_config = ConvNextConfig( depths=depths, hidden_sizes=hidden_sizes, out_features=["stage1", "stage2", "stage3", "stage4"] ) config = UperNetConfig( backbone_config=backbone_config, auxiliary_in_channels=auxiliary_in_channels, num_labels=num_labels, id2label=id2label, label2id=label2id, ) return config # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config): rename_keys = [] # fmt: off # stem rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")) rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")) rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")) rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias")) # stages for i in range(len(config.backbone_config.depths)): for j in range(config.backbone_config.depths[i]): rename_keys.append((f"backbone.stages.{i}.{j}.gamma", f"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter")) rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.weight", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight")) rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.bias", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias")) rename_keys.append((f"backbone.stages.{i}.{j}.norm.weight", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight")) rename_keys.append((f"backbone.stages.{i}.{j}.norm.bias", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias")) rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight")) rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias")) rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight")) rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias")) if i > 0: rename_keys.append((f"backbone.downsample_layers.{i}.0.weight", f"backbone.encoder.stages.{i}.downsampling_layer.0.weight")) rename_keys.append((f"backbone.downsample_layers.{i}.0.bias", f"backbone.encoder.stages.{i}.downsampling_layer.0.bias")) rename_keys.append((f"backbone.downsample_layers.{i}.1.weight", f"backbone.encoder.stages.{i}.downsampling_layer.1.weight")) rename_keys.append((f"backbone.downsample_layers.{i}.1.bias", f"backbone.encoder.stages.{i}.downsampling_layer.1.bias")) rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")) rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")) # decode head rename_keys.extend( [ ("decode_head.conv_seg.weight", "decode_head.classifier.weight"), ("decode_head.conv_seg.bias", "decode_head.classifier.bias"), ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"), ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"), ] ) # fmt: on return rename_keys def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): model_name_to_url = { "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth", "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth", "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth", "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth", "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth", } checkpoint_url = model_name_to_url[model_name] state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["state_dict"] config = get_upernet_config(model_name) model = UperNetForSemanticSegmentation(config) model.eval() # replace "bn" => "batch_norm" for key in state_dict.copy().keys(): val = state_dict.pop(key) if "bn" in key: key = key.replace("bn", "batch_norm") state_dict[key] = val # rename keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) model.load_state_dict(state_dict) # verify on image url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = SegformerImageProcessor() pixel_values = processor(image, return_tensors="pt").pixel_values with torch.no_grad(): outputs = model(pixel_values) if model_name == "upernet-convnext-tiny": expected_slice = torch.tensor( [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ) elif model_name == "upernet-convnext-small": expected_slice = torch.tensor( [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] ) elif model_name == "upernet-convnext-base": expected_slice = torch.tensor( [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] ) elif model_name == "upernet-convnext-large": expected_slice = torch.tensor( [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] ) elif model_name == "upernet-convnext-xlarge": expected_slice = torch.tensor( [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] ) print("Logits:", outputs.logits[0, 0, :3, :3]) assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving processor to {pytorch_dump_folder_path}") processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and processor for {model_name} to hub") model.push_to_hub(f"openmmlab/{model_name}") processor.push_to_hub(f"openmmlab/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="upernet-convnext-tiny", type=str, choices=[f"upernet-convnext-{size}" for size in ["tiny", "small", "base", "large", "xlarge"]], help="Name of the ConvNext UperNet model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

transformers/src/transformers/models/upernet/convert_convnext_upernet_to_pytorch.py/0

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ VisualBERT model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json", "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json", "uclanlp/visualbert-vqa-coco-pre": ( "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json" ), "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json", "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json", "uclanlp/visualbert-vcr-coco-pre": ( "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json" ), "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json", "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json", "uclanlp/visualbert-nlvr2-coco-pre": ( "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json" ), # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert } class VisualBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`VisualBertModel`]. It is used to instantiate an VisualBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the VisualBERT [uclanlp/visualbert-vqa-coco-pre](https://huggingface.co/uclanlp/visualbert-vqa-coco-pre) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the VisualBERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`VisualBertModel`]. Vocabulary size of the model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of [`VisualBertModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. visual_embedding_dim (`int`, *optional*, defaults to 512): Dimensionality of the visual embeddings to be passed to the model. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`VisualBertModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. bypass_transformer (`bool`, *optional*, defaults to `False`): Whether or not the model should bypass the transformer for the visual embeddings. If set to `True`, the model directly concatenates the visual embeddings from [`VisualBertEmbeddings`] with text output from transformers, and then pass it to a self-attention layer. special_visual_initialize (`bool`, *optional*, defaults to `True`): Whether or not the visual token type and position type embedding weights should be initialized the same as the textual token type and positive type embeddings. When set to `True`, the weights of the textual token type and position type embeddings are copied to the respective visual embedding layers. Example: ```python >>> from transformers import VisualBertConfig, VisualBertModel >>> # Initializing a VisualBERT visualbert-vqa-coco-pre style configuration >>> configuration = VisualBertConfig.from_pretrained("uclanlp/visualbert-vqa-coco-pre") >>> # Initializing a model (with random weights) from the visualbert-vqa-coco-pre style configuration >>> model = VisualBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "visual_bert" def __init__( self, vocab_size=30522, hidden_size=768, visual_embedding_dim=512, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, bypass_transformer=False, special_visual_initialize=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.visual_embedding_dim = visual_embedding_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.bypass_transformer = bypass_transformer self.special_visual_initialize = special_visual_initialize

transformers/src/transformers/models/visual_bert/configuration_visual_bert.py/0

# coding=utf-8 # Copyright 2022 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch ViT Hybrid model.""" import collections.abc import math from typing import Dict, List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from ...utils.backbone_utils import load_backbone from .configuration_vit_hybrid import ViTHybridConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "ViTHybridConfig" # Base docstring _CHECKPOINT_FOR_DOC = "google/vit-hybrid-base-bit-384" _EXPECTED_OUTPUT_SHAPE = [1, 197, 768] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "google/vit-hybrid-base-bit-384" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/vit-hybrid-base-bit-384", # See all ViT hybrid models at https://huggingface.co/models?filter=vit-hybrid ] class ViTHybridEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ # Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.__init__ with ViT->ViTHybrid def __init__(self, config: ViTHybridConfig, use_mask_token: bool = False) -> None: super().__init__() self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size)) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None self.patch_embeddings = ViTHybridPatchEmbeddings(config) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 """ num_patches = embeddings.shape[1] - 1 num_positions = self.position_embeddings.shape[1] - 1 if num_patches == num_positions and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, 0] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] height = height // self.config.patch_size width = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 height, width = height + 0.1, width + 0.1 patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, scale_factor=(height / math.sqrt(num_positions), width / math.sqrt(num_positions)), mode="bicubic", align_corners=False, ) if int(height) != patch_pos_embed.shape[-2] or int(width) != patch_pos_embed.shape[-1]: raise ValueError(f"Invalid height or width: {height}, {width}") patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward( self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) if bool_masked_pos is not None: seq_length = embeddings.shape[1] mask_tokens = self.mask_token.expand(batch_size, seq_length, -1) # replace the masked visual tokens by mask_tokens mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings class ViTHybridPatchEmbeddings(nn.Module): """ This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a Transformer. """ def __init__(self, config, feature_size=None): super().__init__() image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) self.backbone = load_backbone(config) if self.backbone.config.model_type != "bit": raise ValueError(f"Backbone model type {self.backbone.model_type} is not supported.") feature_dim = self.backbone.channels[-1] if feature_size is None: feature_map = config.backbone_featmap_shape feature_size = feature_map[-2:] feature_dim = feature_map[1] else: feature_size = ( feature_size if isinstance(feature_size, collections.abc.Iterable) else (feature_size, feature_size) ) feature_dim = self.backbone.channels[-1] self.grid_size = (feature_size[0] // patch_size[0], feature_size[1] // patch_size[1]) self.num_patches = self.grid_size[0] * self.grid_size[1] self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.projection = nn.Conv2d(feature_dim, hidden_size, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: _, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) features = self.backbone(pixel_values).feature_maps[-1] embeddings = self.projection(features).flatten(2).transpose(1, 2) return embeddings # Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->ViTHybrid class ViTHybridSelfAttention(nn.Module): def __init__(self, config: ViTHybridConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->ViTHybrid class ViTHybridSelfOutput(nn.Module): """ The residual connection is defined in ViTHybridLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: ViTHybridConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTHybrid class ViTHybridAttention(nn.Module): def __init__(self, config: ViTHybridConfig) -> None: super().__init__() self.attention = ViTHybridSelfAttention(config) self.output = ViTHybridSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->ViTHybrid class ViTHybridIntermediate(nn.Module): def __init__(self, config: ViTHybridConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->ViTHybrid class ViTHybridOutput(nn.Module): def __init__(self, config: ViTHybridConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class ViTHybridLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: ViTHybridConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = ViTHybridAttention(config) self.intermediate = ViTHybridIntermediate(config) self.output = ViTHybridOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViTHybrid, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection # We assign to correct device for `accelerate`, check: https://github.com/huggingface/transformers/pull/20705/ hidden_states = attention_output + hidden_states.to(attention_output.device) # in ViTHybrid, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTHybrid class ViTHybridEncoder(nn.Module): def __init__(self, config: ViTHybridConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([ViTHybridLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, layer_head_mask, output_attentions, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.vit.modeling_vit.ViTPreTrainedModel with ViT->ViTHybrid class ViTHybridPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ViTHybridConfig base_model_prefix = "vit" main_input_name = "pixel_values" supports_gradient_checkpointing = True _no_split_modules = ["ViTHybridEmbeddings", "ViTHybridLayer"] def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid # `trunc_normal_cpu` not implemented in `half` issues module.weight.data = nn.init.trunc_normal_( module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range ).to(module.weight.dtype) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, ViTHybridEmbeddings): module.position_embeddings.data = nn.init.trunc_normal_( module.position_embeddings.data.to(torch.float32), mean=0.0, std=self.config.initializer_range, ).to(module.position_embeddings.dtype) module.cls_token.data = nn.init.trunc_normal_( module.cls_token.data.to(torch.float32), mean=0.0, std=self.config.initializer_range, ).to(module.cls_token.dtype) VIT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ViTHybridConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTHybridImageProcessor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ViT Hybrid Model transformer outputting raw hidden-states without any specific head on top.", VIT_START_DOCSTRING, ) # Copied from transformers.models.vit.modeling_vit.ViTModel with ViT->ViTHybrid class ViTHybridModel(ViTHybridPreTrainedModel): def __init__(self, config: ViTHybridConfig, add_pooling_layer: bool = True, use_mask_token: bool = False): super().__init__(config) self.config = config self.embeddings = ViTHybridEmbeddings(config, use_mask_token=use_mask_token) self.encoder = ViTHybridEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = ViTHybridPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> ViTHybridPatchEmbeddings: return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype if pixel_values.dtype != expected_dtype: pixel_values = pixel_values.to(expected_dtype) embedding_output = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) # Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->ViTHybrid class ViTHybridPooler(nn.Module): def __init__(self, config: ViTHybridConfig): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output @add_start_docstrings( """ ViT Hybrid Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. """, VIT_START_DOCSTRING, ) # Copied from transformers.models.vit.modeling_vit.ViTForImageClassification with ViT->ViTHybrid class ViTHybridForImageClassification(ViTHybridPreTrainedModel): def __init__(self, config: ViTHybridConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.vit = ViTHybridModel(config, add_pooling_layer=False) # Classifier head self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, ImageClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.vit( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output[:, 0, :]) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(logits.device) if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/vit_hybrid/modeling_vit_hybrid.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for ViTMatte.""" from typing import List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature from ...image_transforms import pad, to_channel_dimension_format from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_kwargs, validate_preprocess_arguments, ) from ...utils import TensorType, logging logger = logging.get_logger(__name__) class VitMatteImageProcessor(BaseImageProcessor): r""" Constructs a ViTMatte image processor. Args: do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. do_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image to make the width and height divisible by `size_divisibility`. Can be overridden by the `do_pad` parameter in the `preprocess` method. size_divisibility (`int`, *optional*, defaults to 32): The width and height of the image will be padded to be divisible by this number. """ model_input_names = ["pixel_values"] def __init__( self, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_pad: bool = True, size_divisibility: int = 32, **kwargs, ) -> None: super().__init__(**kwargs) self.do_rescale = do_rescale self.do_normalize = do_normalize self.do_pad = do_pad self.rescale_factor = rescale_factor self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD self.size_divisibility = size_divisibility self._valid_processor_keys = [ "images", "trimaps", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "do_pad", "size_divisibility", "return_tensors", "data_format", "input_data_format", ] def pad_image( self, image: np.ndarray, size_divisibility: int = 32, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.ndarray: """ Args: image (`np.ndarray`): Image to pad. size_divisibility (`int`, *optional*, defaults to 32): The width and height of the image will be padded to be divisible by this number. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ if input_data_format is None: input_data_format = infer_channel_dimension_format(image) height, width = get_image_size(image, input_data_format) if height % size_divisibility != 0 or width % size_divisibility != 0: pad_height = size_divisibility - height % size_divisibility pad_width = size_divisibility - width % size_divisibility padding = ((0, pad_height), (0, pad_width)) image = pad(image, padding=padding, data_format=data_format, input_data_format=input_data_format) if data_format is not None: image = to_channel_dimension_format(image, data_format, input_data_format) return image def preprocess( self, images: ImageInput, trimaps: ImageInput, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_pad: Optional[bool] = None, size_divisibility: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ): """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. trimaps (`ImageInput`): Trimap to preprocess. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean to use if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use if `do_normalize` is set to `True`. do_pad (`bool`, *optional*, defaults to `self.do_pad`): Whether to pad the image. size_divisibility (`int`, *optional*, defaults to `self.size_divisibility`): The size divisibility to pad the image to if `do_pad` is set to `True`. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ do_rescale = do_rescale if do_rescale is not None else self.do_rescale do_normalize = do_normalize if do_normalize is not None else self.do_normalize do_pad = do_pad if do_pad is not None else self.do_pad rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std size_divisibility = size_divisibility if size_divisibility is not None else self.size_divisibility images = make_list_of_images(images) trimaps = make_list_of_images(trimaps, expected_ndims=2) validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) if not valid_images(trimaps): raise ValueError( "Invalid trimap type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) validate_preprocess_arguments( do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_pad=do_pad, size_divisibility=size_divisibility, ) # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] trimaps = [to_numpy_array(trimap) for trimap in trimaps] if is_scaled_image(images[0]) and do_rescale: logger.warning_once( "It looks like you are trying to rescale already rescaled images. If the input" " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." ) if input_data_format is None: # We assume that all images have the same channel dimension format. input_data_format = infer_channel_dimension_format(images[0]) if do_rescale: images = [ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) for image in images ] trimaps = [ self.rescale(image=trimap, scale=rescale_factor, input_data_format=input_data_format) for trimap in trimaps ] if do_normalize: images = [ self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) for image in images ] # concatenate images and trimaps images = [ np.concatenate([image, np.expand_dims(trimap, axis=-1)], axis=-1) for image, trimap in zip(images, trimaps) ] if do_pad: images = [ self.pad_image(image, size_divisibility=size_divisibility, input_data_format=input_data_format) for image in images ] images = [ to_channel_dimension_format(image=image, channel_dim=data_format, input_channel_dim=input_data_format) for image in images ] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors)

transformers/src/transformers/models/vitmatte/image_processing_vitmatte.py/0

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Feature extractor class for Wav2Vec2 """ from typing import List, Optional, Union import numpy as np from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import PaddingStrategy, TensorType, logging logger = logging.get_logger(__name__) class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor): r""" Constructs a Wav2Vec2 feature extractor. This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: feature_size (`int`, defaults to 1): The feature dimension of the extracted features. sampling_rate (`int`, defaults to 16000): The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). padding_value (`float`, defaults to 0.0): The value that is used to fill the padding values. do_normalize (`bool`, *optional*, defaults to `True`): Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly improve the performance for some models, *e.g.*, [wav2vec2-lv60](https://huggingface.co/models?search=lv60). return_attention_mask (`bool`, *optional*, defaults to `False`): Whether or not [`~Wav2Vec2FeatureExtractor.__call__`] should return `attention_mask`. <Tip> Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask` should be passed. For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be passed for batched inference. </Tip>""" model_input_names = ["input_values", "attention_mask"] def __init__( self, feature_size=1, sampling_rate=16000, padding_value=0.0, return_attention_mask=False, do_normalize=True, **kwargs, ): super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize @staticmethod def zero_mean_unit_var_norm( input_values: List[np.ndarray], attention_mask: List[np.ndarray], padding_value: float = 0.0 ) -> List[np.ndarray]: """ Every array in the list is normalized to have zero mean and unit variance """ if attention_mask is not None: attention_mask = np.array(attention_mask, np.int32) normed_input_values = [] for vector, length in zip(input_values, attention_mask.sum(-1)): normed_slice = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7) if length < normed_slice.shape[0]: normed_slice[length:] = padding_value normed_input_values.append(normed_slice) else: normed_input_values = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values] return normed_input_values def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], padding: Union[bool, str, PaddingStrategy] = False, max_length: Optional[int] = None, truncation: bool = False, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, sampling_rate: Optional[int] = None, **kwargs, ) -> BatchFeature: """ Main method to featurize and prepare for the model one or several sequence(s). Args: raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not stereo, i.e. single float per timestep. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). truncation (`bool`): Activates truncation to cut input sequences longer than *max_length* to *max_length*. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific feature_extractor's default. [What are attention masks?](../glossary#attention-mask) <Tip> Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask` should be passed. For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be passed for batched inference. </Tip> return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. sampling_rate (`int`, *optional*): The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors. padding_value (`float`, defaults to 0.0): """ if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of" f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with" f" {self.sampling_rate} and not {sampling_rate}." ) else: logger.warning( "It is strongly recommended to pass the ``sampling_rate`` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1 if is_batched_numpy and len(raw_speech.shape) > 2: raise ValueError(f"Only mono-channel audio is supported for input to {self}") is_batched = is_batched_numpy or ( isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list))) ) # always return batch if not is_batched: raw_speech = [raw_speech] # convert into correct format for padding encoded_inputs = BatchFeature({"input_values": raw_speech}) padded_inputs = self.pad( encoded_inputs, padding=padding, max_length=max_length, truncation=truncation, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) # convert input values to correct format input_values = padded_inputs["input_values"] if not isinstance(input_values[0], np.ndarray): padded_inputs["input_values"] = [np.asarray(array, dtype=np.float32) for array in input_values] elif ( not isinstance(input_values, np.ndarray) and isinstance(input_values[0], np.ndarray) and input_values[0].dtype is np.dtype(np.float64) ): padded_inputs["input_values"] = [array.astype(np.float32) for array in input_values] elif isinstance(input_values, np.ndarray) and input_values.dtype is np.dtype(np.float64): padded_inputs["input_values"] = input_values.astype(np.float32) # convert attention_mask to correct format attention_mask = padded_inputs.get("attention_mask") if attention_mask is not None: padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask] # zero-mean and unit-variance normalization if self.do_normalize: attention_mask = ( attention_mask if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD else None ) padded_inputs["input_values"] = self.zero_mean_unit_var_norm( padded_inputs["input_values"], attention_mask=attention_mask, padding_value=self.padding_value ) if return_tensors is not None: padded_inputs = padded_inputs.convert_to_tensors(return_tensors) return padded_inputs

transformers/src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py/0

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Wav2Vec2Phoneme.""" import json import os import sys from dataclasses import dataclass from itertools import groupby from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import numpy as np from ...tokenization_utils import PreTrainedTokenizer from ...tokenization_utils_base import AddedToken from ...utils import ( ModelOutput, is_flax_available, is_tf_available, is_torch_available, logging, requires_backends, to_py_obj, ) logger = logging.get_logger(__name__) if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp # noqa: F401 VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/wav2vec2-lv-60-espeak-cv-ft": ( "https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft/resolve/main/vocab.json" ), }, "tokenizer_config_file": { "facebook/wav2vec2-lv-60-espeak-cv-ft": ( "https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft/resolve/main/tokenizer_config.json" ), }, } # Wav2Vec2Phoneme has no max input length PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/wav2vec2-lv-60-espeak-cv-ft": sys.maxsize} ListOfDict = List[Dict[str, Union[int, str]]] @dataclass class Wav2Vec2PhonemeCTCTokenizerOutput(ModelOutput): """ Output type of [` Wav2Vec2PhonemeCTCTokenizer`], with transcription. Args: text (list of `str` or `str`): Decoded logits in text from. Usually the speech transcription. char_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded characters. In combination with sampling rate and model downsampling rate char offsets can be used to compute time stamps for each charater. Total logit score of the beam associated with produced text. """ text: Union[List[str], str] char_offsets: Union[List[ListOfDict], ListOfDict] = None class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2PhonemeCTC tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. do_phonemize (`bool`, *optional*, defaults to `True`): Whether the tokenizer should phonetize the input or not. Only if a sequence of phonemes is passed to the tokenizer, `do_phonemize` should be set to `False`. phonemizer_lang (`str`, *optional*, defaults to `"en-us"`): The language of the phoneme set to which the tokenizer should phonetize the input text to. phonemizer_backend (`str`, *optional*. defaults to `"espeak"`): The backend phonetization library that shall be used by the phonemizer library. Defaults to `espeak-ng`. See the [phonemizer package](https://github.com/bootphon/phonemizer#readme). for more information. **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", phone_delimiter_token=" ", word_delimiter_token=None, do_phonemize=True, phonemizer_lang="en-us", phonemizer_backend="espeak", **kwargs, ): self._word_delimiter_token = word_delimiter_token self._phone_delimiter_token = phone_delimiter_token self.do_phonemize = do_phonemize self.phonemizer_lang = phonemizer_lang self.phonemizer_backend = phonemizer_backend if do_phonemize: self.init_backend(self.phonemizer_lang) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, word_delimiter_token=word_delimiter_token, phone_delimiter_token=phone_delimiter_token, do_phonemize=do_phonemize, phonemizer_lang=phonemizer_lang, phonemizer_backend=phonemizer_backend, **kwargs, ) @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: vocab = dict(self.encoder.copy()) vocab.update(self.added_tokens_encoder) return vocab def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int: # Overwritten to never strip! to_add = [] for token in new_tokens: if isinstance(token, str): to_add.append(AddedToken(token, rstrip=False, lstrip=False, normalized=True, special=special_tokens)) else: to_add.append(token) return super()._add_tokens(to_add, special_tokens) def init_backend(self, phonemizer_lang: str): """ Initializes the backend. Args: phonemizer_lang (`str`): The language to be used. """ requires_backends(self, "phonemizer") from phonemizer.backend import BACKENDS self.backend = BACKENDS[self.phonemizer_backend](phonemizer_lang, language_switch="remove-flags") def prepare_for_tokenization( self, text: str, is_split_into_words: bool = False, phonemizer_lang: Optional[str] = None, do_phonemize: Optional[bool] = None, ) -> Tuple[str, Dict[str, Any]]: """ Performs any necessary transformations before tokenization. This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We test the `kwargs` at the end of the encoding process to be sure all the arguments have been used. Args: text (`str`): The text to prepare. is_split_into_words (`bool`, *optional*, defaults to `False`): Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification. phonemizer_lang (`str`, *optional*): The language of the phoneme set to which the tokenizer should phonetize the input text to. do_phonemize (`bool`, *optional*): Whether the tokenizer should phonetize the input text or not. Only if a sequence of phonemes is passed to the tokenizer, `do_phonemize` should be set to `False`. Returns: `Tuple[str, Dict[str, Any]]`: The prepared text and the unused kwargs. """ if is_split_into_words: text = " " + text # set whether tokenizer should phonemize or not if do_phonemize is not None: self.do_phonemize = do_phonemize # set the correct phonemizer language if phonemizer_lang is not None: self.phonemizer_lang = phonemizer_lang self.init_backend(phonemizer_lang) return (text, {}) def _tokenize(self, text, **kwargs): """ Converts a string into a sequence of tokens (string), using the tokenizer. """ # make sure whitespace is stripped to prevent <unk> text = text.strip() # phonemize if self.do_phonemize: text = text.lower() # create list of phonemes text = self.phonemize(text, self.phonemizer_lang) # make sure ' ' is between phonemes tokens = text.split(" ") tokens = list(filter(lambda p: p.strip() != "", tokens)) return tokens def phonemize(self, text: str, phonemizer_lang: Optional[str] = None) -> str: from phonemizer.separator import Separator word_delimiter = self.word_delimiter_token + " " if self.word_delimiter_token is not None else "" if phonemizer_lang is not None and phonemizer_lang != self.phonemizer_lang: self.init_backend(phonemizer_lang) else: phonemizer_lang = self.phonemizer_lang separator = Separator(phone=self.phone_delimiter_token, word=word_delimiter, syllable="") phonemes = self.backend.phonemize( [text], separator=separator, ) phonemes = phonemes[0].strip() return phonemes @property def word_delimiter_token(self) -> str: """ `str`: Word delimiter token. Log an error if used while not having been set. """ if self._word_delimiter_token is None: if self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @property def phone_delimiter_token(self) -> str: """ `str`: Word delimiter token. Log an error if used while not having been set. """ if self._phone_delimiter_token is None: if self.verbose: logger.error("Using phone_delimiter_token, but it is not set yet.") return None return str(self._phone_delimiter_token) @property def phone_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the phone_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._phone_delimiter_token is None: return None return self.convert_tokens_to_ids(self.phone_delimiter_token) @phone_delimiter_token.setter def phone_delimiter_token(self, value): self._phone_delimiter_token = value @phone_delimiter_token_id.setter def phone_delimiter_token_id(self, value): self._phone_delimiter_token = self.convert_tokens_to_ids(value) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string( self, tokens: List[str], group_tokens: bool = True, spaces_between_special_tokens: bool = False, filter_word_delimiter_token: bool = True, output_char_offsets: bool = False, ) -> str: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ # group same tokens into non-repeating tokens in CTC style decoding if group_tokens: chars, char_repetitions = zip(*((token, len(list(group_iter))) for token, group_iter in groupby(tokens))) else: chars = tokens char_repetitions = len(tokens) * [1] # filter self.pad_token which is used as CTC-blank token processed_chars = list(filter(lambda char: char != self.pad_token, chars)) # also filter self.word_delimiter_token if not not if filter_word_delimiter_token and self.word_delimiter_token is not None: processed_chars = list(filter(lambda token: token != self.word_delimiter_token, processed_chars)) # retrieve offsets char_offsets = None if output_char_offsets: word_delimiter_token_for_offsets = ( self.word_delimiter_token if filter_word_delimiter_token is True else None ) char_offsets = self._compute_offsets( char_repetitions, chars, self.pad_token, word_delimiter_token=word_delimiter_token_for_offsets ) if len(char_offsets) != len(processed_chars): raise ValueError( f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}" " have to be of the same length, but are: `len(offsets)`: " f"{len(char_offsets)} and `len(processed_tokens)`: {len(processed_chars)}" ) # set tokens to correct processed token for i, char in enumerate(processed_chars): char_offsets[i]["char"] = char string = " ".join(processed_chars).strip() return {"text": string, "char_offsets": char_offsets} @staticmethod def _compute_offsets( char_repetitions: List[int], chars: List[str], ctc_token: int, word_delimiter_token: Optional[int] = None ) -> List[Dict[str, Union[str, int]]]: end_indices = np.asarray(char_repetitions).cumsum() start_indices = np.concatenate(([0], end_indices[:-1])) offsets = [ {"char": t, "start_offset": s, "end_offset": e} for t, s, e in zip(chars, start_indices, end_indices) ] # filter out CTC token offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets)) # filter out word delimiter token if necessary if word_delimiter_token is not None: offsets = list(filter(lambda offsets: offsets["char"] != word_delimiter_token, offsets)) return offsets def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, group_tokens: bool = True, filter_word_delimiter_token: bool = True, spaces_between_special_tokens: bool = False, output_char_offsets: bool = False, ) -> str: """ special _decode function is needed for Wav2Vec2PhonemeTokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) string_output = self.convert_tokens_to_string( result, group_tokens=group_tokens, spaces_between_special_tokens=spaces_between_special_tokens, filter_word_delimiter_token=filter_word_delimiter_token, output_char_offsets=output_char_offsets, ) text = string_output["text"] clean_up_tokenization_spaces = ( clean_up_tokenization_spaces if clean_up_tokenization_spaces is not None else self.clean_up_tokenization_spaces ) if clean_up_tokenization_spaces: text = self.clean_up_tokenization(text) if output_char_offsets: return Wav2Vec2PhonemeCTCTokenizerOutput(text=text, char_offsets=string_output["char_offsets"]) else: return text # overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets` here def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, output_char_offsets: bool = False, **kwargs, ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the Example of [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better understand how to make use of `output_word_offsets`. [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works the same way with phonemes. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The decoded sentence. Will be a [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`] when `output_char_offsets == True`. """ # Convert inputs to python lists token_ids = to_py_obj(token_ids) return self._decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, **kwargs, ) # overwritten from `tokenization_utils_base.py` because tokenizer can output # `ModelOutput` which should not be a list for batched output and because # we need docs for `output_char_offsets` here def batch_decode( self, sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, output_char_offsets: bool = False, **kwargs, ) -> List[str]: """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the Example of [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better understand how to make use of `output_word_offsets`. [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works analogous with phonemes and batched output. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The decoded sentence. Will be a [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`] when `output_char_offsets == True`. """ batch_decoded = [ self.decode( seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, **kwargs, ) for seq in sequences ] if output_char_offsets: # transform list of dicts to dict of lists return Wav2Vec2PhonemeCTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]}) return batch_decoded def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,)

transformers/src/transformers/models/wav2vec2_phoneme/tokenization_wav2vec2_phoneme.py/0

# coding=utf-8 # Copyright 2022 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Whisper model.""" import math from typing import Optional, Tuple, Union import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, SequenceClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings, ) from .configuration_whisper import WhisperConfig from .generation_whisper import WhisperGenerationMixin if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa logger = logging.get_logger(__name__) _HIDDEN_STATES_START_POSITION = 1 _CONFIG_FOR_DOC = "WhisperConfig" _CHECKPOINT_FOR_DOC = "openai/whisper-tiny" WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "openai/whisper-base", # See all Whisper models at https://huggingface.co/models?filter=whisper ] # Copied from transformers.models.llama.modeling_llama._get_unpad_data def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) def sinusoids(length: int, channels: int, max_timescale: float = 10000) -> torch.Tensor: """Returns sinusoids for positional embedding""" if channels % 2 != 0: raise ValueError( f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels." ) log_timescale_increment = math.log(max_timescale) / (channels // 2 - 1) inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) scaled_time = torch.arange(length).view(-1, 1) * inv_timescales.view(1, -1) return torch.cat([scaled_time.sin(), scaled_time.cos()], dim=1) # Copied from transformers.models.bart.modeling_bart.shift_tokens_right def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): """ Shift input ids one token to the right. """ shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() shifted_input_ids[:, 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids # Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices def _compute_mask_indices( shape: Tuple[int, int], mask_prob: float, mask_length: int, attention_mask: Optional[torch.LongTensor] = None, min_masks: int = 0, ) -> np.ndarray: """ Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: The shape for which to compute masks. This should be of a tuple of size 2 where the first element is the batch size and the second element is the length of the axis to span. mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of independently generated mask spans of length `mask_length` is computed by `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the actual percentage will be smaller. mask_length: size of the mask min_masks: minimum number of masked spans attention_mask: A (right-padded) attention mask which independently shortens the feature axis of each batch dimension. """ batch_size, sequence_length = shape if mask_length < 1: raise ValueError("`mask_length` has to be bigger than 0.") if mask_length > sequence_length: raise ValueError( f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}" f" and `sequence_length`: {sequence_length}`" ) # epsilon is used for probabilistic rounding epsilon = np.random.rand(1).item() def compute_num_masked_span(input_length): """Given input length, compute how many spans should be masked""" num_masked_span = int(mask_prob * input_length / mask_length + epsilon) num_masked_span = max(num_masked_span, min_masks) # make sure num masked span <= sequence_length if num_masked_span * mask_length > sequence_length: num_masked_span = sequence_length // mask_length # make sure num_masked span is also <= input_length - (mask_length - 1) if input_length - (mask_length - 1) < num_masked_span: num_masked_span = max(input_length - (mask_length - 1), 0) return num_masked_span # compute number of masked spans in batch input_lengths = ( attention_mask.sum(-1).detach().tolist() if attention_mask is not None else [sequence_length for _ in range(batch_size)] ) # SpecAugment mask to fill spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) spec_aug_mask_idxs = [] max_num_masked_span = compute_num_masked_span(sequence_length) if max_num_masked_span == 0: return spec_aug_mask for input_length in input_lengths: # compute num of masked spans for this input num_masked_span = compute_num_masked_span(input_length) # get random indices to mask spec_aug_mask_idx = np.random.choice( np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False ) # pick first sampled index that will serve as a dummy index to pad vector # to ensure same dimension for all batches due to probabilistic rounding # Picking first sample just pads those vectors twice. if len(spec_aug_mask_idx) == 0: # this case can only happen if `input_length` is strictly smaller then # `sequence_length` in which case the last token has to be a padding # token which we can use as a dummy mask id dummy_mask_idx = sequence_length - 1 else: dummy_mask_idx = spec_aug_mask_idx[0] spec_aug_mask_idx = np.concatenate( [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx] ) spec_aug_mask_idxs.append(spec_aug_mask_idx) spec_aug_mask_idxs = np.array(spec_aug_mask_idxs) # expand masked indices to masked spans spec_aug_mask_idxs = np.broadcast_to( spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length) ) spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length) # add offset to the starting indexes so that indexes now create a span offsets = np.arange(mask_length)[None, None, :] offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape( batch_size, max_num_masked_span * mask_length ) spec_aug_mask_idxs = spec_aug_mask_idxs + offsets # ensure that we cannot have indices larger than sequence_length if spec_aug_mask_idxs.max() > sequence_length - 1: spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1 # scatter indices to mask np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) return spec_aug_mask class WhisperPositionalEmbedding(nn.Embedding): def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None): super().__init__(num_positions, embedding_dim) def forward(self, input_ids, past_key_values_length=0, position_ids=None): if position_ids is None: return self.weight[past_key_values_length : past_key_values_length + input_ids.shape[1]] else: return self.weight[position_ids] class WhisperAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[WhisperConfig] = None, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads self.config = config if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.is_causal = is_causal self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) # Copied from transformers.models.bart.modeling_bart.BartAttention._shape with BART->whisper def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() # Copied from transformers.models.bart.modeling_bart.BartAttention.forward with BART->whisper def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.reshape(*proj_shape) value_states = value_states.reshape(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value # Copied from transformers.models.bart.modeling_bart.BartFlashAttention2 with Bart->Whisper class WhisperFlashAttention2(WhisperAttention): """ Whisper flash attention module. This module inherits from `WhisperAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def _reshape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim) def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: # WhisperFlashAttention2 attention does not support output_attentions if output_attentions: raise ValueError("WhisperFlashAttention2 attention does not support output_attentions") # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, q_len, _ = hidden_states.size() # get query proj query_states = self._reshape(self.q_proj(hidden_states), -1, bsz) # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0].transpose(1, 2) value_states = past_key_value[1].transpose(1, 2) elif is_cross_attention: # cross_attentions key_states = self._reshape(self.k_proj(key_value_states), -1, bsz) value_states = self._reshape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._reshape(self.k_proj(hidden_states), -1, bsz) value_states = self._reshape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1) value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states], dim=1) else: # self_attention key_states = self._reshape(self.k_proj(hidden_states), -1, bsz) value_states = self._reshape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states.transpose(1, 2), value_states.transpose(1, 2)) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value[0].shape[-2] # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (LlamaRMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) attn_output = self._flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=self.dropout ) attn_output = attn_output.reshape(bsz, q_len, -1) attn_output = self.out_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`int`, *optional*): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) """ if not self._flash_attn_uses_top_left_mask: causal = self.is_causal else: # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. causal = self.is_causal and query_length != 1 # Contains at least one padding token in the sequence if attention_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query_states, key_states, value_states, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=causal, ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal ) return attn_output # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) class WhisperSdpaAttention(WhisperAttention): # Copied from transformers.models.bart.modeling_bart.BartSdpaAttention.forward with BART->whisper, Bart->Whisper def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" if output_attentions or layer_head_mask is not None: # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented. logger.warning_once( "WhisperModel is using WhisperSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention" ' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( hidden_states, key_value_states=key_value_states, past_key_value=past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) query_states = self._shape(query_states, tgt_len, bsz) # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask, # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577 attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attention_mask, dropout_p=self.dropout if self.training else 0.0, # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1. is_causal=self.is_causal and attention_mask is None and tgt_len > 1, ) if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, None, past_key_value WHISPER_ATTENTION_CLASSES = { "eager": WhisperAttention, "flash_attention_2": WhisperFlashAttention2, "sdpa": WhisperSdpaAttention, } # Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Whisper, MBART->WHISPER class WhisperEncoderLayer(nn.Module): def __init__(self, config: WhisperConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation]( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, config=config, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, layer_head_mask: torch.Tensor, output_attentions: bool = False, ) -> torch.Tensor: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) hidden_states, attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->Whisper, MBART->WHISPER class WhisperDecoderLayer(nn.Module): def __init__(self, config: WhisperConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation]( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, is_causal=True, config=config, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation]( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, config=config, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> torch.Tensor: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs class WhisperPreTrainedModel(PreTrainedModel): config_class = WhisperConfig base_model_prefix = "model" main_input_name = "input_features" supports_gradient_checkpointing = True _no_split_modules = ["WhisperEncoderLayer", "WhisperDecoderLayer"] _supports_flash_attn_2 = True _supports_sdpa = True def _init_weights(self, module): std = self.config.init_std if isinstance(module, (nn.Linear, nn.Conv1d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, WhisperEncoder): with torch.no_grad(): embed_positions = module.embed_positions.weight embed_positions.copy_(sinusoids(*embed_positions.shape)) def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): """ Computes the output length of the convolutional layers """ input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths WHISPER_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`WhisperConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ WHISPER_INPUTS_DOCSTRING = r""" Args: input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`): Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing *SpecAugment* data augmentation on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) Whisper uses the `decoder_start_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should read [`modeling_whisper._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the BART paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ WHISPER_ENCODER_INPUTS_DOCSTRING = r""" Args: input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`): Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class WhisperEncoder(WhisperPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`WhisperEncoderLayer`]. Args: config: WhisperConfig """ def __init__(self, config: WhisperConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.num_mel_bins = config.num_mel_bins self.padding_idx = config.pad_token_id self.max_source_positions = config.max_source_positions self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1) self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1) self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim) self.embed_positions.requires_grad_(False) self.layers = nn.ModuleList([WhisperEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layer_norm = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def _freeze_parameters(self): for param in self.parameters(): param.requires_grad = False self._requires_grad = False def get_input_embeddings(self) -> nn.Module: return self.conv1 def set_input_embeddings(self, value: nn.Module): self.conv1 = value def forward( self, input_features, attention_mask=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`): Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] attention_mask (`torch.Tensor`)`, *optional*): Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but it is not used. By default the silence in the input log mel spectrogram are ignored. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0] if input_features.shape[-1] != expected_seq_length: raise ValueError( f"Whisper expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}." ) output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict inputs_embeds = nn.functional.gelu(self.conv1(input_features)) inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds)) inputs_embeds = inputs_embeds.permute(0, 2, 1) embed_pos = self.embed_positions.weight hidden_states = inputs_embeds + embed_pos hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: assert head_mask.size()[0] == ( len(self.layers) ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}." for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) to_drop = False if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: # skip the layer to_drop = True if to_drop: layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, None, (head_mask[idx] if head_mask is not None else None), output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, None, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) hidden_states = self.layer_norm(hidden_states) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class WhisperDecoder(WhisperPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`WhisperDecoderLayer`] Args: config: WhisperConfig """ main_input_name = "input_ids" def __init__(self, config: WhisperConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_target_positions self.max_source_positions = config.max_source_positions self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) self.embed_positions = WhisperPositionalEmbedding(self.max_target_positions, config.d_model) self.layers = nn.ModuleList([WhisperDecoderLayer(config) for _ in range(config.decoder_layers)]) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self._use_sdpa = config._attn_implementation == "sdpa" self.layer_norm = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, inputs_embeds=None, position_ids=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if self._use_flash_attention_2: # 2d mask is passed through the layers attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None elif self._use_sdpa and head_mask is None and not output_attentions: # output_attentions=True & head_mask can not be supported when using SDPA. attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( attention_mask, input_shape, inputs_embeds, past_key_values_length ) else: # 4d mask is passed through the layers attention_mask = _prepare_4d_causal_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) # embed positions if input_ids is not None: positions = self.embed_positions( input_ids, past_key_values_length=past_key_values_length, position_ids=position_ids ) else: positions = self.embed_positions( inputs_embeds, past_key_values_length=past_key_values_length, position_ids=position_ids ) hidden_states = inputs_embeds + positions hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`..." ) use_cache = False # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None next_decoder_cache = () if use_cache else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: assert attn_mask.size()[0] == (len(self.layers)), ( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, encoder_hidden_states, None, # encoder attention mask head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, None, # past_key_value output_attentions, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=( cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None ), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) hidden_states = self.layer_norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( "The bare Whisper Model outputting raw hidden-states without any specific head on top.", WHISPER_START_DOCSTRING, ) class WhisperModel(WhisperPreTrainedModel): def __init__(self, config: WhisperConfig): super().__init__(config) self.encoder = WhisperEncoder(config) self.decoder = WhisperDecoder(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.decoder.embed_tokens def set_input_embeddings(self, value): self.decoder.embed_tokens = value def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def freeze_encoder(self): """ Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will not be updated during training. """ self.encoder._freeze_parameters() def _mask_input_features( self, input_features: torch.FloatTensor, attention_mask: Optional[torch.LongTensor] = None, ): """ Masks extracted features along time axis and/or along feature axis according to [SpecAugment](https://arxiv.org/abs/1904.08779). """ # `config.apply_spec_augment` can set masking to False if not getattr(self.config, "apply_spec_augment", True): return input_features # generate indices & apply SpecAugment along time axis batch_size, hidden_size, sequence_length = input_features.size() if self.config.mask_time_prob > 0 and self.training: # generate indices & apply SpecAugment along time axis mask_time_indices = _compute_mask_indices( (batch_size, sequence_length), mask_prob=self.config.mask_time_prob, mask_length=self.config.mask_time_length, attention_mask=attention_mask, min_masks=self.config.mask_time_min_masks, ) mask_time_indices = torch.tensor(mask_time_indices, device=input_features.device, dtype=torch.bool) mask_time_indices = mask_time_indices[:, None].expand(-1, hidden_size, -1) input_features[mask_time_indices] = 0 if self.config.mask_feature_prob > 0 and self.training: # generate indices & apply SpecAugment along feature axis mask_feature_indices = _compute_mask_indices( (batch_size, hidden_size), mask_prob=self.config.mask_feature_prob, mask_length=self.config.mask_feature_length, min_masks=self.config.mask_feature_min_masks, ) mask_feature_indices = torch.tensor(mask_feature_indices, device=input_features.device, dtype=torch.bool) input_features[mask_feature_indices] = 0 return input_features @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_features: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.LongTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None, decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]: r""" Returns: Example: ```python >>> import torch >>> from transformers import AutoFeatureExtractor, WhisperModel >>> from datasets import load_dataset >>> model = WhisperModel.from_pretrained("openai/whisper-base") >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") >>> input_features = inputs.input_features >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state >>> list(last_hidden_state.shape) [1, 2, 512] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if encoder_outputs is None: input_features = self._mask_input_features(input_features, attention_mask=attention_mask) encoder_outputs = self.encoder( input_features, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, position_ids=decoder_position_ids, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The Whisper Model with a language modeling head. Can be used for automatic speech recognition.", WHISPER_START_DOCSTRING, ) class WhisperForConditionalGeneration(WhisperGenerationMixin, WhisperPreTrainedModel): base_model_prefix = "model" _tied_weights_keys = ["proj_out.weight"] def __init__(self, config: WhisperConfig): super().__init__(config) self.model = WhisperModel(config) self.proj_out = nn.Linear(config.d_model, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.model.get_encoder() def get_decoder(self): return self.model.get_decoder() def get_output_embeddings(self): return self.proj_out def set_output_embeddings(self, new_embeddings): self.proj_out = new_embeddings def get_input_embeddings(self) -> nn.Module: return self.model.get_input_embeddings() def freeze_encoder(self): """ Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will not be updated during training. """ self.model.encoder._freeze_parameters() @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_features: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.LongTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None, decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> import torch >>> from transformers import AutoProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt") >>> input_features = inputs.input_features >>> generated_ids = model.generate(inputs=input_features) >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] >>> transcription ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) outputs = self.model( input_features, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, decoder_inputs_embeds=decoder_inputs_embeds, decoder_position_ids=decoder_position_ids, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) lm_logits = self.proj_out(outputs[0]) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # move labels to correct device to enable PP labels = labels.to(lm_logits.device) loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.reshape(-1)) if not return_dict: output = (lm_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return Seq2SeqLMOutput( loss=loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) def prepare_inputs_for_generation( self, decoder_input_ids, past_key_values=None, use_cache=None, encoder_outputs=None, attention_mask=None, decoder_attention_mask=None, **kwargs, ): decoder_position_ids = None if decoder_attention_mask is not None: decoder_position_ids = (decoder_attention_mask.cumsum(-1) - 1).clamp(min=0) if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if decoder_input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = decoder_input_ids.shape[1] - 1 decoder_input_ids = decoder_input_ids[:, remove_prefix_length:] if decoder_position_ids is not None and decoder_position_ids.shape[1] > decoder_input_ids.shape[1]: decoder_position_ids = decoder_position_ids[:, remove_prefix_length:] return { "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "use_cache": use_cache, "decoder_attention_mask": decoder_attention_mask, "decoder_position_ids": decoder_position_ids, } @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past class WhisperDecoderWrapper(WhisperPreTrainedModel): """ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is used in combination with the [`EncoderDecoderModel`] framework. """ def __init__(self, config): super().__init__(config) config.is_encoder_decoder = False self.decoder = WhisperDecoder(config) def get_input_embeddings(self): return self.decoder.embed_tokens def set_input_embeddings(self, value): self.decoder.embed_tokens = value def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) @add_start_docstrings( """ Whisper decoder with with a language modeling head on top (linear layer with weights tied to the input embeddings). """, WHISPER_START_DOCSTRING, ) class WhisperForCausalLM(WhisperPreTrainedModel): _tied_weights_keys = ["proj_out.weight"] main_input_name = "input_ids" def __init__(self, config): super().__init__(config) config.is_encoder_decoder = False self.model = WhisperDecoderWrapper(config) self.proj_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.proj_out def set_output_embeddings(self, new_embeddings): self.proj_out = new_embeddings def get_input_embeddings(self) -> nn.Module: return self.model.get_input_embeddings() def set_input_embeddings(self, value): self.model.set_input_embeddings(value) def set_decoder(self, decoder): self.model.decoder = decoder def get_decoder(self): return self.model.decoder @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_outputs (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Returns: Example: ```python >>> from transformers import WhisperForCausalLM, WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from datasets import load_dataset >>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") >>> assistant_model = WhisperForCausalLM.from_pretrained("distil-whisper/distil-large-v2") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor( ... sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt" ... ).input_features >>> predicted_ids = model.generate(input_features, assistant_model=assistant_model) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] >>> transcription ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # If the user passed a tuple or `BaseModelOutput` for encoder_outputs, we extract only the hidden states if isinstance(encoder_outputs, (BaseModelOutput, tuple, list)): encoder_outputs = encoder_outputs[0] # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.decoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_outputs, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) logits = self.proj_out(outputs[0]) loss = None if labels is not None: labels = labels.to(logits.device) loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, use_cache=None, encoder_outputs=None, attention_mask=None, **kwargs, ): if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] return { "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "input_ids": input_ids, "use_cache": use_cache, "attention_mask": attention_mask, } @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past @add_start_docstrings( """ Whisper Encoder Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting. """, WHISPER_ENCODER_INPUTS_DOCSTRING, ) class WhisperForAudioClassification(WhisperPreTrainedModel): def __init__(self, config): super().__init__(config) self.encoder = WhisperEncoder(config) num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings if config.use_weighted_layer_sum: self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size) self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def freeze_encoder(self): """ Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will not be updated during training. Only the projection layers and classification head will be updated. """ self.encoder._freeze_parameters() def get_input_embeddings(self) -> nn.Module: return self.encoder.get_input_embeddings() def set_input_embeddings(self, value: nn.Module): self.encoder.set_input_embeddings(value) @add_start_docstrings_to_model_forward(WHISPER_ENCODER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_features: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Example: ```python >>> import torch >>> from transformers import AutoFeatureExtractor, WhisperForAudioClassification >>> from datasets import load_dataset >>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id") >>> model = WhisperForAudioClassification.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id") >>> ds = load_dataset("google/fleurs", "all", split="validation", streaming=True) >>> sample = next(iter(ds)) >>> inputs = feature_extractor( ... sample["audio"]["array"], sampling_rate=sample["audio"]["sampling_rate"], return_tensors="pt" ... ) >>> input_features = inputs.input_features >>> with torch.no_grad(): ... logits = model(input_features).logits >>> predicted_class_ids = torch.argmax(logits).item() >>> predicted_label = model.config.id2label[predicted_class_ids] >>> predicted_label 'Afrikaans' ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) if self.config.use_weighted_layer_sum: output_hidden_states = True elif output_hidden_states is None: output_hidden_states = self.config.output_hidden_states return_dict = return_dict if return_dict is not None else self.config.use_return_dict if encoder_outputs is None: encoder_outputs = self.encoder( input_features, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if self.config.use_weighted_layer_sum: hidden_states = encoder_outputs[_HIDDEN_STATES_START_POSITION] hidden_states = torch.stack(hidden_states, dim=1) norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) else: hidden_states = encoder_outputs[0] hidden_states = self.projector(hidden_states) pooled_output = hidden_states.mean(dim=1) logits = self.classifier(pooled_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # move labels to correct device to enable PP labels = labels.to(logits.device) loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + encoder_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, )

transformers/src/transformers/models/whisper/modeling_whisper.py/0

# coding=utf-8 # Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for XGLM.""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_xglm import XGLMTokenizer else: XGLMTokenizer = None logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model", }, "tokenizer_file": { "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/xglm-564M": 2048, } class XGLMTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" XGLM tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on [BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models). This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`): Additional special tokens used by the tokenizer. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = XGLMTokenizer def __init__( self, vocab_file=None, tokenizer_file=None, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", **kwargs, ): # Compatibility with the original tokenizer self.num_madeup_words = 7 madeup_words = [f"<madeupword{i}>" for i in range(self.num_madeup_words)] kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or [] kwargs["additional_special_tokens"] += [ word for word in madeup_words if word not in kwargs["additional_special_tokens"] ] super().__init__( vocab_file, tokenizer_file=tokenizer_file, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, **kwargs, ) self.vocab_file = vocab_file @property def can_save_slow_tokenizer(self) -> bool: return os.path.isfile(self.vocab_file) if self.vocab_file else False def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM-RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.sep_token_id] + token_ids_0 sep = [self.sep_token_id] return sep + token_ids_0 + sep + sep + token_ids_1 def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] if token_ids_1 is None: return len(sep + token_ids_0) * [0] return len(sep + token_ids_0 + sep + sep + token_ids_1) * [0] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not self.can_save_slow_tokenizer: raise ValueError( "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " "tokenizer." ) if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory.") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) return (out_vocab_file,)

transformers/src/transformers/models/xglm/tokenization_xglm_fast.py/0

# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License """ Tokenization classes for XLM-RoBERTa model.""" import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "FacebookAI/xlm-roberta-base": "https://huggingface.co/FacebookAI/xlm-roberta-base/resolve/main/sentencepiece.bpe.model", "FacebookAI/xlm-roberta-large": "https://huggingface.co/FacebookAI/xlm-roberta-large/resolve/main/sentencepiece.bpe.model", "FacebookAI/xlm-roberta-large-finetuned-conll02-dutch": ( "https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model" ), "FacebookAI/xlm-roberta-large-finetuned-conll02-spanish": ( "https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model" ), "FacebookAI/xlm-roberta-large-finetuned-conll03-english": ( "https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model" ), "FacebookAI/xlm-roberta-large-finetuned-conll03-german": ( "https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model" ), } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "FacebookAI/xlm-roberta-base": 512, "FacebookAI/xlm-roberta-large": 512, "FacebookAI/xlm-roberta-large-finetuned-conll02-dutch": 512, "FacebookAI/xlm-roberta-large-finetuned-conll02-spanish": 512, "FacebookAI/xlm-roberta-large-finetuned-conll03-english": 512, "FacebookAI/xlm-roberta-large-finetuned-conll03-german": 512, } class XLMRobertaTokenizer(PreTrainedTokenizer): """ Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Attributes: sp_model (`SentencePieceProcessor`): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.fairseq_offset = 1 self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + self.fairseq_offset self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM-RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] @property def vocab_size(self): return len(self.sp_model) + self.fairseq_offset + 1 # Add the <mask> token def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text: str) -> List[str]: # TODO check if the t5/llama PR also applies here return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,)

transformers/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py/0

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ctypes import c_float, sizeof from enum import Enum from typing import TYPE_CHECKING, Optional, Union if TYPE_CHECKING: from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer # tests_ignore class ParameterFormat(Enum): Float = c_float @property def size(self) -> int: """ Number of byte required for this data type Returns: Integer > 0 """ return sizeof(self.value) def compute_effective_axis_dimension(dimension: int, fixed_dimension: int, num_token_to_add: int = 0) -> int: """ Args: dimension: fixed_dimension: num_token_to_add: Returns: """ # < 0 is possible if using a dynamic axis if dimension <= 0: dimension = fixed_dimension dimension -= num_token_to_add return dimension def compute_serialized_parameters_size(num_parameters: int, dtype: ParameterFormat) -> int: """ Compute the size taken by all the parameters in the given the storage format when serializing the model Args: num_parameters: Number of parameters to be saved dtype: The data format each parameter will be saved Returns: Size (in byte) taken to save all the parameters """ return num_parameters * dtype.size def get_preprocessor(model_name: str) -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]: """ Gets a preprocessor (tokenizer, feature extractor or processor) that is available for `model_name`. Args: model_name (`str`): Name of the model for which a preprocessor are loaded. Returns: `Optional[Union[AutoTokenizer, AutoFeatureExtractor, AutoProcessor]]`: If a processor is found, it is returned. Otherwise, if a tokenizer or a feature extractor exists, it is returned. If both a tokenizer and a feature extractor exist, an error is raised. The function returns `None` if no preprocessor is found. """ # Avoid circular imports by only importing this here. from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer # tests_ignore try: return AutoProcessor.from_pretrained(model_name) except (ValueError, OSError, KeyError): tokenizer, feature_extractor = None, None try: tokenizer = AutoTokenizer.from_pretrained(model_name) except (OSError, KeyError): pass try: feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) except (OSError, KeyError): pass if tokenizer is not None and feature_extractor is not None: raise ValueError( f"Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor." ) elif tokenizer is None and feature_extractor is None: return None elif tokenizer is not None: return tokenizer else: return feature_extractor

transformers/src/transformers/onnx/utils.py/0

# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Union import numpy as np from ..utils import ( add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import Pipeline, build_pipeline_init_args if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES logger = logging.get_logger(__name__) @add_end_docstrings(build_pipeline_init_args(has_image_processor=True)) class ImageToImagePipeline(Pipeline): """ Image to Image pipeline using any `AutoModelForImageToImage`. This pipeline generates an image based on a previous image input. Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import pipeline >>> upscaler = pipeline("image-to-image", model="caidas/swin2SR-classical-sr-x2-64") >>> img = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) >>> img = img.resize((64, 64)) >>> upscaled_img = upscaler(img) >>> img.size (64, 64) >>> upscaled_img.size (144, 144) ``` This image to image pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"image-to-image"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=image-to-image). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) requires_backends(self, "vision") self.check_model_type(MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES) def _sanitize_parameters(self, **kwargs): preprocess_params = {} postprocess_params = {} forward_params = {} if "timeout" in kwargs: preprocess_params["timeout"] = kwargs["timeout"] if "head_mask" in kwargs: forward_params["head_mask"] = kwargs["head_mask"] return preprocess_params, forward_params, postprocess_params def __call__( self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs ) -> Union["Image.Image", List["Image.Image"]]: """ Transform the image(s) passed as inputs. Args: images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images, which must then be passed as a string. Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL images. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is used and the call may block forever. Return: An image (Image.Image) or a list of images (List["Image.Image"]) containing result(s). If the input is a single image, the return will be also a single image, if the input is a list of several images, it will return a list of transformed images. """ return super().__call__(images, **kwargs) def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def preprocess(self, image, timeout=None): image = load_image(image, timeout=timeout) inputs = self.image_processor(images=[image], return_tensors="pt") return inputs def postprocess(self, model_outputs): images = [] if "reconstruction" in model_outputs.keys(): outputs = model_outputs.reconstruction for output in outputs: output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() output = np.moveaxis(output, source=0, destination=-1) output = (output * 255.0).round().astype(np.uint8) # float32 to uint8 images.append(Image.fromarray(output)) return images if len(images) > 1 else images[0]

transformers/src/transformers/pipelines/image_to_image.py/0

from collections import UserDict from typing import List, Union from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import Pipeline, build_pipeline_init_args if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): import torch from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES if is_tf_available(): from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES from ..tf_utils import stable_softmax logger = logging.get_logger(__name__) @add_end_docstrings(build_pipeline_init_args(has_image_processor=True)) class ZeroShotImageClassificationPipeline(Pipeline): """ Zero shot image classification pipeline using `CLIPModel`. This pipeline predicts the class of an image when you provide an image and a set of `candidate_labels`. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="google/siglip-so400m-patch14-384") >>> classifier( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["animals", "humans", "landscape"], ... ) [{'score': 0.965, 'label': 'animals'}, {'score': 0.03, 'label': 'humans'}, {'score': 0.005, 'label': 'landscape'}] >>> classifier( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["black and white", "photorealist", "painting"], ... ) [{'score': 0.996, 'label': 'black and white'}, {'score': 0.003, 'label': 'photorealist'}, {'score': 0.0, 'label': 'painting'}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"zero-shot-image-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-image-classification). """ def __init__(self, **kwargs): super().__init__(**kwargs) requires_backends(self, "vision") self.check_model_type( TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES if self.framework == "tf" else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES ) def __call__(self, images: Union[str, List[str], "Image", List["Image"]], **kwargs): """ Assign labels to the image(s) passed as inputs. Args: images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly candidate_labels (`List[str]`): The candidate labels for this image hypothesis_template (`str`, *optional*, defaults to `"This is a photo of {}"`): The sentence used in cunjunction with *candidate_labels* to attempt the image classification by replacing the placeholder with the candidate_labels. Then likelihood is estimated by using logits_per_image timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A list of dictionaries containing result, one dictionary per proposed label. The dictionaries contain the following keys: - **label** (`str`) -- The label identified by the model. It is one of the suggested `candidate_label`. - **score** (`float`) -- The score attributed by the model for that label (between 0 and 1). """ return super().__call__(images, **kwargs) def _sanitize_parameters(self, **kwargs): preprocess_params = {} if "candidate_labels" in kwargs: preprocess_params["candidate_labels"] = kwargs["candidate_labels"] if "timeout" in kwargs: preprocess_params["timeout"] = kwargs["timeout"] if "hypothesis_template" in kwargs: preprocess_params["hypothesis_template"] = kwargs["hypothesis_template"] return preprocess_params, {}, {} def preprocess(self, image, candidate_labels=None, hypothesis_template="This is a photo of {}.", timeout=None): image = load_image(image, timeout=timeout) inputs = self.image_processor(images=[image], return_tensors=self.framework) inputs["candidate_labels"] = candidate_labels sequences = [hypothesis_template.format(x) for x in candidate_labels] padding = "max_length" if self.model.config.model_type == "siglip" else True text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=padding) inputs["text_inputs"] = [text_inputs] return inputs def _forward(self, model_inputs): candidate_labels = model_inputs.pop("candidate_labels") text_inputs = model_inputs.pop("text_inputs") if isinstance(text_inputs[0], UserDict): text_inputs = text_inputs[0] else: # Batching case. text_inputs = text_inputs[0][0] outputs = self.model(**text_inputs, **model_inputs) model_outputs = { "candidate_labels": candidate_labels, "logits": outputs.logits_per_image, } return model_outputs def postprocess(self, model_outputs): candidate_labels = model_outputs.pop("candidate_labels") logits = model_outputs["logits"][0] if self.framework == "pt" and self.model.config.model_type == "siglip": probs = torch.sigmoid(logits).squeeze(-1) scores = probs.tolist() if not isinstance(scores, list): scores = [scores] elif self.framework == "pt": probs = logits.softmax(dim=-1).squeeze(-1) scores = probs.tolist() if not isinstance(scores, list): scores = [scores] elif self.framework == "tf": probs = stable_softmax(logits, axis=-1) scores = probs.numpy().tolist() else: raise ValueError(f"Unsupported framework: {self.framework}") result = [ {"score": score, "label": candidate_label} for score, candidate_label in sorted(zip(scores, candidate_labels), key=lambda x: -x[0]) ] return result

transformers/src/transformers/pipelines/zero_shot_image_classification.py/0

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib.util import json import os import warnings from dataclasses import dataclass, field import torch from ..training_args import TrainingArguments from ..utils import cached_property, is_sagemaker_dp_enabled, logging logger = logging.get_logger(__name__) # TODO: should be moved to `utils` after refactoring of SageMakerTrainer def is_sagemaker_model_parallel_available(): # Get the sagemaker specific mp parameters from smp_options variable. smp_options = os.getenv("SM_HP_MP_PARAMETERS", "{}") try: # Parse it and check the field "partitions" is included, it is required for model parallel. smp_options = json.loads(smp_options) if "partitions" not in smp_options: return False except json.JSONDecodeError: return False # Get the sagemaker specific framework parameters from mpi_options variable. mpi_options = os.getenv("SM_FRAMEWORK_PARAMS", "{}") try: # Parse it and check the field "sagemaker_distributed_dataparallel_enabled". mpi_options = json.loads(mpi_options) if not mpi_options.get("sagemaker_mpi_enabled", False): return False except json.JSONDecodeError: return False # Lastly, check if the `smdistributed` module is present. return importlib.util.find_spec("smdistributed") is not None if is_sagemaker_model_parallel_available(): import smdistributed.modelparallel.torch as smp smp.init() @dataclass class SageMakerTrainingArguments(TrainingArguments): mp_parameters: str = field( default="", metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}, ) def __post_init__(self): super().__post_init__() warnings.warn( "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use " "`TrainingArguments` instead.", FutureWarning, ) @cached_property def _setup_devices(self) -> "torch.device": logger.info("PyTorch: setting up devices") if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1: logger.warning( "torch.distributed process group is initialized, but local_rank == -1. " "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" ) if self.no_cuda: device = torch.device("cpu") self._n_gpu = 0 elif is_sagemaker_model_parallel_available(): local_rank = smp.local_rank() device = torch.device("cuda", local_rank) self._n_gpu = 1 elif is_sagemaker_dp_enabled(): import smdistributed.dataparallel.torch.torch_smddp # noqa: F401 torch.distributed.init_process_group(backend="smddp", timeout=self.ddp_timeout_delta) self.local_rank = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK")) device = torch.device("cuda", self.local_rank) self._n_gpu = 1 elif self.local_rank == -1: # if n_gpu is > 1 we'll use nn.DataParallel. # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0` # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will # trigger an error that a device index is missing. Index 0 takes into account the # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0` # will use the first GPU in that env, i.e. GPU#1 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at # the default value. self._n_gpu = torch.cuda.device_count() else: # Here, we'll use torch.distributed. # Initializes the distributed backend which will take care of synchronizing nodes/GPUs if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="nccl", timeout=self.ddp_timeout_delta) device = torch.device("cuda", self.local_rank) self._n_gpu = 1 if device.type == "cuda": torch.cuda.set_device(device) return device @property def world_size(self): if is_sagemaker_model_parallel_available(): return smp.dp_size() return super().world_size @property def place_model_on_device(self): return not is_sagemaker_model_parallel_available() @property def _no_sync_in_gradient_accumulation(self): return False

transformers/src/transformers/sagemaker/training_args_sm.py/0

#!/usr/bin/env python # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re from ..utils import cached_file # docstyle-ignore CHAT_MESSAGE_PROMPT = """ Human: <<task>> Assistant: """ DEFAULT_PROMPTS_REPO = "huggingface-tools/default-prompts" PROMPT_FILES = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"} def download_prompt(prompt_or_repo_id, agent_name, mode="run"): """ Downloads and caches the prompt from a repo and returns it contents (if necessary) """ if prompt_or_repo_id is None: prompt_or_repo_id = DEFAULT_PROMPTS_REPO # prompt is considered a repo ID when it does not contain any kind of space if re.search("\\s", prompt_or_repo_id) is not None: return prompt_or_repo_id prompt_file = cached_file( prompt_or_repo_id, PROMPT_FILES[mode], repo_type="dataset", user_agent={"agent": agent_name} ) with open(prompt_file, "r", encoding="utf-8") as f: return f.read()

transformers/src/transformers/tools/prompts.py/0

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from huggingface_hub import get_full_repo_name # for backward compatibility from huggingface_hub.constants import HF_HUB_DISABLE_TELEMETRY as DISABLE_TELEMETRY # for backward compatibility from packaging import version from .. import __version__ from .backbone_utils import BackboneConfigMixin, BackboneMixin from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD from .doc import ( add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, copy_func, replace_return_docstrings, ) from .generic import ( ContextManagers, ExplicitEnum, ModelOutput, PaddingStrategy, TensorType, add_model_info_to_auto_map, cached_property, can_return_loss, expand_dims, find_labels, flatten_dict, infer_framework, is_jax_tensor, is_numpy_array, is_tensor, is_tf_symbolic_tensor, is_tf_tensor, is_torch_device, is_torch_dtype, is_torch_tensor, reshape, squeeze, strtobool, tensor_size, to_numpy, to_py_obj, transpose, working_or_temp_dir, ) from .hub import ( CLOUDFRONT_DISTRIB_PREFIX, HF_MODULES_CACHE, HUGGINGFACE_CO_PREFIX, HUGGINGFACE_CO_RESOLVE_ENDPOINT, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, S3_BUCKET_PREFIX, TRANSFORMERS_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, EntryNotFoundError, PushInProgress, PushToHubMixin, RepositoryNotFoundError, RevisionNotFoundError, cached_file, default_cache_path, define_sagemaker_information, download_url, extract_commit_hash, get_cached_models, get_file_from_repo, has_file, http_user_agent, is_offline_mode, is_remote_url, move_cache, send_example_telemetry, try_to_load_from_cache, ) from .import_utils import ( ACCELERATE_MIN_VERSION, ENV_VARS_TRUE_AND_AUTO_VALUES, ENV_VARS_TRUE_VALUES, TORCH_FX_REQUIRED_VERSION, USE_JAX, USE_TF, USE_TORCH, DummyObject, OptionalDependencyNotAvailable, _LazyModule, ccl_version, direct_transformers_import, get_torch_version, is_accelerate_available, is_apex_available, is_aqlm_available, is_auto_awq_available, is_auto_gptq_available, is_bitsandbytes_available, is_bs4_available, is_coloredlogs_available, is_cv2_available, is_cython_available, is_datasets_available, is_decord_available, is_detectron2_available, is_essentia_available, is_faiss_available, is_flash_attn_2_available, is_flash_attn_available, is_flash_attn_greater_or_equal_2_10, is_flax_available, is_fsdp_available, is_ftfy_available, is_g2p_en_available, is_in_notebook, is_ipex_available, is_jieba_available, is_jinja_available, is_jumanpp_available, is_kenlm_available, is_keras_nlp_available, is_levenshtein_available, is_librosa_available, is_natten_available, is_ninja_available, is_nltk_available, is_onnx_available, is_openai_available, is_optimum_available, is_pandas_available, is_peft_available, is_phonemizer_available, is_pretty_midi_available, is_protobuf_available, is_psutil_available, is_py3nvml_available, is_pyctcdecode_available, is_pytesseract_available, is_pytest_available, is_pytorch_quantization_available, is_rjieba_available, is_sacremoses_available, is_safetensors_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_scipy_available, is_sentencepiece_available, is_seqio_available, is_sklearn_available, is_soundfile_availble, is_spacy_available, is_speech_available, is_sudachi_available, is_sudachi_projection_available, is_tensorflow_probability_available, is_tensorflow_text_available, is_tf2onnx_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_bf16_available, is_torch_bf16_available_on_device, is_torch_bf16_cpu_available, is_torch_bf16_gpu_available, is_torch_compile_available, is_torch_cuda_available, is_torch_fp16_available_on_device, is_torch_fx_available, is_torch_fx_proxy, is_torch_mps_available, is_torch_neuroncore_available, is_torch_npu_available, is_torch_sdpa_available, is_torch_tensorrt_fx_available, is_torch_tf32_available, is_torch_tpu_available, is_torch_xpu_available, is_torchaudio_available, is_torchdistx_available, is_torchdynamo_available, is_torchvision_available, is_training_run_on_sagemaker, is_vision_available, requires_backends, torch_only_method, ) from .peft_utils import ( ADAPTER_CONFIG_NAME, ADAPTER_SAFE_WEIGHTS_NAME, ADAPTER_WEIGHTS_NAME, check_peft_version, find_adapter_config_file, ) WEIGHTS_NAME = "pytorch_model.bin" WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json" TF2_WEIGHTS_NAME = "tf_model.h5" TF2_WEIGHTS_INDEX_NAME = "tf_model.h5.index.json" TF_WEIGHTS_NAME = "model.ckpt" FLAX_WEIGHTS_NAME = "flax_model.msgpack" FLAX_WEIGHTS_INDEX_NAME = "flax_model.msgpack.index.json" SAFE_WEIGHTS_NAME = "model.safetensors" SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json" CONFIG_NAME = "config.json" FEATURE_EXTRACTOR_NAME = "preprocessor_config.json" IMAGE_PROCESSOR_NAME = FEATURE_EXTRACTOR_NAME PROCESSOR_NAME = "processor_config.json" GENERATION_CONFIG_NAME = "generation_config.json" MODEL_CARD_NAME = "modelcard.json" SENTENCEPIECE_UNDERLINE = "▁" SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility MULTIPLE_CHOICE_DUMMY_INPUTS = [ [[0, 1, 0, 1], [1, 0, 0, 1]] ] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too. DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]] DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]] def check_min_version(min_version): if version.parse(__version__) < version.parse(min_version): if "dev" in min_version: error_message = ( "This example requires a source install from HuggingFace Transformers (see " "`https://huggingface.co/docs/transformers/installation#install-from-source`)," ) else: error_message = f"This example requires a minimum version of {min_version}," error_message += f" but the version found is {__version__}.\n" raise ImportError( error_message + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other " "versions of HuggingFace Transformers." )

transformers/src/transformers/utils/__init__.py/0

# This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class AlbertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class BartTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class BarthezTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class BertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class BigBirdTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class BlenderbotTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class BlenderbotSmallTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class BloomTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class CamembertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class CLIPTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class CodeLlamaTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class CodeGenTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class ConvBertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class CpmTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class DebertaTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class DebertaV2TokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class RetriBertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class DistilBertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class DPRContextEncoderTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class DPRQuestionEncoderTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class DPRReaderTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class ElectraTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class FNetTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class FunnelTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class GemmaTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class GPT2TokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class GPTNeoXTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class GPTNeoXJapaneseTokenizer(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class HerbertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class LayoutLMTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class LayoutLMv2TokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class LayoutLMv3TokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class LayoutXLMTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class LEDTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class LlamaTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class LongformerTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class LxmertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class MarkupLMTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class MBartTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class MBart50TokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class MobileBertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class MPNetTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class MT5TokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class MvpTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class NllbTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class NougatTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class OpenAIGPTTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class PegasusTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class Qwen2TokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class RealmTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class ReformerTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class RemBertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class RobertaTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class RoFormerTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class SeamlessM4TTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class SplinterTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class SqueezeBertTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class T5TokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class WhisperTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class XGLMTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class XLMRobertaTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class XLNetTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) class PreTrainedTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"])

transformers/src/transformers/utils/dummy_tokenizers_objects.py/0

# coding=utf-8 # Copyright 2022 {{cookiecutter.authors}}. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the {{cookiecutter.modelname}} tokenizer. """ import unittest {% if cookiecutter.has_slow_class == "True" and cookiecutter.has_fast_class == "True" -%} from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}TokenizerFast {% elif cookiecutter.has_slow_class == "True" -%} from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer {% elif cookiecutter.has_fast_class == "True" -%} from transformers import {{cookiecutter.camelcase_modelname}}TokenizerFast {% endif -%} {% if cookiecutter.has_fast_class == "True" and cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%} from transformers.testing_utils import require_sentencepiece, require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_sentencepiece @require_tokenizers {% elif cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%} from transformers.testing_utils import require_sentencepiece from ...test_tokenization_common import TokenizerTesterMixin @require_sentencepiece {% elif cookiecutter.has_fast_class == "True" -%} from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers {% else -%} from ...test_tokenization_common import TokenizerTesterMixin {% endif -%} class {{cookiecutter.camelcase_modelname}}TokenizationTest(TokenizerTesterMixin, unittest.TestCase): {% if cookiecutter.has_slow_class == "True" -%} tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer test_slow_tokenizer = True {% else -%} tokenizer_class = None test_slow_tokenizer = False {% endif -%} {% if cookiecutter.has_fast_class == "True" -%} rust_tokenizer_class = {{cookiecutter.camelcase_modelname}}TokenizerFast test_rust_tokenizer = True {% else -%} rust_tokenizer_class = None test_rust_tokenizer = False {% endif -%} {% if cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%} test_sentencepiece = True {% endif -%} # TODO: Check in `TokenizerTesterMixin` if other attributes need to be changed def setUp(self): super().setUp() raise NotImplementedError( "Here you have to implement the saving of a toy tokenizer in " "`self.tmpdirname`." ) # TODO: add tests with hard-coded target values

transformers/templates/adding_a_missing_tokenization_test/cookiecutter-template-{{cookiecutter.modelname}}/test_tokenization_{{cookiecutter.lowercase_modelname}}.py/0

## Copyright 2022 The HuggingFace Team. All rights reserved. ## ## Licensed under the Apache License, Version 2.0 (the "License"); ## you may not use this file except in compliance with the License. ## You may obtain a copy of the License at ## ## http://www.apache.org/licenses/LICENSE-2.0 ## ## Unless required by applicable law or agreed to in writing, software ## distributed under the License is distributed on an "AS IS" BASIS, ## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ## See the License for the specific language governing permissions and ## limitations under the License. ## This file is made so that specific statements may be copied inside existing files. This is useful to copy ## import statements in __init__.py, or to complete model lists in the AUTO files. ## ## It is to be used as such: ## Put '# To replace in: "FILE_PATH"' in order to indicate the contents will be copied in the file at path FILE_PATH ## Put '# Below: "STATEMENT"' in order to copy the contents below **the first occurrence** of that line in the file at FILE_PATH ## Put '# Replace with:' followed by the lines containing the content to define the content ## End a statement with '# End.'. If starting a new statement without redefining the FILE_PATH, it will continue pasting ## content in that file. ## ## Put '## COMMENT' to comment on the file. # To replace in: "src/transformers/__init__.py" # Below: " # PyTorch models structure" if generating PyTorch # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" %} _import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend( [ "{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST", "{{cookiecutter.camelcase_modelname}}ForMaskedLM", "{{cookiecutter.camelcase_modelname}}ForCausalLM", "{{cookiecutter.camelcase_modelname}}ForMultipleChoice", "{{cookiecutter.camelcase_modelname}}ForQuestionAnswering", "{{cookiecutter.camelcase_modelname}}ForSequenceClassification", "{{cookiecutter.camelcase_modelname}}ForTokenClassification", "{{cookiecutter.camelcase_modelname}}Layer", "{{cookiecutter.camelcase_modelname}}Model", "{{cookiecutter.camelcase_modelname}}PreTrainedModel", "load_tf_weights_in_{{cookiecutter.lowercase_modelname}}", ] ) {% else %} _import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend( [ "{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST", "{{cookiecutter.camelcase_modelname}}ForCausalLM", "{{cookiecutter.camelcase_modelname}}ForConditionalGeneration", "{{cookiecutter.camelcase_modelname}}ForQuestionAnswering", "{{cookiecutter.camelcase_modelname}}ForSequenceClassification", "{{cookiecutter.camelcase_modelname}}Model", "{{cookiecutter.camelcase_modelname}}PreTrainedModel", ] ) {% endif -%} # End. # Below: " # TensorFlow models structure" if generating TensorFlow # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" %} _import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend( [ "TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST", "TF{{cookiecutter.camelcase_modelname}}ForMaskedLM", "TF{{cookiecutter.camelcase_modelname}}ForCausalLM", "TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice", "TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering", "TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification", "TF{{cookiecutter.camelcase_modelname}}ForTokenClassification", "TF{{cookiecutter.camelcase_modelname}}Layer", "TF{{cookiecutter.camelcase_modelname}}Model", "TF{{cookiecutter.camelcase_modelname}}PreTrainedModel", ] ) {% else %} _import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend( [ "TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration", "TF{{cookiecutter.camelcase_modelname}}Model", "TF{{cookiecutter.camelcase_modelname}}PreTrainedModel", ] ) {% endif -%} # End. # Below: " # Flax models structure" if generating Flax # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" %} _import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend( [ "Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM", "Flax{{cookiecutter.camelcase_modelname}}ForCausalLM", "Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice", "Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering", "Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification", "Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification", "Flax{{cookiecutter.camelcase_modelname}}Layer", "Flax{{cookiecutter.camelcase_modelname}}Model", "Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel", ] ) {% else %} _import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend( [ "Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration", "Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering", "Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification", "Flax{{cookiecutter.camelcase_modelname}}Model", "Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel", ] ) {% endif -%} # End. # Below: " # Fast tokenizers structure" # Replace with: _import_structure["models.{{cookiecutter.lowercase_modelname}}"].append("{{cookiecutter.camelcase_modelname}}TokenizerFast") # End. # Below: " # Models" # Replace with: "models.{{cookiecutter.lowercase_modelname}}": ["{{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP", "{{cookiecutter.camelcase_modelname}}Config", "{{cookiecutter.camelcase_modelname}}Tokenizer"], # End. # To replace in: "src/transformers/__init__.py" # Below: " # PyTorch model imports" if generating PyTorch # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" %} from .models.{{cookiecutter.lowercase_modelname}} import ( {{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST, {{cookiecutter.camelcase_modelname}}ForMaskedLM, {{cookiecutter.camelcase_modelname}}ForCausalLM, {{cookiecutter.camelcase_modelname}}ForMultipleChoice, {{cookiecutter.camelcase_modelname}}ForQuestionAnswering, {{cookiecutter.camelcase_modelname}}ForSequenceClassification, {{cookiecutter.camelcase_modelname}}ForTokenClassification, {{cookiecutter.camelcase_modelname}}Layer, {{cookiecutter.camelcase_modelname}}Model, {{cookiecutter.camelcase_modelname}}PreTrainedModel, load_tf_weights_in_{{cookiecutter.lowercase_modelname}}, ) {% else %} from .models.{{cookiecutter.lowercase_modelname}} import ( {{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.camelcase_modelname}}ForCausalLM, {{cookiecutter.camelcase_modelname}}ForQuestionAnswering, {{cookiecutter.camelcase_modelname}}ForSequenceClassification, {{cookiecutter.camelcase_modelname}}Model, {{cookiecutter.camelcase_modelname}}PreTrainedModel, ) {% endif -%} # End. # Below: " # TensorFlow model imports" if generating TensorFlow # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" %} from .models.{{cookiecutter.lowercase_modelname}} import ( TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST, TF{{cookiecutter.camelcase_modelname}}ForMaskedLM, TF{{cookiecutter.camelcase_modelname}}ForCausalLM, TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice, TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification, TF{{cookiecutter.camelcase_modelname}}ForTokenClassification, TF{{cookiecutter.camelcase_modelname}}Layer, TF{{cookiecutter.camelcase_modelname}}Model, TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, ) {% else %} from .models.{{cookiecutter.lowercase_modelname}} import ( TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model, TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, ) {% endif -%} # End. # Below: " # Flax model imports" if generating Flax # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" %} from .models.{{cookiecutter.lowercase_modelname}} import ( Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM, Flax{{cookiecutter.camelcase_modelname}}ForCausalLM, Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification, Flax{{cookiecutter.camelcase_modelname}}Layer, Flax{{cookiecutter.camelcase_modelname}}Model, Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel, ) {% else %} from .models.{{cookiecutter.lowercase_modelname}} import ( Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, Flax{{cookiecutter.camelcase_modelname}}Model, Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel, ) {% endif -%} # End. # Below: " # Fast tokenizers imports" # Replace with: from .models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}TokenizerFast # End. # Below: " from .models.albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig" # Replace with: from .models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP, {{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}Tokenizer # End. # To replace in: "src/transformers/models/__init__.py" # Below: "from . import (" # Replace with: {{cookiecutter.lowercase_modelname}}, # End. # To replace in: "src/transformers/models/auto/configuration_auto.py" # Below: "# Add configs here" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}Config"), # End. # Below: "# Add archive maps here" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP"), # End. # Below: "# Add full (and cased) model names here" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}"), # End. # To replace in: "src/transformers/models/auto/modeling_auto.py" if generating PyTorch # Below: "# Base model mapping" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}Model"), # End. # Below: "# Model with LM heads mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMaskedLM"), {% else %} ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"), {% endif -%} # End. # Below: "# Model for Causal LM mapping" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForCausalLM"), # End. # Below: "# Model for Masked LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMaskedLM"), {% else -%} {% endif -%} # End. # Below: "# Model for Sequence Classification mapping" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForSequenceClassification"), # End. # Below: "# Model for Question Answering mapping" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"), # End. # Below: "# Model for Token Classification mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForTokenClassification"), {% else -%} {% endif -%} # End. # Below: "# Model for Multiple Choice mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMultipleChoice"), {% else -%} {% endif -%} # End. # Below: "# Model for Seq2Seq Causal LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} {% else %} ("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"), {% endif -%} # End. # To replace in: "src/transformers/models/auto/modeling_tf_auto.py" if generating TensorFlow # Below: "# Base model mapping" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}Model"), # End. # Below: "# Model with LM heads mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMaskedLM"), {% else %} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"), {% endif -%} # End. # Below: "# Model for Causal LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForCausalLM"), {% else -%} {% endif -%} # End. # Below: "# Model for Masked LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMaskedLM"), {% else -%} {% endif -%} # End. # Below: "# Model for Sequence Classification mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification"), {% else -%} {% endif -%} # End. # Below: "# Model for Question Answering mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"), {% else -%} {% endif -%} # End. # Below: "# Model for Token Classification mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForTokenClassification"), {% else -%} {% endif -%} # End. # Below: "# Model for Multiple Choice mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice"), {% else -%} {% endif -%} # End. # Below: "# Model for Seq2Seq Causal LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} {% else %} ("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"), {% endif -%} # End. # To replace in: "src/transformers/models/auto/modeling_flax_auto.py" if generating Flax # Below: "# Base model mapping" # Replace with: ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}Model"), # End. # Below: "# Model for Masked LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM"), {% else %} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"), {% endif -%} # End. # Below: "# Model for Causal LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForCausalLM"), {% else -%} {% endif -%} # End. # Below: "# Model for Masked LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM"), {% else -%} {% endif -%} # End. # Below: "# Model for Sequence Classification mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification"), {% else %} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification"), {% endif -%} # End. # Below: "# Model for Question Answering mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"), {% else %} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"), {% endif -%} # End. # Below: "# Model for Token Classification mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification"), {% else -%} {% endif -%} # End. # Below: "# Model for Multiple Choice mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice"), {% else -%} {% endif -%} # End. # Below: "# Model for Seq2Seq Causal LM mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} {% else %} ("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"), {% endif -%} # End. # To replace in: "utils/check_repo.py" if generating PyTorch # Below: "models to ignore for model xxx mapping" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} {% else -%} "{{cookiecutter.camelcase_modelname}}Encoder", "{{cookiecutter.camelcase_modelname}}Decoder", "{{cookiecutter.camelcase_modelname}}DecoderWrapper", {% endif -%} # End. # Below: "models to ignore for not tested" # Replace with: {% if cookiecutter.is_encoder_decoder_model == "False" -%} {% else -%} "{{cookiecutter.camelcase_modelname}}Encoder", # Building part of bigger (tested) model. "{{cookiecutter.camelcase_modelname}}Decoder", # Building part of bigger (tested) model. "{{cookiecutter.camelcase_modelname}}DecoderWrapper", # Building part of bigger (tested) model. {% endif -%} # End.

transformers/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/to_replace_{{cookiecutter.lowercase_modelname}}.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Team Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a clone of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import os import tempfile import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers import is_tensorflow_text_available, is_tf_available from transformers.testing_utils import require_tensorflow_text, require_tf, slow from ..test_modeling_tf_common import floats_tensor from .test_framework_agnostic import GenerationIntegrationTestsMixin if is_tf_available(): import tensorflow as tf from transformers import ( AutoTokenizer, TFAutoModelForCausalLM, TFAutoModelForSeq2SeqLM, TFAutoModelForSpeechSeq2Seq, TFAutoModelForVision2Seq, TFBartForConditionalGeneration, TFLogitsProcessorList, TFMinLengthLogitsProcessor, tf_top_k_top_p_filtering, ) from transformers.modeling_tf_utils import keras if is_tensorflow_text_available(): import tensorflow_text as text @require_tf class UtilsFunctionsTest(unittest.TestCase): # tests whether the top_k_top_p_filtering function behaves as expected def test_top_k_top_p_filtering(self): logits = tf.convert_to_tensor( [ [ 8.2220991, # 3rd highest value; idx. 0 -0.5620044, 5.23229752, 4.0386393, -6.8798378, -0.54785802, -3.2012153, 2.92777176, 1.88171953, 7.35341276, # 5th highest value; idx. 9 8.43207833, # 2nd highest value; idx. 10 -9.85711836, -5.96209236, -1.13039161, -7.1115294, -0.8369633, -5.3186408, 7.06427407, 0.81369344, -0.82023817, -5.9179796, 0.58813443, -6.99778438, 4.71551189, -0.18771637, 7.44020759, # 4th highest value; idx. 25 9.38450987, # 1st highest value; idx. 26 2.12662941, -9.32562038, 2.35652522, ], # cummulative prob of 5 highest values <= 0.6 [ 0.58425518, 4.53139238, -5.57510464, -6.28030699, -7.19529503, -4.02122551, 1.39337037, -6.06707057, 1.59480517, -9.643119, 0.03907799, 0.67231762, -8.88206726, 6.27115922, # 4th highest value; idx. 13 2.28520723, 4.82767506, 4.30421368, 8.8275313, # 2nd highest value; idx. 17 5.44029958, # 5th highest value; idx. 18 -4.4735794, 7.38579536, # 3rd highest value; idx. 20 -2.91051663, 2.61946077, -2.5674762, -9.48959302, -4.02922645, -1.35416918, 9.67702323, # 1st highest value; idx. 27 -5.89478553, 1.85370467, ], # cummulative prob of 5 highest values <= 0.6 ], dtype=tf.float32, ) non_inf_expected_idx = tf.convert_to_tensor( [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]], dtype=tf.int32, ) # expected non filtered idx as noted above non_inf_expected_output = tf.convert_to_tensor( [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023], dtype=tf.float32, ) # expected non filtered values as noted above output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4) non_inf_output = output[output != -float("inf")] non_inf_idx = tf.cast( tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))), dtype=tf.int32, ) tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12) tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx) @require_tf class TFGenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMixin): # setting framework_dependent_parameters needs to be gated, just like its contents' imports if is_tf_available(): framework_dependent_parameters = { "AutoModelForCausalLM": TFAutoModelForCausalLM, "AutoModelForSpeechSeq2Seq": TFAutoModelForSpeechSeq2Seq, "AutoModelForSeq2SeqLM": TFAutoModelForSeq2SeqLM, "AutoModelForVision2Seq": TFAutoModelForVision2Seq, "LogitsProcessorList": TFLogitsProcessorList, "MinLengthLogitsProcessor": TFMinLengthLogitsProcessor, "create_tensor_fn": tf.convert_to_tensor, "floats_tensor": floats_tensor, "return_tensors": "tf", } @slow def test_generate_tf_function_export_fixed_input_length(self): # TF-only test: tf.saved_model export test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2") input_length = 2 max_new_tokens = 2 class DummyModel(tf.Module): def __init__(self, model): super(DummyModel, self).__init__() self.model = model @tf.function( input_signature=( tf.TensorSpec((None, input_length), tf.int32, name="input_ids"), tf.TensorSpec((None, input_length), tf.int32, name="attention_mask"), ), jit_compile=True, ) def serving(self, input_ids, attention_mask): outputs = self.model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, return_dict_in_generate=True, ) return {"sequences": outputs["sequences"]} dummy_input_ids = [[2, 0], [102, 103]] dummy_attention_masks = [[1, 0], [1, 1]] dummy_model = DummyModel(model=test_model) with tempfile.TemporaryDirectory() as tmp_dir: tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving}) serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"] for batch_size in range(1, len(dummy_input_ids) + 1): inputs = { "input_ids": tf.constant(dummy_input_ids[:batch_size]), "attention_mask": tf.constant(dummy_attention_masks[:batch_size]), } tf_func_outputs = serving_func(**inputs)["sequences"] tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_new_tokens) tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs) @slow def test_generate_tf_function_export_fixed_batch_size(self): # TF-only test: tf.saved_model export test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2") batch_size = 1 max_new_tokens = 2 class DummyModel(tf.Module): def __init__(self, model): super(DummyModel, self).__init__() self.model = model @tf.function( input_signature=( tf.TensorSpec((batch_size, None), tf.int32, name="input_ids"), tf.TensorSpec((batch_size, None), tf.int32, name="attention_mask"), ), jit_compile=True, ) def serving(self, input_ids, attention_mask): outputs = self.model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, return_dict_in_generate=True, ) return {"sequences": outputs["sequences"]} dummy_input_ids = [[2], [102, 103]] dummy_attention_masks = [[1], [1, 1]] dummy_model = DummyModel(model=test_model) with tempfile.TemporaryDirectory() as tmp_dir: tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving}) serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"] for input_row in range(len(dummy_input_ids)): inputs = { "input_ids": tf.constant([dummy_input_ids[input_row]]), "attention_mask": tf.constant([dummy_attention_masks[input_row]]), } tf_func_outputs = serving_func(**inputs)["sequences"] tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_new_tokens) tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs) @slow @require_tensorflow_text def test_generate_tf_function_export_with_tf_tokenizer(self): # TF-only test: tf.saved_model export with tempfile.TemporaryDirectory() as tmp_dir: # file needed to load the TF tokenizer hf_hub_download(repo_id="google/flan-t5-small", filename="spiece.model", local_dir=tmp_dir) class CompleteSentenceTransformer(keras.layers.Layer): def __init__(self): super().__init__() self.tokenizer = text.SentencepieceTokenizer( model=tf.io.gfile.GFile(os.path.join(tmp_dir, "spiece.model"), "rb").read() ) self.model = TFAutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-t5") def call(self, inputs, *args, **kwargs): tokens = self.tokenizer.tokenize(inputs) input_ids, attention_mask = text.pad_model_inputs( tokens, max_seq_length=64, pad_value=self.model.config.pad_token_id ) outputs = self.model.generate(input_ids=input_ids, attention_mask=attention_mask) return self.tokenizer.detokenize(outputs) complete_model = CompleteSentenceTransformer() inputs = keras.layers.Input(shape=(1,), dtype=tf.string, name="inputs") outputs = complete_model(inputs) keras_model = keras.Model(inputs, outputs) keras_model.save(tmp_dir) def test_eos_token_id_int_and_list_top_k_top_sampling(self): # Has PT equivalent: this test relies on random sampling generation_kwargs = { "do_sample": True, "num_beams": 1, "top_p": 0.7, "top_k": 10, "temperature": 0.7, } expectation = 14 tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2") text = """Hello, my dog is cute and""" tokens = tokenizer(text, return_tensors="tf") model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2") eos_token_id = 638 # forces the generation to happen on CPU, to avoid GPU-related quirks with tf.device(":/CPU:0"): tf.random.set_seed(0) generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs) self.assertTrue(expectation == len(generated_tokens[0])) eos_token_id = [638, 198] with tf.device(":/CPU:0"): tf.random.set_seed(0) generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs) self.assertTrue(expectation == len(generated_tokens[0])) def test_model_kwarg_encoder_signature_filtering(self): # Has PT equivalent: ample use of framework-specific code bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") article = """Hugging Face is a technology company based in New York and Paris.""" input_ids = bart_tokenizer(article, return_tensors="tf").input_ids bart_model = TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart") output = bart_model.generate(input_ids).numpy() # Let's create a fake model that has a different signature. In particular, this fake model accepts "foo" as an # argument. Because "foo" is not in the encoder signature and doesn't start with "decoder_", it will be part of # the encoder kwargs prior to signature filtering, which would lead to an exception. But filtering kicks in and # saves the day. class FakeBart(TFBartForConditionalGeneration): def call(self, input_ids, foo=None, **kwargs): return super().call(input_ids, **kwargs) bart_model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart") fake_output = bart_model.generate(input_ids, foo="bar").numpy() self.assertTrue(np.array_equal(output, fake_output)) # Encoder signature filtering only kicks in if it doesn't accept wildcard kwargs. The following test will fail # because it doesn't do signature filtering. class FakeEncoder(bart_model.model.encoder.__class__): def call(self, input_ids, **kwargs): return super().call(input_ids, **kwargs) fake_encoder = FakeEncoder(bart_model.config, bart_model.model.shared) bart_model.model.encoder = fake_encoder # Normal generation still works (the output will be different because the encoder weights are different) fake_output = bart_model.generate(input_ids).numpy() with self.assertRaises(ValueError): # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo" bart_model.generate(input_ids, foo="bar")

transformers/tests/generation/test_tf_utils.py/0

# coding=utf-8 # Copyright 2021, The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch BART model. """ import copy import tempfile import unittest import timeout_decorator # noqa from transformers import BartConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, slow, torch_device, ) from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( AutoModelForSequenceClassification, BartForCausalLM, BartForConditionalGeneration, BartForQuestionAnswering, BartForSequenceClassification, BartModel, BartTokenizer, pipeline, ) from transformers.models.bart.modeling_bart import BartDecoder, BartEncoder, shift_tokens_right def prepare_bart_inputs_dict( config, input_ids, decoder_input_ids=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id) if head_mask is None: head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device) if decoder_head_mask is None: decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class BartModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id # forcing a certain token to be generated, sets all other tokens to -inf # if however the token to be generated is already at -inf then it can lead token # `nan` values and thus break generation self.forced_bos_token_id = None self.forced_eos_token_id = None def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp( 3, ) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.get_config() inputs_dict = prepare_bart_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return BartConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, forced_bos_token_id=self.forced_bos_token_id, forced_eos_token_id=self.forced_eos_token_id, ) def get_pipeline_config(self): config = self.get_config() config.max_position_embeddings = 100 config.vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BartModel(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] head_mask = inputs_dict["head_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = BartModel(config=config).to(torch_device).eval() outputs = model(**inputs_dict) encoder_last_hidden_state = outputs.encoder_last_hidden_state last_hidden_state = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: encoder = model.get_encoder() encoder.save_pretrained(tmpdirname) encoder = BartEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[ 0 ] self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = BartDecoder.from_pretrained(tmpdirname).to(torch_device) last_hidden_state_2 = decoder( input_ids=inputs_dict["decoder_input_ids"], attention_mask=inputs_dict["decoder_attention_mask"], encoder_hidden_states=encoder_last_hidden_state, encoder_attention_mask=inputs_dict["attention_mask"], )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) @require_torch class BartHeadTests(unittest.TestCase): vocab_size = 99 def _get_config_and_data(self): input_ids = torch.tensor( [ [71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 82, 2], [5, 97, 17, 39, 94, 40, 2], [76, 83, 94, 25, 70, 78, 2], [87, 59, 41, 35, 48, 66, 2], [55, 13, 16, 58, 5, 2, 1], # note padding [64, 27, 31, 51, 12, 75, 2], [52, 64, 86, 17, 83, 39, 2], [48, 61, 9, 24, 71, 82, 2], [26, 1, 60, 48, 22, 13, 2], [21, 5, 62, 28, 14, 76, 2], [45, 98, 37, 86, 59, 48, 2], [70, 70, 50, 9, 28, 0, 2], ], dtype=torch.long, device=torch_device, ) batch_size = input_ids.shape[0] config = BartConfig( vocab_size=self.vocab_size, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, ) return config, input_ids, batch_size def test_sequence_classification_forward(self): config, input_ids, batch_size = self._get_config_and_data() labels = _long_tensor([2] * batch_size).to(torch_device) model = BartForSequenceClassification(config) model.to(torch_device) outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, labels=labels) expected_shape = torch.Size((batch_size, config.num_labels)) self.assertEqual(outputs["logits"].shape, expected_shape) self.assertIsInstance(outputs["loss"].item(), float) def test_question_answering_forward(self): config, input_ids, batch_size = self._get_config_and_data() sequence_labels = ids_tensor([batch_size], 2).to(torch_device) model = BartForQuestionAnswering(config) model.to(torch_device) outputs = model( input_ids=input_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.assertEqual(outputs["start_logits"].shape, input_ids.shape) self.assertEqual(outputs["end_logits"].shape, input_ids.shape) self.assertIsInstance(outputs["loss"].item(), float) @timeout_decorator.timeout(1) def test_lm_forward(self): config, input_ids, batch_size = self._get_config_and_data() lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size).to(torch_device) lm_model = BartForConditionalGeneration(config) lm_model.to(torch_device) outputs = lm_model(input_ids=input_ids, labels=lm_labels) expected_shape = (batch_size, input_ids.shape[1], config.vocab_size) self.assertEqual(outputs["logits"].shape, expected_shape) self.assertIsInstance(outputs["loss"].item(), float) def test_lm_uneven_forward(self): config = BartConfig( vocab_size=self.vocab_size, d_model=14, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=8, decoder_ffn_dim=8, max_position_embeddings=48, ) lm_model = BartForConditionalGeneration(config).to(torch_device) context = torch.tensor( [[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], device=torch_device, dtype=torch.long ) summary = torch.tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], device=torch_device, dtype=torch.long) outputs = lm_model(input_ids=context, decoder_input_ids=summary, labels=summary) expected_shape = (*summary.shape, config.vocab_size) self.assertEqual(outputs["logits"].shape, expected_shape) def test_generate_beam_search(self): input_ids = torch.tensor([[71, 82, 2], [68, 34, 2]], device=torch_device, dtype=torch.long) config = BartConfig( vocab_size=self.vocab_size, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, ) lm_model = BartForConditionalGeneration(config).to(torch_device) lm_model.eval() max_length = 5 generated_ids = lm_model.generate( input_ids.clone(), do_sample=True, num_return_sequences=1, num_beams=2, no_repeat_ngram_size=3, max_length=max_length, ) self.assertEqual(generated_ids.shape, (input_ids.shape[0], max_length)) def test_shift_tokens_right(self): input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long) shifted = shift_tokens_right(input_ids, 1, 2) n_pad_before = input_ids.eq(1).float().sum() n_pad_after = shifted.eq(1).float().sum() self.assertEqual(shifted.shape, input_ids.shape) self.assertEqual(n_pad_after, n_pad_before - 1) self.assertTrue(torch.eq(shifted[:, 0], 2).all()) @slow def test_tokenization(self): tokenizer = BartTokenizer.from_pretrained("facebook/bart-large") examples = [" Hello world", " DomDramg"] # need leading spaces for equality fairseq_results = [ torch.tensor([0, 20920, 232, 2]), torch.tensor([0, 11349, 495, 4040, 571, 2]), ] for ex, desired_result in zip(examples, fairseq_results): bart_toks = tokenizer.encode(ex, return_tensors="pt").squeeze() assert_tensors_close(desired_result.long(), bart_toks, prefix=ex) @require_torch_fp16 def test_generate_fp16(self): config, input_ids, batch_size = self._get_config_and_data() attention_mask = input_ids.ne(1).to(torch_device) model = BartForConditionalGeneration(config).eval().to(torch_device) model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_dummy_inputs(self): config, *_ = self._get_config_and_data() model = BartForConditionalGeneration(config).eval().to(torch_device) model(**model.dummy_inputs) def test_resize_tokens_embeddings_more(self): config, input_ids, _ = self._get_config_and_data() def _get_embs(m): return (m.get_input_embeddings().weight.data.clone(), m.get_output_embeddings().weight.data.clone()) model = BartForConditionalGeneration(config).eval().to(torch_device) input, output = _get_embs(model) self.assertTrue(torch.eq(input, output).all()) new_vocab_size = 45 model.resize_token_embeddings(new_vocab_size) input_new, output_new = _get_embs(model) self.assertEqual(input_new.shape, (new_vocab_size, config.d_model)) self.assertEqual(output_new.shape, (new_vocab_size, config.d_model)) self.assertTrue(torch.eq(input_new, output_new).all()) @require_torch class BartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (BartModel, BartForConditionalGeneration, BartForSequenceClassification, BartForQuestionAnswering) if is_torch_available() else () ) all_generative_model_classes = (BartForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "conversational": BartForConditionalGeneration, "feature-extraction": BartModel, "fill-mask": BartForConditionalGeneration, "question-answering": BartForQuestionAnswering, "summarization": BartForConditionalGeneration, "text-classification": BartForSequenceClassification, "text-generation": BartForCausalLM, "text2text-generation": BartForConditionalGeneration, "translation": BartForConditionalGeneration, "zero-shot": BartForSequenceClassification, } if is_torch_available() else {} ) is_encoder_decoder = True fx_compatible = False # Fix me Michael test_pruning = False def setUp(self): self.model_tester = BartModelTester(self) self.config_tester = ConfigTester(self, config_class=BartConfig) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_encoder_decoder_model_standalone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs) # BartForSequenceClassification does not support inputs_embeds def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in (BartModel, BartForConditionalGeneration, BartForQuestionAnswering): model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) wte = model.get_input_embeddings() if not self.is_encoder_decoder: inputs["inputs_embeds"] = wte(input_ids) else: inputs["inputs_embeds"] = wte(encoder_input_ids) inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = BartForConditionalGeneration(config).eval().to(torch_device) model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) @unittest.skip("Does not support conversations.") def test_pipeline_conversational(self): pass def assert_tensors_close(a, b, atol=1e-12, prefix=""): """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if torch.allclose(a, b, atol=atol): return True raise except Exception: pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item() if a.numel() > 100: msg = f"tensor values are {pct_different:.1%} percent different." else: msg = f"{a} != {b}" if prefix: msg = prefix + ": " + msg raise AssertionError(msg) def _long_tensor(tok_lst): return torch.tensor(tok_lst, dtype=torch.long, device=torch_device) @require_torch @slow class FastIntegrationTests(unittest.TestCase): """These tests are useful for debugging since they operate on a model with 1 encoder layer and 1 decoder layer.""" @cached_property def tok(self): return BartTokenizer.from_pretrained("facebook/bart-large") @cached_property def xsum_1_1_model(self): return BartForConditionalGeneration.from_pretrained("sshleifer/distilbart-xsum-1-1") def test_xsum_1_1_generation(self): hf = self.xsum_1_1_model tok = self.tok ARTICLE = ( "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes." ) EXPECTED = ( " The International Criminal Court (ICC) has announced that it has been announced by the International" " Criminal court." ) dct = tok(ARTICLE, return_tensors="pt") generated_ids = hf.generate(**dct, num_beams=4) result = tok.batch_decode(generated_ids, skip_special_tokens=True)[0] assert EXPECTED == result def test_xsum_1_1_batch_generation(self): # test batch batch = self.tok( [ "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories." " The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is" " based. The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted" ' its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including' ' East Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination' " into the situation in Palestinian territories, paving the way for possible war crimes investigations" " against Israelis. As members of the court, Palestinians may be subject to counter-charges as well." " Israel and the United States, neither of which is an ICC member, opposed the Palestinians' efforts" " to join the body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony," ' said it was a move toward greater justice. "As Palestine formally becomes a State Party to the Rome' ' Statute today, the world is also a step closer to ending a long era of impunity and injustice," he' ' said, according to an ICC news release. "Indeed, today brings us closer to our shared goals of' ' justice and peace." Judge Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was' ' just the first step for the Palestinians. "As the Rome Statute today enters into force for the State' " of Palestine, Palestine acquires all the rights as well as responsibilities that come with being a" ' State Party to the Statute. These are substantive commitments, which cannot be taken lightly," she' ' said. Rights group Human Rights Watch welcomed the development. "Governments seeking to penalize' " Palestine for joining the ICC should immediately end their pressure, and countries that support" " universal acceptance of the court's treaty should speak out to welcome its membership,\" said" " Balkees Jarrah, international justice counsel for the group. \"What's objectionable is the attempts" " to undermine international justice, not Palestine's decision to join a treaty to which over 100" ' countries around the world are members." In January, when the preliminary ICC examination was' " opened, Israeli Prime Minister Benjamin Netanyahu described it as an outrage, saying the court was" ' overstepping its boundaries. The United States also said it "strongly" disagreed with the court\'s' ' decision. "As we have said repeatedly, we do not believe that Palestine is a state and therefore we' ' do not believe that it is eligible to join the ICC," the State Department said in a statement. It' ' urged the warring sides to resolve their differences through direct negotiations. "We will continue' ' to oppose actions against Israel at the ICC as counterproductive to the cause of peace," it said.' " But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows' " the court to review evidence and determine whether to investigate suspects on both sides. Prosecutor" ' Fatou Bensouda said her office would "conduct its analysis in full independence and impartiality."' " The war between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The" " inquiry will include alleged war crimes committed since June. The International Criminal Court was" " set up in 2002 to prosecute genocide, crimes against humanity and war crimes.", "The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted" " Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor" ' Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A' " person who has such a video needs to immediately give it to the investigators.\" Robin's comments" " follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the" " French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was" " recovered from a phone at the wreckage site. The two publications described the supposed video, but" " did not post it on their websites. The publications said that they watched the video, which was" " found by a source close to the investigation. \"One can hear cries of 'My God' in several" ' languages," Paris Match reported. "Metallic banging can also be heard more than three times, perhaps' " of the pilot trying to open the cockpit door with a heavy object. Towards the end, after a heavy" ' shake, stronger than the others, the screaming intensifies. Then nothing." "It is a very disturbing' " scene,\" said Julian Reichelt, editor-in-chief of Bild online. An official with France's accident" " investigation agency, the BEA, said the agency is not aware of any such video. Lt. Col. Jean-Marc" " Menichini, a French Gendarmerie spokesman in charge of communications on rescue efforts around the" ' Germanwings crash site, told CNN that the reports were "completely wrong" and "unwarranted." Cell' ' phones have been collected at the site, he said, but that they "hadn\'t been exploited yet."' " Menichini said he believed the cell phones would need to be sent to the Criminal Research Institute" " in Rosny sous-Bois, near Paris, in order to be analyzed by specialized technicians working" " hand-in-hand with investigators. But none of the cell phones found so far have been sent to the" " institute, Menichini said. Asked whether staff involved in the search could have leaked a memory" ' card to the media, Menichini answered with a categorical "no." Reichelt told "Erin Burnett:' ' Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match are' ' "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is' " something we did not know before. ... Overall we can say many things of the investigation weren't" ' revealed by the investigation at the beginning," he said. What was mental state of Germanwings' " co-pilot? German airline Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled" " depression years before he took the controls of Germanwings Flight 9525, which he's accused of" " deliberately crashing last week in the French Alps. Lubitz told his Lufthansa flight training school" ' in 2009 that he had a "previous episode of severe depression," the airline said Tuesday. Email' " correspondence between Lubitz and the school discovered in an internal investigation, Lufthansa" " said, included medical documents he submitted in connection with resuming his flight training. The" " announcement indicates that Lufthansa, the parent company of Germanwings, knew of Lubitz's battle" " with depression, allowed him to continue training and ultimately put him in the cockpit. Lufthansa," " whose CEO Carsten Spohr previously said Lubitz was 100% fit to fly, described its statement Tuesday" ' as a "swift and seamless clarification" and said it was sharing the information and documents --' " including training and medical records -- with public prosecutors. Spohr traveled to the crash site" " Wednesday, where recovery teams have been working for the past week to recover human remains and" " plane debris scattered across a steep mountainside. He saw the crisis center set up in" " Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash site, where grieving" " families have left flowers at a simple stone memorial. Menichini told CNN late Tuesday that no" " visible human remains were left at the site but recovery teams would keep searching. French" " President Francois Hollande, speaking Tuesday, said that it should be possible to identify all the" " victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini" " said. Among those personal belongings could be more cell phones belonging to the 144 passengers and" " six crew on board. Check out the latest from our correspondents . The details about Lubitz's" " correspondence with the flight school during his training were among several developments as" " investigators continued to delve into what caused the crash and Lubitz's possible motive for" " downing the jet. A Lufthansa spokesperson told CNN on Tuesday that Lubitz had a valid medical" ' certificate, had passed all his examinations and "held all the licenses required." Earlier, a' " spokesman for the prosecutor's office in Dusseldorf, Christoph Kumpa, said medical records reveal" " Lubitz suffered from suicidal tendencies at some point before his aviation career and underwent" " psychotherapy before he got his pilot's license. Kumpa emphasized there's no evidence suggesting" " Lubitz was suicidal or acting aggressively before the crash. Investigators are looking into whether" " Lubitz feared his medical condition would cause him to lose his pilot's license, a European" ' government official briefed on the investigation told CNN on Tuesday. While flying was "a big part' " of his life,\" the source said, it's only one theory being considered. Another source, a law" " enforcement official briefed on the investigation, also told CNN that authorities believe the" " primary motive for Lubitz to bring down the plane was that he feared he would not be allowed to fly" " because of his medical problems. Lubitz's girlfriend told investigators he had seen an eye doctor" " and a neuropsychologist, both of whom deemed him unfit to work recently and concluded he had" " psychological issues, the European government official said. But no matter what details emerge about" " his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the' " fact that maybe they weren't going to keep doing their job and they're upset about that and so" ' they\'re suicidal," he said. "But there is no mental illness that explains why somebody then feels' " entitled to also take that rage and turn it outward on 149 other people who had nothing to do with" " the person's problems.\" Germanwings crash compensation: What we know . Who was the captain of" " Germanwings Flight 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from" " Dusseldorf, while Laura Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff," " Antonia Mortensen, Sandrine Amiel and Anna-Maja Rappard contributed to this report.", ], return_tensors="pt", padding="longest", truncation=True, ) generated_ids = self.xsum_1_1_model.generate(**batch, num_beams=4) result = self.tok.batch_decode(generated_ids, skip_special_tokens=True) assert ( result[0] == " The International Criminal Court (ICC) has announced that it has been announced by the International" " Criminal court." ) assert ( result[1] == " An investigation into the crash that killed at least 10 people in the French capital has been" " released by the French police investigating the crash." ) def test_encoder_equiv(self): # test batch batch = self.tok( [ "The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories." " The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is" " based. The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted" ' its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including' ' East Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination' " into the situation in Palestinian territories, paving the way for possible war crimes investigations" " against Israelis. As members of the court, Palestinians may be subject to counter-charges as well." " Israel and the United States, neither of which is an ICC member, opposed the Palestinians' efforts" " to join the body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony," ' said it was a move toward greater justice. "As Palestine formally becomes a State Party to the Rome' ' Statute today, the world is also a step closer to ending a long era of impunity and injustice," he' ' said, according to an ICC news release. "Indeed, today brings us closer to our shared goals of' ' justice and peace." Judge Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was' ' just the first step for the Palestinians. "As the Rome Statute today enters into force for the State' " of Palestine, Palestine acquires all the rights as well as responsibilities that come with being a" ' State Party to the Statute. These are substantive commitments, which cannot be taken lightly," she' ' said. Rights group Human Rights Watch welcomed the development. "Governments seeking to penalize' " Palestine for joining the ICC should immediately end their pressure, and countries that support" " universal acceptance of the court's treaty should speak out to welcome its membership,\" said" " Balkees Jarrah, international justice counsel for the group. \"What's objectionable is the attempts" " to undermine international justice, not Palestine's decision to join a treaty to which over 100" ' countries around the world are members." In January, when the preliminary ICC examination was' " opened, Israeli Prime Minister Benjamin Netanyahu described it as an outrage, saying the court was" ' overstepping its boundaries. The United States also said it "strongly" disagreed with the court\'s' ' decision. "As we have said repeatedly, we do not believe that Palestine is a state and therefore we' ' do not believe that it is eligible to join the ICC," the State Department said in a statement. It' ' urged the warring sides to resolve their differences through direct negotiations. "We will continue' ' to oppose actions against Israel at the ICC as counterproductive to the cause of peace," it said.' " But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows' " the court to review evidence and determine whether to investigate suspects on both sides. Prosecutor" ' Fatou Bensouda said her office would "conduct its analysis in full independence and impartiality."' " The war between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The" " inquiry will include alleged war crimes committed since June. The International Criminal Court was" " set up in 2002 to prosecute genocide, crimes against humanity and war crimes.", "The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted" " Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor" ' Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A' " person who has such a video needs to immediately give it to the investigators.\" Robin's comments" " follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the" " French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was" " recovered from a phone at the wreckage site. The two publications described the supposed video, but" " did not post it on their websites. The publications said that they watched the video, which was" " found by a source close to the investigation. \"One can hear cries of 'My God' in several" ' languages," Paris Match reported. "Metallic banging can also be heard more than three times, perhaps' " of the pilot trying to open the cockpit door with a heavy object. Towards the end, after a heavy" ' shake, stronger than the others, the screaming intensifies. Then nothing." "It is a very disturbing' " scene,\" said Julian Reichelt, editor-in-chief of Bild online. An official with France's accident" " investigation agency, the BEA, said the agency is not aware of any such video. Lt. Col. Jean-Marc" " Menichini, a French Gendarmerie spokesman in charge of communications on rescue efforts around the" ' Germanwings crash site, told CNN that the reports were "completely wrong" and "unwarranted." Cell' ' phones have been collected at the site, he said, but that they "hadn\'t been exploited yet."' " Menichini said he believed the cell phones would need to be sent to the Criminal Research Institute" " in Rosny sous-Bois, near Paris, in order to be analyzed by specialized technicians working" " hand-in-hand with investigators. But none of the cell phones found so far have been sent to the" " institute, Menichini said. Asked whether staff involved in the search could have leaked a memory" ' card to the media, Menichini answered with a categorical "no." Reichelt told "Erin Burnett:' ' Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match are' ' "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is' " something we did not know before. ... Overall we can say many things of the investigation weren't" ' revealed by the investigation at the beginning," he said. What was mental state of Germanwings' " co-pilot? German airline Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled" " depression years before he took the controls of Germanwings Flight 9525, which he's accused of" " deliberately crashing last week in the French Alps. Lubitz told his Lufthansa flight training school" ' in 2009 that he had a "previous episode of severe depression," the airline said Tuesday. Email' " correspondence between Lubitz and the school discovered in an internal investigation, Lufthansa" " said, included medical documents he submitted in connection with resuming his flight training. The" " announcement indicates that Lufthansa, the parent company of Germanwings, knew of Lubitz's battle" " with depression, allowed him to continue training and ultimately put him in the cockpit. Lufthansa," " whose CEO Carsten Spohr previously said Lubitz was 100% fit to fly, described its statement Tuesday" ' as a "swift and seamless clarification" and said it was sharing the information and documents --' " including training and medical records -- with public prosecutors. Spohr traveled to the crash site" " Wednesday, where recovery teams have been working for the past week to recover human remains and" " plane debris scattered across a steep mountainside. He saw the crisis center set up in" " Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash site, where grieving" " families have left flowers at a simple stone memorial. Menichini told CNN late Tuesday that no" " visible human remains were left at the site but recovery teams would keep searching. French" " President Francois Hollande, speaking Tuesday, said that it should be possible to identify all the" " victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini" " said. Among those personal belongings could be more cell phones belonging to the 144 passengers and" " six crew on board. Check out the latest from our correspondents . The details about Lubitz's" " correspondence with the flight school during his training were among several developments as" " investigators continued to delve into what caused the crash and Lubitz's possible motive for" " downing the jet. A Lufthansa spokesperson told CNN on Tuesday that Lubitz had a valid medical" ' certificate, had passed all his examinations and "held all the licenses required." Earlier, a' " spokesman for the prosecutor's office in Dusseldorf, Christoph Kumpa, said medical records reveal" " Lubitz suffered from suicidal tendencies at some point before his aviation career and underwent" " psychotherapy before he got his pilot's license. Kumpa emphasized there's no evidence suggesting" " Lubitz was suicidal or acting aggressively before the crash. Investigators are looking into whether" " Lubitz feared his medical condition would cause him to lose his pilot's license, a European" ' government official briefed on the investigation told CNN on Tuesday. While flying was "a big part' " of his life,\" the source said, it's only one theory being considered. Another source, a law" " enforcement official briefed on the investigation, also told CNN that authorities believe the" " primary motive for Lubitz to bring down the plane was that he feared he would not be allowed to fly" " because of his medical problems. Lubitz's girlfriend told investigators he had seen an eye doctor" " and a neuropsychologist, both of whom deemed him unfit to work recently and concluded he had" " psychological issues, the European government official said. But no matter what details emerge about" " his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the' " fact that maybe they weren't going to keep doing their job and they're upset about that and so" ' they\'re suicidal," he said. "But there is no mental illness that explains why somebody then feels' " entitled to also take that rage and turn it outward on 149 other people who had nothing to do with" " the person's problems.\" Germanwings crash compensation: What we know . Who was the captain of" " Germanwings Flight 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from" " Dusseldorf, while Laura Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff," " Antonia Mortensen, Sandrine Amiel and Anna-Maja Rappard contributed to this report.", ], return_tensors="pt", padding="longest", truncation=True, ) features = self.xsum_1_1_model.get_encoder()(**batch).last_hidden_state expected = [[-0.0828, -0.0251, -0.0674], [0.1277, 0.3311, -0.0255], [0.2613, -0.0840, -0.2763]] assert_tensors_close(features[0, :3, :3], torch.tensor(expected), atol=1e-3) @require_torch @require_sentencepiece @require_tokenizers class BartModelIntegrationTests(unittest.TestCase): @cached_property def default_tokenizer(self): return BartTokenizer.from_pretrained("facebook/bart-large") @slow def test_inference_no_head(self): model = BartModel.from_pretrained("facebook/bart-large").to(torch_device) input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) attention_mask = input_ids.ne(model.config.pad_token_id) with torch.no_grad(): output = model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state expected_shape = torch.Size((1, 11, 1024)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], device=torch_device ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)) @slow def test_base_mask_filling(self): pbase = pipeline(task="fill-mask", model="facebook/bart-base") src_text = [" I went to the <mask>."] results = [x["token_str"] for x in pbase(src_text)] assert " bathroom" in results @slow def test_large_mask_filling(self): plarge = pipeline(task="fill-mask", model="facebook/bart-large") src_text = [" I went to the <mask>."] results = [x["token_str"] for x in plarge(src_text)] expected_results = [" bathroom", " gym", " wrong", " movies", " hospital"] self.assertListEqual(results, expected_results) @slow def test_mnli_inference(self): example_b = [0, 31414, 232, 328, 740, 1140, 69, 46078, 1588, 2, 1] input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2], example_b]) model = AutoModelForSequenceClassification.from_pretrained("facebook/bart-large-mnli").to( torch_device ) # eval called in from_pre attention_mask = input_ids.ne(model.config.pad_token_id) # Test that model hasn't changed with torch.no_grad(): outputs = model(input_ids=input_ids, attention_mask=attention_mask) batched_logits = outputs.logits expected_shape = torch.Size((2, 3)) self.assertEqual(batched_logits.shape, expected_shape) expected_slice = torch.tensor([[0.1907, 1.4342, -1.0289]], device=torch_device) logits_arr = batched_logits[0].detach() # Test that padding does not change results input_ids_no_pad = _long_tensor([example_b[:-1]]) attention_mask_no_pad = input_ids_no_pad.ne(model.config.pad_token_id) with torch.no_grad(): logits2 = model(input_ids=input_ids_no_pad, attention_mask=attention_mask_no_pad).logits.squeeze() assert_tensors_close(batched_logits[1], logits2, atol=1e-3) assert_tensors_close(expected_slice, logits_arr, atol=1e-3) @slow def test_xsum_summarization_same_as_fairseq(self): model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-xsum").to(torch_device) tok = self.default_tokenizer PGE_ARTICLE = """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""" EXPECTED_SUMMARY = ( "California's largest power company has begun shutting off electricity to thousands of customers in the" " state." ) dct = tok.batch_encode_plus( [PGE_ARTICLE], max_length=1024, padding="max_length", truncation=True, return_tensors="pt", ).to(torch_device) hypotheses_batch = model.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], num_beams=2, max_length=62, min_length=11, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True, decoder_start_token_id=model.config.eos_token_id, ) decoded = tok.batch_decode( hypotheses_batch, skip_special_tokens=True, ) self.assertEqual(EXPECTED_SUMMARY, decoded[0]) def test_xsum_config_generation_params(self): config = BartConfig.from_pretrained("facebook/bart-large-xsum") expected_params = {"num_beams": 6, "do_sample": False, "early_stopping": True, "length_penalty": 1.0} config_params = {k: getattr(config, k, "MISSING") for k, v in expected_params.items()} self.assertDictEqual(expected_params, config_params) @slow def test_cnn_summarization_same_as_fairseq(self): hf = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn").to(torch_device) tok = BartTokenizer.from_pretrained("facebook/bart-large") FRANCE_ARTICLE = ( # @noq " Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings" " Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane." ' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."' ' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s' " comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French" " Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a" " phone at the wreckage site. The two publications described the supposed video, but did not post it on" " their websites. The publications said that they watched the video, which was found by a source close to" " the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported." ' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the' " cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the" ' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,' " editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said" " the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman" " in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the" ' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,' ' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be' " sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by" " specialized technicians working hand-in-hand with investigators. But none of the cell phones found so" " far have been sent to the institute, Menichini said. Asked whether staff involved in the search could" ' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin' ' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match' ' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is something' " we did not know before. ... Overall we can say many things of the investigation weren't revealed by the" ' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline' " Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the" " controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the" ' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of' ' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school' " discovered in an internal investigation, Lufthansa said, included medical documents he submitted in" " connection with resuming his flight training. The announcement indicates that Lufthansa, the parent" " company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and" " ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%" ' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was' " sharing the information and documents -- including training and medical records -- with public" " prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the" " past week to recover human remains and plane debris scattered across a steep mountainside. He saw the" " crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash" " site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late" " Tuesday that no visible human remains were left at the site but recovery teams would keep searching." " French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all" " the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said." " Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew" " on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with" " the flight school during his training were among several developments as investigators continued to" " delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa" " spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his" ' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in' " Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at" " some point before his aviation career and underwent psychotherapy before he got his pilot's license." " Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the" " crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to" " lose his pilot's license, a European government official briefed on the investigation told CNN on" ' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being' " considered. Another source, a law enforcement official briefed on the investigation, also told CNN that" " authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would" " not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had" " seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded" " he had psychological issues, the European government official said. But no matter what details emerge" " about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact' " that maybe they weren't going to keep doing their job and they're upset about that and so they're" ' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to' " also take that rage and turn it outward on 149 other people who had nothing to do with the person's" ' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight' " 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura" " Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine" " Amiel and Anna-Maja Rappard contributed to this report." ) SHORTER_ARTICLE = ( " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder" " and Faith Karimi contributed to this report." ) # The below article tests that we don't add any hypotheses outside of the top n_beams IRAN_ARTICLE = ( " (CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran" " in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively" " block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger." " Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli" " Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a" " letter to the Iranian leadership warning them away from a deal. The debate that has already begun since" " the announcement of the new framework will likely result in more heat than light. It will not be helped" " by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ." " The most misleading assertion, despite universal rejection by experts, is that the negotiations'" " objective at the outset was the total elimination of any nuclear program in Iran. That is the position" " of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it" " had been, there would have been no Iranian team at the negotiating table. Rather, the objective has" " always been to structure an agreement or series of agreements so that Iran could not covertly develop a" " nuclear arsenal before the United States and its allies could respond. The new framework has exceeded" " expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by" " two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another" " dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite" " sharp accusations by some in the United States and its allies, Iran denies having such a program, and" " U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's" " continued cooperation with International Atomic Energy Agency inspections is further evidence on this" " point, and we'll know even more about Iran's program in the coming months and years because of the deal." " In fact, the inspections provisions that are part of this agreement are designed to protect against any" " covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that" " the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter" " warning that a deal might be killed by Congress or a future president). This of course is not the case." " The talks were between Iran and the five permanent members of the U.N. Security Council (United States," " United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has" " played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement" " reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran" " and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement" " contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the" " case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased" " or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes" " Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear" " sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going" " forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such" " a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the" ' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not' " suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New" " START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement" " with Iran will not be so balanced. The restrictions and obligations in the final framework agreement" " will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove" " most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally" " some insist that any agreement must address Iranian missile programs, human rights violations or support" " for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are" " unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in" " the negotiations would be a poison pill. This agreement should be judged on its merits and on how it" " affects the security of our negotiating partners and allies, including Israel. Those judgments should be" " fact-based, not based on questionable assertions or dubious assumptions." ) ARTICLE_SUBWAY = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) dct = tok.batch_encode_plus( [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY], max_length=1024, padding="max_length", truncation_strategy="only_first", truncation=True, return_tensors="pt", ) self.assertEqual(1024, dct["input_ids"].shape[1]) hypotheses_batch = hf.generate( input_ids=dct["input_ids"].to(torch_device), attention_mask=dct["attention_mask"].to(torch_device), num_beams=2, ) assert hypotheses_batch[:, 1].eq(0).all().item() EXPECTED = [ "A French prosecutor says he is not aware of any video footage from on board the plane. Two German " "magazines claim to have found a cell phone video showing the crash. The publications say they watched " "the video, which was found by a source close to the investigation. All 150 on board Germanwings Flight " "9525 were killed.", "Palestinian Authority becomes 123rd member of the International Criminal Court. The move gives the court " "jurisdiction over alleged crimes in Palestinian territories. Israel and the United States opposed the " "Palestinians' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki said it was a " "move toward greater justice.", "U.S. and its negotiating partners reached a strong framework agreement with Iran. Peter Bergen: The " "debate that has already begun will likely result in more heat than light. He says critics have made " "dubious assumptions and doubtful assertions. Bergen says the goal was to block Iran from building a " "nuclear weapon.", "Liana Barrientos, 39, has been married 10 times, sometimes within two weeks of each other. Prosecutors " "say the marriages were part of an immigration scam. She pleaded not guilty at State Supreme Court in the " "Bronx on Friday. If convicted, she faces up to four years in prison.", ] generated_summaries = tok.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated_summaries == EXPECTED @slow def test_contrastive_search_bart(self): article = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn") bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn").to(torch_device) input_ids = bart_tokenizer( article, add_special_tokens=False, truncation=True, max_length=512, return_tensors="pt" ).input_ids.to(torch_device) outputs = bart_model.generate(input_ids, penalty_alpha=0.5, top_k=5, max_length=64, num_beams=1) generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "Liana Barrientos, 39, pleaded not guilty to charges related to false marriage statements. " "Prosecutors say she married at least 10 times, sometimes within two weeks of each other. She is " "accused of being part of an immigration scam to get permanent residency. If convicted, she faces up " "to four years in" ], ) @slow def test_decoder_attention_mask(self): model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", forced_bos_token_id=0).to( torch_device ) tokenizer = self.default_tokenizer sentence = "UN Chief Says There Is No <mask> in Syria" input_ids = tokenizer(sentence, return_tensors="pt").input_ids.to(torch_device) padding_size = 3 decoder_input_ids = torch.tensor( [ [model.config.decoder_start_token_id] + padding_size * [model.config.pad_token_id] + [model.config.bos_token_id] ], dtype=torch.long, device=torch_device, ) decoder_attention_mask = torch.where(decoder_input_ids == model.config.pad_token_id, 0, 1).to(torch_device) generated_ids = model.generate( input_ids=input_ids, use_cache=False, max_new_tokens=20, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) generated_sentence = tokenizer.batch_decode(generated_ids)[0] expected_sentence = "</s><pad><pad><pad><s>UN Chief Says There Is No Plan B for Peace in Syria</s>" self.assertEqual(generated_sentence, expected_sentence) class BartStandaloneDecoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, d_model=16, decoder_seq_length=7, is_training=True, is_decoder=True, use_attention_mask=True, use_cache=False, use_labels=True, decoder_start_token_id=2, decoder_ffn_dim=32, decoder_layers=2, encoder_attention_heads=4, decoder_attention_heads=4, max_position_embeddings=30, is_encoder_decoder=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, scope=None, ): self.parent = parent self.batch_size = batch_size self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.d_model = d_model self.hidden_size = d_model self.num_hidden_layers = decoder_layers self.decoder_layers = decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_attention_heads = encoder_attention_heads self.decoder_attention_heads = decoder_attention_heads self.num_attention_heads = decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.max_position_embeddings = max_position_embeddings self.is_encoder_decoder = is_encoder_decoder self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 2 self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = BartConfig( vocab_size=self.vocab_size, d_model=self.d_model, encoder_layers=self.decoder_layers, decoder_layers=self.decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_attention_heads=self.encoder_attention_heads, decoder_attention_heads=self.decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, max_position_embeddings=self.max_position_embeddings, is_encoder_decoder=self.is_encoder_decoder, ) return ( config, input_ids, attention_mask, lm_labels, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, attention_mask, lm_labels, ) = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.decoder_seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) return ( config, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): config.use_cache = True model = BartDecoder(config=config).to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def create_and_check_decoder_model_attention_mask_past( self, config, input_ids, attention_mask, lm_labels, ): model = BartDecoder(config=config).to(torch_device).eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=attn_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class BartStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BartDecoder, BartForCausalLM) if is_torch_available() else () all_generative_model_classes = (BartForCausalLM,) if is_torch_available() else () fx_comptatible = True test_pruning = False is_encoder_decoder = False test_missing_keys = False def setUp( self, ): self.model_tester = BartStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=BartConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_attn_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients return def test_save_load_fast_init_from_base(self): pass @unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :) def test_left_padding_compatibility(self): pass

transformers/tests/models/bart/test_modeling_bart.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch BioGPT model. """ import math import unittest from transformers import BioGptConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification, BioGptModel, BioGptTokenizer, ) from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST class BioGptModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return BioGptConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BioGptModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = BioGptForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_biogpt_model_attention_mask_past( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = BioGptModel(config=config) model.to(torch_device) model.eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = self.seq_length // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past = model(input_ids, attention_mask=attn_mask).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_biogpt_model_past_large_inputs( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = BioGptModel(config=config).to(torch_device).eval() attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_forward_and_backwards( self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False ): model = BioGptForCausalLM(config) model.to(torch_device) if gradient_checkpointing: model.gradient_checkpointing_enable() result = model(input_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) result.loss.backward() def create_and_check_biogpt_weight_initialization(self, config, *args): model = BioGptModel(config) model_std = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers) for key in model.state_dict().keys(): if "c_proj" in key and "weight" in key: self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001) self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01) def create_and_check_biogpt_for_token_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): config.num_labels = self.num_labels model = BioGptForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class BioGptModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification) if is_torch_available() else () ) all_generative_model_classes = (BioGptForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": BioGptModel, "text-classification": BioGptForSequenceClassification, "text-generation": BioGptForCausalLM, "token-classification": BioGptForTokenClassification, "zero-shot": BioGptForSequenceClassification, } if is_torch_available() else {} ) test_pruning = False def setUp(self): self.model_tester = BioGptModelTester(self) self.config_tester = ConfigTester(self, config_class=BioGptConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_biogpt_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_biogpt_model_attention_mask_past(*config_and_inputs) def test_biogpt_gradient_checkpointing(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True) def test_biogpt_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_biogpt_model_past_large_inputs(*config_and_inputs) def test_biogpt_weight_initialization(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_biogpt_weight_initialization(*config_and_inputs) def test_biogpt_token_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_biogpt_for_token_classification(*config_and_inputs) @slow def test_batch_generation(self): model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") model.to(torch_device) tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") tokenizer.padding_side = "left" # Define PAD Token = EOS Token = 50256 tokenizer.pad_token = tokenizer.eos_token model.config.pad_token_id = model.config.eos_token_id # use different length sentences to test batching sentences = [ "Hello, my dog is a little", "Today, I", ] inputs = tokenizer(sentences, return_tensors="pt", padding=True) input_ids = inputs["input_ids"].to(torch_device) outputs = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), ) inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device) output_non_padded = model.generate(input_ids=inputs_non_padded) num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item() inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device) output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings) batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True) non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True) expected_output_sentence = [ "Hello, my dog is a little bit bigger than a little bit.", "Today, I have a good idea of how to use the information", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence]) @slow def test_model_from_pretrained(self): for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BioGptModel.from_pretrained(model_name) self.assertIsNotNone(model) # Copied from tests.models.opt.test_modeling_opt.OPTModelTest.test_opt_sequence_classification_model with OPT->BioGpt,opt->biogpt,prepare_config_and_inputs->prepare_config_and_inputs_for_common def test_biogpt_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = BioGptForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) # Copied from tests.models.opt.test_modeling_opt.OPTModelTest.test_opt_sequence_classification_model_for_multi_label with OPT->BioGpt,opt->biogpt,prepare_config_and_inputs->prepare_config_and_inputs_for_common def test_biogpt_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = BioGptForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @require_torch class BioGptModelIntegrationTest(unittest.TestCase): @slow def test_inference_lm_head_model(self): model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") input_ids = torch.tensor([[2, 4805, 9, 656, 21]]) output = model(input_ids)[0] vocab_size = 42384 expected_shape = torch.Size((1, 5, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @slow def test_biogpt_generation(self): tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") model.to(torch_device) torch.manual_seed(0) tokenized = tokenizer("COVID-19 is", return_tensors="pt").to(torch_device) output_ids = model.generate( **tokenized, min_length=100, max_length=1024, num_beams=5, early_stopping=True, ) output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True) EXPECTED_OUTPUT_STR = ( "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the" " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and" " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK)," " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and" " more than 800,000 deaths." ) self.assertEqual(output_str, EXPECTED_OUTPUT_STR)

transformers/tests/models/biogpt/test_modeling_biogpt.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Blip model. """ import inspect import os import tempfile import unittest import numpy as np import requests from transformers import BlipConfig, BlipTextConfig, BlipVisionConfig from transformers.testing_utils import ( require_torch, require_torch_accelerator, require_torch_fp16, require_vision, slow, torch_device, ) from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( BlipForConditionalGeneration, BlipForImageTextRetrieval, BlipForQuestionAnswering, BlipModel, BlipTextModel, BlipVisionModel, ) from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import BlipProcessor class BlipVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=1e-10, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return BlipVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = BlipVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class BlipVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Blip does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (BlipVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = BlipVisionModelTester(self) self.config_tester = ConfigTester(self, config_class=BlipVisionConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Blip does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="BlipVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="BlipVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BlipVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class BlipTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, bos_token_id=0, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope self.bos_token_id = bos_token_id def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return BlipTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, bos_token_id=self.bos_token_id, ) def create_and_check_model(self, config, input_ids, input_mask): model = BlipTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class BlipTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (BlipTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = BlipTextModelTester(self) self.config_tester = ConfigTester(self, config_class=BlipTextConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Blip does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BlipTextModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_pt_tf_model_equivalence(self): super().test_pt_tf_model_equivalence(allow_missing_keys=True) class BlipModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = BlipTextModelTester(parent, **text_kwargs) self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return BlipConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = BlipModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "return_loss": True, } return config, inputs_dict @require_torch class BlipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (BlipModel,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": BlipModel, "image-to-text": BlipForConditionalGeneration, } if is_torch_available() else {} ) fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = BlipModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="BlipModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for Blip def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # Blip needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save BlipConfig and check if we can load BlipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save BlipConfig and check if we can load BlipTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = BlipTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BlipModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_pt_tf_model_equivalence(self): super().test_pt_tf_model_equivalence(allow_missing_keys=True) class BlipTextRetrievalModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = BlipTextModelTester(parent, **text_kwargs) self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return BlipConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = BlipModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict class BlipTextImageModelsModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = BlipTextModelTester(parent, **text_kwargs) self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return BlipConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = BlipModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "labels": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict class BlipVQAModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = BlipTextModelTester(parent, **text_kwargs) self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return BlipConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = BlipModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "labels": input_ids, "decoder_input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict @require_torch @require_vision class BlipVQAModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (BlipForQuestionAnswering,) if is_torch_available() else () fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_torchscript = False def setUp(self): self.model_tester = BlipVQAModelTester(self) def _prepare_inputs_for_vqa(self): _, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() inputs_dict["labels"] = inputs_dict["input_ids"] inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"] inputs_dict.pop("return_loss") return inputs_dict def test_class_name_consistency(self): """ Tests that all VQA models have a class name that ends with "ForQuestionAnswering" """ for model_class in self.all_model_classes: model = model_class(self.model_tester.get_config()) self.assertTrue( model.__class__.__name__.endswith("ForQuestionAnswering"), f"Class name should end with 'ForVisualQuestionAnswering' got {model.__class__.__name__}", ) def test_training(self): """ Tests that all VQA models can be trained on a single batch """ for model_class in self.all_model_classes: model = model_class(self.model_tester.get_config()).to(torch_device) model.train() loss = model(**self.model_tester.prepare_config_and_inputs_for_common()[1]).loss loss.backward() # verify the gradients are not None for name, param in model.named_parameters(): self.assertIsNotNone(param.grad, f"Gradients should not be None - got {param.grad} for {name}") def test_forward_signature(self): """ Test if the forward function has the expected arguments. """ for model_class in self.all_model_classes: model = model_class(self.model_tester.get_config()) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so args are the first n entries args = list(signature.parameters.keys()) expected_args = [ "input_ids", "attention_mask", "labels", "decoder_input_ids", "decoder_attention_mask", ] for arg in expected_args: self.assertTrue( arg in args, f"Argument {arg} of forward function signature should include {arg}. Found {args}.", ) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="BlipModel does not have input/output embeddings") def test_model_common_attributes(self): pass @require_torch class BlipTextRetrievalModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (BlipForImageTextRetrieval,) if is_torch_available() else () fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_torchscript = False def setUp(self): self.model_tester = BlipTextRetrievalModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="BlipModel does not have input/output embeddings") def test_model_common_attributes(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["input_ids"] if model_class != BlipForConditionalGeneration else ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes[:-1]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) # hardcode labels to be the same as input_ids inputs["labels"] = inputs["input_ids"] loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes[:-1]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) # hardcode labels to be the same as input_ids inputs["labels"] = inputs["input_ids"] loss = model(**inputs).loss loss.backward() @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass # override as the `logit_scale` parameter initilization is different for Blip def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # Blip needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save BlipConfig and check if we can load BlipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save BlipConfig and check if we can load BlipTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = BlipTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BlipModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class BlipTextImageModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (BlipForConditionalGeneration,) if is_torch_available() else () fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_torchscript = False def setUp(self): self.model_tester = BlipTextImageModelsModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="BlipModel does not have input/output embeddings") def test_model_common_attributes(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["input_ids"] if model_class != BlipForConditionalGeneration else ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes[:-1]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) # hardcode labels to be the same as input_ids inputs["labels"] = inputs["input_ids"] loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes[:-1]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) # hardcode labels to be the same as input_ids inputs["labels"] = inputs["input_ids"] loss = model(**inputs).loss loss.backward() # override as the `logit_scale` parameter initilization is different for Blip def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # Blip needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save BlipConfig and check if we can load BlipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save BlipConfig and check if we can load BlipTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = BlipTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BlipModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @require_vision @require_torch @slow class BlipModelIntegrationTest(unittest.TestCase): def test_inference_image_captioning(self): model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(torch_device) processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") image = prepare_img() # image only inputs = processor(images=image, return_tensors="pt").to(torch_device) predictions = model.generate(**inputs) # Test output self.assertEqual(predictions[0].tolist(), [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]) # image and context context = ["a picture of"] inputs = processor(images=image, text=context, return_tensors="pt").to(torch_device) predictions = model.generate(**inputs) # Test output self.assertEqual( predictions[0].tolist(), [30522, 1037, 3861, 1997, 1037, 2450, 1998, 2014, 3899, 2006, 1996, 3509, 102], ) @require_torch_accelerator @require_torch_fp16 def test_inference_image_captioning_fp16(self): model = BlipForConditionalGeneration.from_pretrained( "Salesforce/blip-image-captioning-base", torch_dtype=torch.float16 ).to(torch_device) processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") image = prepare_img() # image only inputs = processor(images=image, return_tensors="pt").to(torch_device, torch.float16) predictions = model.generate(**inputs) # Test output self.assertEqual(predictions[0].tolist(), [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]) # image and context context = ["a picture of"] inputs = processor(images=image, text=context, return_tensors="pt").to(torch_device, torch.float16) predictions = model.generate(**inputs) # Test output self.assertEqual( predictions[0].tolist(), [30522, 1037, 3861, 1997, 1037, 2450, 1998, 2014, 3899, 2006, 1996, 3509, 102], ) def test_inference_vqa(self): model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(torch_device) processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") image = prepare_img() text = "how many dogs are in the picture?" inputs = processor(image, text=text, return_tensors="pt").to(torch_device) out = model.generate(**inputs) # Test output self.assertEqual(out[0].tolist(), [30522, 1015, 102]) def test_inference_itm(self): model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco").to(torch_device) processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco") image = prepare_img() text = "A woman and her dog sitting in a beach" inputs = processor(image, text, return_tensors="pt").to(torch_device) out_itm = model(**inputs) out = model(**inputs, use_itm_head=False) expected_scores = torch.Tensor([[0.0029, 0.9971]]) self.assertTrue(torch.allclose(torch.nn.Softmax()(out_itm[0].cpu()), expected_scores, rtol=1e-3, atol=1e-3)) self.assertTrue(torch.allclose(out[0].cpu(), torch.Tensor([[0.5162]]), rtol=1e-3, atol=1e-3))

transformers/tests/models/blip/test_modeling_blip.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Bros model. """ import copy import unittest from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import is_torch_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( BrosConfig, BrosForTokenClassification, BrosModel, BrosSpadeEEForTokenClassification, BrosSpadeELForTokenClassification, ) from transformers.models.bros.modeling_bros import ( BROS_PRETRAINED_MODEL_ARCHIVE_LIST, ) class BrosModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_bbox_first_token_mask=True, use_labels=True, vocab_size=99, hidden_size=64, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_bbox_first_token_mask = use_bbox_first_token_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) bbox = ids_tensor([self.batch_size, self.seq_length, 8], 1) # Ensure that bbox is legal for i in range(bbox.shape[0]): for j in range(bbox.shape[1]): if bbox[i, j, 3] < bbox[i, j, 1]: t = bbox[i, j, 3] bbox[i, j, 3] = bbox[i, j, 1] bbox[i, j, 1] = t if bbox[i, j, 2] < bbox[i, j, 0]: t = bbox[i, j, 2] bbox[i, j, 2] = bbox[i, j, 0] bbox[i, j, 0] = t input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) bbox_first_token_mask = None if self.use_bbox_first_token_mask: bbox_first_token_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.bool).to(torch_device) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) token_labels = None if self.use_labels: token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) initial_token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) subsequent_token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) config = self.get_config() return ( config, input_ids, bbox, token_type_ids, input_mask, bbox_first_token_mask, token_labels, initial_token_labels, subsequent_token_labels, ) def get_config(self): return BrosConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def create_and_check_model( self, config, input_ids, bbox, token_type_ids, input_mask, bbox_first_token_mask, token_labels, initial_token_labels, subsequent_token_labels, ): model = BrosModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_token_classification( self, config, input_ids, bbox, token_type_ids, input_mask, bbox_first_token_mask, token_labels, initial_token_labels, subsequent_token_labels, ): config.num_labels = self.num_labels model = BrosForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_spade_ee_token_classification( self, config, input_ids, bbox, token_type_ids, input_mask, bbox_first_token_mask, token_labels, initial_token_labels, subsequent_token_labels, ): config.num_labels = self.num_labels model = BrosSpadeEEForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, attention_mask=input_mask, bbox_first_token_mask=bbox_first_token_mask, token_type_ids=token_type_ids, initial_token_labels=token_labels, subsequent_token_labels=token_labels, ) self.parent.assertEqual(result.initial_token_logits.shape, (self.batch_size, self.seq_length, self.num_labels)) self.parent.assertEqual( result.subsequent_token_logits.shape, (self.batch_size, self.seq_length, self.seq_length + 1) ) def create_and_check_for_spade_el_token_classification( self, config, input_ids, bbox, token_type_ids, input_mask, bbox_first_token_mask, token_labels, initial_token_labels, subsequent_token_labels, ): config.num_labels = self.num_labels model = BrosSpadeELForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, attention_mask=input_mask, bbox_first_token_mask=bbox_first_token_mask, token_type_ids=token_type_ids, labels=token_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.seq_length + 1)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, bbox, token_type_ids, input_mask, bbox_first_token_mask, token_labels, initial_token_labels, subsequent_token_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "bbox": bbox, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_torch class BrosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): test_pruning = False test_torchscript = False test_mismatched_shapes = False all_model_classes = ( ( BrosForTokenClassification, BrosSpadeEEForTokenClassification, BrosSpadeELForTokenClassification, BrosModel, ) if is_torch_available() else () ) all_generative_model_classes = () if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": BrosModel, "token-classification": BrosForTokenClassification} if is_torch_available() else {} ) # BROS requires `bbox` in the inputs which doesn't fit into the above 2 pipelines' input formats. # see https://github.com/huggingface/transformers/pull/26294 def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True def setUp(self): self.model_tester = BrosModelTester(self) self.config_tester = ConfigTester(self, config_class=BrosConfig, hidden_size=37) def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if return_labels: if model_class.__name__ in ["BrosForTokenClassification", "BrosSpadeELForTokenClassification"]: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device, ) inputs_dict["bbox_first_token_mask"] = torch.ones( [self.model_tester.batch_size, self.model_tester.seq_length], dtype=torch.bool, device=torch_device, ) elif model_class.__name__ in ["BrosSpadeEEForTokenClassification"]: inputs_dict["initial_token_labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device, ) inputs_dict["subsequent_token_labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device, ) inputs_dict["bbox_first_token_mask"] = torch.ones( [self.model_tester.batch_size, self.model_tester.seq_length], dtype=torch.bool, device=torch_device, ) return inputs_dict def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_multi_gpu_data_parallel_forward(self): super().test_multi_gpu_data_parallel_forward() def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_spade_ee_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_spade_ee_token_classification(*config_and_inputs) def test_for_spade_el_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_spade_el_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in BROS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BrosModel.from_pretrained(model_name) self.assertIsNotNone(model) def prepare_bros_batch_inputs(): attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) bbox = torch.tensor( [ [ [0.0000, 0.0000, 0.0000, 0.0000], [0.5223, 0.5590, 0.5787, 0.5720], [0.5853, 0.5590, 0.6864, 0.5720], [0.5853, 0.5590, 0.6864, 0.5720], [0.1234, 0.5700, 0.2192, 0.5840], [0.2231, 0.5680, 0.2782, 0.5780], [0.2874, 0.5670, 0.3333, 0.5780], [0.3425, 0.5640, 0.4344, 0.5750], [0.0866, 0.7770, 0.1181, 0.7870], [0.1168, 0.7770, 0.1522, 0.7850], [0.1535, 0.7750, 0.1864, 0.7850], [0.1890, 0.7750, 0.2572, 0.7850], [1.0000, 1.0000, 1.0000, 1.0000], ], [ [0.0000, 0.0000, 0.0000, 0.0000], [0.4396, 0.6720, 0.4659, 0.6850], [0.4698, 0.6720, 0.4843, 0.6850], [0.1575, 0.6870, 0.2021, 0.6980], [0.2047, 0.6870, 0.2730, 0.7000], [0.1299, 0.7010, 0.1430, 0.7140], [0.1299, 0.7010, 0.1430, 0.7140], [0.1562, 0.7010, 0.2441, 0.7120], [0.1562, 0.7010, 0.2441, 0.7120], [0.2454, 0.7010, 0.3150, 0.7120], [0.3176, 0.7010, 0.3320, 0.7110], [0.3333, 0.7000, 0.4029, 0.7140], [1.0000, 1.0000, 1.0000, 1.0000], ], ] ) input_ids = torch.tensor( [ [101, 1055, 8910, 1012, 5719, 3296, 5366, 3378, 2146, 2846, 10807, 13494, 102], [101, 2112, 1997, 3671, 6364, 1019, 1012, 5057, 1011, 4646, 2030, 2974, 102], ] ) return input_ids, bbox, attention_mask @require_torch class BrosModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = BrosModel.from_pretrained("jinho8345/bros-base-uncased").to(torch_device) input_ids, bbox, attention_mask = prepare_bros_batch_inputs() with torch.no_grad(): outputs = model( input_ids.to(torch_device), bbox.to(torch_device), attention_mask=attention_mask.to(torch_device), return_dict=True, ) # verify the logits expected_shape = torch.Size((2, 13, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.3074, 0.1363, 0.3143], [0.0925, -0.1155, 0.1050], [0.0221, 0.0003, 0.1285]] ).to(torch_device) torch.set_printoptions(sci_mode=False) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))

transformers/tests/models/bros/test_modeling_bros.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch CLAP model. """ import inspect import os import tempfile import unittest import numpy as np from datasets import load_dataset from transformers import ClapAudioConfig, ClapConfig, ClapProcessor, ClapTextConfig from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import is_torch_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( ClapAudioModel, ClapAudioModelWithProjection, ClapModel, ClapTextModel, ClapTextModelWithProjection, ) from transformers.models.clap.modeling_clap import CLAP_PRETRAINED_MODEL_ARCHIVE_LIST class ClapAudioModelTester: def __init__( self, parent, batch_size=12, image_size=60, num_mel_bins=16, window_size=4, spec_size=64, patch_size=2, patch_stride=2, seq_length=16, freq_ratio=2, num_channels=3, is_training=True, hidden_size=32, patch_embeds_hidden_size=16, projection_dim=32, depths=[2, 2], num_hidden_layers=2, num_heads=[2, 2], intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_mel_bins = num_mel_bins self.window_size = window_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.depths = depths self.num_heads = num_heads self.num_attention_heads = num_heads[0] self.seq_length = seq_length self.spec_size = spec_size self.freq_ratio = freq_ratio self.patch_stride = patch_stride self.patch_embeds_hidden_size = patch_embeds_hidden_size self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): input_features = floats_tensor([self.batch_size, 1, self.hidden_size, self.num_mel_bins]) config = self.get_config() return config, input_features def get_config(self): return ClapAudioConfig( image_size=self.image_size, patch_size=self.patch_size, num_mel_bins=self.num_mel_bins, window_size=self.window_size, num_channels=self.num_channels, hidden_size=self.hidden_size, patch_stride=self.patch_stride, projection_dim=self.projection_dim, depths=self.depths, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, spec_size=self.spec_size, freq_ratio=self.freq_ratio, patch_embeds_hidden_size=self.patch_embeds_hidden_size, ) def create_and_check_model(self, config, input_features): model = ClapAudioModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_features) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_model_with_projection(self, config, input_features): model = ClapAudioModelWithProjection(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_features) self.parent.assertEqual(result.audio_embeds.shape, (self.batch_size, self.projection_dim)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_features = config_and_inputs inputs_dict = {"input_features": input_features} return config, inputs_dict @require_torch class ClapAudioModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as CLAP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ClapAudioModel, ClapAudioModelWithProjection) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = ClapAudioModelTester(self) self.config_tester = ConfigTester(self, config_class=ClapAudioConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ClapAudioModel does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-2:]), [2 * self.model_tester.patch_embeds_hidden_size, 2 * self.model_tester.patch_embeds_hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass") def test_retain_grad_hidden_states_attentions(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["input_features"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_projection(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_projection(*config_and_inputs) @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass") def test_training(self): pass @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="ClapAudioModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="ClapAudioModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapAudioModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow def test_model_with_projection_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapAudioModelWithProjection.from_pretrained(model_name) self.assertIsNotNone(model) self.assertTrue(hasattr(model, "audio_projection")) class ClapTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, scope=None, projection_hidden_act="relu", ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope self.projection_hidden_act = projection_hidden_act def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return ClapTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, projection_hidden_act=self.projection_hidden_act, ) def create_and_check_model(self, config, input_ids, input_mask): model = ClapTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_model_with_projection(self, config, input_ids, input_mask): model = ClapTextModelWithProjection(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.text_embeds.shape, (self.batch_size, self.projection_dim)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class ClapTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (ClapTextModel, ClapTextModelWithProjection) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = ClapTextModelTester(self) self.config_tester = ConfigTester(self, config_class=ClapTextConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_projection(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_projection(*config_and_inputs) @unittest.skip(reason="ClapTextModel does not output any loss term in the forward pass") def test_training(self): pass @unittest.skip(reason="ClapTextModel does not output any loss term in the forward pass") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="ClapTextModel does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ClapTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="ClapTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapTextModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow def test_model_with_projection_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapTextModelWithProjection.from_pretrained(model_name) self.assertIsNotNone(model) self.assertTrue(hasattr(model, "text_projection")) class ClapModelTester: def __init__(self, parent, text_kwargs=None, audio_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if audio_kwargs is None: audio_kwargs = {} self.parent = parent self.text_model_tester = ClapTextModelTester(parent, **text_kwargs) self.audio_model_tester = ClapAudioModelTester(parent, **audio_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() _, input_features = self.audio_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, input_features def get_config(self): return ClapConfig.from_text_audio_configs( self.text_model_tester.get_config(), self.audio_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, input_features): model = ClapModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, input_features, attention_mask) self.parent.assertEqual( result.logits_per_audio.shape, (self.audio_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.audio_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, input_features = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "input_features": input_features, "return_loss": True, } return config, inputs_dict @require_torch class ClapModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (ClapModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": ClapModel} if is_torch_available() else {} fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = ClapModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="ClapModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for CLAP def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] input_features = inputs_dict["input_features"] # CLAP needs input_features traced_model = torch.jit.trace(model, (input_ids, input_features)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_audio_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save ClapConfig and check if we can load ClapAudioConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) audio_config = ClapAudioConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.audio_config.to_dict(), audio_config.to_dict()) # Save ClapConfig and check if we can load ClapTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = ClapTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow @require_torch class ClapModelIntegrationTest(unittest.TestCase): paddings = ["repeatpad", "repeat", "pad"] def test_integration_unfused(self): EXPECTED_MEANS_UNFUSED = { "repeatpad": 0.0024, "pad": 0.0020, "repeat": 0.0023, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[-1] model_id = "laion/clap-htsat-unfused" model = ClapModel.from_pretrained(model_id).to(torch_device) processor = ClapProcessor.from_pretrained(model_id) for padding in self.paddings: inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt", padding=padding).to( torch_device ) audio_embed = model.get_audio_features(**inputs) expected_mean = EXPECTED_MEANS_UNFUSED[padding] self.assertTrue( torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3) ) def test_integration_fused(self): EXPECTED_MEANS_FUSED = { "repeatpad": 0.00069, "repeat": 0.00196, "pad": -0.000379, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[-1] model_id = "laion/clap-htsat-fused" model = ClapModel.from_pretrained(model_id).to(torch_device) processor = ClapProcessor.from_pretrained(model_id) for padding in self.paddings: inputs = processor( audios=audio_sample["audio"]["array"], return_tensors="pt", padding=padding, truncation="fusion" ).to(torch_device) audio_embed = model.get_audio_features(**inputs) expected_mean = EXPECTED_MEANS_FUSED[padding] self.assertTrue( torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3) ) def test_batched_fused(self): EXPECTED_MEANS_FUSED = { "repeatpad": 0.0010, "repeat": 0.0020, "pad": 0.0006, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]] model_id = "laion/clap-htsat-fused" model = ClapModel.from_pretrained(model_id).to(torch_device) processor = ClapProcessor.from_pretrained(model_id) for padding in self.paddings: inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding, truncation="fusion").to( torch_device ) audio_embed = model.get_audio_features(**inputs) expected_mean = EXPECTED_MEANS_FUSED[padding] self.assertTrue( torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3) ) def test_batched_unfused(self): EXPECTED_MEANS_FUSED = { "repeatpad": 0.0016, "repeat": 0.0019, "pad": 0.0019, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]] model_id = "laion/clap-htsat-unfused" model = ClapModel.from_pretrained(model_id).to(torch_device) processor = ClapProcessor.from_pretrained(model_id) for padding in self.paddings: inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding).to(torch_device) audio_embed = model.get_audio_features(**inputs) expected_mean = EXPECTED_MEANS_FUSED[padding] self.assertTrue( torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3) )

transformers/tests/models/clap/test_modeling_clap.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Data2VecAudio model. """ import math import unittest import numpy as np from datasets import load_dataset from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask from transformers import Data2VecAudioConfig, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_soundfile, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( Data2VecAudioForAudioFrameClassification, Data2VecAudioForCTC, Data2VecAudioForSequenceClassification, Data2VecAudioForXVector, Data2VecAudioModel, Wav2Vec2Processor, ) from transformers.models.data2vec.modeling_data2vec_audio import _compute_mask_indices class Data2VecAudioModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=2, num_attention_heads=2, hidden_dropout_prob=0.1, intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, num_adapter_layers=1, adapter_stride=2, tdnn_dim=(32, 32), tdnn_kernel=(5, 3), tdnn_dilation=(1, 2), xvector_output_dim=32, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.num_adapter_layers = num_adapter_layers self.adapter_stride = adapter_stride self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.scope = scope self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1 def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() return config, input_values, attention_mask def get_config(self): return Data2VecAudioConfig( hidden_size=self.hidden_size, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, num_adapter_layers=self.num_adapter_layers, adapter_stride=self.adapter_stride, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, ) def create_and_check_model(self, config, input_values, attention_mask): model = Data2VecAudioModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter(self, config, input_values, attention_mask): config.add_adapter = True model = Data2VecAudioModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 8 model = Data2VecAudioModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, config.output_hidden_size), ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = Data2VecAudioModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = Data2VecAudioForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = Data2VecAudioForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Data2VecAudioForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lengths are at least # one shorter than logit lengths to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Data2VecAudioForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_xvector_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Data2VecAudioForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = Data2VecAudioForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with self.parent.assertRaises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class Data2VecAudioModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( Data2VecAudioForCTC, Data2VecAudioModel, Data2VecAudioForSequenceClassification, Data2VecAudioForAudioFrameClassification, Data2VecAudioForXVector, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "audio-classification": Data2VecAudioForSequenceClassification, "automatic-speech-recognition": Data2VecAudioForCTC, "feature-extraction": Data2VecAudioModel, } if is_torch_available() else {} ) test_pruning = False test_headmasking = False def setUp(self): self.model_tester = Data2VecAudioModelTester(self) self.config_tester = ConfigTester(self, config_class=Data2VecAudioConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_adapter(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter(*config_and_inputs) def test_model_with_adapter_proj_dim(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_xvector_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_xvector_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # Data2VecAudio has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # Data2VecAudio cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # Data2VecAudio has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_flax_to_pt(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_pt_to_flax(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "feature_projection.projection.weight", "feature_projection.projection.bias", "objective.weight", ] if param.requires_grad: if any(x in name for x in uniform_init_parms): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = Data2VecAudioForCTC.from_pretrained( "hf-internal-testing/tiny-random-data2vec-seq-class", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = Data2VecAudioForCTC.from_pretrained( "facebook/data2vec-audio-base-960h", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 299, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = Data2VecAudioModel.from_pretrained("facebook/data2vec-audio-base") self.assertIsNotNone(model) @require_torch class Data2VecAudioUtilsTest(unittest.TestCase): def test_compute_mask_indices(self): batch_size = 4 sequence_length = 60 mask_prob = 0.5 mask_length = 1 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) def test_compute_mask_indices_low_prob(self): # with these settings num_masked_spans=0.5, which means probabilistic rounding # ensures that in 5 out of 10 method calls, num_masked_spans=0, and in # the other 5 out of 10, cases num_masked_spans=1 n_trials = 100 batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 count_dimensions_masked = 0 count_dimensions_not_masked = 0 for _ in range(n_trials): mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) num_masks = torch.sum(mask).item() if num_masks > 0: count_dimensions_masked += 1 else: count_dimensions_not_masked += 1 # as we test for at least 10 masked dimension and at least # 10 non-masked dimension, this test could fail with probability: # P(100 coin flips, at most 9 heads) = 1.66e-18 self.assertGreater(count_dimensions_masked, int(n_trials * 0.1)) self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1)) def test_compute_mask_indices_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) def test_compute_mask_indices_attn_mask_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) attention_mask[:2, sequence_length // 2 :] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask ) mask = torch.from_numpy(mask).to(torch_device) for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0) def test_compute_mask_indices_short_audio(self): batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) # force one example to be heavily padded attention_mask[0, 5:] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2 ) # make sure that non-padded examples cannot be padded self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any()) @require_torch @require_soundfile @slow class Data2VecAudioModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter( lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)] )[:num_samples]["audio"] return [x["array"] for x in speech_samples] def _load_superb(self, task, num_samples): ds = load_dataset("anton-l/superb_dummy", task, split="test") return ds[:num_samples] def test_inference_ctc_normal(self): model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True) input_speech = self._load_datasamples(1) input_values = processor(input_speech, return_tensors="pt").input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_ctc_batched(self): model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h").to(torch_device) processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True) input_speech = self._load_datasamples(4) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore", "the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around" " him with thousands of spectators were trivialities not worth thinking about", "his instant of panic was followed by a small sharp blow high on his chest", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)

transformers/tests/models/data2vec/test_modeling_data2vec_audio.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Deformable DETR model. """ import inspect import math import unittest from typing import Dict, List, Tuple from transformers import DeformableDetrConfig, ResNetConfig, is_torch_available, is_vision_available from transformers.file_utils import cached_property from transformers.testing_utils import ( require_timm, require_torch, require_torch_accelerator, require_vision, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import DeformableDetrForObjectDetection, DeformableDetrModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class DeformableDetrModelTester: def __init__( self, parent, batch_size=8, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=8, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, num_queries=12, num_channels=3, image_size=196, n_targets=8, num_labels=91, num_feature_levels=4, encoder_n_points=2, decoder_n_points=6, ): self.parent = parent self.batch_size = batch_size self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.num_queries = num_queries self.num_channels = num_channels self.image_size = image_size self.n_targets = n_targets self.num_labels = num_labels self.num_feature_levels = num_feature_levels self.encoder_n_points = encoder_n_points self.decoder_n_points = decoder_n_points # we also set the expected seq length for both encoder and decoder self.encoder_seq_length = ( math.ceil(self.image_size / 8) ** 2 + math.ceil(self.image_size / 16) ** 2 + math.ceil(self.image_size / 32) ** 2 + math.ceil(self.image_size / 64) ** 2 ) self.decoder_seq_length = self.num_queries def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device) labels = None if self.use_labels: # labels is a list of Dict (each Dict being the labels for a given example in the batch) labels = [] for i in range(self.batch_size): target = {} target["class_labels"] = torch.randint( high=self.num_labels, size=(self.n_targets,), device=torch_device ) target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device) target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device) labels.append(target) config = self.get_config() return config, pixel_values, pixel_mask, labels def get_config(self): resnet_config = ResNetConfig( num_channels=3, embeddings_size=10, hidden_sizes=[10, 20, 30, 40], depths=[1, 1, 2, 1], hidden_act="relu", num_labels=3, out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], ) return DeformableDetrConfig( d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, num_queries=self.num_queries, num_labels=self.num_labels, num_feature_levels=self.num_feature_levels, encoder_n_points=self.encoder_n_points, decoder_n_points=self.decoder_n_points, use_timm_backbone=False, backbone=None, backbone_config=resnet_config, use_pretrained_backbone=False, ) def prepare_config_and_inputs_for_common(self): config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs() inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask} return config, inputs_dict def create_and_check_deformable_detr_model(self, config, pixel_values, pixel_mask, labels): model = DeformableDetrModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size)) def create_and_check_deformable_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels): model = DeformableDetrForObjectDetection(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) @require_torch class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection} if is_torch_available() else {} ) is_encoder_decoder = True test_torchscript = False test_pruning = False test_head_masking = False test_missing_keys = False # special case for head models def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class.__name__ == "DeformableDetrForObjectDetection": labels = [] for i in range(self.model_tester.batch_size): target = {} target["class_labels"] = torch.ones( size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long ) target["boxes"] = torch.ones( self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float ) target["masks"] = torch.ones( self.model_tester.n_targets, self.model_tester.image_size, self.model_tester.image_size, device=torch_device, dtype=torch.float, ) labels.append(target) inputs_dict["labels"] = labels return inputs_dict def setUp(self): self.model_tester = DeformableDetrModelTester(self) self.config_tester = ConfigTester(self, config_class=DeformableDetrConfig, has_text_modality=False) def test_config(self): # we don't test common_properties and arguments_init as these don't apply for Deformable DETR self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() def test_deformable_detr_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deformable_detr_model(*config_and_inputs) def test_deformable_detr_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deformable_detr_object_detection_head_model(*config_and_inputs) @unittest.skip(reason="Deformable DETR does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Deformable DETR does not have a get_input_embeddings method") def test_model_common_attributes(self): pass @unittest.skip(reason="Deformable DETR is not a generative model") def test_generate_without_input_ids(self): pass @unittest.skip(reason="Deformable DETR does not use token embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, self.model_tester.num_feature_levels, self.model_tester.encoder_n_points, ], ) out_len = len(outputs) correct_outlen = 8 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Object Detection model returns pred_logits and pred_boxes if model_class.__name__ == "DeformableDetrForObjectDetection": correct_outlen += 2 self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, self.model_tester.num_feature_levels, self.model_tester.decoder_n_points, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, self.model_tester.num_feature_levels, self.model_tester.encoder_n_points, ], ) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif isinstance(tuple_object, Dict): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values(), dict_object.values() ): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: print("Model class:", model_class) model = model_class(config) model.to(torch_device) model.eval() tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence( model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True} ) def test_retain_grad_hidden_states_attentions(self): # removed retain_grad and grad on decoder_hidden_states, as queries don't require grad config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) # we take the second output since last_hidden_state is the second item output = outputs[1] encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_attentions = outputs.encoder_attentions[0] encoder_hidden_states.retain_grad() encoder_attentions.retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(decoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) def test_forward_auxiliary_loss(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.auxiliary_loss = True # only test for object detection and segmentation model for model_class in self.all_model_classes[1:]: model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) outputs = model(**inputs) self.assertIsNotNone(outputs.auxiliary_outputs) self.assertEqual(len(outputs.auxiliary_outputs), self.model_tester.num_hidden_layers - 1) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = ["pixel_values", "pixel_mask"] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" in arg_names else [] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["pixel_values", "pixel_mask"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_different_timm_backbone(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # let's pick a random timm backbone config.backbone = "tf_mobilenetv3_small_075" config.use_timm_backbone = True config.backbone_config = None for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if model_class.__name__ == "DeformableDetrForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, self.model_tester.num_labels, ) self.assertEqual(outputs.logits.shape, expected_shape) self.assertTrue(outputs) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: print("Model class:", model_class) model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: if param.requires_grad: if ( "level_embed" in name or "sampling_offsets.bias" in name or "value_proj" in name or "output_proj" in name or "reference_points" in name ): continue self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_two_stage_training(self): model_class = DeformableDetrForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True config.two_stage = True config.auxiliary_loss = True config.with_box_refine = True model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def create_and_check_model_fp16_forward(self): model_class = DeformableDetrForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) model.to(torch_device) model.half() model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) output = model(**inputs)["last_hidden_state"] self.parent.assertFalse(torch.isnan(output).any().item()) TOLERANCE = 1e-4 # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_timm @require_vision @slow class DeformableDetrModelIntegrationTests(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") if is_vision_available() else None def test_inference_object_detection_head(self): model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr").to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt").to(torch_device) pixel_values = encoding["pixel_values"].to(torch_device) pixel_mask = encoding["pixel_mask"].to(torch_device) with torch.no_grad(): outputs = model(pixel_values, pixel_mask) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_logits = torch.tensor( [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]] ).to(torch_device) expected_boxes = torch.tensor( [[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4)) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4)) # verify postprocessing results = image_processor.post_process_object_detection( outputs, threshold=0.3, target_sizes=[image.size[::-1]] )[0] expected_scores = torch.tensor([0.7999, 0.7894, 0.6331, 0.4720, 0.4382]).to(torch_device) expected_labels = [17, 17, 75, 75, 63] expected_slice_boxes = torch.tensor([16.5028, 52.8390, 318.2544, 470.7841]).to(torch_device) self.assertEqual(len(results["scores"]), 5) self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4)) self.assertSequenceEqual(results["labels"].tolist(), expected_labels) self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes)) def test_inference_object_detection_head_with_box_refine_two_stage(self): model = DeformableDetrForObjectDetection.from_pretrained( "SenseTime/deformable-detr-with-box-refine-two-stage" ).to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt").to(torch_device) pixel_values = encoding["pixel_values"].to(torch_device) pixel_mask = encoding["pixel_mask"].to(torch_device) with torch.no_grad(): outputs = model(pixel_values, pixel_mask) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_logits = torch.tensor( [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]] ).to(torch_device) expected_boxes = torch.tensor( [[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4)) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4)) @require_torch_accelerator def test_inference_object_detection_head_equivalence_cpu_gpu(self): image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt") pixel_values = encoding["pixel_values"] pixel_mask = encoding["pixel_mask"] # 1. run model on CPU model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr-single-scale") with torch.no_grad(): cpu_outputs = model(pixel_values, pixel_mask) # 2. run model on GPU model.to(torch_device) with torch.no_grad(): gpu_outputs = model(pixel_values.to(torch_device), pixel_mask.to(torch_device)) # 3. assert equivalence for key in cpu_outputs.keys(): assert torch.allclose(cpu_outputs[key], gpu_outputs[key].cpu(), atol=1e-4) expected_logits = torch.tensor( [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]] ) assert torch.allclose(cpu_outputs.logits[0, :3, :3], expected_logits, atol=1e-4)

transformers/tests/models/deformable_detr/test_modeling_deformable_detr.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Dinov2 model. """ import unittest from transformers import Dinov2Config from transformers.testing_utils import ( is_flaky, require_torch, require_vision, slow, torch_device, ) from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import Dinov2Backbone, Dinov2ForImageClassification, Dinov2Model from transformers.models.dinov2.modeling_dinov2 import DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class Dinov2ModelTester: def __init__( self, parent, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope # in Dinov2, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return Dinov2Config( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels): model = Dinov2Model(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_backbone(self, config, pixel_values, labels): model = Dinov2Backbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify hidden states self.parent.assertEqual(len(result.feature_maps), len(config.out_features)) expected_size = self.image_size // config.patch_size self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], expected_size, expected_size] ) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = Dinov2Backbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], expected_size, expected_size] ) # verify channels self.parent.assertEqual(len(model.channels), 1) # verify backbone works with apply_layernorm=False and reshape_hidden_states=False config.apply_layernorm = False config.reshape_hidden_states = False model = Dinov2Backbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.seq_length, self.hidden_size] ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = Dinov2ForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) # test greyscale images config.num_channels = 1 model = Dinov2ForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, pixel_values, labels, ) = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class Dinov2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Dinov2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( Dinov2Model, Dinov2ForImageClassification, Dinov2Backbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-feature-extraction": Dinov2Model, "image-classification": Dinov2ForImageClassification} if is_torch_available() else {} ) fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = Dinov2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Dinov2Config, has_text_modality=False, hidden_size=37) @is_flaky(max_attempts=3, description="`torch.nn.init.trunc_normal_` is flaky.") def test_initialization(self): super().test_initialization() def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Dinov2 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*config_and_inputs) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @unittest.skip(reason="Dinov2 does not support feedforward chunking yet") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): for model_name in DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = Dinov2Model.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class Dinov2ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("facebook/dinov2-base") if is_vision_available() else None @slow def test_inference_no_head(self): model = Dinov2Model.from_pretrained("facebook/dinov2-base").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the last hidden states expected_shape = torch.Size((1, 257, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-2.1747, -0.4729, 1.0936], [-3.2780, -0.8269, -0.9210], [-2.9129, 1.1284, -0.7306]], device=torch_device, ) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4)) @require_torch class Dinov2BackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (Dinov2Backbone,) if is_torch_available() else () config_class = Dinov2Config has_attentions = False def setUp(self): self.model_tester = Dinov2ModelTester(self)

transformers/tests/models/dinov2/test_modeling_dinov2.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import unittest from transformers import EsmConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers.modeling_tf_utils import keras from transformers.models.esm.modeling_tf_esm import ( TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST, TFEsmForMaskedLM, TFEsmForSequenceClassification, TFEsmForTokenClassification, TFEsmModel, ) # copied from tests.test_modeling_tf_roberta class TFEsmModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = EsmConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, pad_token_id=1, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = TFEsmModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFEsmModel(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, encoder_hidden_states=encoder_hidden_states) # Also check the case where encoder outputs are not passed result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFEsmForMaskedLM(config=config) result = model([input_ids, input_mask]) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_token_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFEsmForTokenClassification(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TFEsmModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFEsmModel, TFEsmForMaskedLM, TFEsmForSequenceClassification, TFEsmForTokenClassification, ) if is_tf_available() else () ) pipeline_model_mapping = ( { "feature-extraction": TFEsmModel, "fill-mask": TFEsmForMaskedLM, "text-classification": TFEsmForSequenceClassification, "token-classification": TFEsmForTokenClassification, "zero-shot": TFEsmForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFEsmModelTester(self) self.config_tester = ConfigTester(self, config_class=EsmConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): """Test the base model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_as_decoder(self): """Test the base model as a decoder (of an encoder-decoder architecture) is_deocder=True + cross_attention + pass encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFEsmModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip("Protein models do not support embedding resizing.") def test_resize_token_embeddings(self): pass @unittest.skip("Protein models do not support embedding resizing.") def test_save_load_after_resize_token_embeddings(self): pass def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), keras.layers.Layer) if model_class is TFEsmForMaskedLM: # Output embedding test differs from the main test because they're a matrix, not a layer name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None @require_tf class TFEsmModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = [1, 6, 33] self.assertEqual(list(output.numpy().shape), expected_shape) # compare the actual values for a slice. expected_slice = tf.constant( [ [ [8.921518, -10.589814, -6.4671307], [-6.3967156, -13.911377, -1.1211915], [-7.781247, -13.951557, -3.740592], ] ] ) self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-2)) @slow def test_inference_no_head(self): model = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D") input_ids = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]) output = model(input_ids)[0] # compare the actual values for a slice. expected_slice = tf.constant( [ [ [0.14443092, 0.54125327, 0.3247739], [0.30340484, 0.00526676, 0.31077722], [0.32278043, -0.24987096, 0.3414628], ] ] ) self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))

transformers/tests/models/esm/test_modeling_tf_esm.py/0

# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Gemma model. """ import tempfile import unittest import pytest from parameterized import parameterized from transformers import AutoModelForCausalLM, AutoTokenizer, GemmaConfig, is_torch_available from transformers.testing_utils import ( require_bitsandbytes, require_flash_attn, require_torch, require_torch_gpu, require_torch_sdpa, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import GemmaForCausalLM, GemmaForSequenceClassification, GemmaModel class GemmaModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=2, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, pad_token_id=0, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.pad_token_id = pad_token_id self.scope = scope self.head_dim = self.hidden_size // self.num_attention_heads # Copied from tests.models.mistral.test_modeling_mistral.MistralModelTester.prepare_config_and_inputs def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels # Ignore copy def get_config(self): return GemmaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, head_dim=self.head_dim, ) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Gemma def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = GemmaModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Gemma def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = GemmaModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Gemma def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = GemmaForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Gemma def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = GemmaForCausalLM(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common with Llama->Gemma def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (GemmaModel, GemmaForCausalLM, GemmaForSequenceClassification) if is_torch_available() else () all_generative_model_classes = (GemmaForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": GemmaModel, "text-classification": GemmaForSequenceClassification, "text-generation": GemmaForCausalLM, "zero-shot": GemmaForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146 def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True def setUp(self): self.model_tester = GemmaModelTester(self) self.config_tester = ConfigTester(self, config_class=GemmaConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_Gemma_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() print(config) config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = GemmaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_Gemma_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = GemmaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_Gemma_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = GemmaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @unittest.skip("TODO @gante fix this for Llama") @parameterized.expand([(1, False), (1, True), (4, False)]) def test_new_cache_format(self, num_beams, do_sample): pass @unittest.skip("Gemma buffers include complex numbers, which breaks this test") def test_save_load_fast_init_from_base(self): pass @unittest.skip("Gemma uses GQA on all models so the KV cache is a non standard format") def test_past_key_values_format(self): pass @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_padding_right(self): import torch for model_class in self.all_generative_model_classes: config, _ = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to( torch_device ) dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device) dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device) model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) with self.assertRaises(ValueError): _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_use_cache(self): import torch max_new_tokens = 30 for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) # NOTE: Gemma apparently does not support right padding + use_cache with FA2. dummy_attention_mask[:, -1] = 1 model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) # Just test that a large cache works as expected _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False, use_cache=True, ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_padding_right(self): self.skipTest("Gemma flash attention does not support right padding") @require_torch_sdpa @require_torch_gpu @slow def test_sdpa_equivalence(self): for model_class in self.all_model_classes: if not model_class._supports_sdpa: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_sdpa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="sdpa" ) model_sdpa.to(torch_device) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="eager") model.to(torch_device) dummy_input = inputs_dict[model_class.main_input_name] dummy_input = dummy_input.to(torch_device) outputs = model(dummy_input, output_hidden_states=True) outputs_sdpa = model_sdpa(dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_sdpa = outputs_sdpa.hidden_states[-1] # gemma sdpa needs a high tolerance assert torch.allclose(logits_sdpa, logits, atol=3e-3) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_equivalence(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="eager") model.to(torch_device) dummy_input = inputs_dict[model_class.main_input_name] dummy_input = dummy_input.to(torch_device) outputs = model(dummy_input, output_hidden_states=True) outputs_fa = model_fa(dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] # gemma flash attention 2 needs a high tolerance assert torch.allclose(logits_fa, logits, atol=3e-3) @require_torch_gpu @slow class GemmaIntegrationTest(unittest.TestCase): input_text = ["Hello I am doing", "Hi today"] def test_model_2b_fp32(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) def test_model_2b_fp16(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) def test_model_2b_fp16_static_cache(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) model.generation_config.cache_implementation = "static" tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) def test_model_2b_bf16(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to( torch_device ) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) def test_model_2b_eager(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I am looking for some information on the ", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained( model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager" ) model.to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_torch_sdpa def test_model_2b_sdpa(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi", ] model = AutoModelForCausalLM.from_pretrained( model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="sdpa" ) model.to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @pytest.mark.flash_attn_test @require_flash_attn def test_model_2b_flash_attn(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained( model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model.to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_bitsandbytes def test_model_2b_4bit(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project and I need to make a 3d model of a house. I have been using", "Hi today I'd like to share with you my experience with the new wattpad wattpad wattpad wattpad wattpad wattpad wattpad", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @unittest.skip("The test will not fit our CI runners") def test_model_7b_fp32(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ "Hello my name is ***** ***** I will be assisting you today. I am sorry to hear about your issue. I will", "Hi,\n\nI have a problem with my 2005 1.6 16", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) def test_model_7b_fp16(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ """Hello I am doing a project on a 1999 4.0L 4x4. I""", "Hi today I am going to show you how to make a simple and easy to make a DIY 3D", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) def test_model_7b_bf16(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ """Hello I am doing a project on a 1991 240sx and I am trying to find""", "Hi today I am going to show you how to make a very simple and easy to make a very simple and", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to( torch_device ) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) def test_model_7b_fp16_static_cache(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ """Hello I am doing a project on a 1999 4.0L 4x4. I""", "Hi today I am going to show you how to make a simple and easy to make a DIY 3D", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) model.generation_config.cache_implementation = "static" tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_bitsandbytes def test_model_7b_4bit(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ "Hello I am doing a project for my school and I am trying to make a program that will take a number and then", """Hi today I am going to talk about the new update for the game called "The new update" and I""", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS)

transformers/tests/models/gemma/test_modeling_gemma.py/0

# coding=utf-8 # Copyright 2023 Toshiyuki Sakamoto(tanreinama) and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import unittest from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import ( VOCAB_FILES_NAMES, GPTSanJapaneseTokenizer, ) from transformers.testing_utils import require_jinja, require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class GPTSanJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = GPTSanJapaneseTokenizer test_rust_tokenizer = False from_pretrained_kwargs = {"do_clean_text": False, "add_prefix_space": False} def setUp(self): super().setUp() vocab_tokens = ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"] # fmt: skip emoji_tokens = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}} # 😀 self.special_tokens_map = {"unk_token": "<unk>"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.emoji_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["emoji_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) with open(self.emoji_file, "w") as emoji_writer: emoji_writer.write(json.dumps(emoji_tokens)) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname, **kwargs) # Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.get_input_output_texts def get_input_output_texts(self, tokenizer): input_text = "こんにちは、世界。 \nこんばんは、㔺界。😀" output_text = "こんにちは、世界。 \nこんばんは、世界。😀" return input_text, output_text # Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.get_clean_sequence def get_clean_sequence(self, tokenizer): input_text, output_text = self.get_input_output_texts(tokenizer) ids = tokenizer.encode(output_text, add_special_tokens=False) text = tokenizer.decode(ids, clean_up_tokenization_spaces=False) return text, ids # Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_pretokenized_inputs def test_pretokenized_inputs(self): pass # TODO add if relevant # Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_maximum_encoding_length_pair_input def test_maximum_encoding_length_pair_input(self): pass # TODO add if relevant # Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_maximum_encoding_length_single_input def test_maximum_encoding_length_single_input(self): pass # TODO add if relevant # Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_full_tokenizer def test_full_tokenizer(self): tokenizer = self.get_tokenizer() # Testing tokenization input_text = "こんにちは、世界。　こんばんは、㔺界。" expected_token = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"] tokens = tokenizer.tokenize(input_text) self.assertListEqual(tokens, expected_token) # Testing conversion to ids without special tokens expected_ids = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6] input_ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual(input_ids, expected_ids) # Testing conversion to ids with special tokens input_tokens = tokens + [tokenizer.unk_token] expected_ids = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19] input_ids = tokenizer.convert_tokens_to_ids(input_tokens) self.assertListEqual(input_ids, expected_ids) def test_token_bagging(self): tokenizer = self.get_tokenizer() # Testing tokenization input_text = "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。" expected_text = "こんにちは、、、、世界。こんばんは、、、、世界。" tokens = tokenizer.encode(input_text) output_text = tokenizer.decode(tokens) self.assertEqual(output_text, expected_text) @slow def test_prefix_input(self): tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese") # Testing tokenization prefix_text = "こんにちは、世界。" input_text = "こんばんは、㔺界。😀" expected_text = "こんにちは、世界。こんばんは、世界。😀" tokens_1 = tokenizer.encode(prefix_text + input_text) tokens_2 = tokenizer.encode("", prefix_text=prefix_text + input_text) tokens_3 = tokenizer.encode(input_text, prefix_text=prefix_text) output_text_1 = tokenizer.decode(tokens_1) output_text_2 = tokenizer.decode(tokens_2) output_text_3 = tokenizer.decode(tokens_3) self.assertEqual(output_text_1, expected_text) self.assertEqual(output_text_2, expected_text) self.assertEqual(output_text_3, expected_text) @slow def test_token_type_ids(self): tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese") # Testing tokenization prefix_text = "こんにちは、世界。" input_text = "こんばんは、㔺界。😀" len_prefix = len(tokenizer.encode(prefix_text)) - 2 len_text = len(tokenizer.encode(input_text)) - 2 expected_mask_1 = [1] + [0] * (len_prefix + len_text + 1) expected_mask_2 = [1] * (len_prefix + len_text + 1) + [0] expected_mask_3 = [1] + [1] * (len_prefix) + [0] * (len_text + 1) type_id_1 = tokenizer(prefix_text + input_text).token_type_ids type_id_2 = tokenizer("", prefix_text=prefix_text + input_text).token_type_ids type_id_3 = tokenizer(input_text, prefix_text=prefix_text).token_type_ids self.assertListEqual(type_id_1, expected_mask_1) self.assertListEqual(type_id_2, expected_mask_2) self.assertListEqual(type_id_3, expected_mask_3) @slow def test_prefix_tokens(self): tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese") x_token_1 = tokenizer.encode("あンいワ") x_token_2 = tokenizer.encode("", prefix_text="あンいワ") x_token_3 = tokenizer.encode("いワ", prefix_text="あン") self.assertEqual(tokenizer.decode(x_token_1), tokenizer.decode(x_token_2)) self.assertEqual(tokenizer.decode(x_token_1), tokenizer.decode(x_token_3)) self.assertNotEqual(x_token_1, x_token_2) self.assertNotEqual(x_token_1, x_token_3) self.assertEqual(x_token_1[1], x_token_2[-1]) # SEG token self.assertEqual(x_token_1[1], x_token_3[3]) # SEG token @slow def test_batch_encode(self): tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese") input_pairs = [["武田信玄", "は、"], ["織田信長", "の配下の、"]] x_token = tokenizer(input_pairs, padding=True) x_token_2 = tokenizer.batch_encode_plus(input_pairs, padding=True) # fmt: off expected_outputs = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]] expected_typeids = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]] expected_attmask = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]] # fmt: on self.assertListEqual(x_token.input_ids, expected_outputs) self.assertListEqual(x_token.token_type_ids, expected_typeids) self.assertListEqual(x_token.attention_mask, expected_attmask) self.assertListEqual(x_token_2.input_ids, expected_outputs) self.assertListEqual(x_token_2.token_type_ids, expected_typeids) self.assertListEqual(x_token_2.attention_mask, expected_attmask) # Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_conversion_reversible def test_conversion_reversible(self): # Intentionally convert some words to accommodate character fluctuations unique to Japanese pass # Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_padding_different_model_input_name def test_padding_different_model_input_name(self): # tokenizer has no padding token pass @require_jinja def test_tokenization_for_chat(self): tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese") # This is in English, but it's just here to make sure the chat control tokens are being added properly test_chats = [ [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}], [ {"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Nice to meet you."}, ], [{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}], ] tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats] # fmt: off expected_tokens = [ [35993, 35998, 35637, 35659, 35665, 35716, 35645, 35662, 35649, 35716, 35645, 35716, 35652, 35649, 35656, 35660, 35650, 35665, 35656, 35716, 35647, 35652, 35645, 35664, 35646, 35659, 35664, 35595, 35716, 35999, 35993, 35998, 35620, 35649, 35656, 35656, 35659, 35582, 35716, 35999], [35993, 35998, 35637, 35659, 35665, 35716, 35645, 35662, 35649, 35716, 35645, 35716, 35652, 35649, 35656, 35660, 35650, 35665, 35656, 35716, 35647, 35652, 35645, 35664, 35646, 35659, 35664, 35595, 35716, 35999, 35993, 35998, 35620, 35649, 35656, 35656, 35659, 35582, 35716, 35999, 35993, 35998, 35626, 35653, 35647, 35649, 35716, 35664, 35659, 35716, 35657, 35649, 35649, 35664, 35716, 35669, 35659, 35665, 35595, 35716, 35999], [35993, 35998, 35626, 35653, 35647, 35649, 35716, 35664, 35659, 35716, 35657, 35649, 35649, 35664, 35716, 35669, 35659, 35665, 35595, 35716, 35999, 35993, 35998, 35620, 35649, 35656, 35656, 35659, 35582, 35716, 35999] ] # fmt: on for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens): self.assertListEqual(tokenized_chat, expected_tokens)

transformers/tests/models/gptsan_japanese/test_tokenization_gptsan_japanese.py/0

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from transformers.testing_utils import TestCasePlus, require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ( AutoProcessor, IdeficsImageProcessor, IdeficsProcessor, LlamaTokenizerFast, PreTrainedTokenizerFast, ) @require_torch @require_vision class IdeficsProcessorTest(TestCasePlus): def setUp(self): super().setUp() self.checkpoint_path = self.get_auto_remove_tmp_dir() image_processor = IdeficsImageProcessor() tokenizer = LlamaTokenizerFast.from_pretrained("HuggingFaceM4/tiny-random-idefics") processor = IdeficsProcessor(image_processor, tokenizer) processor.save_pretrained(self.checkpoint_path) self.input_keys = ["pixel_values", "input_ids", "attention_mask", "image_attention_mask"] def get_tokenizer(self, **kwargs): return AutoProcessor.from_pretrained(self.checkpoint_path, **kwargs).tokenizer def get_image_processor(self, **kwargs): return AutoProcessor.from_pretrained(self.checkpoint_path, **kwargs).image_processor def prepare_prompts(self): """This function prepares a list of PIL images""" num_images = 2 images = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8) for x in range(num_images)] images = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in images] # print([type(x) for x in images]) # die prompts = [ # text and 1 image [ "User:", images[0], "Describe this image.\nAssistant:", ], # text and images [ "User:", images[0], "Describe this image.\nAssistant: An image of two dogs.\n", "User:", images[1], "Describe this image.\nAssistant:", ], # only text [ "User:", "Describe this image.\nAssistant: An image of two kittens.\n", "User:", "Describe this image.\nAssistant:", ], # only images [ images[0], images[1], ], ] return prompts def test_save_load_pretrained_additional_features(self): processor = IdeficsProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) processor.save_pretrained(self.checkpoint_path) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0) processor = IdeficsProcessor.from_pretrained( self.checkpoint_path, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0 ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, IdeficsImageProcessor) def test_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor) prompts = self.prepare_prompts() # test that all prompts succeeded input_processor = processor(prompts, return_tensors="pt") for key in self.input_keys: assert torch.is_tensor(input_processor[key]) def test_tokenizer_decode(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor) predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] decoded_processor = processor.batch_decode(predicted_ids) decoded_tok = tokenizer.batch_decode(predicted_ids) self.assertListEqual(decoded_tok, decoded_processor) def test_tokenizer_padding(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer(padding_side="right") processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor) predicted_tokens = [ "<s> Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk>", "<s> Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>", ] prompts = [[prompt] for prompt in self.prepare_prompts()[2]] max_length = processor(prompts, padding="max_length", truncation=True, max_length=20) longest = processor(prompts, padding="longest", truncation=True, max_length=30) decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1]) decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1]) self.assertEqual(decoded_max_length, predicted_tokens[1]) self.assertEqual(decoded_longest, predicted_tokens[0]) def test_model_input_names(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor) prompts = self.prepare_prompts() inputs = processor(prompts) # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask'] self.assertSetEqual(set(inputs.keys()), set(self.input_keys))

transformers/tests/models/idefics/test_processor_idefics.py/0

# coding=utf-8 # Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import LayoutLMConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( LayoutLMForMaskedLM, LayoutLMForQuestionAnswering, LayoutLMForSequenceClassification, LayoutLMForTokenClassification, LayoutLMModel, ) class LayoutLMModelTester: """You can also import this e.g from .test_modeling_layoutlm import LayoutLMModelTester""" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, range_bbox=1000, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.range_bbox = range_bbox def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox) # Ensure that bbox is legal for i in range(bbox.shape[0]): for j in range(bbox.shape[1]): if bbox[i, j, 3] < bbox[i, j, 1]: t = bbox[i, j, 3] bbox[i, j, 3] = bbox[i, j, 1] bbox[i, j, 1] = t if bbox[i, j, 2] < bbox[i, j, 0]: t = bbox[i, j, 2] bbox[i, j, 2] = bbox[i, j, 0] bbox[i, j, 0] = t input_mask = None if self.use_input_mask: input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return LayoutLMConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) def create_and_check_model( self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LayoutLMModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, bbox, token_type_ids=token_type_ids) result = model(input_ids, bbox) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LayoutLMForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_sequence_classification( self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = LayoutLMForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = LayoutLMForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LayoutLMForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "bbox": bbox, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_torch class LayoutLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( LayoutLMModel, LayoutLMForMaskedLM, LayoutLMForSequenceClassification, LayoutLMForTokenClassification, LayoutLMForQuestionAnswering, ) if is_torch_available() else None ) pipeline_model_mapping = ( { "document-question-answering": LayoutLMForQuestionAnswering, "feature-extraction": LayoutLMModel, "fill-mask": LayoutLMForMaskedLM, "text-classification": LayoutLMForSequenceClassification, "token-classification": LayoutLMForTokenClassification, "zero-shot": LayoutLMForSequenceClassification, } if is_torch_available() else {} ) fx_compatible = True def setUp(self): self.model_tester = LayoutLMModelTester(self) self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass def prepare_layoutlm_batch_inputs(): # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on: # fmt: off input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]],device=torch_device) # noqa: E231 attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],device=torch_device) # noqa: E231 bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],device=torch_device) # noqa: E231 token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],device=torch_device) # noqa: E231 # these are sequence labels (i.e. at the token level) labels = torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],device=torch_device) # noqa: E231 # fmt: on return input_ids, attention_mask, bbox, token_type_ids, labels @require_torch class LayoutLMModelIntegrationTest(unittest.TestCase): @slow def test_forward_pass_no_head(self): model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device) input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs() # forward pass outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids) # test the sequence output on [0, :3, :3] expected_slice = torch.tensor( [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]], device=torch_device, ) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3)) # test the pooled output on [1, :3] expected_slice = torch.tensor([-0.6580, -0.0214, 0.8552], device=torch_device) self.assertTrue(torch.allclose(outputs.pooler_output[1, :3], expected_slice, atol=1e-3)) @slow def test_forward_pass_sequence_classification(self): # initialize model with randomly initialized sequence classification head model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2).to( torch_device ) input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs() # forward pass outputs = model( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=torch.tensor([1, 1], device=torch_device), ) # test whether we get a loss as a scalar loss = outputs.loss expected_shape = torch.Size([]) self.assertEqual(loss.shape, expected_shape) # test the shape of the logits logits = outputs.logits expected_shape = torch.Size((2, 2)) self.assertEqual(logits.shape, expected_shape) @slow def test_forward_pass_token_classification(self): # initialize model with randomly initialized token classification head model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13).to( torch_device ) input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs() # forward pass outputs = model( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels ) # test the loss calculation to be around 2.65 # expected_loss = torch.tensor(2.65, device=torch_device) # The loss is currently somewhat random and can vary between 0.1-0.3 atol. # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1)) # test the shape of the logits logits = outputs.logits expected_shape = torch.Size((2, 25, 13)) self.assertEqual(logits.shape, expected_shape) @slow def test_forward_pass_question_answering(self): # initialize model with randomly initialized token classification head model = LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device) input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs() # forward pass outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids) # test the shape of the logits expected_shape = torch.Size((2, 25)) self.assertEqual(outputs.start_logits.shape, expected_shape) self.assertEqual(outputs.end_logits.shape, expected_shape)

transformers/tests/models/layoutlm/test_modeling_layoutlm.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch MaskFormer model. """ import copy import unittest import numpy as np from tests.test_modeling_common import floats_tensor from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available from transformers.testing_utils import ( require_torch, require_torch_accelerator, require_torch_fp16, require_torch_multi_gpu, require_vision, slow, torch_device, ) from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel if is_vision_available(): from transformers import MaskFormerImageProcessor if is_vision_available(): from PIL import Image class MaskFormerModelTester: def __init__( self, parent, batch_size=2, is_training=True, use_auxiliary_loss=False, num_queries=10, num_channels=3, min_size=32 * 4, max_size=32 * 6, num_labels=4, mask_feature_size=32, num_hidden_layers=2, num_attention_heads=2, ): self.parent = parent self.batch_size = batch_size self.is_training = is_training self.use_auxiliary_loss = use_auxiliary_loss self.num_queries = num_queries self.num_channels = num_channels self.min_size = min_size self.max_size = max_size self.num_labels = num_labels self.mask_feature_size = mask_feature_size # This is passed to the decoder config. We add it to the model tester here for testing self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to( torch_device ) pixel_mask = torch.ones([self.batch_size, self.min_size, self.max_size], device=torch_device) mask_labels = ( torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size], device=torch_device) > 0.5 ).float() class_labels = (torch.rand((self.batch_size, self.num_labels), device=torch_device) > 0.5).long() config = self.get_config() return config, pixel_values, pixel_mask, mask_labels, class_labels def get_config(self): return MaskFormerConfig.from_backbone_and_decoder_configs( backbone_config=SwinConfig( depths=[1, 1, 1, 1], embed_dim=16, hidden_size=32, num_heads=[1, 1, 2, 2], ), backbone=None, decoder_config=DetrConfig( decoder_ffn_dim=64, decoder_layers=self.num_hidden_layers, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=64, encoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, num_queries=self.num_queries, d_model=self.mask_feature_size, ), mask_feature_size=self.mask_feature_size, fpn_feature_size=self.mask_feature_size, num_channels=self.num_channels, num_labels=self.num_labels, ) def prepare_config_and_inputs_for_common(self): config, pixel_values, pixel_mask, _, _ = self.prepare_config_and_inputs() inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask} return config, inputs_dict def check_output_hidden_state(self, output, config): encoder_hidden_states = output.encoder_hidden_states pixel_decoder_hidden_states = output.pixel_decoder_hidden_states transformer_decoder_hidden_states = output.transformer_decoder_hidden_states self.parent.assertTrue(len(encoder_hidden_states), len(config.backbone_config.depths)) self.parent.assertTrue(len(pixel_decoder_hidden_states), len(config.backbone_config.depths)) self.parent.assertTrue(len(transformer_decoder_hidden_states), config.decoder_config.decoder_layers) def create_and_check_maskformer_model(self, config, pixel_values, pixel_mask, output_hidden_states=False): with torch.no_grad(): model = MaskFormerModel(config=config) model.to(torch_device) model.eval() output = model(pixel_values=pixel_values, pixel_mask=pixel_mask) output = model(pixel_values, output_hidden_states=True) # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the # encoder and pixel decoder self.parent.assertEqual( output.transformer_decoder_last_hidden_state.shape, (self.batch_size, self.num_queries, self.mask_feature_size), ) # let's ensure the other two hidden state exists self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None) self.parent.assertTrue(output.encoder_last_hidden_state is not None) if output_hidden_states: self.check_output_hidden_state(output, config) def create_and_check_maskformer_instance_segmentation_head_model( self, config, pixel_values, pixel_mask, mask_labels, class_labels ): model = MaskFormerForInstanceSegmentation(config=config) model.to(torch_device) model.eval() def comm_check_on_output(result): # let's still check that all the required stuff is there self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None) self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None) self.parent.assertTrue(result.encoder_last_hidden_state is not None) # okay, now we need to check the logits shape # due to the encoder compression, masks have a //4 spatial size self.parent.assertEqual( result.masks_queries_logits.shape, (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4), ) # + 1 for null class self.parent.assertEqual( result.class_queries_logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1) ) with torch.no_grad(): result = model(pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(pixel_values) comm_check_on_output(result) result = model( pixel_values=pixel_values, pixel_mask=pixel_mask, mask_labels=mask_labels, class_labels=class_labels ) comm_check_on_output(result) self.parent.assertTrue(result.loss is not None) self.parent.assertEqual(result.loss.shape, torch.Size([])) @require_torch class MaskFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation} if is_torch_available() else {} ) is_encoder_decoder = False test_pruning = False test_head_masking = False test_missing_keys = False def setUp(self): self.model_tester = MaskFormerModelTester(self) self.config_tester = ConfigTester(self, config_class=MaskFormerConfig, has_text_modality=False) def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if return_labels: if model_class in [MaskFormerForInstanceSegmentation]: inputs_dict["mask_labels"] = torch.zeros( ( self.model_tester.batch_size, self.model_tester.num_labels, self.model_tester.min_size, self.model_tester.max_size, ), dtype=torch.float32, device=torch_device, ) inputs_dict["class_labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.num_labels), dtype=torch.long, device=torch_device ) return inputs_dict def test_config(self): self.config_tester.run_common_tests() def test_maskformer_model(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskformer_model(config, **inputs, output_hidden_states=False) def test_maskformer_instance_segmentation_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*config_and_inputs) @unittest.skip(reason="MaskFormer does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method") def test_model_common_attributes(self): pass @unittest.skip(reason="MaskFormer is not a generative model") def test_generate_without_input_ids(self): pass @unittest.skip(reason="MaskFormer does not use token embeddings") def test_resize_tokens_embeddings(self): pass @require_torch_multi_gpu @unittest.skip( reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" ) def test_multi_gpu_data_parallel_forward(self): pass @slow def test_model_from_pretrained(self): for model_name in ["facebook/maskformer-swin-small-coco"]: model = MaskFormerModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_model_with_labels(self): size = (self.model_tester.min_size,) * 2 inputs = { "pixel_values": torch.randn((2, 3, *size), device=torch_device), "mask_labels": torch.randn((2, 10, *size), device=torch_device), "class_labels": torch.zeros(2, 10, device=torch_device).long(), } model = MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(torch_device) outputs = model(**inputs) self.assertTrue(outputs.loss is not None) def test_hidden_states_output(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskformer_model(config, **inputs, output_hidden_states=True) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # Check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) # encoder_hidden_states, pixel_decoder_hidden_states, transformer_decoder_hidden_states, hidden_states added_hidden_states = 4 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) def test_retain_grad_hidden_states_attentions(self): # only MaskFormerForInstanceSegmentation has the loss model_class = self.all_model_classes[1] config, pixel_values, pixel_mask, mask_labels, class_labels = self.model_tester.prepare_config_and_inputs() config.output_hidden_states = True config.output_attentions = True model = model_class(config) model.to(torch_device) model.train() outputs = model(pixel_values, mask_labels=mask_labels, class_labels=class_labels) encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() pixel_decoder_hidden_states = outputs.pixel_decoder_hidden_states[0] pixel_decoder_hidden_states.retain_grad() # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't transformer_decoder_hidden_states = outputs.transformer_decoder_hidden_states[0] transformer_decoder_hidden_states.retain_grad() attentions = outputs.attentions[0] attentions.retain_grad() outputs.loss.backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(pixel_decoder_hidden_states.grad) self.assertIsNotNone(transformer_decoder_hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_forward_auxiliary_loss(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_auxiliary_loss = True config.output_auxiliary_logits = True config.output_hidden_states = True # only test for object detection and segmentation model for model_class in self.all_model_classes[1:]: model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) outputs = model(**inputs) self.assertIsNotNone(outputs.auxiliary_logits) self.assertEqual(len(outputs.auxiliary_logits), self.model_tester.num_channels - 1) TOLERANCE = 1e-4 # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_vision @slow class MaskFormerModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco") if is_vision_available() else None ) def test_inference_no_head(self): model = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device) inputs_shape = inputs["pixel_values"].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0) # check size self.assertEqual(inputs_shape, (1, 3, 800, 1088)) with torch.no_grad(): outputs = model(**inputs) expected_slice_hidden_state = torch.tensor( [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]] ).to(torch_device) self.assertTrue( torch.allclose( outputs.encoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE ) ) expected_slice_hidden_state = torch.tensor( [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]] ).to(torch_device) self.assertTrue( torch.allclose( outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE ) ) expected_slice_hidden_state = torch.tensor( [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]] ).to(torch_device) self.assertTrue( torch.allclose( outputs.transformer_decoder_last_hidden_state[0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE ) ) def test_inference_instance_segmentation_head(self): model = ( MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco") .to(torch_device) .eval() ) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device) inputs_shape = inputs["pixel_values"].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0) # check size self.assertEqual(inputs_shape, (1, 3, 800, 1088)) with torch.no_grad(): outputs = model(**inputs) # masks_queries_logits masks_queries_logits = outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape, (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4), ) expected_slice = [ [-1.3737124, -1.7724937, -1.9364233], [-1.5977281, -1.9867939, -2.1523695], [-1.5795398, -1.9269832, -2.093942], ] expected_slice = torch.tensor(expected_slice).to(torch_device) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], expected_slice, atol=TOLERANCE)) # class_queries_logits class_queries_logits = outputs.class_queries_logits self.assertEqual( class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) ) expected_slice = torch.tensor( [ [1.6512e00, -5.2572e00, -3.3519e00], [3.6169e-02, -5.9025e00, -2.9313e00], [1.0766e-04, -7.7630e00, -5.1263e00], ] ).to(torch_device) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_slice, atol=TOLERANCE)) def test_inference_instance_segmentation_head_resnet_backbone(self): model = ( MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff") .to(torch_device) .eval() ) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device) inputs_shape = inputs["pixel_values"].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0) # check size self.assertEqual(inputs_shape, (1, 3, 800, 1088)) with torch.no_grad(): outputs = model(**inputs) # masks_queries_logits masks_queries_logits = outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape, (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4), ) expected_slice = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]] expected_slice = torch.tensor(expected_slice).to(torch_device) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], expected_slice, atol=TOLERANCE)) # class_queries_logits class_queries_logits = outputs.class_queries_logits self.assertEqual( class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) ) expected_slice = torch.tensor( [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_slice, atol=TOLERANCE)) @require_torch_accelerator @require_torch_fp16 def test_inference_fp16(self): model = ( MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff") .to(torch_device, dtype=torch.float16) .eval() ) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device, dtype=torch.float16) with torch.no_grad(): _ = model(**inputs) def test_with_segmentation_maps_and_loss(self): model = ( MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco") .to(torch_device) .eval() ) image_processor = self.default_image_processor inputs = image_processor( [np.zeros((3, 400, 333)), np.zeros((3, 400, 333))], segmentation_maps=[np.zeros((384, 384)).astype(np.float32), np.zeros((384, 384)).astype(np.float32)], return_tensors="pt", ) inputs["pixel_values"] = inputs["pixel_values"].to(torch_device) inputs["mask_labels"] = [el.to(torch_device) for el in inputs["mask_labels"]] inputs["class_labels"] = [el.to(torch_device) for el in inputs["class_labels"]] with torch.no_grad(): outputs = model(**inputs) self.assertTrue(outputs.loss is not None)

transformers/tests/models/maskformer/test_modeling_maskformer.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch MGP-STR model. """ import unittest import requests from transformers import MgpstrConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import MgpstrForSceneTextRecognition, MgpstrModel if is_vision_available(): from PIL import Image from transformers import MgpstrProcessor class MgpstrModelTester: def __init__( self, parent, is_training=False, batch_size=13, image_size=(32, 128), patch_size=4, num_channels=3, max_token_length=27, num_character_labels=38, num_bpe_labels=99, num_wordpiece_labels=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, mlp_ratio=4.0, patch_embeds_hidden_size=257, output_hidden_states=None, ): self.parent = parent self.is_training = is_training self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.max_token_length = max_token_length self.num_character_labels = num_character_labels self.num_bpe_labels = num_bpe_labels self.num_wordpiece_labels = num_wordpiece_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.mlp_ratio = mlp_ratio self.patch_embeds_hidden_size = patch_embeds_hidden_size self.output_hidden_states = output_hidden_states def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]) config = self.get_config() return config, pixel_values def get_config(self): return MgpstrConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, max_token_length=self.max_token_length, num_character_labels=self.num_character_labels, num_bpe_labels=self.num_bpe_labels, num_wordpiece_labels=self.num_wordpiece_labels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, mlp_ratio=self.mlp_ratio, output_hidden_states=self.output_hidden_states, ) def create_and_check_model(self, config, pixel_values): model = MgpstrForSceneTextRecognition(config) model.to(torch_device) model.eval() with torch.no_grad(): generated_ids = model(pixel_values) self.parent.assertEqual( generated_ids[0][0].shape, (self.batch_size, self.max_token_length, self.num_character_labels) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class MgpstrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (MgpstrForSceneTextRecognition,) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": MgpstrForSceneTextRecognition, "image-feature-extraction": MgpstrModel} if is_torch_available() else {} ) fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False test_attention_outputs = False def setUp(self): self.model_tester = MgpstrModelTester(self) self.config_tester = ConfigTester(self, config_class=MgpstrConfig, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="MgpstrModel does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) @unittest.skip(reason="MgpstrModel does not support feedforward chunking") def test_feed_forward_chunking(self): pass def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue config.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) # override as the `logit_scale` parameter initilization is different for MgpstrModel def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if isinstance(param, (nn.Linear, nn.Conv2d, nn.LayerNorm)): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass # We will verify our results on an image from the IIIT-5k dataset def prepare_img(): url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png" im = Image.open(requests.get(url, stream=True).raw).convert("RGB") return im @require_vision @require_torch class MgpstrModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "alibaba-damo/mgp-str-base" model = MgpstrForSceneTextRecognition.from_pretrained(model_name).to(torch_device) processor = MgpstrProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor(images=image, return_tensors="pt").pixel_values.to(torch_device) # forward pass with torch.no_grad(): outputs = model(inputs) # verify the logits self.assertEqual(outputs.logits[0].shape, torch.Size((1, 27, 38))) out_strs = processor.batch_decode(outputs.logits) expected_text = "ticket" self.assertEqual(out_strs["generated_text"][0], expected_text) expected_slice = torch.tensor( [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]], device=torch_device, ) self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4], expected_slice, atol=1e-4))

transformers/tests/models/mgp_str/test_modeling_mgp_str.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch MobileNetV1 model. """ import unittest from transformers import MobileNetV1Config from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MobileNetV1ForImageClassification, MobileNetV1Model from transformers.models.mobilenet_v1.modeling_mobilenet_v1 import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import MobileNetV1ImageProcessor class MobileNetV1ConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "tf_padding")) self.parent.assertTrue(hasattr(config, "depth_multiplier")) class MobileNetV1ModelTester: def __init__( self, parent, batch_size=13, num_channels=3, image_size=32, depth_multiplier=0.25, min_depth=8, tf_padding=True, last_hidden_size=1024, output_stride=32, hidden_act="relu6", classifier_dropout_prob=0.1, initializer_range=0.02, is_training=True, use_labels=True, num_labels=10, scope=None, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.depth_multiplier = depth_multiplier self.min_depth = min_depth self.tf_padding = tf_padding self.last_hidden_size = int(last_hidden_size * depth_multiplier) self.output_stride = output_stride self.hidden_act = hidden_act self.classifier_dropout_prob = classifier_dropout_prob self.use_labels = use_labels self.is_training = is_training self.num_labels = num_labels self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None pixel_labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.num_labels) pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels, pixel_labels def get_config(self): return MobileNetV1Config( num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, min_depth=self.min_depth, tf_padding=self.tf_padding, hidden_act=self.hidden_act, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = MobileNetV1Model(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.last_hidden_state.shape, ( self.batch_size, self.last_hidden_size, self.image_size // self.output_stride, self.image_size // self.output_stride, ), ) def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = MobileNetV1ForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels, pixel_labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class MobileNetV1ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as MobileNetV1 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (MobileNetV1Model, MobileNetV1ForImageClassification) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": MobileNetV1Model, "image-classification": MobileNetV1ForImageClassification} if is_torch_available() else {} ) test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = MobileNetV1ModelTester(self) self.config_tester = MobileNetV1ConfigTester(self, config_class=MobileNetV1Config, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="MobileNetV1 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="MobileNetV1 does not support input and output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="MobileNetV1 does not output attentions") def test_attention_outputs(self): pass def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_stages = 26 self.assertEqual(len(hidden_states), expected_num_stages) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = MobileNetV1Model.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class MobileNetV1ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( MobileNetV1ImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = MobileNetV1ForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 1001)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-4.1739, -1.1233, 3.1205]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

transformers/tests/models/mobilenet_v1/test_modeling_mobilenet_v1.py/0

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import unittest from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase): """Tests OpenAIGPTTokenizer that uses BERT BasicTokenizer.""" tokenizer_class = OpenAIGPTTokenizer rust_tokenizer_class = OpenAIGPTTokenizerFast test_rust_tokenizer = True test_seq2seq = False def setUp(self): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "w</w>", "r</w>", "t</w>", "lo", "low", "er</w>", "low</w>", "lowest</w>", "newer</w>", "wider</w>", "<unk>", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens)) with open(self.merges_file, "w") as fp: fp.write("\n".join(merges)) def get_input_output_texts(self, tokenizer): return "lower newer", "lower newer" def test_full_tokenizer(self): tokenizer = OpenAIGPTTokenizer(self.vocab_file, self.merges_file) text = "lower" bpe_tokens = ["low", "er</w>"] tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + ["<unk>"] input_bpe_tokens = [14, 15, 20] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_padding(self, max_length=15): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) # Simple input s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, padding="max_length", ) # Pair input self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, padding="max_length", ) # tokenizer has no padding token def test_padding_different_model_input_name(self): pass @require_ftfy @require_spacy @require_tokenizers class OpenAIGPTTokenizationTestWithSpacy(OpenAIGPTTokenizationTest): """Tests OpenAIGPTTokenizer that uses SpaCy and ftfy.""" pass

transformers/tests/models/openai/test_tokenization_openai.py/0

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team, The Microsoft Research team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import tempfile import unittest from transformers import ProphetNetConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( ProphetNetDecoder, ProphetNetEncoder, ProphetNetForCausalLM, ProphetNetForConditionalGeneration, ProphetNetModel, ProphetNetTokenizer, ) from transformers.modeling_outputs import BaseModelOutput class ProphetNetModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, hidden_size=16, encoder_seq_length=7, decoder_seq_length=9, # For common tests is_training=True, use_attention_mask=True, use_labels=True, decoder_start_token_id=0, encoder_ffn_dim=32, num_encoder_layers=2, num_encoder_attention_heads=4, decoder_ffn_dim=32, num_decoder_layers=2, num_decoder_attention_heads=4, max_position_embeddings=30, is_encoder_decoder=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, ngram=2, num_buckets=32, relative_max_distance=128, disable_ngram_loss=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_decoder_layers self.num_encoder_layers = num_encoder_layers self.num_decoder_layers = num_decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_ffn_dim = encoder_ffn_dim self.num_attention_heads = num_decoder_attention_heads self.num_encoder_attention_heads = num_encoder_attention_heads self.num_decoder_attention_heads = num_decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.ngram = ngram self.num_buckets = num_buckets self.relative_max_distance = relative_max_distance self.disable_ngram_loss = disable_ngram_loss self.max_position_embeddings = max_position_embeddings self.is_encoder_decoder = is_encoder_decoder self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 7 self.num_hidden_states_types = 3 # encoder, decoder_main, decoder_ngram self.decoder_attention_idx = 2 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None decoder_attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = self.get_config() return ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) def get_config(self): return ProphetNetConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_encoder_layers=self.num_encoder_layers, num_decoder_layers=self.num_decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_ffn_dim=self.encoder_ffn_dim, num_encoder_attention_heads=self.num_encoder_attention_heads, num_decoder_attention_heads=self.num_decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, ngram=self.ngram, num_buckets=self.num_buckets, relative_max_distance=self.relative_max_distance, disable_ngram_loss=self.disable_ngram_loss, max_position_embeddings=self.max_position_embeddings, is_encoder_decoder=self.is_encoder_decoder, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.encoder_seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) return ( config, decoder_input_ids, decoder_attention_mask, encoder_hidden_states, encoder_attention_mask, lm_labels, ) def check_prepare_lm_labels_via_shift_left( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = ProphetNetModel(config=config) model.to(torch_device) model.eval() # make sure that lm_labels are correctly padded from the right lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id) # add casaul pad token mask triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not() lm_labels.masked_fill_(triangular_mask, self.pad_token_id) decoder_input_ids = model._shift_right(lm_labels) for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)): # first item self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id) if i < decoder_input_ids_slice.shape[-1]: if i < decoder_input_ids.shape[-1] - 1: # items before diagonal self.parent.assertListEqual( decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist() ) # pad items after diagonal if i < decoder_input_ids.shape[-1] - 2: self.parent.assertListEqual( decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist() ) else: # all items after square self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist()) def create_and_check_model( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = ProphetNetModel(config=config) model.to(torch_device) model.eval() result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) decoder_output = result.last_hidden_state decoder_past = result.past_key_values encoder_output = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size)) self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size)) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(decoder_past), config.num_decoder_layers) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0]), 4) # cross-attention + uni-directional self-attention def create_and_check_with_lm_head( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = ProphetNetForConditionalGeneration(config=config).to(torch_device).eval() outputs = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, labels=lm_labels, ) self.parent.assertEqual(len(outputs), 5) self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size)) self.parent.assertEqual(outputs["loss"].size(), ()) def create_and_check_causal_lm_decoder( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = ProphetNetForCausalLM(config=config).to(torch_device).eval() outputs = model( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, labels=lm_labels, ) self.parent.assertEqual(len(outputs), 4) self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size)) self.parent.assertEqual(outputs["loss"].size(), ()) def create_and_check_generate_with_past_key_value_states( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = ProphetNetForConditionalGeneration(config=config).to(torch_device).eval() torch.manual_seed(0) output_without_past_cache = model.generate( input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False ) torch.manual_seed(0) output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True) self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache)) def create_and_check_decoder_generate_with_past_key_value_states( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = ProphetNetForCausalLM(config=config).to(torch_device).eval() torch.manual_seed(0) output_without_past_cache = model.generate( input_ids[:1], num_beams=2, max_length=10, do_sample=True, use_cache=False ) torch.manual_seed(0) output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=10, do_sample=True) self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache)) def create_and_check_model_fp16_forward( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = ProphetNetModel(config=config).to(torch_device).half().eval() output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"] self.parent.assertFalse(torch.isnan(output).any().item()) def create_and_check_encoder_decoder_shared_weights( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): for model_class in [ProphetNetModel, ProphetNetForConditionalGeneration]: torch.manual_seed(0) model = model_class(config=config).to(torch_device).eval() # load state dict copies weights but does not tie them if model_class == ProphetNetForConditionalGeneration: model.prophetnet.encoder.load_state_dict(model.prophetnet.decoder.state_dict(), strict=False) else: model.encoder.load_state_dict(model.decoder.state_dict(), strict=False) torch.manual_seed(0) tied_config = copy.deepcopy(config) tied_config.tie_encoder_decoder = True tied_model = model_class(config=tied_config).to(torch_device).eval() model_result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) tied_model_result = tied_model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) # check that models has less parameters self.parent.assertLess( sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters()) ) random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item() # check that outputs are equal self.parent.assertTrue( torch.allclose( model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4 ) ) # check that outputs after saving and loading are equal with tempfile.TemporaryDirectory() as tmpdirname: tied_model.save_pretrained(tmpdirname) tied_model = model_class.from_pretrained(tmpdirname) tied_model.to(torch_device) tied_model.eval() # check that models has less parameters self.parent.assertLess( sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters()) ) random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item() tied_model_result = tied_model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) # check that outputs are equal self.parent.assertTrue( torch.allclose( model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4, ) ) def check_fast_integration( self, config, *args, ): input_ids = torch.tensor([[7, 4, 78, 0, 24, 52, 43]], device=torch_device, dtype=torch.long) decoder_input_ids = torch.tensor([[12, 62, 25, 11, 47, 15, 14]], device=torch_device, dtype=torch.long) attention_mask = torch.tensor([[1, 1, 1, 0, 1, 0, 0]], device=torch_device, dtype=torch.long) decoder_attention_mask = torch.tensor([[1, 1, 1, 0, 0, 1, 0]], device=torch_device, dtype=torch.long) lm_labels = torch.tensor([[62, 25, 11, 47, 15, 14, 24]], device=torch_device, dtype=torch.long) torch.manual_seed(0) config.ngram = 4 model = ProphetNetForConditionalGeneration(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, labels=lm_labels, ) self.parent.assertTrue(torch.allclose(result.loss, torch.tensor(4.5892, device=torch_device), atol=1e-3)) expected_logit_slice = torch.tensor( [-0.0184, 0.0758, -0.0543, -0.0093, 0.0050, -0.0660, -0.1453], device=torch_device ) self.parent.assertTrue(torch.allclose(result.logits[0, :, 1], expected_logit_slice, atol=1e-3)) def check_model_with_attn_mask(self, config, input_ids, decoder_input_ids, *args): model = ProphetNetModel(config=config) model.to(torch_device) model.eval() outputs_no_mask = model(input_ids=input_ids[:, :5], decoder_input_ids=decoder_input_ids[:, :5]) attention_mask = torch.ones_like(input_ids) decoder_attention_mask = torch.ones_like(decoder_input_ids) attention_mask[:, 5:] = 0 outputs_with_mask = model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) # check encoder self.parent.assertTrue( torch.allclose( outputs_no_mask.encoder_last_hidden_state[0, :, 0], outputs_with_mask.encoder_last_hidden_state[0, :5, 0], atol=1e-3, ) ) # check decoder # main stream self.parent.assertTrue( torch.allclose( outputs_no_mask.last_hidden_state[0, :, 0], outputs_with_mask.last_hidden_state[0, :5, 0], atol=1e-3 ) ) # predict stream self.parent.assertTrue( torch.allclose( outputs_no_mask.last_hidden_state_ngram[0, :5, 0], outputs_with_mask.last_hidden_state_ngram[0, :5, 0], atol=1e-2, ) ) def check_causal_lm_from_pretrained( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, *args ): model = ProphetNetForConditionalGeneration(config).to(torch_device).eval() with tempfile.TemporaryDirectory() as tmp_dirname: model.save_pretrained(tmp_dirname) decoder = ProphetNetForCausalLM.from_pretrained(tmp_dirname).to(torch_device) encoder_hidden_states = model.prophetnet.encoder(input_ids).last_hidden_state model_outputs = model( encoder_outputs=BaseModelOutput(last_hidden_state=encoder_hidden_states), decoder_input_ids=decoder_input_ids, ) dec_outputs = decoder(encoder_hidden_states=encoder_hidden_states, input_ids=decoder_input_ids) self.parent.assertTrue( torch.allclose( model_outputs.logits[0, :5], dec_outputs.logits[0, :5], atol=1e-3, ) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "use_cache": False, } return config, inputs_dict class ProphetNetStandaloneDecoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, hidden_size=16, encoder_seq_length=7, decoder_seq_length=7, # For common tests is_training=True, is_decoder=True, use_attention_mask=True, add_cross_attention=False, use_cache=False, use_labels=True, decoder_start_token_id=0, encoder_ffn_dim=32, num_encoder_layers=2, num_encoder_attention_heads=4, decoder_ffn_dim=32, num_decoder_layers=2, num_decoder_attention_heads=4, max_position_embeddings=30, is_encoder_decoder=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, ngram=2, num_buckets=32, relative_max_distance=128, disable_ngram_loss=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_decoder_layers self.num_encoder_layers = num_encoder_layers self.num_decoder_layers = num_decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_ffn_dim = encoder_ffn_dim self.num_attention_heads = num_decoder_attention_heads self.num_encoder_attention_heads = num_encoder_attention_heads self.num_decoder_attention_heads = num_decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.ngram = ngram self.num_buckets = num_buckets self.relative_max_distance = relative_max_distance self.use_cache = use_cache self.disable_ngram_loss = disable_ngram_loss self.max_position_embeddings = max_position_embeddings self.add_cross_attention = add_cross_attention self.is_encoder_decoder = is_encoder_decoder self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 2 self.num_hidden_states_types = 2 # decoder_main, decoder_ngram self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) config = ProphetNetConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_encoder_layers=self.num_encoder_layers, num_decoder_layers=self.num_decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_ffn_dim=self.encoder_ffn_dim, num_encoder_attention_heads=self.num_encoder_attention_heads, num_decoder_attention_heads=self.num_decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, ngram=self.ngram, num_buckets=self.num_buckets, relative_max_distance=self.relative_max_distance, disable_ngram_loss=self.disable_ngram_loss, max_position_embeddings=self.max_position_embeddings, add_cross_attention=self.add_cross_attention, is_encoder_decoder=self.is_encoder_decoder, ) return ( config, input_ids, attention_mask, lm_labels, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, attention_mask, lm_labels, ) = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.encoder_seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) return ( config, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): config.use_cache = True model = ProphetNetDecoder(config=config).to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def create_and_check_decoder_model_attention_mask_past( self, config, input_ids, attention_mask, lm_labels, ): model = ProphetNetDecoder(config=config).to(torch_device).eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict class ProphetNetStandaloneEncoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, hidden_size=16, encoder_seq_length=7, decoder_seq_length=7, # For common tests is_training=True, is_decoder=False, use_attention_mask=True, add_cross_attention=False, use_cache=False, use_labels=True, decoder_start_token_id=0, encoder_ffn_dim=32, num_encoder_layers=2, num_encoder_attention_heads=4, decoder_ffn_dim=32, num_decoder_layers=2, num_decoder_attention_heads=4, max_position_embeddings=30, is_encoder_decoder=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, num_buckets=32, relative_max_distance=128, disable_ngram_loss=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_decoder_layers self.num_encoder_layers = num_encoder_layers self.num_decoder_layers = num_decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_ffn_dim = encoder_ffn_dim self.num_attention_heads = num_decoder_attention_heads self.num_encoder_attention_heads = num_encoder_attention_heads self.num_decoder_attention_heads = num_decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.num_buckets = num_buckets self.relative_max_distance = relative_max_distance self.use_cache = use_cache self.disable_ngram_loss = disable_ngram_loss self.max_position_embeddings = max_position_embeddings self.add_cross_attention = add_cross_attention self.is_encoder_decoder = is_encoder_decoder self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 1 self.num_hidden_states_types = 1 self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) config = ProphetNetConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_encoder_layers=self.num_encoder_layers, num_decoder_layers=self.num_decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_ffn_dim=self.encoder_ffn_dim, num_encoder_attention_heads=self.num_encoder_attention_heads, num_decoder_attention_heads=self.num_decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, num_buckets=self.num_buckets, relative_max_distance=self.relative_max_distance, disable_ngram_loss=self.disable_ngram_loss, max_position_embeddings=self.max_position_embeddings, add_cross_attention=self.add_cross_attention, is_encoder_decoder=self.is_encoder_decoder, ) return ( config, input_ids, attention_mask, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (ProphetNetModel, ProphetNetForConditionalGeneration) if is_torch_available() else () all_generative_model_classes = (ProphetNetForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "conversational": ProphetNetForConditionalGeneration, "feature-extraction": ProphetNetModel, "summarization": ProphetNetForConditionalGeneration, "text-generation": ProphetNetForCausalLM, "text2text-generation": ProphetNetForConditionalGeneration, "translation": ProphetNetForConditionalGeneration, } if is_torch_available() else {} ) test_pruning = False test_resize_embeddings = False is_encoder_decoder = True # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if pipeline_test_casse_name == "TextGenerationPipelineTests": # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`. # `ProphetNetConfig` was never used in pipeline tests: cannot create a simple # tokenizer. return True return False def setUp(self): self.model_tester = ProphetNetModelTester(self) self.config_tester = ConfigTester(self, config_class=ProphetNetConfig) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_lm_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_with_lm_head(*config_and_inputs) def test_only_decoder_causal_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_decoder(*config_and_inputs) def test_fast_integration(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_fast_integration(*config_and_inputs) def test_shared_weights(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs) def test_shift_labels_via_shift_left(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs) @unittest.skip("Flaky test with no simple resolution. TODO Fix me @patrickvonplaten") def test_decoder_model_generate(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_generate_with_past_key_value_states(*config_and_inputs) def test_encoder_decoder_model_generate(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_generate_with_past_key_value_states(*config_and_inputs) def test_attn_mask_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_model_with_attn_mask(*config_and_inputs) def test_config_save(self): config = self.model_tester.prepare_config_and_inputs()[0] config.add_cross_attention = False with tempfile.TemporaryDirectory() as tmp_dirname: config.save_pretrained(tmp_dirname) config = ProphetNetConfig.from_pretrained(tmp_dirname) self.assertFalse(config.add_cross_attention) def test_causal_lm_from_pretrained(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_causal_lm_from_pretrained(*config_and_inputs) @unittest.skipIf(torch_device == "cpu", "Cant do half precision") def test_fp16_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs) # methods overwrite method in `test_modeling_common.py` def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) if chunk_length is not None: self.assertListEqual( list(attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) correct_outlen = 7 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, (self.model_tester.ngram + 1) * decoder_seq_length, encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) if chunk_length is not None: self.assertListEqual( list(self_attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_attentions = outputs.encoder_attentions[0] encoder_hidden_states.retain_grad() encoder_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(encoder_attentions.grad) def test_generate_with_head_masking(self): """Generating with head_masking has not been implemented for ProphetNet models yet.""" pass @require_torch class ProphetNetStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (ProphetNetDecoder, ProphetNetForCausalLM) if is_torch_available() else () all_generative_model_classes = (ProphetNetForCausalLM,) if is_torch_available() else () test_pruning = False test_resize_embeddings = False is_encoder_decoder = False def setUp(self): self.model_tester = ProphetNetStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=ProphetNetConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_attn_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients return @unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :) def test_left_padding_compatibility(self): pass @require_torch class ProphetNetStandaloneEncoderModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (ProphetNetEncoder,) if is_torch_available() else () test_pruning = False test_resize_embeddings = False is_encoder_decoder = False def setUp(self): self.model_tester = ProphetNetStandaloneEncoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=ProphetNetConfig) def test_config(self): self.config_tester.run_common_tests() @require_torch class ProphetNetModelIntegrationTest(unittest.TestCase): @slow def test_pretrained_checkpoint_hidden_states(self): model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased") model.to(torch_device) # encoder-decoder outputs encoder_ids = torch.tensor( [ [ 2871, 102, 2048, 3176, 2780, 1997, 2871, 26727, 2169, 2097, 12673, 1996, 8457, 2006, 2049, 8240, 2859, 2799, 1012, 2023, 6512, 2038, 2174, 13977, 2195, 25962, 1012, 102, ] ] ).to(torch_device) decoder_prev_ids = torch.tensor([[102, 2129, 2116, 2372, 2024, 2006, 2169, 1997, 2122, 2048, 2780, 1029]]).to( torch_device ) output = model( input_ids=encoder_ids, attention_mask=None, encoder_outputs=None, decoder_input_ids=decoder_prev_ids, ) output_predited_logits = output[0] expected_shape = torch.Size((1, 12, 30522)) self.assertEqual(output_predited_logits.shape, expected_shape) expected_slice = torch.tensor( [[[-7.7729, -8.0343, -8.26001], [-7.74213, -7.8629, -8.6000], [-7.7328, -7.8269, -8.5264]]] ).to(torch_device) # self.assertTrue(torch.allclose(output_predited_logits[:, :3, :3], expected_slice, atol=1e-4)) assert torch.allclose(output_predited_logits[:, :3, :3], expected_slice, atol=1e-4) # encoder outputs encoder_outputs = model.prophetnet.encoder(encoder_ids)[0] expected_encoder_outputs_slice = torch.tensor( [[[-0.2526, -0.1951, -0.2185], [-0.8923, 0.2992, -0.4623], [-0.4585, 0.0165, -0.6652]]] ).to(torch_device) expected_shape_encoder = torch.Size((1, 28, 1024)) self.assertEqual(encoder_outputs.shape, expected_shape_encoder) # self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4)) assert torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4) # decoder outputs decoder_outputs = model.prophetnet.decoder(decoder_prev_ids, encoder_hidden_states=encoder_outputs) predicting_streams = decoder_outputs[1].view(1, model.config.ngram, 12, -1) predicting_streams_logits = model.lm_head(predicting_streams) next_first_stream_logits = predicting_streams_logits[:, 0] # self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4)) assert torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4) @slow def test_cnndm_inference(self): model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased-cnndm") model.config.max_length = 512 model.to(torch_device) tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-cnndm") ARTICLE_TO_SUMMARIZE = ( "USTC was founded in Beijing by the Chinese Academy of Sciences (CAS) in September 1958. The Director of" " CAS, Mr. Guo Moruo was appointed the first president of USTC. USTC's founding mission was to develop a" " high-level science and technology workforce, as deemed critical for development of China's economy," ' defense, and science and technology education. The establishment was hailed as "A Major Event in the' ' History of Chinese Education and Science." CAS has supported USTC by combining most of its institutes' " with the departments of the university. USTC is listed in the top 16 national key universities, becoming" " the youngest national key university.".lower() ) input_ids = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=511, return_tensors="pt").input_ids input_ids = input_ids.to(torch_device) summary_ids = model.generate( input_ids, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True ) EXPECTED_SUMMARIZE_512 = ( "us ##tc was founded by the chinese academy of sciences ( cas ) in 1958 . [X_SEP] us ##tc is listed in the" " top 16 national key universities ." ) generated_titles = [ " ".join(tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)) for g in summary_ids ] self.assertListEqual( [EXPECTED_SUMMARIZE_512], generated_titles, ) input_ids = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=99, return_tensors="pt").input_ids input_ids = input_ids.to(torch_device) # actually 98 tokens are used. max_length=100 contains bos and eos. summary_ids = model.generate( input_ids, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True ) EXPECTED_SUMMARIZE_100 = ( r"us ##tc was founded in beijing by the chinese academy of sciences ( cas ) in 1958 . [X_SEP] us ##tc " "'" " s founding mission was to develop a high - level science and technology workforce . [X_SEP]" ' establishment hailed as " a major event in the history of chinese education and science "' ) generated_titles = [ " ".join(tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)) for g in summary_ids ] self.assertListEqual( [EXPECTED_SUMMARIZE_100], generated_titles, ) @slow def test_question_gen_inference(self): model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg") model.to(torch_device) tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg") INPUTS = [ "Bill Gates [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.", "1975 [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.", "April 4, 1975 [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.", ] input_ids = tokenizer(INPUTS, truncation=True, padding=True, return_tensors="pt").input_ids input_ids = input_ids.to(torch_device) gen_output = model.generate(input_ids, num_beams=5, early_stopping=True) generated_questions = tokenizer.batch_decode(gen_output, skip_special_tokens=True) EXPECTED_QUESTIONS = [ "along with paul allen, who founded microsoft?", "what year was microsoft founded?", "when was microsoft founded?", ] self.assertListEqual( EXPECTED_QUESTIONS, generated_questions, )

transformers/tests/models/prophetnet/test_modeling_prophetnet.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch REALM model. """ import copy import unittest import numpy as np from transformers import RealmConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( RealmEmbedder, RealmForOpenQA, RealmKnowledgeAugEncoder, RealmReader, RealmRetriever, RealmScorer, RealmTokenizer, ) class RealmModelTester: def __init__( self, parent, batch_size=13, retriever_proj_size=128, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, layer_norm_eps=1e-12, span_hidden_size=50, max_span_width=10, reader_layer_norm_eps=1e-3, reader_beam_size=4, reader_seq_len=288 + 32, num_block_records=13353718, searcher_beam_size=8, searcher_seq_len=64, num_labels=3, num_choices=4, num_candidates=10, scope=None, ): # General config self.parent = parent self.batch_size = batch_size self.retriever_proj_size = retriever_proj_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps # Reader config self.span_hidden_size = span_hidden_size self.max_span_width = max_span_width self.reader_layer_norm_eps = reader_layer_norm_eps self.reader_beam_size = reader_beam_size self.reader_seq_len = reader_seq_len # Searcher config self.num_block_records = num_block_records self.searcher_beam_size = searcher_beam_size self.searcher_seq_len = searcher_seq_len self.num_labels = num_labels self.num_choices = num_choices self.num_candidates = num_candidates self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) candiate_input_ids = ids_tensor([self.batch_size, self.num_candidates, self.seq_length], self.vocab_size) reader_input_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], self.vocab_size) input_mask = None candiate_input_mask = None reader_input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) candiate_input_mask = random_attention_mask([self.batch_size, self.num_candidates, self.seq_length]) reader_input_mask = random_attention_mask([self.reader_beam_size, self.reader_seq_len]) token_type_ids = None candidate_token_type_ids = None reader_token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) candidate_token_type_ids = ids_tensor( [self.batch_size, self.num_candidates, self.seq_length], self.type_vocab_size ) reader_token_type_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() # inputs with additional num_candidates axis. scorer_encoder_inputs = (candiate_input_ids, candiate_input_mask, candidate_token_type_ids) # reader inputs reader_inputs = (reader_input_ids, reader_input_mask, reader_token_type_ids) return ( config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ) def get_config(self): return RealmConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, retriever_proj_size=self.retriever_proj_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_candidates=self.num_candidates, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) def create_and_check_embedder( self, config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ): model = RealmEmbedder(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.projected_score.shape, (self.batch_size, self.retriever_proj_size)) def create_and_check_encoder( self, config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ): model = RealmKnowledgeAugEncoder(config=config) model.to(torch_device) model.eval() relevance_score = floats_tensor([self.batch_size, self.num_candidates]) result = model( scorer_encoder_inputs[0], attention_mask=scorer_encoder_inputs[1], token_type_ids=scorer_encoder_inputs[2], relevance_score=relevance_score, labels=token_labels, ) self.parent.assertEqual( result.logits.shape, (self.batch_size * self.num_candidates, self.seq_length, self.vocab_size) ) def create_and_check_reader( self, config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ): model = RealmReader(config=config) model.to(torch_device) model.eval() relevance_score = floats_tensor([self.reader_beam_size]) result = model( reader_inputs[0], attention_mask=reader_inputs[1], token_type_ids=reader_inputs[2], relevance_score=relevance_score, ) self.parent.assertEqual(result.block_idx.shape, ()) self.parent.assertEqual(result.candidate.shape, ()) self.parent.assertEqual(result.start_pos.shape, ()) self.parent.assertEqual(result.end_pos.shape, ()) def create_and_check_scorer( self, config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ): model = RealmScorer(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, candidate_input_ids=scorer_encoder_inputs[0], candidate_attention_mask=scorer_encoder_inputs[1], candidate_token_type_ids=scorer_encoder_inputs[2], ) self.parent.assertEqual(result.relevance_score.shape, (self.batch_size, self.num_candidates)) self.parent.assertEqual(result.query_score.shape, (self.batch_size, self.retriever_proj_size)) self.parent.assertEqual( result.candidate_score.shape, (self.batch_size, self.num_candidates, self.retriever_proj_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class RealmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( RealmEmbedder, RealmKnowledgeAugEncoder, # RealmScorer is excluded from common tests as it is a container model # consisting of two RealmEmbedders & a simple inner product calculation. # RealmScorer ) if is_torch_available() else () ) all_generative_model_classes = () pipeline_model_mapping = {} if is_torch_available() else {} # disable these tests because there is no base_model in Realm test_save_load_fast_init_from_base = False test_save_load_fast_init_to_base = False def setUp(self): self.test_pruning = False self.model_tester = RealmModelTester(self) self.config_tester = ConfigTester(self, config_class=RealmConfig) def test_config(self): self.config_tester.run_common_tests() def test_embedder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_embedder(*config_and_inputs) def test_encoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_encoder(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_embedder(*config_and_inputs) self.model_tester.create_and_check_encoder(*config_and_inputs) def test_scorer(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_scorer(*config_and_inputs) def test_training(self): if not self.model_tester.is_training: return config, *inputs = self.model_tester.prepare_config_and_inputs() input_ids, token_type_ids, input_mask, scorer_encoder_inputs = inputs[0:4] config.return_dict = True tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa") # RealmKnowledgeAugEncoder training model = RealmKnowledgeAugEncoder(config) model.to(torch_device) model.train() inputs_dict = { "input_ids": scorer_encoder_inputs[0].to(torch_device), "attention_mask": scorer_encoder_inputs[1].to(torch_device), "token_type_ids": scorer_encoder_inputs[2].to(torch_device), "relevance_score": floats_tensor([self.model_tester.batch_size, self.model_tester.num_candidates]), } inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs = inputs_dict loss = model(**inputs).loss loss.backward() # RealmForOpenQA training openqa_config = copy.deepcopy(config) openqa_config.vocab_size = 30522 # the retrieved texts will inevitably have more than 99 vocabs. openqa_config.num_block_records = 5 openqa_config.searcher_beam_size = 2 block_records = np.array( [ b"This is the first record.", b"This is the second record.", b"This is the third record.", b"This is the fourth record.", b"This is the fifth record.", ], dtype=object, ) retriever = RealmRetriever(block_records, tokenizer) model = RealmForOpenQA(openqa_config, retriever) model.to(torch_device) model.train() inputs_dict = { "input_ids": input_ids[:1].to(torch_device), "attention_mask": input_mask[:1].to(torch_device), "token_type_ids": token_type_ids[:1].to(torch_device), "answer_ids": input_ids[:1].tolist(), } inputs = self._prepare_for_class(inputs_dict, RealmForOpenQA) loss = model(**inputs).reader_output.loss loss.backward() # Test model.block_embedding_to device = torch.device("cpu") model.block_embedding_to(device) loss = model(**inputs).reader_output.loss loss.backward() self.assertEqual(model.block_emb.device.type, device.type) @slow def test_embedder_from_pretrained(self): model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder") self.assertIsNotNone(model) @slow def test_encoder_from_pretrained(self): model = RealmKnowledgeAugEncoder.from_pretrained("google/realm-cc-news-pretrained-encoder") self.assertIsNotNone(model) @slow def test_open_qa_from_pretrained(self): model = RealmForOpenQA.from_pretrained("google/realm-orqa-nq-openqa") self.assertIsNotNone(model) @slow def test_reader_from_pretrained(self): model = RealmReader.from_pretrained("google/realm-orqa-nq-reader") self.assertIsNotNone(model) @slow def test_scorer_from_pretrained(self): model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer") self.assertIsNotNone(model) @require_torch class RealmModelIntegrationTest(unittest.TestCase): @slow def test_inference_embedder(self): retriever_projected_size = 128 model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder") input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = torch.Size((1, retriever_projected_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[-0.0714, -0.0837, -0.1314]]) self.assertTrue(torch.allclose(output[:, :3], expected_slice, atol=1e-4)) @slow def test_inference_encoder(self): num_candidates = 2 vocab_size = 30522 model = RealmKnowledgeAugEncoder.from_pretrained( "google/realm-cc-news-pretrained-encoder", num_candidates=num_candidates ) input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]) relevance_score = torch.tensor([[0.3, 0.7]], dtype=torch.float32) output = model(input_ids, relevance_score=relevance_score)[0] expected_shape = torch.Size((2, 6, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[[-11.0888, -11.2544], [-10.2170, -10.3874]]]) self.assertTrue(torch.allclose(output[1, :2, :2], expected_slice, atol=1e-4)) @slow def test_inference_open_qa(self): from transformers.models.realm.retrieval_realm import RealmRetriever tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa") retriever = RealmRetriever.from_pretrained("google/realm-orqa-nq-openqa") model = RealmForOpenQA.from_pretrained( "google/realm-orqa-nq-openqa", retriever=retriever, ) question = "Who is the pioneer in modern computer science?" question = tokenizer( [question], padding=True, truncation=True, max_length=model.config.searcher_seq_len, return_tensors="pt", ).to(model.device) predicted_answer_ids = model(**question).predicted_answer_ids predicted_answer = tokenizer.decode(predicted_answer_ids) self.assertEqual(predicted_answer, "alan mathison turing") @slow def test_inference_reader(self): config = RealmConfig(reader_beam_size=2, max_span_width=3) model = RealmReader.from_pretrained("google/realm-orqa-nq-reader", config=config) concat_input_ids = torch.arange(10).view((2, 5)) concat_token_type_ids = torch.tensor([[0, 0, 1, 1, 1], [0, 0, 1, 1, 1]], dtype=torch.int64) concat_block_mask = torch.tensor([[0, 0, 1, 1, 0], [0, 0, 1, 1, 0]], dtype=torch.int64) relevance_score = torch.tensor([0.3, 0.7], dtype=torch.float32) output = model( concat_input_ids, token_type_ids=concat_token_type_ids, relevance_score=relevance_score, block_mask=concat_block_mask, return_dict=True, ) block_idx_expected_shape = torch.Size(()) start_pos_expected_shape = torch.Size((1,)) end_pos_expected_shape = torch.Size((1,)) self.assertEqual(output.block_idx.shape, block_idx_expected_shape) self.assertEqual(output.start_pos.shape, start_pos_expected_shape) self.assertEqual(output.end_pos.shape, end_pos_expected_shape) expected_block_idx = torch.tensor(1) expected_start_pos = torch.tensor(3) expected_end_pos = torch.tensor(3) self.assertTrue(torch.allclose(output.block_idx, expected_block_idx, atol=1e-4)) self.assertTrue(torch.allclose(output.start_pos, expected_start_pos, atol=1e-4)) self.assertTrue(torch.allclose(output.end_pos, expected_end_pos, atol=1e-4)) @slow def test_inference_scorer(self): num_candidates = 2 model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer", num_candidates=num_candidates) input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) candidate_input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]) output = model(input_ids, candidate_input_ids=candidate_input_ids)[0] expected_shape = torch.Size((1, 2)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[0.7410, 0.7170]]) self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))

transformers/tests/models/realm/test_modeling_realm.py/0

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ResNet model. """ import unittest from transformers import ResNetConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class ResNetModelTester: def __init__( self, parent, batch_size=3, image_size=32, num_channels=3, embeddings_size=10, hidden_sizes=[10, 20, 30, 40], depths=[1, 1, 2, 1], is_training=True, use_labels=True, hidden_act="relu", num_labels=3, scope=None, out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.embeddings_size = embeddings_size self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.num_labels = num_labels self.scope = scope self.num_stages = len(hidden_sizes) self.out_features = out_features self.out_indices = out_indices def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return ResNetConfig( num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, out_features=self.out_features, out_indices=self.out_indices, ) def create_and_check_model(self, config, pixel_values, labels): model = ResNetModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.num_labels model = ResNetForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_backbone(self, config, pixel_values, labels): model = ResNetBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), len(config.out_features)) self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 4, 4]) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) self.parent.assertListEqual(model.channels, config.hidden_sizes[1:]) # verify backbone works with out_features=None config.out_features = None model = ResNetBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1]) # verify channels self.parent.assertEqual(len(model.channels), 1) self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]]) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class ResNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ResNet does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( ResNetModel, ResNetForImageClassification, ResNetBackbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification} if is_torch_available() else {} ) fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = ResNetModelTester(self) self.config_tester = ConfigTester(self, config_class=ResNetConfig, has_text_modality=False) def test_config(self): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def create_and_test_config_common_properties(self): return @unittest.skip(reason="ResNet does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ResNet does not support input and output embeddings") def test_model_common_attributes(self): pass def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*config_and_inputs) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config=config) for name, module in model.named_modules(): if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)): self.assertTrue( torch.all(module.weight == 1), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) self.assertTrue( torch.all(module.bias == 0), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # ResNet's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() layers_type = ["basic", "bottleneck"] for model_class in self.all_model_classes: for layer_type in layers_type: config.layer_type = layer_type inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @unittest.skip(reason="ResNet does not use feedforward chunking") def test_feed_forward_chunking(self): pass def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ResNetModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class ResNetModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-11.1069, -9.7877, -8.3777]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)) @require_torch class ResNetBackboneTest(BackboneTesterMixin, unittest.TestCase): all_model_classes = (ResNetBackbone,) if is_torch_available() else () has_attentions = False config_class = ResNetConfig def setUp(self): self.model_tester = ResNetModelTester(self)

transformers/tests/models/resnet/test_modeling_resnet.py/0

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch RoFormer model. """ import unittest from transformers import RoFormerConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( RoFormerForCausalLM, RoFormerForMaskedLM, RoFormerForMultipleChoice, RoFormerForQuestionAnswering, RoFormerForSequenceClassification, RoFormerForTokenClassification, RoFormerModel, ) from transformers.models.roformer.modeling_roformer import ( ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, RoFormerSelfAttention, RoFormerSinusoidalPositionalEmbedding, ) class RoFormerModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return RoFormerConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RoFormerModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = RoFormerModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = RoFormerForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_generate_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = RoFormerForCausalLM(config=config).to(torch_device).eval() torch.manual_seed(0) output_without_past_cache = model.generate( input_ids[:1], num_beams=2, max_length=15, do_sample=True, use_cache=False ) torch.manual_seed(0) output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=15, do_sample=True) self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RoFormerForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = RoFormerForCausalLM(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RoFormerForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = RoFormerForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = RoFormerForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = RoFormerForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class RoFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( RoFormerModel, RoFormerForMaskedLM, RoFormerForCausalLM, RoFormerForMultipleChoice, RoFormerForQuestionAnswering, RoFormerForSequenceClassification, RoFormerForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (RoFormerForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": RoFormerModel, "fill-mask": RoFormerForMaskedLM, "question-answering": RoFormerForQuestionAnswering, "text-classification": RoFormerForSequenceClassification, "text-generation": RoFormerForCausalLM, "token-classification": RoFormerForTokenClassification, "zero-shot": RoFormerForSequenceClassification, } if is_torch_available() else {} ) def setUp(self): self.model_tester = RoFormerModelTester(self) self.config_tester = ConfigTester(self, config_class=RoFormerConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_generate_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_generate_causal_lm(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_model_as_decoder_with_default_input_mask(self): # This regression test was failing with PyTorch < 1.3 ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) @slow def test_model_from_pretrained(self): for model_name in ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = RoFormerModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @require_torch class RoFormerModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = RoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base") input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) with torch.no_grad(): output = model(input_ids)[0] # TODO Replace vocab size vocab_size = 50000 expected_shape = torch.Size((1, 6, vocab_size)) self.assertEqual(output.shape, expected_shape) # TODO Replace values below with what was printed above. expected_slice = torch.tensor( [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @require_torch class RoFormerSinusoidalPositionalEmbeddingTest(unittest.TestCase): tolerance = 1e-4 def test_basic(self): input_ids = torch.tensor([[4, 10]], dtype=torch.long, device=torch_device) emb1 = RoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6).to(torch_device) emb = emb1(input_ids.shape) desired_weights = torch.tensor( [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] ).to(torch_device) self.assertTrue( torch.allclose(emb, desired_weights, atol=self.tolerance), msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n", ) def test_positional_emb_weights_against_roformer(self): desired_weights = torch.tensor( [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.8415, 0.8219, 0.8020, 0.7819, 0.7617], [0.9093, 0.9364, 0.9581, 0.9749, 0.9870], ] ).to(torch_device) emb1 = RoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512).to(torch_device) weights = emb1.weight.data[:3, :5].to(torch_device) self.assertTrue( torch.allclose(weights, desired_weights, atol=self.tolerance), msg=f"\nexp:\n{desired_weights}\ngot:\n{weights}\n", ) @require_torch class RoFormerSelfAttentionRotaryPositionEmbeddingTest(unittest.TestCase): tolerance = 1e-4 def test_apply_rotary_position_embeddings(self): # 2,12,16,64 query_layer = ( torch.arange(2 * 12 * 16 * 64, dtype=torch.float, device=torch_device).reshape(2, 12, 16, 64) / 100 ).to(torch_device) key_layer = ( -torch.arange(2 * 12 * 16 * 64, dtype=torch.float, device=torch_device).reshape(2, 12, 16, 64) / 100 ).to(torch_device) embed_positions = RoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64).to(torch_device) sinusoidal_pos = embed_positions([2, 16, 768])[None, None, :, :] query_layer, key_layer = RoFormerSelfAttention.apply_rotary_position_embeddings( sinusoidal_pos, query_layer, key_layer ) desired_query_layer = torch.tensor( [ [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700], [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343], [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985], [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871], [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980], [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253], ] ).to(torch_device) desired_key_layer = torch.tensor( [ [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700], [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343], [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985], [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871], [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980], [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253], ] ).to(torch_device) self.assertTrue( torch.allclose(query_layer[0, 0, :6, :8], desired_query_layer, atol=self.tolerance), msg=f"\nexp:\n{desired_query_layer}\ngot:\n{query_layer}\n", ) self.assertTrue( torch.allclose(key_layer[0, 0, :6, :8], desired_key_layer, atol=self.tolerance), msg=f"\nexp:\n{desired_key_layer}\ngot:\n{key_layer}\n", )

transformers/tests/models/roformer/test_modeling_roformer.py/0

# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools import os import random import tempfile import unittest import numpy as np from transformers import Speech2TextFeatureExtractor from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin global_rng = random.Random() # Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list def floats_list(shape, scale=1.0, rng=None, name=None): """Creates a random float32 tensor""" if rng is None: rng = global_rng values = [] for batch_idx in range(shape[0]): values.append([]) for _ in range(shape[1]): values[-1].append(rng.random() * scale) return values @require_torch @require_torchaudio class Speech2TextFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=24, num_mel_bins=24, padding_value=0.0, sampling_rate=16_000, return_attention_mask=True, do_normalize=True, ): self.parent = parent self.batch_size = batch_size self.min_seq_length = min_seq_length self.max_seq_length = max_seq_length self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) self.feature_size = feature_size self.num_mel_bins = num_mel_bins self.padding_value = padding_value self.sampling_rate = sampling_rate self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "num_mel_bins": self.num_mel_bins, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } def prepare_inputs_for_common(self, equal_length=False, numpify=False): def _flatten(list_of_lists): return list(itertools.chain(*list_of_lists)) if equal_length: speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)] else: # make sure that inputs increase in size speech_inputs = [ floats_list((x, self.feature_size)) for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff) ] if numpify: speech_inputs = [np.asarray(x) for x in speech_inputs] return speech_inputs @require_torch @require_torchaudio class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = Speech2TextFeatureExtractor def setUp(self): self.feat_extract_tester = Speech2TextFeatureExtractionTester(self) def _check_zero_mean_unit_variance(self, input_vector): self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3)) self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3)) def test_call(self): # Tests that all call wrap to encode_plus and batch_encode_plus feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) # create three inputs of length 800, 1000, and 1200 speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] # Test feature size input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features self.assertTrue(input_features.ndim == 3) self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size) # Test not batched input encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3)) # Test batched encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2): self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3)) # Test 2-D numpy arrays are batched. speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)] np_speech_inputs = np.asarray(speech_inputs) encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2): self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3)) def test_cepstral_mean_and_variance_normalization(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] paddings = ["longest", "max_length", "do_not_pad"] max_lengths = [None, 16, None] for max_length, padding in zip(max_lengths, paddings): inputs = feature_extractor( speech_inputs, padding=padding, max_length=max_length, return_attention_mask=True ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = [np.sum(x) for x in attention_mask] self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]) def test_cepstral_mean_and_variance_normalization_np(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] paddings = ["longest", "max_length", "do_not_pad"] max_lengths = [None, 16, None] for max_length, padding in zip(max_lengths, paddings): inputs = feature_extractor( speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = [np.sum(x) for x in attention_mask] self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]) self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6) self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]) self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6) self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]) def test_cepstral_mean_and_variance_normalization_trunc_max_length(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] inputs = feature_extractor( speech_inputs, padding="max_length", max_length=4, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1]) self._check_zero_mean_unit_variance(input_features[2]) def test_cepstral_mean_and_variance_normalization_trunc_longest(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] inputs = feature_extractor( speech_inputs, padding="longest", max_length=4, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2]) # make sure that if max_length < longest -> then pad to max_length self.assertEqual(input_features.shape, (3, 4, 24)) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] inputs = feature_extractor( speech_inputs, padding="longest", max_length=16, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2]) # make sure that if max_length < longest -> then pad to max_length self.assertEqual(input_features.shape, (3, 6, 24)) def test_double_precision_pad(self): import torch feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) np_speech_inputs = np.random.rand(100, 32).astype(np.float64) py_speech_inputs = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np") self.assertTrue(np_processed.input_features.dtype == np.float32) pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt") self.assertTrue(pt_processed.input_features.dtype == torch.float32) def _load_datasamples(self, num_samples): from datasets import load_dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_integration(self): # fmt: off expected = np.array([ -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241, -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128, -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625, ]) # fmt: on input_speech = self._load_datasamples(1) feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) input_features = feature_extractor(input_speech, return_tensors="pt").input_features self.assertEquals(input_features.shape, (1, 584, 24)) self.assertTrue(np.allclose(input_features[0, 0, :30], expected, atol=1e-4)) def test_feat_extract_from_and_save_pretrained(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: saved_file = feat_extract_first.save_pretrained(tmpdirname)[0] check_json_file_has_correct_format(saved_file) feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname) dict_first = feat_extract_first.to_dict() dict_second = feat_extract_second.to_dict() self.assertDictEqual(dict_first, dict_second) def test_feat_extract_to_json_file(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "feat_extract.json") feat_extract_first.to_json_file(json_file_path) feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path) dict_first = feat_extract_first.to_dict() dict_second = feat_extract_second.to_dict() self.assertEqual(dict_first, dict_second) # exact same tests than before, except that we simulate that torchaudio is not available @require_torch @unittest.mock.patch( "transformers.models.speech_to_text.feature_extraction_speech_to_text.is_speech_available", lambda: False ) class Speech2TextFeatureExtractionWithoutTorchaudioTest(Speech2TextFeatureExtractionTest): def test_using_audio_utils(self): # Tests that it uses audio_utils instead of torchaudio feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) self.assertTrue(hasattr(feat_extract, "window")) self.assertTrue(hasattr(feat_extract, "mel_filters")) from transformers.models.speech_to_text.feature_extraction_speech_to_text import is_speech_available self.assertFalse(is_speech_available())

transformers/tests/models/speech_to_text/test_feature_extraction_speech_to_text.py/0

# coding=utf-8 # Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import SqueezeBertConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, SqueezeBertModel, ) class SqueezeBertModelTester(object): def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=64, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, q_groups=2, k_groups=2, v_groups=2, post_attention_groups=2, intermediate_groups=4, output_groups=1, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.q_groups = q_groups self.k_groups = k_groups self.v_groups = v_groups self.post_attention_groups = post_attention_groups self.intermediate_groups = intermediate_groups self.output_groups = output_groups def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return SqueezeBertConfig( embedding_size=self.hidden_size, vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, attention_probs_dropout_prob=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, q_groups=self.q_groups, k_groups=self.k_groups, v_groups=self.v_groups, post_attention_groups=self.post_attention_groups, intermediate_groups=self.intermediate_groups, output_groups=self.output_groups, ) def create_and_check_squeezebert_model( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = SqueezeBertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_squeezebert_for_masked_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = SqueezeBertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_squeezebert_for_question_answering( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = SqueezeBertForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_squeezebert_for_sequence_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = SqueezeBertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_squeezebert_for_token_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = SqueezeBertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_squeezebert_for_multiple_choice( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = SqueezeBertForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class SqueezeBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( SqueezeBertModel, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, ) if is_torch_available() else None ) pipeline_model_mapping = ( { "feature-extraction": SqueezeBertModel, "fill-mask": SqueezeBertForMaskedLM, "question-answering": SqueezeBertForQuestionAnswering, "text-classification": SqueezeBertForSequenceClassification, "token-classification": SqueezeBertForTokenClassification, "zero-shot": SqueezeBertForSequenceClassification, } if is_torch_available() else {} ) test_pruning = False test_resize_embeddings = True test_head_masking = False def setUp(self): self.model_tester = SqueezeBertModelTester(self) self.config_tester = ConfigTester(self, config_class=SqueezeBertConfig, dim=37) def test_config(self): self.config_tester.run_common_tests() def test_squeezebert_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_squeezebert_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_squeezebert_for_masked_lm(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_squeezebert_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_squeezebert_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_squeezebert_for_token_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_squeezebert_for_multiple_choice(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = SqueezeBertModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_sentencepiece @require_tokenizers @require_torch class SqueezeBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_classification_head(self): model = SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli") input_ids = torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]]) output = model(input_ids)[0] expected_shape = torch.Size((1, 3)) self.assertEqual(output.shape, expected_shape) expected_tensor = torch.tensor([[0.6401, -0.0349, -0.6041]]) self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))

transformers/tests/models/squeezebert/test_modeling_squeezebert.py/0

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import inspect import unittest from transformers import AutoBackbone from transformers.configuration_utils import PretrainedConfig from transformers.testing_utils import require_timm, require_torch, torch_device from transformers.utils.import_utils import is_torch_available from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor if is_torch_available(): import torch from transformers import TimmBackbone, TimmBackboneConfig from ...test_pipeline_mixin import PipelineTesterMixin class TimmBackboneModelTester: def __init__( self, parent, out_indices=None, out_features=None, stage_names=None, backbone="resnet18", batch_size=3, image_size=32, num_channels=3, is_training=True, use_pretrained_backbone=True, ): self.parent = parent self.out_indices = out_indices if out_indices is not None else [4] self.stage_names = stage_names self.out_features = out_features self.backbone = backbone self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.use_pretrained_backbone = use_pretrained_backbone self.is_training = is_training def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return TimmBackboneConfig( image_size=self.image_size, num_channels=self.num_channels, out_features=self.out_features, out_indices=self.out_indices, stage_names=self.stage_names, use_pretrained_backbone=self.use_pretrained_backbone, backbone=self.backbone, ) def create_and_check_model(self, config, pixel_values): model = TimmBackbone(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) self.parent.assertEqual( result.feature_map[-1].shape, (self.batch_size, model.channels[-1], 14, 14), ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch @require_timm class TimmBackboneModelTest(ModelTesterMixin, BackboneTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TimmBackbone,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": TimmBackbone} if is_torch_available() else {} test_resize_embeddings = False test_head_masking = False test_pruning = False has_attentions = False def setUp(self): self.config_class = PretrainedConfig self.model_tester = TimmBackboneModelTester(self) self.config_tester = ConfigTester(self, config_class=self.config_class, has_text_modality=False) def test_config(self): self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def test_timm_transformer_backbone_equivalence(self): timm_checkpoint = "resnet18" transformers_checkpoint = "microsoft/resnet-18" timm_model = AutoBackbone.from_pretrained(timm_checkpoint, use_timm_backbone=True) transformers_model = AutoBackbone.from_pretrained(transformers_checkpoint) self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features)) self.assertEqual(len(timm_model.stage_names), len(transformers_model.stage_names)) self.assertEqual(timm_model.channels, transformers_model.channels) # Out indices are set to the last layer by default. For timm models, we don't know # the number of layers in advance, so we set it to (-1,), whereas for transformers # models, we set it to [len(stage_names) - 1] (kept for backward compatibility). self.assertEqual(timm_model.out_indices, (-1,)) self.assertEqual(transformers_model.out_indices, [len(timm_model.stage_names) - 1]) timm_model = AutoBackbone.from_pretrained(timm_checkpoint, use_timm_backbone=True, out_indices=[1, 2, 3]) transformers_model = AutoBackbone.from_pretrained(transformers_checkpoint, out_indices=[1, 2, 3]) self.assertEqual(timm_model.out_indices, transformers_model.out_indices) self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features)) self.assertEqual(timm_model.channels, transformers_model.channels) @unittest.skip("TimmBackbone doesn't support feed forward chunking") def test_feed_forward_chunking(self): pass @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute") def test_hidden_states_output(self): pass @unittest.skip("TimmBackbone initialization is managed on the timm side") def test_initialization(self): pass @unittest.skip("TimmBackbone models doesn't have inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("TimmBackbone models doesn't have inputs_embeds") def test_model_common_attributes(self): pass @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint") def test_from_pretrained_no_checkpoint(self): pass @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone") def test_save_load(self): pass @unittest.skip("model weights aren't tied in TimmBackbone.") def test_tie_model_weights(self): pass @unittest.skip("model weights aren't tied in TimmBackbone.") def test_tied_model_weights_key_ignore(self): pass @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone") def test_load_save_without_tied_weights(self): pass @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone") def test_model_weights_reload_no_missing_tied_weights(self): pass @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration.") def test_channels(self): pass @unittest.skip("TimmBackbone doesn't support output_attentions.") def test_torchscript_output_attentions(self): pass @unittest.skip("Safetensors is not supported by timm.") def test_can_use_safetensors(self): pass @unittest.skip("Need to use a timm backbone and there is no tiny model available.") def test_model_is_small(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0][-1] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNotNone(attentions.grad) # TimmBackbone config doesn't have out_features attribute def test_create_from_modified_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() result = model(**inputs_dict) self.assertEqual(len(result.feature_maps), len(config.out_indices)) self.assertEqual(len(model.channels), len(config.out_indices)) # Check output of last stage is taken if out_features=None, out_indices=None modified_config = copy.deepcopy(config) modified_config.out_indices = None model = model_class(modified_config) model.to(torch_device) model.eval() result = model(**inputs_dict) self.assertEqual(len(result.feature_maps), 1) self.assertEqual(len(model.channels), 1) # Check backbone can be initialized with fresh weights modified_config = copy.deepcopy(config) modified_config.use_pretrained_backbone = False model = model_class(modified_config) model.to(torch_device) model.eval() result = model(**inputs_dict)

transformers/tests/models/timm_backbone/test_modeling_timm_backbone.py/0

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch UniSpeechSat model. """ import math import unittest import numpy as np import pytest from datasets import load_dataset from transformers import UniSpeechSatConfig, is_torch_available from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( UniSpeechSatForAudioFrameClassification, UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, UniSpeechSatModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) class UniSpeechSatModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=2, num_attention_heads=2, hidden_dropout_prob=0.1, # this is most likely not correctly set yet intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, do_stable_layer_norm=False, tdnn_dim=(32, 32), tdnn_kernel=(3, 3), tdnn_dilation=(1, 1), xvector_output_dim=32, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim self.scope = scope output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() return config, input_values, attention_mask def get_config(self): return UniSpeechSatConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, ) def create_and_check_model(self, config, input_values, attention_mask): model = UniSpeechSatModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = UniSpeechSatModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = UniSpeechSatForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = UniSpeechSatForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = UniSpeechSatForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lengths are at least # one shorter than logit lengths to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = UniSpeechSatForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_xvector_training(self, config, *args): config.ctc_zero_infinity = True model = UniSpeechSatForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() # use a longer sequence length to account for TDNN temporal downsampling input_values = floats_tensor([self.batch_size, self.seq_length * 2], scale=1.0) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = UniSpeechSatForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with pytest.raises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class UniSpeechSatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification, UniSpeechSatForAudioFrameClassification, UniSpeechSatForXVector, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "audio-classification": UniSpeechSatForSequenceClassification, "automatic-speech-recognition": UniSpeechSatForCTC, "feature-extraction": UniSpeechSatModel, } if is_torch_available() else {} ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = UniSpeechSatModelTester(self) self.config_tester = ConfigTester(self, config_class=UniSpeechSatConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_xvector_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_xvector_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # UniSpeechSat has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # UniSpeechSat cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # UniSpeechSat has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "conv.parametrizations.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "feature_projection.projection.weight", "feature_projection.projection.bias", "label_embeddings_concat", "objective.weight", ] if param.requires_grad: if any(x in name for x in uniform_init_parms): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-base-plus") self.assertIsNotNone(model) @require_torch class UniSpeechSatRobustModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( (UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = UniSpeechSatModelTester( self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True ) self.config_tester = ConfigTester(self, config_class=UniSpeechSatConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_batched_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_batch_inference(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # UniSpeechSat has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # UniSpeechSat cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # UniSpeechSat has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "conv.parametrizations.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "feature_projection.projection.weight", "feature_projection.projection.bias", "label_embeddings_concat", "objective.weight", ] if param.requires_grad: if any(x in name for x in uniform_init_parms): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_feature_prob_ctc_single_batch(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_feature_prob=0.2, mask_time_length=2, mask_feature_length=2, ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (1, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large") self.assertIsNotNone(model) @require_torch @require_soundfile @slow class UniSpeechSatModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter( lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)] )[:num_samples]["audio"] return [x["array"] for x in speech_samples] def _load_superb(self, task, num_samples): ds = load_dataset("anton-l/superb_dummy", task, split="test") return ds[:num_samples] def test_inference_encoder_base(self): model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-base-plus") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), ) # fmt: off expected_hidden_states_slice = torch.tensor( [[[-0.0743, 0.1384], [-0.0845, 0.1704]], [[-0.0954, 0.1936], [-0.1123, 0.2095]]], device=torch_device, ) # fmt: on self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3)) def test_inference_encoder_large(self): model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large-xlsr-53") input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), ) # fmt: off expected_hidden_states_slice = torch.tensor( [[[-0.1172, -0.0797], [-0.0012, 0.0213]], [[-0.1225, -0.1277], [-0.0668, -0.0585]]], device=torch_device, ) # fmt: on self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3)) def test_inference_diarization(self): model = UniSpeechSatForAudioFrameClassification.from_pretrained("microsoft/unispeech-sat-base-plus-sd").to( torch_device ) processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base-plus-sd") input_data = self._load_superb("sd", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True, sampling_rate=16_000) input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) # labels is a one-hot array of shape (num_frames, num_speakers) labels = (outputs.logits > 0).long() # s3prl logits for the same batch expected_logits = torch.tensor( [ [[-5.6119, -5.5845], [-3.7772, -5.4824], [-3.6914, -5.1619], [-4.7560, -5.0496]], [[-6.3785, -4.8365], [-5.5863, -5.4149], [-5.5639, -4.8469], [-6.1511, -4.0052]], [[-6.0355, -3.7414], [-5.5968, -4.8061], [-5.4620, -4.7310], [-5.5864, -4.6078]], [[-5.9493, -4.8963], [-4.4050, -5.4476], [-4.1755, -5.1395], [-4.0272, -4.3705]], ], device=torch_device, ) self.assertEqual(labels[0, :, 0].sum(), 270) self.assertEqual(labels[0, :, 1].sum(), 647) self.assertTrue(torch.allclose(outputs.logits[:, :4], expected_logits, atol=1e-2)) def test_inference_speaker_verification(self): model = UniSpeechSatForXVector.from_pretrained("microsoft/unispeech-sat-base-plus-sv").to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base-plus-sv") input_data = self._load_superb("si", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) labels = torch.tensor([5, 1, 1, 3], device=torch_device).T with torch.no_grad(): input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) outputs = model(input_values, attention_mask=attention_mask, labels=labels) embeddings = torch.nn.functional.normalize(outputs.embeddings, dim=-1) cosine_sim = torch.nn.CosineSimilarity(dim=-1) # id10002 vs id10002 self.assertAlmostEqual(cosine_sim(embeddings[1], embeddings[2]).item(), 0.9671, 3) # id10006 vs id10002 self.assertAlmostEqual(cosine_sim(embeddings[0], embeddings[1]).item(), 0.4941, 3) # id10002 vs id10004 self.assertAlmostEqual(cosine_sim(embeddings[2], embeddings[3]).item(), 0.5616, 3) self.assertAlmostEqual(outputs.loss.item(), 18.5925, 2)

transformers/tests/models/unispeech_sat/test_modeling_unispeech_sat.py/0

# coding=utf-8 # Copyright 2022 HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow VisionEncoderDecoder model. """ from __future__ import annotations import copy import os import tempfile import unittest import numpy as np from transformers import is_tf_available, is_torch_available, is_vision_available from transformers.testing_utils import ( is_pt_tf_cross_test, require_tf, require_torch, require_vision, slow, torch_device, ) from transformers.utils.generic import ModelOutput from ...test_modeling_tf_common import floats_tensor, ids_tensor from ..gpt2.test_modeling_tf_gpt2 import TFGPT2ModelTester from ..vit.test_modeling_tf_vit import TFViTModelTester if is_tf_available(): import tensorflow as tf from transformers import ( AutoConfig, AutoImageProcessor, AutoTokenizer, TFAutoModel, TFAutoModelForCausalLM, TFGPT2LMHeadModel, TFVisionEncoderDecoderModel, TFViTModel, VisionEncoderDecoderConfig, ) from transformers.modeling_tf_outputs import TFBaseModelOutput if is_torch_available(): import torch from transformers import GPT2LMHeadModel, VisionEncoderDecoderModel, ViTModel if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor @require_tf class TFVisionEncoderDecoderMixin: def get_encoder_decoder_model(self, config, decoder_config): raise NotImplementedError def prepare_config_and_inputs(self): raise NotImplementedError def get_pretrained_model(self): raise NotImplementedError def check_encoder_decoder_model_from_pretrained_configs( self, config, pixel_values, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs, ): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) self.assertTrue(encoder_decoder_config.decoder.is_decoder) enc_dec_model = TFVisionEncoderDecoderModel(encoder_decoder_config) self.assertTrue(enc_dec_model.config.is_encoder_decoder) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, kwargs=kwargs, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) ) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0]) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size) def check_encoder_decoder_model( self, config, pixel_values, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs, ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) self.assertTrue(enc_dec_model.config.decoder.is_decoder) self.assertTrue(enc_dec_model.config.decoder.add_cross_attention) self.assertTrue(enc_dec_model.config.is_encoder_decoder) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, kwargs=kwargs, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) ) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0]) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size) encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_hidden_states) outputs_encoder_decoder = enc_dec_model( pixel_values=None, encoder_outputs=encoder_outputs, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, kwargs=kwargs, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) ) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0]) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size) def check_encoder_decoder_model_from_pretrained( self, config, pixel_values, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, return_dict, **kwargs, ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict} enc_dec_model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, return_dict=True, kwargs=kwargs, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) ) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0]) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size) def check_save_and_load( self, config, pixel_values, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs, ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) outputs = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, kwargs=kwargs, ) out_2 = np.array(outputs[0]) out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: enc_dec_model.save_pretrained(tmpdirname) enc_dec_model = TFVisionEncoderDecoderModel.from_pretrained(tmpdirname) after_outputs = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, kwargs=kwargs, ) out_1 = np.array(after_outputs[0]) out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) def check_encoder_decoder_model_labels( self, config, pixel_values, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, labels, **kwargs, ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, labels=labels, kwargs=kwargs, ) # Make sure `loss` exist self.assertIn("loss", outputs_encoder_decoder) batch_size, seq_len = decoder_input_ids.shape expected_shape = (batch_size, seq_len, decoder_config.vocab_size) self.assertEqual(outputs_encoder_decoder["logits"].shape, expected_shape) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0]) self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size) def check_encoder_decoder_model_output_attentions( self, config, pixel_values, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs, ): # make the decoder inputs a different shape from the encoder inputs to harden the test decoder_input_ids = decoder_input_ids[:, :-1] decoder_attention_mask = decoder_attention_mask[:, :-1] encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, output_attentions=True, kwargs=kwargs, ) encoder_attentions = outputs_encoder_decoder["encoder_attentions"] self.assertEqual(len(encoder_attentions), config.num_hidden_layers) self.assertEqual(encoder_attentions[0].shape[-3:-2], (config.num_attention_heads,)) decoder_attentions = outputs_encoder_decoder["decoder_attentions"] num_decoder_layers = ( decoder_config.num_decoder_layers if hasattr(decoder_config, "num_decoder_layers") else decoder_config.num_hidden_layers ) self.assertEqual(len(decoder_attentions), num_decoder_layers) self.assertEqual( decoder_attentions[0].shape[-3:], (decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]), ) cross_attentions = outputs_encoder_decoder["cross_attentions"] self.assertEqual(len(cross_attentions), num_decoder_layers) cross_attention_input_seq_len = decoder_input_ids.shape[-1] * ( 1 + (decoder_config.ngram if hasattr(decoder_config, "ngram") else 0) ) self.assertEqual( cross_attentions[0].shape[-3:-1], (decoder_config.num_attention_heads, cross_attention_input_seq_len), ) def check_encoder_decoder_model_generate(self, pixel_values, config, decoder_config, **kwargs): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) # Generate until max length if hasattr(enc_dec_model.config, "eos_token_id"): enc_dec_model.config.eos_token_id = None if hasattr(enc_dec_model.config, "decoder") and hasattr(enc_dec_model.config.decoder, "eos_token_id"): enc_dec_model.config.decoder.eos_token_id = None if hasattr(enc_dec_model.generation_config, "eos_token_id"): enc_dec_model.generation_config.eos_token_id = None # Bert does not have a bos token id, so use pad_token_id instead generated_output = enc_dec_model.generate( pixel_values, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id ) self.assertEqual( tuple(generated_output.shape.as_list()), (pixel_values.shape[0],) + (decoder_config.max_length,) ) def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way. Args: model_class: The class of the model that is currently testing. For example, `TFBertModel`, TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative error messages. name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc. attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element being a named field in the output. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(tf_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is", ) tf_keys = [k for k, v in tf_outputs.items() if v is not None] pt_keys = [k for k, v in pt_outputs.items() if v is not None] self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `names` attributes = tuple([f"{name}.{k}" for k in tf_keys]) self.check_pt_tf_outputs( tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(tf_outputs) in [tuple, list]: self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch") self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch") if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(tf_outputs), f"{name}: The tuple `names` should have the same length as `tf_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `names` attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))]) for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes): self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(tf_outputs, tf.Tensor): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is" ) tf_outputs = tf_outputs.numpy() pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(tf_outputs): tf_outputs = np.array([tf_outputs]) pt_outputs = np.array([pt_outputs]) tf_nans = np.isnan(tf_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[tf_nans] = 0 tf_outputs[tf_nans] = 0 pt_outputs[pt_nans] = 0 tf_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(tf_outputs - pt_outputs)) self.assertLessEqual(max_diff, tol, f"{name}: Difference between torch and tf is {max_diff} (>= {tol}).") else: raise ValueError( "`tf_outputs` should be an instance of `tf.Tensor`, a `tuple`, or an instance of `tf.Tensor`. Got" f" {type(tf_outputs)} instead." ) def prepare_pt_inputs_from_tf_inputs(self, tf_inputs_dict): pt_inputs_dict = {} for name, key in tf_inputs_dict.items(): if isinstance(key, bool): pt_inputs_dict[name] = key elif name == "input_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) elif name == "pixel_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) elif name == "input_features": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) # other general float inputs elif tf_inputs_dict[name].dtype.is_floating: pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) else: pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long) return pt_inputs_dict def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict): pt_inputs_dict = self.prepare_pt_inputs_from_tf_inputs(tf_inputs_dict) # send pytorch inputs to the correct device pt_inputs_dict = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items() } # send pytorch model to the correct device pt_model.to(torch_device) # Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences pt_model.eval() with torch.no_grad(): pt_outputs = pt_model(**pt_inputs_dict) tf_outputs = tf_model(tf_inputs_dict) # tf models returned loss is usually a tensor rather than a scalar. # (see `hf_compute_loss`: it uses `keras.losses.Reduction.NONE`) # Change it here to a scalar to match PyTorch models' loss tf_loss = getattr(tf_outputs, "loss", None) if tf_loss is not None: tf_outputs.loss = tf.math.reduce_mean(tf_loss) self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(tf_model)) def check_pt_tf_equivalence(self, tf_model, pt_model, tf_inputs_dict): """Wrap `check_pt_tf_models` to further check PT -> TF again""" self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) # PT -> TF with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) tf_model = TFVisionEncoderDecoderModel.from_pretrained(tmpdirname) self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) def check_pt_to_tf_equivalence(self, config, decoder_config, tf_inputs_dict): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) # Output all for aggressive testing encoder_decoder_config.output_hidden_states = True # All models tested in this file have attentions encoder_decoder_config.output_attentions = True pt_model = VisionEncoderDecoderModel(encoder_decoder_config) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) tf_model = TFVisionEncoderDecoderModel.from_pretrained(tmpdirname) self.check_pt_tf_equivalence(tf_model, pt_model, tf_inputs_dict) def check_tf_to_pt_equivalence(self, config, decoder_config, tf_inputs_dict): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) # Output all for aggressive testing encoder_decoder_config.output_hidden_states = True # TODO: A generalizable way to determine this attribute encoder_decoder_config.output_attentions = True tf_model = TFVisionEncoderDecoderModel(encoder_decoder_config) # Make sure model is built before saving tf_model(**tf_inputs_dict) with tempfile.TemporaryDirectory() as tmpdirname: tf_model.save_pretrained(tmpdirname, safe_serialization=False) pt_model = VisionEncoderDecoderModel.from_pretrained(tmpdirname, from_tf=True) self.check_pt_tf_equivalence(tf_model, pt_model, tf_inputs_dict) def test_encoder_decoder_model(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model(**config_inputs_dict) def test_encoder_decoder_model_from_pretrained_configs(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_from_pretrained_configs(**config_inputs_dict) def test_encoder_decoder_model_from_pretrained(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_from_pretrained(**config_inputs_dict, return_dict=False) def test_encoder_decoder_model_from_pretrained_return_dict(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_from_pretrained(**config_inputs_dict, return_dict=True) def test_save_and_load_from_pretrained(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_save_and_load(**config_inputs_dict) def test_encoder_decoder_model_labels(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_labels(**config_inputs_dict) def test_encoder_decoder_model_output_attentions(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_output_attentions(**config_inputs_dict) def test_encoder_decoder_model_generate(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_generate(**config_inputs_dict) def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float): diff = np.abs((a - b)).max() self.assertLessEqual(diff, tol, f"Difference between torch and tf is {diff} (>= {tol}).") @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): config_inputs_dict = self.prepare_config_and_inputs() labels = config_inputs_dict.pop("decoder_token_labels") # Keep only common arguments arg_names = [ "config", "pixel_values", "decoder_config", "decoder_input_ids", "decoder_attention_mask", "encoder_hidden_states", ] config_inputs_dict = {k: v for k, v in config_inputs_dict.items() if k in arg_names} config = config_inputs_dict.pop("config") decoder_config = config_inputs_dict.pop("decoder_config") # Output all for aggressive testing config.output_hidden_states = True decoder_config.output_hidden_states = True # All models tested in this file have attentions config.output_attentions = True decoder_config.output_attentions = True tf_inputs_dict = config_inputs_dict # `encoder_hidden_states` is not used in model call/forward del tf_inputs_dict["encoder_hidden_states"] # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`. for k in ["decoder_attention_mask"]: attention_mask = tf_inputs_dict[k] # Make sure no all 0s attention masks - to avoid failure at this moment. # Put `1` at the beginning of sequences to make it still work when combining causal attention masks. # TODO: remove this line once a fix regarding large negative values for attention mask is done. attention_mask = tf.concat( [tf.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], axis=-1 ) tf_inputs_dict[k] = attention_mask tf_inputs_dict_with_labels = copy.copy(tf_inputs_dict) tf_inputs_dict_with_labels["labels"] = labels self.assertTrue(decoder_config.cross_attention_hidden_size is None) # Original test: check without `labels` and without `enc_to_dec_proj` projection self.assertTrue(config.hidden_size == decoder_config.hidden_size) self.check_pt_to_tf_equivalence(config, decoder_config, tf_inputs_dict) self.check_tf_to_pt_equivalence(config, decoder_config, tf_inputs_dict) # check with `labels` self.check_pt_to_tf_equivalence(config, decoder_config, tf_inputs_dict_with_labels) self.check_tf_to_pt_equivalence(config, decoder_config, tf_inputs_dict_with_labels) # check `enc_to_dec_proj` work as expected decoder_config.hidden_size = decoder_config.hidden_size * 2 self.assertTrue(config.hidden_size != decoder_config.hidden_size) self.check_pt_to_tf_equivalence(config, decoder_config, tf_inputs_dict) self.check_tf_to_pt_equivalence(config, decoder_config, tf_inputs_dict) @slow def test_real_model_save_load_from_pretrained(self): model_2 = self.get_pretrained_model() pixel_values = floats_tensor( [ 13, model_2.config.encoder.num_channels, model_2.config.encoder.image_size, model_2.config.encoder.image_size, ] ) decoder_input_ids = ids_tensor([13, 1], model_2.config.decoder.vocab_size) outputs = model_2( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, ) out_2 = np.array(outputs[0]) out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmp_dirname: model_2.save_pretrained(tmp_dirname) model_1 = TFVisionEncoderDecoderModel.from_pretrained(tmp_dirname) after_outputs = model_1(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids) out_1 = np.array(after_outputs[0]) out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) @require_tf class TFViT2GPT2EncoderDecoderModelTest(TFVisionEncoderDecoderMixin, unittest.TestCase): def get_pretrained_model(self): return TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( "google/vit-base-patch16-224-in21k", "openai-community/gpt2" ) def get_encoder_decoder_model(self, config, decoder_config): encoder_model = TFViTModel(config, name="encoder") decoder_model = TFGPT2LMHeadModel(decoder_config, name="decoder") return encoder_model, decoder_model def prepare_config_and_inputs(self): model_tester_encoder = TFViTModelTester(self, batch_size=13) model_tester_decoder = TFGPT2ModelTester(self) encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs() decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder() (config, pixel_values, labels) = encoder_config_and_inputs ( decoder_config, decoder_input_ids, decoder_attention_mask, decoder_head_mask, decoder_token_type_ids, decoder_sequence_labels, decoder_token_labels, decoder_choice_labels, encoder_hidden_states, encoder_attention_mask, ) = decoder_config_and_inputs # make sure that cross attention layers are added decoder_config.add_cross_attention = True # disable cache for now decoder_config.use_cache = False return { "config": config, "pixel_values": pixel_values, "decoder_config": decoder_config, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "decoder_token_labels": decoder_token_labels, "encoder_hidden_states": encoder_hidden_states, # This is not used in the tests. "labels": decoder_token_labels, } @require_tf class TFVisionEncoderDecoderModelTest(unittest.TestCase): def get_from_encoderdecoder_pretrained_model(self): return TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( "google/vit-base-patch16-224-in21k", "openai-community/gpt2" ) def get_decoder_config(self): config = AutoConfig.from_pretrained("openai-community/gpt2") config.is_decoder = True config.add_cross_attention = True return config def get_encoderdecoder_model(self): return TFVisionEncoderDecoderModel.from_pretrained("ydshieh/vit-gpt2-coco-en") def get_encoder_decoder_models(self): encoder_model = TFViTModel.from_pretrained("google/vit-base-patch16-224-in21k", name="encoder") decoder_model = TFGPT2LMHeadModel.from_pretrained( "openai-community/gpt2", config=self.get_decoder_config(), name="decoder" ) return {"encoder": encoder_model, "decoder": decoder_model} def _check_configuration_tie(self, model): assert id(model.decoder.config) == id(model.config.decoder) assert id(model.encoder.config) == id(model.config.encoder) @slow def test_configuration_tie(self): model = self.get_from_encoderdecoder_pretrained_model() self._check_configuration_tie(model) model = TFVisionEncoderDecoderModel(**self.get_encoder_decoder_models()) self._check_configuration_tie(model) model = self.get_encoderdecoder_model() self._check_configuration_tie(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_tf class TFVisionEncoderDecoderModelSaveLoadTests(unittest.TestCase): def get_encoder_decoder_config(self): encoder_config = AutoConfig.from_pretrained("google/vit-base-patch16-224-in21k") decoder_config = AutoConfig.from_pretrained("openai-community/gpt2", is_decoder=True, add_cross_attention=True) return VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder_config, decoder_config) def get_encoder_decoder_config_small(self): encoder_config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-vit") decoder_config = AutoConfig.from_pretrained( "hf-internal-testing/tiny-random-gpt2", is_decoder=True, add_cross_attention=True ) return VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder_config, decoder_config) def test_encoder_decoder_save_load_from_encoder_decoder(self): config = self.get_encoder_decoder_config_small() # create two random ViT/GPT2 models for vit-gpt2 & initialize weights (+cross_attention weights) encoder = TFViTModel(config.encoder) encoder.build_in_name_scope() decoder = TFGPT2LMHeadModel(config.decoder) decoder.build_in_name_scope() encoder_decoder_orig = TFVisionEncoderDecoderModel(encoder=encoder, decoder=decoder) pixel_values = floats_tensor( [ 13, encoder.config.num_channels, encoder.config.image_size, encoder.config.image_size, ] ) decoder_input_ids = ids_tensor([13, 1], decoder.config.vocab_size) logits_orig = encoder_decoder_orig(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits with tempfile.TemporaryDirectory() as tmp_dirname: encoder_path = os.path.join(tmp_dirname, "encoder") decoder_path = os.path.join(tmp_dirname, "decoder") encoder.save_pretrained(encoder_path) decoder.save_pretrained(decoder_path) encoder_decoder = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_path, decoder_path) logits_1 = encoder_decoder(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits self.assertTrue(logits_orig.numpy().sum() - logits_1.numpy().sum() < 1e-3) max_diff = np.max(np.abs(logits_1.numpy() - logits_orig.numpy())) self.assertAlmostEqual(max_diff, 0.0, places=4) with tempfile.TemporaryDirectory() as tmp_dirname: encoder_decoder.save_pretrained(tmp_dirname) encoder_decoder = TFVisionEncoderDecoderModel.from_pretrained(tmp_dirname) logits_2 = encoder_decoder(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits max_diff = np.max(np.abs(logits_2.numpy() - logits_orig.numpy())) self.assertAlmostEqual(max_diff, 0.0, places=4) @require_torch @is_pt_tf_cross_test def test_encoder_decoder_save_load_from_encoder_decoder_from_pt(self): config = self.get_encoder_decoder_config_small() # create two random ViT/GPT2 models for vit-gpt2 & initialize weights (+cross_attention weights) encoder_pt = ViTModel(config.encoder).to(torch_device).eval() decoder_pt = GPT2LMHeadModel(config.decoder).to(torch_device).eval() encoder_decoder_pt = VisionEncoderDecoderModel(encoder=encoder_pt, decoder=decoder_pt).to(torch_device).eval() pixel_values = floats_tensor( [ 13, encoder_pt.config.num_channels, encoder_pt.config.image_size, encoder_pt.config.image_size, ] ) decoder_input_ids = ids_tensor([13, 1], decoder_pt.config.vocab_size) pt_pixel_values = torch.tensor(pixel_values.numpy(), device=torch_device, dtype=torch.float) pt_decoder_input_ids = torch.tensor(decoder_input_ids.numpy(), device=torch_device, dtype=torch.long) logits_pt = encoder_decoder_pt(pixel_values=pt_pixel_values, decoder_input_ids=pt_decoder_input_ids).logits # PyTorch => TensorFlow with tempfile.TemporaryDirectory() as tmp_dirname_1, tempfile.TemporaryDirectory() as tmp_dirname_2: encoder_decoder_pt.encoder.save_pretrained(tmp_dirname_1) encoder_decoder_pt.decoder.save_pretrained(tmp_dirname_2) encoder_decoder_tf = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( tmp_dirname_1, tmp_dirname_2 ) logits_tf = encoder_decoder_tf(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits max_diff = np.max(np.abs(logits_pt.detach().cpu().numpy() - logits_tf.numpy())) self.assertAlmostEqual(max_diff, 0.0, places=3) # Make sure `from_pretrained` following `save_pretrained` work and give the same result # (See https://github.com/huggingface/transformers/pull/14016) with tempfile.TemporaryDirectory() as tmp_dirname: encoder_decoder_tf.save_pretrained(tmp_dirname, safe_serialization=False) encoder_decoder_tf = TFVisionEncoderDecoderModel.from_pretrained(tmp_dirname) logits_tf_2 = encoder_decoder_tf(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits max_diff = np.max(np.abs(logits_tf_2.numpy() - logits_tf.numpy())) self.assertAlmostEqual(max_diff, 0.0, places=3) @require_vision @slow def test_encoder_decoder_from_pretrained(self): load_weight_prefix = TFVisionEncoderDecoderModel.load_weight_prefix config = self.get_encoder_decoder_config() image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") decoder_tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2") img = prepare_img() pixel_values = image_processor(images=img, return_tensors="tf").pixel_values decoder_input_ids = decoder_tokenizer("Linda Davis", return_tensors="tf").input_ids with tempfile.TemporaryDirectory() as tmp_dirname: # Since most of HF's models don't have pretrained cross-attention layers, they are randomly # initialized even if we create models using `from_pretrained` method. # For the tests, the decoder need to be a model with pretrained cross-attention layers. # So we create pretrained models (without `load_weight_prefix`), save them, and later, # we load them using `from_pretrained`. # (we don't need to do this for encoder, but let's make the code more similar between encoder/decoder) encoder = TFAutoModel.from_pretrained("google/vit-base-patch16-224-in21k", name="encoder") # It's necessary to specify `add_cross_attention=True` here. decoder = TFAutoModelForCausalLM.from_pretrained( "openai-community/gpt2", is_decoder=True, add_cross_attention=True, name="decoder" ) pretrained_encoder_dir = os.path.join(tmp_dirname, "pretrained_encoder") pretrained_decoder_dir = os.path.join(tmp_dirname, "pretrained_decoder") encoder.save_pretrained(pretrained_encoder_dir) decoder.save_pretrained(pretrained_decoder_dir) del encoder del decoder enc_dec_model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( pretrained_encoder_dir, pretrained_decoder_dir, ) enc_dec_model.build_in_name_scope() # check that the from pretrained methods work enc_dec_model.save_pretrained(tmp_dirname) enc_dec_model = TFVisionEncoderDecoderModel.from_pretrained(tmp_dirname) output = enc_dec_model(pixel_values, decoder_input_ids=decoder_input_ids, labels=decoder_input_ids) loss_pretrained = output.loss del enc_dec_model # Create the model using `__init__` with loaded ``pretrained`` encoder / decoder encoder = TFAutoModel.from_pretrained( pretrained_encoder_dir, load_weight_prefix=load_weight_prefix, name="encoder" ) decoder = TFAutoModelForCausalLM.from_pretrained( pretrained_decoder_dir, load_weight_prefix=load_weight_prefix, name="decoder" ) enc_dec_model = TFVisionEncoderDecoderModel(config=config, encoder=encoder, decoder=decoder) output = enc_dec_model(pixel_values, decoder_input_ids=decoder_input_ids, labels=decoder_input_ids) loss_init = output.loss max_diff = np.max(np.abs(loss_pretrained - loss_init)) expected_diff = 0.0 self.assertAlmostEqual(max_diff, expected_diff, places=4) @require_vision @require_tf class TFViT2GPT2ModelIntegrationTest(unittest.TestCase): @slow def test_inference_coco_en(self): loc = "ydshieh/vit-gpt2-coco-en" image_processor = ViTImageProcessor.from_pretrained(loc) tokenizer = AutoTokenizer.from_pretrained(loc) model = TFVisionEncoderDecoderModel.from_pretrained(loc) # We will verify our results on an image of cute cats img = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") pixel_values = image_processor(images=img, return_tensors="tf").pixel_values decoder_input_ids = tf.constant([[model.config.decoder_start_token_id]]) logits = model(pixel_values, decoder_input_ids)[0].numpy() # verify the logits expected_shape = (1, 1, model.config.decoder.vocab_size) self.assertEqual(logits.shape, expected_shape) EXPECTED_LOGIT_SLICE = np.array( [ -38.705807, -30.639929, -31.41903, -39.012012, -38.38696, -34.887207, -33.290855, -35.68447, -38.508484, -36.124645, ] ) max_diff = np.amax(np.abs(logits[0, 0, :10] - EXPECTED_LOGIT_SLICE)) self.assertLessEqual(max_diff, 1e-4) def generate_step(pixel_values): outputs = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True) output_ids = outputs.sequences preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) preds = [pred.strip() for pred in preds] return preds preds = generate_step(pixel_values) # should produce # ["a cat laying on top of a couch next to another cat"] self.assertEqual(preds, ["a cat laying on top of a couch next to another cat"])

transformers/tests/models/vision_encoder_decoder/test_modeling_tf_vision_encoder_decoder.py/0

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import gc import logging import os import sys import tempfile import unittest from pathlib import Path import datasets import numpy as np from huggingface_hub import HfFolder, Repository, create_repo, delete_repo from requests.exceptions import HTTPError from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DistilBertForSequenceClassification, TextClassificationPipeline, TFAutoModelForSequenceClassification, pipeline, ) from transformers.pipelines import PIPELINE_REGISTRY, get_task from transformers.pipelines.base import Pipeline, _pad from transformers.testing_utils import ( TOKEN, USER, CaptureLogger, RequestCounter, backend_empty_cache, is_pipeline_test, is_staging_test, nested_simplify, require_tensorflow_probability, require_tf, require_torch, require_torch_accelerator, require_torch_or_tf, slow, torch_device, ) from transformers.utils import direct_transformers_import, is_tf_available, is_torch_available from transformers.utils import logging as transformers_logging sys.path.append(str(Path(__file__).parent.parent.parent / "utils")) from test_module.custom_pipeline import PairClassificationPipeline # noqa E402 logger = logging.getLogger(__name__) PATH_TO_TRANSFORMERS = os.path.join(Path(__file__).parent.parent.parent, "src/transformers") # Dynamically import the Transformers module to grab the attribute classes of the processor form their names. transformers_module = direct_transformers_import(PATH_TO_TRANSFORMERS) class ANY: def __init__(self, *_types): self._types = _types def __eq__(self, other): return isinstance(other, self._types) def __repr__(self): return f"ANY({', '.join(_type.__name__ for _type in self._types)})" @is_pipeline_test class CommonPipelineTest(unittest.TestCase): @require_torch def test_pipeline_iteration(self): from torch.utils.data import Dataset class MyDataset(Dataset): data = [ "This is a test", "This restaurant is great", "This restaurant is awful", ] def __len__(self): return 3 def __getitem__(self, i): return self.data[i] text_classifier = pipeline( task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt" ) dataset = MyDataset() for output in text_classifier(dataset): self.assertEqual(output, {"label": ANY(str), "score": ANY(float)}) @require_torch def test_check_task_auto_inference(self): pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert") self.assertIsInstance(pipe, TextClassificationPipeline) @require_torch def test_pipeline_batch_size_global(self): pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert") self.assertEqual(pipe._batch_size, None) self.assertEqual(pipe._num_workers, None) pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert", batch_size=2, num_workers=1) self.assertEqual(pipe._batch_size, 2) self.assertEqual(pipe._num_workers, 1) @require_torch def test_pipeline_pathlike(self): pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert") with tempfile.TemporaryDirectory() as d: pipe.save_pretrained(d) path = Path(d) newpipe = pipeline(task="text-classification", model=path) self.assertIsInstance(newpipe, TextClassificationPipeline) @require_torch def test_pipeline_override(self): class MyPipeline(TextClassificationPipeline): pass text_classifier = pipeline(model="hf-internal-testing/tiny-random-distilbert", pipeline_class=MyPipeline) self.assertIsInstance(text_classifier, MyPipeline) def test_check_task(self): task = get_task("openai-community/gpt2") self.assertEqual(task, "text-generation") with self.assertRaises(RuntimeError): # Wrong framework get_task("espnet/siddhana_slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best") @require_torch def test_iterator_data(self): def data(n: int): for _ in range(n): yield "This is a test" pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert") results = [] for out in pipe(data(10)): self.assertEqual(nested_simplify(out), {"label": "LABEL_0", "score": 0.504}) results.append(out) self.assertEqual(len(results), 10) # When using multiple workers on streamable data it should still work # This will force using `num_workers=1` with a warning for now. results = [] for out in pipe(data(10), num_workers=2): self.assertEqual(nested_simplify(out), {"label": "LABEL_0", "score": 0.504}) results.append(out) self.assertEqual(len(results), 10) @require_tf def test_iterator_data_tf(self): def data(n: int): for _ in range(n): yield "This is a test" pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert", framework="tf") out = pipe("This is a test") results = [] for out in pipe(data(10)): self.assertEqual(nested_simplify(out), {"label": "LABEL_0", "score": 0.504}) results.append(out) self.assertEqual(len(results), 10) @require_torch def test_unbatch_attentions_hidden_states(self): model = DistilBertForSequenceClassification.from_pretrained( "hf-internal-testing/tiny-random-distilbert", output_hidden_states=True, output_attentions=True ) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-distilbert") text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer) # Used to throw an error because `hidden_states` are a tuple of tensors # instead of the expected tensor. outputs = text_classifier(["This is great !"] * 20, batch_size=32) self.assertEqual(len(outputs), 20) @is_pipeline_test class PipelineScikitCompatTest(unittest.TestCase): @require_torch def test_pipeline_predict_pt(self): data = ["This is a test"] text_classifier = pipeline( task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt" ) expected_output = [{"label": ANY(str), "score": ANY(float)}] actual_output = text_classifier.predict(data) self.assertEqual(expected_output, actual_output) @require_tf def test_pipeline_predict_tf(self): data = ["This is a test"] text_classifier = pipeline( task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf" ) expected_output = [{"label": ANY(str), "score": ANY(float)}] actual_output = text_classifier.predict(data) self.assertEqual(expected_output, actual_output) @require_torch def test_pipeline_transform_pt(self): data = ["This is a test"] text_classifier = pipeline( task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt" ) expected_output = [{"label": ANY(str), "score": ANY(float)}] actual_output = text_classifier.transform(data) self.assertEqual(expected_output, actual_output) @require_tf def test_pipeline_transform_tf(self): data = ["This is a test"] text_classifier = pipeline( task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf" ) expected_output = [{"label": ANY(str), "score": ANY(float)}] actual_output = text_classifier.transform(data) self.assertEqual(expected_output, actual_output) @is_pipeline_test class PipelinePadTest(unittest.TestCase): @require_torch def test_pipeline_padding(self): import torch items = [ { "label": "label1", "input_ids": torch.LongTensor([[1, 23, 24, 2]]), "attention_mask": torch.LongTensor([[0, 1, 1, 0]]), }, { "label": "label2", "input_ids": torch.LongTensor([[1, 23, 24, 43, 44, 2]]), "attention_mask": torch.LongTensor([[0, 1, 1, 1, 1, 0]]), }, ] self.assertEqual(_pad(items, "label", 0, "right"), ["label1", "label2"]) self.assertTrue( torch.allclose( _pad(items, "input_ids", 10, "right"), torch.LongTensor([[1, 23, 24, 2, 10, 10], [1, 23, 24, 43, 44, 2]]), ) ) self.assertTrue( torch.allclose( _pad(items, "input_ids", 10, "left"), torch.LongTensor([[10, 10, 1, 23, 24, 2], [1, 23, 24, 43, 44, 2]]), ) ) self.assertTrue( torch.allclose( _pad(items, "attention_mask", 0, "right"), torch.LongTensor([[0, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 0]]) ) ) @require_torch def test_pipeline_image_padding(self): import torch items = [ { "label": "label1", "pixel_values": torch.zeros((1, 3, 10, 10)), }, { "label": "label2", "pixel_values": torch.zeros((1, 3, 10, 10)), }, ] self.assertEqual(_pad(items, "label", 0, "right"), ["label1", "label2"]) self.assertTrue( torch.allclose( _pad(items, "pixel_values", 10, "right"), torch.zeros((2, 3, 10, 10)), ) ) @require_torch def test_pipeline_offset_mapping(self): import torch items = [ { "offset_mappings": torch.zeros([1, 11, 2], dtype=torch.long), }, { "offset_mappings": torch.zeros([1, 4, 2], dtype=torch.long), }, ] self.assertTrue( torch.allclose( _pad(items, "offset_mappings", 0, "right"), torch.zeros((2, 11, 2), dtype=torch.long), ), ) @is_pipeline_test class PipelineUtilsTest(unittest.TestCase): @require_torch def test_pipeline_dataset(self): from transformers.pipelines.pt_utils import PipelineDataset dummy_dataset = [0, 1, 2, 3] def add(number, extra=0): return number + extra dataset = PipelineDataset(dummy_dataset, add, {"extra": 2}) self.assertEqual(len(dataset), 4) outputs = [dataset[i] for i in range(4)] self.assertEqual(outputs, [2, 3, 4, 5]) @require_torch def test_pipeline_iterator(self): from transformers.pipelines.pt_utils import PipelineIterator dummy_dataset = [0, 1, 2, 3] def add(number, extra=0): return number + extra dataset = PipelineIterator(dummy_dataset, add, {"extra": 2}) self.assertEqual(len(dataset), 4) outputs = list(dataset) self.assertEqual(outputs, [2, 3, 4, 5]) @require_torch def test_pipeline_iterator_no_len(self): from transformers.pipelines.pt_utils import PipelineIterator def dummy_dataset(): for i in range(4): yield i def add(number, extra=0): return number + extra dataset = PipelineIterator(dummy_dataset(), add, {"extra": 2}) with self.assertRaises(TypeError): len(dataset) outputs = list(dataset) self.assertEqual(outputs, [2, 3, 4, 5]) @require_torch def test_pipeline_batch_unbatch_iterator(self): from transformers.pipelines.pt_utils import PipelineIterator dummy_dataset = [{"id": [0, 1, 2]}, {"id": [3]}] def add(number, extra=0): return {"id": [i + extra for i in number["id"]]} dataset = PipelineIterator(dummy_dataset, add, {"extra": 2}, loader_batch_size=3) outputs = list(dataset) self.assertEqual(outputs, [{"id": 2}, {"id": 3}, {"id": 4}, {"id": 5}]) @require_torch def test_pipeline_batch_unbatch_iterator_tensors(self): import torch from transformers.pipelines.pt_utils import PipelineIterator dummy_dataset = [{"id": torch.LongTensor([[10, 20], [0, 1], [0, 2]])}, {"id": torch.LongTensor([[3]])}] def add(number, extra=0): return {"id": number["id"] + extra} dataset = PipelineIterator(dummy_dataset, add, {"extra": 2}, loader_batch_size=3) outputs = list(dataset) self.assertEqual( nested_simplify(outputs), [{"id": [[12, 22]]}, {"id": [[2, 3]]}, {"id": [[2, 4]]}, {"id": [[5]]}] ) @require_torch def test_pipeline_chunk_iterator(self): from transformers.pipelines.pt_utils import PipelineChunkIterator def preprocess_chunk(n: int): for i in range(n): yield i dataset = [2, 3] dataset = PipelineChunkIterator(dataset, preprocess_chunk, {}, loader_batch_size=3) outputs = list(dataset) self.assertEqual(outputs, [0, 1, 0, 1, 2]) @require_torch def test_pipeline_pack_iterator(self): from transformers.pipelines.pt_utils import PipelinePackIterator def pack(item): return {"id": item["id"] + 1, "is_last": item["is_last"]} dataset = [ {"id": 0, "is_last": False}, {"id": 1, "is_last": True}, {"id": 0, "is_last": False}, {"id": 1, "is_last": False}, {"id": 2, "is_last": True}, ] dataset = PipelinePackIterator(dataset, pack, {}) outputs = list(dataset) self.assertEqual( outputs, [ [ {"id": 1}, {"id": 2}, ], [ {"id": 1}, {"id": 2}, {"id": 3}, ], ], ) @require_torch def test_pipeline_pack_unbatch_iterator(self): from transformers.pipelines.pt_utils import PipelinePackIterator dummy_dataset = [{"id": [0, 1, 2], "is_last": [False, True, False]}, {"id": [3], "is_last": [True]}] def add(number, extra=0): return {"id": [i + extra for i in number["id"]], "is_last": number["is_last"]} dataset = PipelinePackIterator(dummy_dataset, add, {"extra": 2}, loader_batch_size=3) outputs = list(dataset) self.assertEqual(outputs, [[{"id": 2}, {"id": 3}], [{"id": 4}, {"id": 5}]]) # is_false Across batch dummy_dataset = [{"id": [0, 1, 2], "is_last": [False, False, False]}, {"id": [3], "is_last": [True]}] def add(number, extra=0): return {"id": [i + extra for i in number["id"]], "is_last": number["is_last"]} dataset = PipelinePackIterator(dummy_dataset, add, {"extra": 2}, loader_batch_size=3) outputs = list(dataset) self.assertEqual(outputs, [[{"id": 2}, {"id": 3}, {"id": 4}, {"id": 5}]]) def test_pipeline_negative_device(self): # To avoid regressing, pipeline used to accept device=-1 classifier = pipeline("text-generation", "hf-internal-testing/tiny-random-bert", device=-1) expected_output = [{"generated_text": ANY(str)}] actual_output = classifier("Test input.") self.assertEqual(expected_output, actual_output) @slow @require_torch def test_load_default_pipelines_pt(self): import torch from transformers.pipelines import SUPPORTED_TASKS set_seed_fn = lambda: torch.manual_seed(0) # noqa: E731 for task in SUPPORTED_TASKS.keys(): if task == "table-question-answering": # test table in seperate test due to more dependencies continue self.check_default_pipeline(task, "pt", set_seed_fn, self.check_models_equal_pt) # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() backend_empty_cache(torch_device) @slow @require_tf def test_load_default_pipelines_tf(self): import tensorflow as tf from transformers.pipelines import SUPPORTED_TASKS set_seed_fn = lambda: tf.random.set_seed(0) # noqa: E731 for task in SUPPORTED_TASKS.keys(): if task == "table-question-answering": # test table in seperate test due to more dependencies continue self.check_default_pipeline(task, "tf", set_seed_fn, self.check_models_equal_tf) # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() @slow @require_torch def test_load_default_pipelines_pt_table_qa(self): import torch set_seed_fn = lambda: torch.manual_seed(0) # noqa: E731 self.check_default_pipeline("table-question-answering", "pt", set_seed_fn, self.check_models_equal_pt) # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() backend_empty_cache(torch_device) @slow @require_torch @require_torch_accelerator def test_pipeline_accelerator(self): pipe = pipeline("text-generation", device=torch_device) _ = pipe("Hello") @slow @require_torch @require_torch_accelerator def test_pipeline_accelerator_indexed(self): pipe = pipeline("text-generation", device=torch_device) _ = pipe("Hello") @slow @require_tf @require_tensorflow_probability def test_load_default_pipelines_tf_table_qa(self): import tensorflow as tf set_seed_fn = lambda: tf.random.set_seed(0) # noqa: E731 self.check_default_pipeline("table-question-answering", "tf", set_seed_fn, self.check_models_equal_tf) # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() def check_default_pipeline(self, task, framework, set_seed_fn, check_models_equal_fn): from transformers.pipelines import SUPPORTED_TASKS, pipeline task_dict = SUPPORTED_TASKS[task] # test to compare pipeline to manually loading the respective model model = None relevant_auto_classes = task_dict[framework] if len(relevant_auto_classes) == 0: # task has no default logger.debug(f"{task} in {framework} has no default") return # by default use first class auto_model_cls = relevant_auto_classes[0] # retrieve correct model ids if task == "translation": # special case for translation pipeline which has multiple languages model_ids = [] revisions = [] tasks = [] for translation_pair in task_dict["default"].keys(): model_id, revision = task_dict["default"][translation_pair]["model"][framework] model_ids.append(model_id) revisions.append(revision) tasks.append(task + f"_{'_to_'.join(translation_pair)}") else: # normal case - non-translation pipeline model_id, revision = task_dict["default"]["model"][framework] model_ids = [model_id] revisions = [revision] tasks = [task] # check for equality for model_id, revision, task in zip(model_ids, revisions, tasks): # load default model try: set_seed_fn() model = auto_model_cls.from_pretrained(model_id, revision=revision) except ValueError: # first auto class is possible not compatible with model, go to next model class auto_model_cls = relevant_auto_classes[1] set_seed_fn() model = auto_model_cls.from_pretrained(model_id, revision=revision) # load default pipeline set_seed_fn() default_pipeline = pipeline(task, framework=framework) # compare pipeline model with default model models_are_equal = check_models_equal_fn(default_pipeline.model, model) self.assertTrue(models_are_equal, f"{task} model doesn't match pipeline.") logger.debug(f"{task} in {framework} succeeded with {model_id}.") def check_models_equal_pt(self, model1, model2): models_are_equal = True for model1_p, model2_p in zip(model1.parameters(), model2.parameters()): if model1_p.data.ne(model2_p.data).sum() > 0: models_are_equal = False return models_are_equal def check_models_equal_tf(self, model1, model2): models_are_equal = True for model1_p, model2_p in zip(model1.weights, model2.weights): if np.abs(model1_p.numpy() - model2_p.numpy()).sum() > 1e-5: models_are_equal = False return models_are_equal class CustomPipeline(Pipeline): def _sanitize_parameters(self, **kwargs): preprocess_kwargs = {} if "maybe_arg" in kwargs: preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"] return preprocess_kwargs, {}, {} def preprocess(self, text, maybe_arg=2): input_ids = self.tokenizer(text, return_tensors="pt") return input_ids def _forward(self, model_inputs): outputs = self.model(**model_inputs) return outputs def postprocess(self, model_outputs): return model_outputs["logits"].softmax(-1).numpy() @is_pipeline_test class CustomPipelineTest(unittest.TestCase): def test_warning_logs(self): transformers_logging.set_verbosity_debug() logger_ = transformers_logging.get_logger("transformers.pipelines.base") alias = "text-classification" # Get the original task, so we can restore it at the end. # (otherwise the subsequential tests in `TextClassificationPipelineTests` will fail) _, original_task, _ = PIPELINE_REGISTRY.check_task(alias) try: with CaptureLogger(logger_) as cm: PIPELINE_REGISTRY.register_pipeline(alias, PairClassificationPipeline) self.assertIn(f"{alias} is already registered", cm.out) finally: # restore PIPELINE_REGISTRY.supported_tasks[alias] = original_task def test_register_pipeline(self): PIPELINE_REGISTRY.register_pipeline( "custom-text-classification", pipeline_class=PairClassificationPipeline, pt_model=AutoModelForSequenceClassification if is_torch_available() else None, tf_model=TFAutoModelForSequenceClassification if is_tf_available() else None, default={"pt": "hf-internal-testing/tiny-random-distilbert"}, type="text", ) assert "custom-text-classification" in PIPELINE_REGISTRY.get_supported_tasks() _, task_def, _ = PIPELINE_REGISTRY.check_task("custom-text-classification") self.assertEqual(task_def["pt"], (AutoModelForSequenceClassification,) if is_torch_available() else ()) self.assertEqual(task_def["tf"], (TFAutoModelForSequenceClassification,) if is_tf_available() else ()) self.assertEqual(task_def["type"], "text") self.assertEqual(task_def["impl"], PairClassificationPipeline) self.assertEqual(task_def["default"], {"model": {"pt": "hf-internal-testing/tiny-random-distilbert"}}) # Clean registry for next tests. del PIPELINE_REGISTRY.supported_tasks["custom-text-classification"] @require_torch_or_tf def test_dynamic_pipeline(self): PIPELINE_REGISTRY.register_pipeline( "pair-classification", pipeline_class=PairClassificationPipeline, pt_model=AutoModelForSequenceClassification if is_torch_available() else None, tf_model=TFAutoModelForSequenceClassification if is_tf_available() else None, ) classifier = pipeline("pair-classification", model="hf-internal-testing/tiny-random-bert") # Clean registry as we won't need the pipeline to be in it for the rest to work. del PIPELINE_REGISTRY.supported_tasks["pair-classification"] with tempfile.TemporaryDirectory() as tmp_dir: classifier.save_pretrained(tmp_dir) # checks self.assertDictEqual( classifier.model.config.custom_pipelines, { "pair-classification": { "impl": "custom_pipeline.PairClassificationPipeline", "pt": ("AutoModelForSequenceClassification",) if is_torch_available() else (), "tf": ("TFAutoModelForSequenceClassification",) if is_tf_available() else (), } }, ) # Fails if the user forget to pass along `trust_remote_code=True` with self.assertRaises(ValueError): _ = pipeline(model=tmp_dir) new_classifier = pipeline(model=tmp_dir, trust_remote_code=True) # Using trust_remote_code=False forces the traditional pipeline tag old_classifier = pipeline("text-classification", model=tmp_dir, trust_remote_code=False) # Can't make an isinstance check because the new_classifier is from the PairClassificationPipeline class of a # dynamic module self.assertEqual(new_classifier.__class__.__name__, "PairClassificationPipeline") self.assertEqual(new_classifier.task, "pair-classification") results = new_classifier("I hate you", second_text="I love you") self.assertDictEqual( nested_simplify(results), {"label": "LABEL_0", "score": 0.505, "logits": [-0.003, -0.024]}, ) self.assertEqual(old_classifier.__class__.__name__, "TextClassificationPipeline") self.assertEqual(old_classifier.task, "text-classification") results = old_classifier("I hate you", text_pair="I love you") self.assertListEqual( nested_simplify(results), [{"label": "LABEL_0", "score": 0.505}], ) @require_torch_or_tf def test_cached_pipeline_has_minimum_calls_to_head(self): # Make sure we have cached the pipeline. _ = pipeline("text-classification", model="hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = pipeline("text-classification", model="hf-internal-testing/tiny-random-bert") self.assertEqual(counter["GET"], 0) self.assertEqual(counter["HEAD"], 1) self.assertEqual(counter.total_calls, 1) @require_torch def test_chunk_pipeline_batching_single_file(self): # Make sure we have cached the pipeline. pipe = pipeline(model="hf-internal-testing/tiny-random-Wav2Vec2ForCTC") ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id") audio = ds[40]["audio"]["array"] pipe = pipeline(model="hf-internal-testing/tiny-random-Wav2Vec2ForCTC") # For some reason scoping doesn't work if not using `self.` self.COUNT = 0 forward = pipe.model.forward def new_forward(*args, **kwargs): self.COUNT += 1 return forward(*args, **kwargs) pipe.model.forward = new_forward for out in pipe(audio, return_timestamps="char", chunk_length_s=3, stride_length_s=[1, 1], batch_size=1024): pass self.assertEqual(self.COUNT, 1) @require_torch @is_staging_test class DynamicPipelineTester(unittest.TestCase): vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "I", "love", "hate", "you"] @classmethod def setUpClass(cls): cls._token = TOKEN HfFolder.save_token(TOKEN) @classmethod def tearDownClass(cls): try: delete_repo(token=cls._token, repo_id="test-dynamic-pipeline") except HTTPError: pass def test_push_to_hub_dynamic_pipeline(self): from transformers import BertConfig, BertForSequenceClassification, BertTokenizer PIPELINE_REGISTRY.register_pipeline( "pair-classification", pipeline_class=PairClassificationPipeline, pt_model=AutoModelForSequenceClassification, ) config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertForSequenceClassification(config).eval() with tempfile.TemporaryDirectory() as tmp_dir: create_repo(f"{USER}/test-dynamic-pipeline", token=self._token) repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-pipeline", token=self._token) vocab_file = os.path.join(tmp_dir, "vocab.txt") with open(vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens])) tokenizer = BertTokenizer(vocab_file) classifier = pipeline("pair-classification", model=model, tokenizer=tokenizer) # Clean registry as we won't need the pipeline to be in it for the rest to work. del PIPELINE_REGISTRY.supported_tasks["pair-classification"] classifier.save_pretrained(tmp_dir) # checks self.assertDictEqual( classifier.model.config.custom_pipelines, { "pair-classification": { "impl": "custom_pipeline.PairClassificationPipeline", "pt": ("AutoModelForSequenceClassification",), "tf": (), } }, ) repo.push_to_hub() # Fails if the user forget to pass along `trust_remote_code=True` with self.assertRaises(ValueError): _ = pipeline(model=f"{USER}/test-dynamic-pipeline") new_classifier = pipeline(model=f"{USER}/test-dynamic-pipeline", trust_remote_code=True) # Can't make an isinstance check because the new_classifier is from the PairClassificationPipeline class of a # dynamic module self.assertEqual(new_classifier.__class__.__name__, "PairClassificationPipeline") results = classifier("I hate you", second_text="I love you") new_results = new_classifier("I hate you", second_text="I love you") self.assertDictEqual(nested_simplify(results), nested_simplify(new_results)) # Using trust_remote_code=False forces the traditional pipeline tag old_classifier = pipeline( "text-classification", model=f"{USER}/test-dynamic-pipeline", trust_remote_code=False ) self.assertEqual(old_classifier.__class__.__name__, "TextClassificationPipeline") self.assertEqual(old_classifier.task, "text-classification") new_results = old_classifier("I hate you", text_pair="I love you") self.assertListEqual( nested_simplify([{"label": results["label"], "score": results["score"]}]), nested_simplify(new_results) )

transformers/tests/pipelines/test_pipelines_common.py/0

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import ( MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, Text2TextGenerationPipeline, pipeline, ) from transformers.testing_utils import is_pipeline_test, require_tf, require_torch from transformers.utils import is_torch_available from .test_pipelines_common import ANY if is_torch_available(): import torch @is_pipeline_test class Text2TextGenerationPipelineTests(unittest.TestCase): model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING def get_test_pipeline(self, model, tokenizer, processor): generator = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer) return generator, ["Something to write", "Something else"] def run_pipeline_test(self, generator, _): outputs = generator("Something there") self.assertEqual(outputs, [{"generated_text": ANY(str)}]) # These are encoder decoder, they don't just append to incoming string self.assertFalse(outputs[0]["generated_text"].startswith("Something there")) outputs = generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True) self.assertEqual( outputs, [ [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}], [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}], ], ) outputs = generator( ["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True ) self.assertEqual( outputs, [ [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}], [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}], ], ) with self.assertRaises(ValueError): generator(4) @require_torch def test_small_model_pt(self): generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="pt") # do_sample=False necessary for reproducibility outputs = generator("Something there", do_sample=False) self.assertEqual(outputs, [{"generated_text": ""}]) num_return_sequences = 3 outputs = generator( "Something there", num_return_sequences=num_return_sequences, num_beams=num_return_sequences, ) target_outputs = [ {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"}, {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"}, {"generated_text": ""}, ] self.assertEqual(outputs, target_outputs) outputs = generator("This is a test", do_sample=True, num_return_sequences=2, return_tensors=True) self.assertEqual( outputs, [ {"generated_token_ids": ANY(torch.Tensor)}, {"generated_token_ids": ANY(torch.Tensor)}, ], ) generator.tokenizer.pad_token_id = generator.model.config.eos_token_id generator.tokenizer.pad_token = "<pad>" outputs = generator( ["This is a test", "This is a second test"], do_sample=True, num_return_sequences=2, batch_size=2, return_tensors=True, ) self.assertEqual( outputs, [ [ {"generated_token_ids": ANY(torch.Tensor)}, {"generated_token_ids": ANY(torch.Tensor)}, ], [ {"generated_token_ids": ANY(torch.Tensor)}, {"generated_token_ids": ANY(torch.Tensor)}, ], ], ) @require_tf def test_small_model_tf(self): generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="tf") # do_sample=False necessary for reproducibility outputs = generator("Something there", do_sample=False) self.assertEqual(outputs, [{"generated_text": ""}])

transformers/tests/pipelines/test_pipelines_text2text_generation.py/0

# Testing mixed int8 quantization  The following is the recipe on how to effectively debug `bitsandbytes` integration on Hugging Face `transformers`. ## Library requirements + `transformers>=4.22.0` + `accelerate>=0.12.0` + `bitsandbytes>=0.31.5`. ## Hardware requirements The following instructions are tested with 2 NVIDIA-Tesla T4 GPUs. To run successfully `bitsandbytes` you would need a 8-bit core tensor supported GPU. Note that Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100, A6000 should be supported. ## Virutal envs ```bash conda create --name int8-testing python==3.8 pip install bitsandbytes>=0.31.5 pip install accelerate>=0.12.0 pip install transformers>=4.23.0 ``` if `transformers>=4.23.0` is not released yet, then use: ```bash pip install git+https://github.com/huggingface/transformers.git ``` ## Troubleshooting A list of common errors: ### Torch does not correctly do the operations on GPU First check that: ```py import torch vec = torch.randn(1, 2, 3).to(0) ``` Works without any error. If not, install torch using `conda` like: ```bash conda create --name int8-testing python==3.8 conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge pip install bitsandbytes>=0.31.5 pip install accelerate>=0.12.0 pip install transformers>=4.23.0 ``` For the latest pytorch instructions please see [this](https://pytorch.org/get-started/locally/) and the snippet above should work. ### ` bitsandbytes operations are not supported under CPU!` This happens when some Linear weights are set to the CPU when using `accelerate`. Please check carefully `model.hf_device_map` and make sure that there is no `Linear` module that is assigned to CPU. It is fine to have the last module (usually the Lm_head) set on CPU. ### `To use the type as a Parameter, please correct the detach() semantics defined by __torch_dispatch__() implementation.` Use the latest version of `accelerate` with a command such as: `pip install -U accelerate` and the problem should be solved. ### `Parameter has no attribue .CB` Same solution as above. ### `RuntimeError: CUDA error: an illegal memory access was encountered ... consider passing CUDA_LAUNCH_BLOCKING=1` Run your script by pre-pending `CUDA_LAUNCH_BLOCKING=1` and you should observe an error as described in the next section. ### `CUDA illegal memory error: an illegal memory access at line...`: Check the CUDA verisons with: ```bash nvcc --version ``` and confirm it is the same version as the one detected by `bitsandbytes`. If not, run: ```bash ls -l $CONDA_PREFIX/lib/libcudart.so ``` or ```bash ls -l $LD_LIBRARY_PATH ``` Check if `libcudart.so` has a correct symlink that is set. Sometimes `nvcc` detects the correct CUDA version but `bitsandbytes` doesn't. You have to make sure that the symlink that is set for the file `libcudart.so` is redirected to the correct CUDA file. Here is an example of a badly configured CUDA installation: `nvcc --version` gives:  which means that the detected CUDA version is 11.3 but `bitsandbytes` outputs:  First check: ```bash echo $LD_LIBRARY_PATH ``` If this contains multiple paths separated by `:`. Then you have to make sure that the correct CUDA version is set. By doing: ```bash ls -l $path/libcudart.so ``` On each path (`$path`) separated by `:`. If not, simply run ```bash ls -l $LD_LIBRARY_PATH/libcudart.so ``` and you can see  If you see that the file is linked to the wrong CUDA version (here 10.2), find the correct location for `libcudart.so` (`find --name libcudart.so`) and replace the environment variable `LD_LIBRARY_PATH` with the one containing the correct `libcudart.so` file.

transformers/tests/quantization/bnb/README.md/0

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Finetuning the library models for sequence classification on GLUE.""" # You can also adapt this script on your own text classification task. Pointers for this are left as comments. import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import numpy as np from datasets import load_dataset, load_metric import transformers from transformers import ( # Trainer,; TrainingArguments, AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, PretrainedConfig, default_data_collator, set_seed, ) # Will import SageMaker Model parallelism specific Trainer from transformers.sagemaker import SageMakerTrainer as Trainer from transformers.sagemaker import SageMakerTrainingArguments as TrainingArguments from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.4.2") task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } logger = logging.getLogger(__name__) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ task_name: Optional[str] = field( default=None, metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())}, ) max_seq_length: int = field( default=128, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} ) pad_to_max_length: bool = field( default=True, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_val_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of validation examples to this " "value if set." ) }, ) max_test_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of test examples to this " "value if set." ) }, ) train_file: Optional[str] = field( default=None, metadata={"help": "A csv or a json file containing the training data."} ) validation_file: Optional[str] = field( default=None, metadata={"help": "A csv or a json file containing the validation data."} ) test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."}) def __post_init__(self): if self.task_name is not None: self.task_name = self.task_name.lower() if self.task_name not in task_to_keys.keys(): raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys())) elif self.train_file is None or self.validation_file is None: raise ValueError("Need either a GLUE task or a training/validation file.") else: train_extension = self.train_file.split(".")[-1] assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file." validation_extension = self.validation_file.split(".")[-1] assert ( validation_extension == train_extension ), "`validation_file` should have the same extension (csv or json) as `train_file`." @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if training_args.should_log else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if training_args.should_log: transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named # label if at least two columns are provided. # # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this # single column. You can easily tweak this behavior (see below) # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.task_name is not None: # Downloading and loading a dataset from the hub. datasets = load_dataset("glue", data_args.task_name) else: # Loading a dataset from your local files. # CSV/JSON training and evaluation files are needed. data_files = {"train": data_args.train_file, "validation": data_args.validation_file} # Get the test dataset: you can provide your own CSV/JSON test file (see below) # when you use `do_predict` without specifying a GLUE benchmark task. if training_args.do_predict: if data_args.test_file is not None: train_extension = data_args.train_file.split(".")[-1] test_extension = data_args.test_file.split(".")[-1] assert ( test_extension == train_extension ), "`test_file` should have the same extension (csv or json) as `train_file`." data_files["test"] = data_args.test_file else: raise ValueError("Need either a GLUE task or a test file for `do_predict`.") for key in data_files.keys(): logger.info(f"load a local file for {key}: {data_files[key]}") if data_args.train_file.endswith(".csv"): # Loading a dataset from local csv files datasets = load_dataset("csv", data_files=data_files) else: # Loading a dataset from local json files datasets = load_dataset("json", data_files=data_files) # See more about loading any type of standard or custom dataset at # https://huggingface.co/docs/datasets/loading_datasets. # Labels if data_args.task_name is not None: is_regression = data_args.task_name == "stsb" if not is_regression: label_list = datasets["train"].features["label"].names num_labels = len(label_list) else: num_labels = 1 else: # Trying to have good defaults here, don't hesitate to tweak to your needs. is_regression = datasets["train"].features["label"].dtype in ["float32", "float64"] if is_regression: num_labels = 1 else: # A useful fast method: # https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique label_list = datasets["train"].unique("label") label_list.sort() # Let's sort it for determinism num_labels = len(label_list) # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=num_labels, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=True if model_args.use_auth_token else None, ) tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, token=True if model_args.use_auth_token else None, ) model = AutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=True if model_args.use_auth_token else None, ) # Preprocessing the datasets if data_args.task_name is not None: sentence1_key, sentence2_key = task_to_keys[data_args.task_name] else: # Again, we try to have some nice defaults but don't hesitate to tweak to your use case. non_label_column_names = [name for name in datasets["train"].column_names if name != "label"] if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names: sentence1_key, sentence2_key = "sentence1", "sentence2" else: if len(non_label_column_names) >= 2: sentence1_key, sentence2_key = non_label_column_names[:2] else: sentence1_key, sentence2_key = non_label_column_names[0], None # Padding strategy if data_args.pad_to_max_length: padding = "max_length" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch padding = False # Some models have set the order of the labels to use, so let's make sure we do use it. label_to_id = None if ( model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id and data_args.task_name is not None and not is_regression ): # Some have all caps in their config, some don't. label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()} if sorted(label_name_to_id.keys()) == sorted(label_list): label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)} else: logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}." "\nIgnoring the model labels as a result.", ) elif data_args.task_name is None and not is_regression: label_to_id = {v: i for i, v in enumerate(label_list)} if data_args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the " f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) def preprocess_function(examples): # Tokenize the texts args = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True) # Map labels to IDs (not necessary for GLUE tasks) if label_to_id is not None and "label" in examples: result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]] return result datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache) if training_args.do_train: if "train" not in datasets: raise ValueError("--do_train requires a train dataset") train_dataset = datasets["train"] if data_args.max_train_samples is not None: train_dataset = train_dataset.select(range(data_args.max_train_samples)) if training_args.do_eval: if "validation" not in datasets and "validation_matched" not in datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = datasets["validation_matched" if data_args.task_name == "mnli" else "validation"] if data_args.max_val_samples is not None: eval_dataset = eval_dataset.select(range(data_args.max_val_samples)) if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None: if "test" not in datasets and "test_matched" not in datasets: raise ValueError("--do_predict requires a test dataset") test_dataset = datasets["test_matched" if data_args.task_name == "mnli" else "test"] if data_args.max_test_samples is not None: test_dataset = test_dataset.select(range(data_args.max_test_samples)) # Log a few random samples from the training set: if training_args.do_train: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # Get the metric function if data_args.task_name is not None: metric = load_metric("glue", data_args.task_name) # TODO: When datasets metrics include regular accuracy, make an else here and remove special branch from # compute_metrics # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(p: EvalPrediction): preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1) if data_args.task_name is not None: result = metric.compute(predictions=preds, references=p.label_ids) if len(result) > 1: result["combined_score"] = np.mean(list(result.values())).item() return result elif is_regression: return {"mse": ((preds - p.label_ids) ** 2).mean().item()} else: return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()} # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: data_collator = default_data_collator elif training_args.fp16: data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) else: data_collator = None # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, compute_metrics=compute_metrics, tokenizer=tokenizer, data_collator=data_collator, ) # Training if training_args.do_train: checkpoint = None if last_checkpoint is not None: checkpoint = last_checkpoint elif os.path.isdir(model_args.model_name_or_path): # Check the config from that potential checkpoint has the right number of labels before using it as a # checkpoint. if AutoConfig.from_pretrained(model_args.model_name_or_path).num_labels == num_labels: checkpoint = model_args.model_name_or_path train_result = trainer.train(resume_from_checkpoint=checkpoint) metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) ) metrics["train_samples"] = min(max_train_samples, len(train_dataset)) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("*** Evaluate ***") # Loop to handle MNLI double evaluation (matched, mis-matched) tasks = [data_args.task_name] eval_datasets = [eval_dataset] if data_args.task_name == "mnli": tasks.append("mnli-mm") eval_datasets.append(datasets["validation_mismatched"]) for eval_dataset, task in zip(eval_datasets, tasks): metrics = trainer.evaluate(eval_dataset=eval_dataset) max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset) metrics["eval_samples"] = min(max_val_samples, len(eval_dataset)) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) if training_args.do_predict: logger.info("*** Test ***") # Loop to handle MNLI double evaluation (matched, mis-matched) tasks = [data_args.task_name] test_datasets = [test_dataset] if data_args.task_name == "mnli": tasks.append("mnli-mm") test_datasets.append(datasets["test_mismatched"]) for test_dataset, task in zip(test_datasets, tasks): # Removing the `label` columns because it contains -1 and Trainer won't like that. test_dataset = test_dataset.remove_columns("label") predictions = trainer.predict(test_dataset=test_dataset).predictions predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1) output_test_file = os.path.join(training_args.output_dir, f"test_results_{task}.txt") if trainer.is_world_process_zero(): with open(output_test_file, "w") as writer: logger.info(f"***** Test results {task} *****") writer.write("index\tprediction\n") for index, item in enumerate(predictions): if is_regression: writer.write(f"{index}\t{item:3.3f}\n") else: item = label_list[item] writer.write(f"{index}\t{item}\n") def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

transformers/tests/sagemaker/scripts/pytorch/run_glue_model_parallelism.py/0

# coding=utf-8 # Copyright 2019 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections import copy import gc import inspect import os import os.path import pickle import random import re import tempfile import warnings from collections import defaultdict from typing import Dict, List, Tuple import numpy as np from parameterized import parameterized from pytest import mark import transformers from transformers import ( AutoModel, AutoModelForCausalLM, AutoModelForSequenceClassification, PretrainedConfig, PreTrainedModel, is_torch_available, logging, set_seed, ) from transformers.models.auto import get_values from transformers.models.auto.modeling_auto import ( MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES, MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES, MODEL_FOR_MASKED_LM_MAPPING_NAMES, MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES, MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES, MODEL_MAPPING_NAMES, ) from transformers.testing_utils import ( CaptureLogger, is_flaky, is_pt_flax_cross_test, is_pt_tf_cross_test, require_accelerate, require_bitsandbytes, require_flash_attn, require_safetensors, require_torch, require_torch_gpu, require_torch_multi_gpu, require_torch_sdpa, slow, torch_device, ) from transformers.utils import ( CONFIG_NAME, GENERATION_CONFIG_NAME, SAFE_WEIGHTS_NAME, is_accelerate_available, is_flax_available, is_tf_available, is_torch_bf16_available_on_device, is_torch_fp16_available_on_device, is_torch_fx_available, is_torch_sdpa_available, ) from transformers.utils.generic import ContextManagers, ModelOutput if is_accelerate_available(): from accelerate.utils import compute_module_sizes if is_torch_available(): import torch from safetensors.torch import load_file as safe_load_file from safetensors.torch import save_file as safe_save_file from torch import nn from transformers import MODEL_MAPPING, AdaptiveEmbedding from transformers.modeling_utils import load_state_dict, no_init_weights from transformers.pytorch_utils import id_tensor_storage if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp from tests.test_modeling_flax_utils import check_models_equal from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) if is_torch_fx_available(): from transformers.utils.fx import _FX_SUPPORTED_MODELS_WITH_KV_CACHE, symbolic_trace def _config_zero_init(config): configs_no_init = copy.deepcopy(config) for key in configs_no_init.__dict__.keys(): if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key: setattr(configs_no_init, key, 1e-10) if isinstance(getattr(configs_no_init, key, None), PretrainedConfig): no_init_subconfig = _config_zero_init(getattr(configs_no_init, key)) setattr(configs_no_init, key, no_init_subconfig) return configs_no_init def _mock_init_weights(self, module): for name, param in module.named_parameters(recurse=False): # Use the first letter of the name to get a value and go from a <> -13 to z <> 12 value = ord(name[0].lower()) - 110 param.data.fill_(value) def _mock_all_init_weights(self): # Prune heads if needed if self.config.pruned_heads: self.prune_heads(self.config.pruned_heads) import transformers.modeling_utils if transformers.modeling_utils._init_weights: for module in self.modules(): module._is_hf_initialized = False # Initialize weights self.apply(self._initialize_weights) # Tie weights should be skipped when not initializing all weights # since from_pretrained(...) calls tie weights anyways self.tie_weights() @require_torch class ModelTesterMixin: model_tester = None all_model_classes = () all_generative_model_classes = () fx_compatible = False test_torchscript = True test_pruning = True test_resize_embeddings = True test_resize_position_embeddings = False test_head_masking = True test_mismatched_shapes = True test_missing_keys = True test_model_parallel = False is_encoder_decoder = False has_attentions = True model_split_percents = [0.5, 0.7, 0.9] def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if model_class.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES): inputs_dict = { k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() if isinstance(v, torch.Tensor) and v.ndim > 1 else v for k, v in inputs_dict.items() } elif model_class.__name__ in get_values(MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES): inputs_dict.pop("attention_mask") if return_labels: if model_class.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES): inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device) elif model_class.__name__ in [ *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES), *get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES), ]: inputs_dict["start_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["end_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class.__name__ in [ *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES), *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES), *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES), *get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES), *get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES), ]: inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class.__name__ in [ *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES), *get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES), *get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES), *get_values(MODEL_FOR_MASKED_LM_MAPPING_NAMES), *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES), ]: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) elif model_class.__name__ in get_values(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES): num_patches = self.model_tester.image_size // self.model_tester.patch_size inputs_dict["bool_masked_pos"] = torch.zeros( (self.model_tester.batch_size, num_patches**2), dtype=torch.long, device=torch_device ) elif model_class.__name__ in get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES): batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = torch.zeros( [self.model_tester.batch_size, height, width], device=torch_device ).long() return inputs_dict def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_save_load(out1, out2): # make sure we don't have nans out_2 = out2.cpu().numpy() out_2[np.isnan(out_2)] = 0 out_1 = out1.cpu().numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): first = model(**self._prepare_for_class(inputs_dict, model_class))[0] with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # the config file (and the generation config file, if it can generate) should be saved self.assertTrue(os.path.exists(os.path.join(tmpdirname, CONFIG_NAME))) self.assertEqual( model.can_generate(), os.path.exists(os.path.join(tmpdirname, GENERATION_CONFIG_NAME)) ) model = model_class.from_pretrained(tmpdirname) model.to(torch_device) with torch.no_grad(): second = model(**self._prepare_for_class(inputs_dict, model_class))[0] if isinstance(first, tuple) and isinstance(second, tuple): for tensor1, tensor2 in zip(first, second): check_save_load(tensor1, tensor2) else: check_save_load(first, second) def test_from_pretrained_no_checkpoint(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) state_dict = model.state_dict() new_model = model_class.from_pretrained( pretrained_model_name_or_path=None, config=config, state_dict=state_dict ) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_keep_in_fp32_modules(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._keep_in_fp32_modules is None: return model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16) for name, param in model.named_parameters(): if any(n in model_class._keep_in_fp32_modules for n in name.split(".")): self.assertTrue(param.dtype == torch.float32) else: self.assertTrue(param.dtype == torch.float16, name) def test_save_load_keys_to_ignore_on_save(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) _keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None) if _keys_to_ignore_on_save is None: continue # check the keys are in the original state_dict for k in _keys_to_ignore_on_save: self.assertIn(k, model.state_dict().keys(), "\n".join(model.state_dict().keys())) # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) output_model_file = os.path.join(tmpdirname, SAFE_WEIGHTS_NAME) state_dict_saved = safe_load_file(output_model_file) for k in _keys_to_ignore_on_save: self.assertNotIn(k, state_dict_saved.keys(), "\n".join(state_dict_saved.keys())) # Test we can load the state dict in the model, necessary for the checkpointing API in Trainer. load_result = model.load_state_dict(state_dict_saved, strict=False) keys_to_ignore = set(model._keys_to_ignore_on_save) if hasattr(model, "_tied_weights_keys"): keys_to_ignore.update(set(model._tied_weights_keys)) self.assertTrue(len(load_result.missing_keys) == 0 or set(load_result.missing_keys) == keys_to_ignore) self.assertTrue(len(load_result.unexpected_keys) == 0) def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue config.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) def test_gradient_checkpointing_enable_disable(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue # at init model should have gradient checkpointing disabled model = model_class(config) self.assertFalse(model.is_gradient_checkpointing) # check enable works model.gradient_checkpointing_enable() self.assertTrue(model.is_gradient_checkpointing) # Loop over all modules and check that relevant modules have gradient_checkpointing set to True for n, m in model.named_modules(): if hasattr(m, "gradient_checkpointing"): self.assertTrue( m.gradient_checkpointing, f"Module {n} does not have gradient_checkpointing set to True" ) # check disable works model.gradient_checkpointing_disable() self.assertFalse(model.is_gradient_checkpointing) # Loop over all modules and check that relevant modules have gradient_checkpointing set to False for n, m in model.named_modules(): if hasattr(m, "gradient_checkpointing"): self.assertFalse( m.gradient_checkpointing, f"Module {n} does not have gradient_checkpointing set to False" ) @is_flaky(description="low likelihood of failure, reason not yet discovered") def test_save_load_fast_init_from_base(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if config.__class__ not in MODEL_MAPPING: return base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(model_class): pass model_class_copy = CopyClass # make sure that all keys are expected for test model_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless model_class_copy._init_weights = _mock_init_weights model_class_copy.init_weights = _mock_all_init_weights model = base_class(config) state_dict = model.state_dict() # this will often delete a single weight of a multi-weight module # to test an edge case random_key_to_del = random.choice(list(state_dict.keys())) del state_dict[random_key_to_del] # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) model_fast_init = model_class_copy.from_pretrained(tmpdirname) model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False) # Before we test anything for key in model_fast_init.state_dict().keys(): if isinstance(model_slow_init.state_dict()[key], torch.BoolTensor): max_diff = (model_slow_init.state_dict()[key] ^ model_fast_init.state_dict()[key]).sum().item() else: max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_fast_init_context_manager(self): # 1. Create a dummy class. Should have buffers as well? To make sure we test __init__ class MyClass(PreTrainedModel): config_class = PretrainedConfig def __init__(self, config=None): super().__init__(config if config is not None else PretrainedConfig()) self.linear = nn.Linear(10, 10, bias=True) self.embedding = nn.Embedding(10, 10) self.std = 1 def _init_weights(self, module): if isinstance(module, nn.Linear): module.weight.data = nn.init.kaiming_uniform_(module.weight.data, np.sqrt(5)) if module.bias is not None: module.bias.data.normal_(mean=0.0, std=self.std) # 2. Make sure a linear layer's reset params is properly skipped: with ContextManagers([no_init_weights(True)]): no_init_instance = MyClass() set_seed(0) expected_bias = torch.tensor( ([0.2975, 0.2131, -0.1379, -0.0796, -0.3012, -0.0057, -0.2381, -0.2439, -0.0174, 0.0475]) ) init_instance = MyClass() torch.testing.assert_allclose(init_instance.linear.bias, expected_bias, rtol=1e-3, atol=1e-4) set_seed(0) torch.testing.assert_allclose( init_instance.linear.weight, nn.init.kaiming_uniform_(no_init_instance.linear.weight, np.sqrt(5)) ) # 3. Make sure weights that are not present use init_weight_ and get expected values with tempfile.TemporaryDirectory() as tmpdirname: state_dict = init_instance.state_dict() del state_dict["linear.weight"] init_instance.config.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) set_seed(0) model_fast_init = MyClass.from_pretrained(tmpdirname) set_seed(0) model_slow_init = MyClass.from_pretrained(tmpdirname, _fast_init=False) for key in model_fast_init.state_dict().keys(): max_diff = torch.max(torch.abs(model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key])) self.assertLessEqual(max_diff.item(), 1e-3, msg=f"{key} not identical") def test_fast_init_tied_embeddings(self): class MyClass(PreTrainedModel): config_class = PretrainedConfig _tied_weights_keys = ["output_embeddings.weight"] def __init__(self, config=None): super().__init__(config if config is not None else PretrainedConfig()) self.input_embeddings = nn.Embedding(10, 10) self.output_embeddings = nn.Linear(10, 10, bias=False) self.tie_weights() def get_output_embeddings(self): return self.output_embeddings def set_output_embeddings(self, output_embeddings): self.output_embeddings = output_embeddings def get_input_embeddings(self): return self.input_embeddings def set_input_embeddings(self, input_embeddings): self.input_embeddings = input_embeddings def _init_weights(self, module): if module is self.output_embeddings: raise ValueError("unnecessarily initialized tied output embedding!") model = MyClass() with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # throws if it initializes the tied output_embeddings MyClass.from_pretrained(tmpdirname) def test_save_load_fast_init_to_base(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if config.__class__ not in MODEL_MAPPING: return base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(base_class): pass base_class_copy = CopyClass # make sure that all keys are expected for test base_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless base_class_copy._init_weights = _mock_init_weights base_class_copy.init_weights = _mock_all_init_weights model = model_class(config) state_dict = model.state_dict() # this will often delete a single weight of a multi-weight module # to test an edge case random_key_to_del = random.choice(list(state_dict.keys())) del state_dict[random_key_to_del] # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.config.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) model_fast_init = base_class_copy.from_pretrained(tmpdirname) model_slow_init = base_class_copy.from_pretrained(tmpdirname, _fast_init=False) for key in model_fast_init.state_dict().keys(): if isinstance(model_slow_init.state_dict()[key], torch.BoolTensor): max_diff = torch.max( model_slow_init.state_dict()[key] ^ model_fast_init.state_dict()[key] ).item() else: max_diff = torch.max( torch.abs(model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]) ).item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_torch_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if config.__class__ not in MODEL_MAPPING: return base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(base_class): pass base_class_copy = CopyClass # make sure that all keys are expected for test base_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless base_class_copy._init_weights = _mock_init_weights base_class_copy.init_weights = _mock_all_init_weights model = model_class(config) state_dict = model.state_dict() def check_equal(loaded): for key in state_dict.keys(): max_diff = torch.max( state_dict()[key] ^ loaded[key] if isinstance(state_dict[key], torch.BoolTensor) else torch.abs(state_dict[key] - loaded[key]) ).item() self.assertLessEqual(max_diff, 1e-6, msg=f"{key} not identical") # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: pt_checkpoint_path = os.path.join(tmpdirname, "pytorch_model.bin") torch.save(state_dict, pt_checkpoint_path, _use_new_zipfile_serialization=True) check_equal(load_state_dict(pt_checkpoint_path)) torch.save(state_dict, pt_checkpoint_path, _use_new_zipfile_serialization=False) check_equal(load_state_dict(pt_checkpoint_path)) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_determinism(first, second): out_1 = first.cpu().numpy() out_2 = second.cpu().numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): first = model(**self._prepare_for_class(inputs_dict, model_class))[0] second = model(**self._prepare_for_class(inputs_dict, model_class))[0] if isinstance(first, tuple) and isinstance(second, tuple): for tensor1, tensor2 in zip(first, second): check_determinism(tensor1, tensor2) else: check_determinism(first, second) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) elif model_class.__name__ in [*get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)] and self.has_attentions: expected_arg_names = ["pixel_values", "output_hidden_states", "output_attentions", "return_dict"] self.assertListEqual(arg_names, expected_arg_names) elif model_class.__name__ in [*get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)] and not self.has_attentions: expected_arg_names = ["pixel_values", "output_hidden_states", "return_dict"] self.assertListEqual(arg_names, expected_arg_names) else: expected_arg_names = [model.main_input_name] self.assertListEqual(arg_names[:1], expected_arg_names) def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=None): if not self.model_tester.is_training: return for model_class in self.all_model_classes: if ( model_class.__name__ in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)] or not model_class.supports_gradient_checkpointing ): continue config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs) model.train() # unfreeze additional layers for p in model.parameters(): p.requires_grad_(True) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() optimizer.step() for k, v in model.named_parameters(): if v.requires_grad: self.assertTrue(v.grad is not None, f"{k} in {model_class.__name__} has no gradient!") def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True if model_class.__name__ in [ *get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES), ]: continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): # Scenario - 1 default behaviour self.check_training_gradient_checkpointing() def test_training_gradient_checkpointing_use_reentrant(self): # Scenario - 2 with `use_reentrant=True` - this is the default value that is used in pytorch's # torch.utils.checkpoint.checkpoint self.check_training_gradient_checkpointing(gradient_checkpointing_kwargs={"use_reentrant": True}) def test_training_gradient_checkpointing_use_reentrant_false(self): # Scenario - 3 with `use_reentrant=False` pytorch suggests users to use this value for # future releases: https://pytorch.org/docs/stable/checkpoint.html self.check_training_gradient_checkpointing(gradient_checkpointing_kwargs={"use_reentrant": False}) def test_attention_outputs(self): if not self.has_attentions: self.skipTest(reason="Model does not output attentions") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) if chunk_length is not None: self.assertListEqual( list(attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Question Answering model returns start_logits and end_logits if model_class.__name__ in [ *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES), *get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES), ]: correct_outlen += 1 # start_logits and end_logits instead of only 1 output if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) if chunk_length is not None: self.assertListEqual( list(self_attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) @slow def test_torchscript_simple(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torchscript(config, inputs_dict) @slow def test_torchscript_output_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_attentions = True self._create_and_check_torchscript(config, inputs_dict) @slow def test_torchscript_output_hidden_state(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True self._create_and_check_torchscript(config, inputs_dict) # This is copied from `torch/testing/_internal/jit_utils.py::clear_class_registry` def clear_torch_jit_class_registry(self): torch._C._jit_clear_class_registry() torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore() # torch 1.8 has no `_clear_class_state` in `torch.jit._state` if hasattr(torch.jit._state, "_clear_class_state"): torch.jit._state._clear_class_state() def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True for model_class in self.all_model_classes: for attn_implementation in ["eager", "sdpa"]: if attn_implementation == "sdpa" and (not model_class._supports_sdpa or not is_torch_sdpa_available()): continue configs_no_init._attn_implementation = attn_implementation model = model_class(config=configs_no_init) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class) main_input_name = model_class.main_input_name try: if model.config.is_encoder_decoder: model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward main_input = inputs[main_input_name] attention_mask = inputs["attention_mask"] decoder_input_ids = inputs["decoder_input_ids"] decoder_attention_mask = inputs["decoder_attention_mask"] model(main_input, attention_mask, decoder_input_ids, decoder_attention_mask) traced_model = torch.jit.trace( model, (main_input, attention_mask, decoder_input_ids, decoder_attention_mask) ) elif "bbox" in inputs and "image" in inputs: # LayoutLMv2 requires additional inputs input_ids = inputs["input_ids"] bbox = inputs["bbox"] image = inputs["image"].tensor model(input_ids, bbox, image) traced_model = torch.jit.trace( model, (input_ids, bbox, image), check_trace=False ) # when traced model is checked, an error is produced due to name mangling elif "bbox" in inputs: # Bros requires additional inputs (bbox) input_ids = inputs["input_ids"] bbox = inputs["bbox"] model(input_ids, bbox) traced_model = torch.jit.trace( model, (input_ids, bbox), check_trace=False ) # when traced model is checked, an error is produced due to name mangling else: main_input = inputs[main_input_name] if model.config._attn_implementation == "sdpa": trace_input = {main_input_name: main_input} if "attention_mask" in inputs: trace_input["attention_mask"] = inputs["attention_mask"] else: self.skipTest("testing SDPA without attention_mask is not supported") model(main_input, attention_mask=inputs["attention_mask"]) # example_kwarg_inputs was introduced in torch==2.0, but it is fine here since SDPA has a requirement on torch>=2.1. traced_model = torch.jit.trace(model, example_kwarg_inputs=trace_input) else: model(main_input) traced_model = torch.jit.trace(model, (main_input,)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): if layer_name in loaded_model_state_dict: p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) # Avoid memory leak. Without this, each call increase RAM usage by ~20MB. # (Even with this call, there are still memory leak by ~0.04MB) self.clear_torch_jit_class_registry() def test_torch_fx(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torch_fx_tracing(config, inputs_dict) def test_torch_fx_output_loss(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torch_fx_tracing(config, inputs_dict, output_loss=True) def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False): if not is_torch_fx_available() or not self.fx_compatible: self.skipTest( f"Either torch.fx is not available, or the model type {config.model_type} is not compatible with torch.fx" ) configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss) # We may want to test several inputs (various shapes, etc.). inputs_to_test = [inputs] if model.config.is_encoder_decoder: model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward labels = inputs.get("labels", None) input_names = [ "attention_mask", "decoder_attention_mask", "decoder_input_ids", "input_features", "input_ids", "input_values", ] if labels is not None: input_names.append("labels") else: input_names = [ "attention_mask", "bbox", "input_features", "input_ids", "input_values", "pixel_values", "token_type_ids", "visual_feats", "visual_pos", ] labels = inputs.get("labels", None) start_positions = inputs.get("start_positions", None) end_positions = inputs.get("end_positions", None) if labels is not None: input_names.append("labels") if start_positions is not None: input_names.append("start_positions") if end_positions is not None: input_names.append("end_positions") if model.config.model_type in _FX_SUPPORTED_MODELS_WITH_KV_CACHE: input_names.append("past_key_values") # Generally model_tester.prepare_config_and_inputs_for_common seem not to generate past key values inputs. if "past_key_values" not in inputs: batch_size = inputs[next(iter(inputs))].shape[0] num_heads = model.config.num_attention_heads head_dim = model.config.hidden_size // model.config.num_attention_heads cache_shape = (batch_size, num_heads, 0, head_dim) empty_pkv = tuple( ( torch.rand(cache_shape, dtype=torch.float, device=torch_device), torch.rand(cache_shape, dtype=torch.float, device=torch_device), ) for i in range(model.config.num_hidden_layers) ) cache_length = 9 cache_shape = (batch_size, num_heads, cache_length, head_dim) non_empty_pkv = tuple( ( torch.rand(cache_shape, dtype=torch.float, device=torch_device), torch.rand(cache_shape, dtype=torch.float, device=torch_device), ) for i in range(model.config.num_hidden_layers) ) inps = copy.deepcopy(inputs_to_test[0]) inputs_to_test[0]["past_key_values"] = empty_pkv inps["past_key_values"] = non_empty_pkv inputs_to_test.append(inps) past_mask = torch.ones(batch_size, cache_length, device=torch_device, dtype=torch.float) inputs_to_test[1]["attention_mask"] = torch.cat( (past_mask, inputs_to_test[1]["attention_mask"]), dim=1 ) for inps in inputs_to_test: filtered_inputs = {k: v for (k, v) in inps.items() if k in input_names} input_names = list(filtered_inputs.keys()) if model.__class__.__name__ in set(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values()) and ( not hasattr(model.config, "problem_type") or model.config.problem_type is None ): model.config.problem_type = "single_label_classification" traced_model = symbolic_trace(model, input_names) with torch.no_grad(): traced_output = traced_model(**filtered_inputs) model_output = model(**filtered_inputs) def flatten_output(output): flatten = [] for x in output: if isinstance(x, (tuple, list)): flatten += flatten_output(x) elif not isinstance(x, torch.Tensor): continue else: flatten.append(x) return flatten model_output = flatten_output(model_output) traced_output = flatten_output(traced_output) num_outputs = len(model_output) for i in range(num_outputs): self.assertTrue( torch.allclose(model_output[i], traced_output[i]), f"traced {i}th output doesn't match model {i}th output for {model_class}", ) # Test that the model can be serialized and restored properly with tempfile.TemporaryDirectory() as tmp_dir_name: pkl_file_name = os.path.join(tmp_dir_name, "model.pkl") try: with open(pkl_file_name, "wb") as f: pickle.dump(traced_model, f) with open(pkl_file_name, "rb") as f: loaded = pickle.load(f) except Exception as e: self.fail(f"Couldn't serialize / deserialize the traced model: {e}") loaded_output = loaded(**filtered_inputs) loaded_output = flatten_output(loaded_output) for i in range(num_outputs): self.assertTrue( torch.allclose(model_output[i], loaded_output[i]), f"serialized model {i}th output doesn't match model {i}th output for {model_class}", ) # Avoid memory leak. Without this, each call increase RAM usage by ~20MB. # (Even with this call, there are still memory leak by ~0.04MB) self.clear_torch_jit_class_registry() def test_headmasking(self): if not self.test_head_masking: return global_rng.seed(42) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() global_rng.seed() inputs_dict["output_attentions"] = True config.output_hidden_states = True configs_no_init = _config_zero_init(config) # To be sure we have no Nan for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() # Prepare head_mask # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior) head_mask = torch.ones( self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device, ) head_mask[0, 0] = 0 head_mask[-1, :-1] = 0 head_mask.requires_grad_(requires_grad=True) inputs = self._prepare_for_class(inputs_dict, model_class).copy() inputs["head_mask"] = head_mask if model.config.is_encoder_decoder: signature = inspect.signature(model.forward) arg_names = [*signature.parameters.keys()] if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model inputs["decoder_head_mask"] = head_mask if "cross_attn_head_mask" in arg_names: inputs["cross_attn_head_mask"] = head_mask outputs = model(**inputs, return_dict=True) # Test that we can get a gradient back for importance score computation output = sum(t.sum() for t in outputs[0]) output = output.sum() output.backward() multihead_outputs = head_mask.grad self.assertIsNotNone(multihead_outputs) self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers) def check_attentions_validity(attentions): # Remove Nan for t in attentions: self.assertLess( torch.sum(torch.isnan(t)), t.numel() / 4 ) # Check we don't have more than 25% nans (arbitrary) attentions = [ t.masked_fill(torch.isnan(t), 0.0) for t in attentions ] # remove them (the test is less complete) self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0) self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0) if len(attentions) > 2: # encoder-decoder models have only 2 layers in each module self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0) self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0) self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0) if model.config.is_encoder_decoder: check_attentions_validity(outputs.encoder_attentions) check_attentions_validity(outputs.decoder_attentions) check_attentions_validity(outputs.cross_attentions) else: check_attentions_validity(outputs.attentions) def test_head_pruning(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False model = model_class(config=config) model.to(torch_device) model.eval() heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } model.prune_heads(heads_to_prune) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) # TODO: To have this check, we will need at least 3 layers. Do we really need it? # self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_save_load_from_pretrained(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False model = model_class(config=config) model.to(torch_device) model.eval() heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } model.prune_heads(heads_to_prune) with tempfile.TemporaryDirectory() as temp_dir_name: model.save_pretrained(temp_dir_name) model = model_class.from_pretrained(temp_dir_name) model.to(torch_device) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) # TODO: To have this check, we will need at least 3 layers. Do we really need it? # self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_save_load_from_config_init(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False heads_to_prune = { 0: list(range(1, self.model_tester.num_attention_heads)), -1: [0], } config.pruned_heads = heads_to_prune model = model_class(config=config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) # TODO: To have this check, we will need at least 3 layers. Do we really need it? # self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) def test_head_pruning_integration(self): if not self.test_pruning: return for model_class in self.all_model_classes: ( config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if "head_mask" in inputs_dict: del inputs_dict["head_mask"] inputs_dict["output_attentions"] = True config.output_hidden_states = False heads_to_prune = {1: [1, 2]} config.pruned_heads = heads_to_prune model = model_class(config=config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 0) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) with tempfile.TemporaryDirectory() as temp_dir_name: model.save_pretrained(temp_dir_name) model = model_class.from_pretrained(temp_dir_name) model.to(torch_device) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 0) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) heads_to_prune = {0: [0], 1: [1, 2]} model.prune_heads(heads_to_prune) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2]}) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1: seq_length = seq_length * self.model_tester.chunk_length else: seq_length = self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] if config.is_encoder_decoder: # Seq2Seq models encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() decoder_hidden_states = outputs.decoder_hidden_states[0] decoder_hidden_states.retain_grad() if self.has_attentions: encoder_attentions = outputs.encoder_attentions[0] encoder_attentions.retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(decoder_hidden_states.grad) if self.has_attentions: self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(decoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) else: # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNotNone(attentions.grad) def test_feed_forward_chunking(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: torch.manual_seed(0) config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model.eval() hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0] torch.manual_seed(0) config.chunk_size_feed_forward = 1 model = model_class(config) model.to(torch_device) model.eval() hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0] self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3)) def test_resize_position_vector_embeddings(self): if not self.test_resize_position_embeddings: return ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() max_position_embeddings = config.max_position_embeddings # Retrieve the embeddings and clone theme if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() encoder_cloned_embeddings = encoder_model_embed.weight.clone() decoder_cloned_embeddings = decoder_model_embed.weight.clone() else: model_embed = model.get_position_embeddings() cloned_embeddings = model_embed.weight.clone() # Check that resizing the position embeddings with a larger max_position_embeddings increases # the model's postion embeddings size model.resize_position_embeddings(max_position_embeddings + 10) self.assertEqual(model.config.max_position_embeddings, max_position_embeddings + 10) # Check that it actually resizes the embeddings matrix if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] + 10) self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] + 10) else: model_embed = model.get_position_embeddings() self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the position embeddings with a smaller max_position_embeddings decreases # the model's max_position_embeddings model.resize_position_embeddings(max_position_embeddings - 5) self.assertEqual(model.config.max_position_embeddings, max_position_embeddings - 5) # Check that it actually resizes the embeddings matrix if model.config.is_encoder_decoder: encoder_model_embed, decoder_model_embed = model.get_position_embeddings() self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] - 5) self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] - 5) else: model_embed = model.get_position_embeddings() self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 5) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True if model.config.is_encoder_decoder: for p1, p2 in zip(encoder_cloned_embeddings, encoder_model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False for p1, p2 in zip(decoder_cloned_embeddings, decoder_model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False else: for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_tokens_embeddings(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) # make sure that decoder_input_ids are resized as well if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1) self.assertTrue(model.config.vocab_size + 10, model_vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) self.assertTrue(model_embed.weight.shape[0], model.config.vocab_size) self.assertTrue(model.config.vocab_size, model.vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) # Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size target_dimension = 128 model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0], target_dimension) with self.assertRaisesRegex( ValueError, "Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer", ): model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3) def test_resize_embeddings_untied(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return original_config.tie_word_embeddings = False # if model cannot untied embeddings -> leave test if original_config.tie_word_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding, AdaptiveEmbedding)) model.set_input_embeddings(nn.Embedding(10, 10)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_model_main_input_name(self): for model_class in self.all_model_classes: model_signature = inspect.signature(getattr(model_class, "forward")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(model_class.main_input_name, observed_main_input_name) def test_correct_missing_keys(self): if not self.test_missing_keys: return config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) base_model_prefix = model.base_model_prefix if hasattr(model, base_model_prefix): extra_params = {k: v for k, v in model.named_parameters() if not k.startswith(base_model_prefix)} extra_params.update({k: v for k, v in model.named_buffers() if not k.startswith(base_model_prefix)}) # Some models define this as None if model._keys_to_ignore_on_load_missing: for key in model._keys_to_ignore_on_load_missing: extra_params.pop(key, None) if not extra_params: # In that case, we *are* on a head model, but every # single key is not actual parameters and this is # tested in `test_tied_model_weights_key_ignore` test. continue with tempfile.TemporaryDirectory() as temp_dir_name: model.base_model.save_pretrained(temp_dir_name) model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True) self.assertGreater(len(loading_info["missing_keys"]), 0, model.__class__.__name__) def test_tie_model_weights(self): if not self.test_torchscript: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_same_values(layer_1, layer_2): equal = True for p1, p2 in zip(layer_1.weight, layer_2.weight): if p1.data.ne(p2.data).sum() > 0: equal = False return equal for model_class in self.all_model_classes: config.torchscript = True model_not_tied = model_class(config) if model_not_tied.get_output_embeddings() is None: continue config_tied = copy.deepcopy(config) config_tied.torchscript = False model_tied = model_class(config_tied) params_tied = list(model_tied.parameters()) # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(check_same_values(embeddings, decoding)) # # Check that after modification, they remain the same. # embeddings.weight.data.div_(2) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(embeddings.weight.shape, decoding.weight.shape) # self.assertTrue(check_same_values(embeddings, decoding)) # # Check that after modification, they remain the same. # decoding.weight.data.div_(4) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(embeddings.weight.shape, decoding.weight.shape) # self.assertTrue(check_same_values(embeddings, decoding)) # Check that after resize they remain tied. model_tied.resize_token_embeddings(config.vocab_size + 10) params_tied_2 = list(model_tied.parameters()) self.assertEqual(len(params_tied_2), len(params_tied)) # decoding.weight.data.mul_(20) # # Check that the embedding layer and decoding layer are the same in size and in value # self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape) # self.assertTrue(check_same_values(model.transformer.wte, model.lm_head)) @require_safetensors def test_can_use_safetensors(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model_tied = model_class(config) with tempfile.TemporaryDirectory() as d: try: model_tied.save_pretrained(d, safe_serialization=True) except Exception as e: raise Exception(f"Class {model_class.__name__} cannot be saved using safetensors: {e}") model_reloaded, infos = model_class.from_pretrained(d, output_loading_info=True) # Checking the state dicts are correct reloaded_state = model_reloaded.state_dict() for k, v in model_tied.state_dict().items(): self.assertIn(k, reloaded_state, f"Key {k} is missing from reloaded") torch.testing.assert_close( v, reloaded_state[k], msg=lambda x: f"{model_class.__name__}: Tensor {k}: {x}" ) # Checking there was no complain of missing weights self.assertEqual(infos["missing_keys"], []) # Checking the tensor sharing are correct ptrs = defaultdict(list) for k, v in model_tied.state_dict().items(): ptrs[v.data_ptr()].append(k) shared_ptrs = {k: v for k, v in ptrs.items() if len(v) > 1} for _, shared_names in shared_ptrs.items(): reloaded_ptrs = {reloaded_state[k].data_ptr() for k in shared_names} self.assertEqual( len(reloaded_ptrs), 1, f"The shared pointers are incorrect, found different pointers for keys {shared_names}", ) def test_load_save_without_tied_weights(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() config.tie_word_embeddings = False for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as d: model.save_pretrained(d) model_reloaded, infos = model_class.from_pretrained(d, output_loading_info=True) # Checking the state dicts are correct reloaded_state = model_reloaded.state_dict() for k, v in model.state_dict().items(): self.assertIn(k, reloaded_state, f"Key {k} is missing from reloaded") torch.testing.assert_close( v, reloaded_state[k], msg=lambda x: f"{model_class.__name__}: Tensor {k}: {x}" ) # Checking there was no complain of missing weights self.assertEqual(infos["missing_keys"], []) def test_tied_weights_keys(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() config.tie_word_embeddings = True for model_class in self.all_model_classes: model_tied = model_class(config) ptrs = collections.defaultdict(list) for name, tensor in model_tied.state_dict().items(): ptrs[id_tensor_storage(tensor)].append(name) # These are all the pointers of shared tensors. tied_params = [names for _, names in ptrs.items() if len(names) > 1] tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else [] # Detect we get a hit for each key for key in tied_weight_keys: if not any(re.search(key, p) for group in tied_params for p in group): raise ValueError(f"{key} is not a tied weight key for {model_class}.") # Removed tied weights found from tied params -> there should only be one left after for key in tied_weight_keys: for i in range(len(tied_params)): tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None] tied_params = [group for group in tied_params if len(group) > 1] self.assertListEqual( tied_params, [], f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.", ) def test_model_weights_reload_no_missing_tied_weights(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) # We are nuking ALL weights on file, so every parameter should # yell on load. We're going to detect if we yell too much, or too little. placeholder_dict = {"tensor": torch.tensor([1, 2])} safe_save_file(placeholder_dict, os.path.join(tmp_dir, "model.safetensors"), metadata={"format": "pt"}) model_reloaded, infos = model_class.from_pretrained(tmp_dir, output_loading_info=True) prefix = f"{model_reloaded.base_model_prefix}." params = dict(model_reloaded.named_parameters()) params.update(dict(model_reloaded.named_buffers())) param_names = {k[len(prefix) :] if k.startswith(prefix) else k for k in params.keys()} missing_keys = set(infos["missing_keys"]) extra_missing = missing_keys - param_names # Remove tied weights from extra missing: they are normally not warned as missing if their tied # counterpart is present but here there are no weights at all so we do get the warning. ptrs = collections.defaultdict(list) for name, tensor in model_reloaded.state_dict().items(): ptrs[id_tensor_storage(tensor)].append(name) tied_params = [names for _, names in ptrs.items() if len(names) > 1] for group in tied_params: group = {k[len(prefix) :] if k.startswith(prefix) else k for k in group} # We remove the group from extra_missing if not all weights from group are in it if len(group - extra_missing) > 0: extra_missing = extra_missing - set(group) self.assertEqual( extra_missing, set(), f"This model {model_class.__name__} might be missing some `keys_to_ignore`: {extra_missing}. " f"For debugging, tied parameters are {tied_params}", ) missed_missing = param_names - missing_keys # Remove nonpersistent buffers from missed_missing buffers = [n for n, _ in model_reloaded.named_buffers()] nonpersistent_buffers = {n for n in buffers if n not in model_reloaded.state_dict()} nonpersistent_buffers = { k[len(prefix) :] if k.startswith(prefix) else k for k in nonpersistent_buffers } missed_missing = missed_missing - nonpersistent_buffers if model_reloaded._keys_to_ignore_on_load_missing is None: expected_missing = set() else: expected_missing = set(model_reloaded._keys_to_ignore_on_load_missing) self.assertEqual( missed_missing, expected_missing, f"This model {model_class.__name__} ignores keys {missed_missing} but they look like real" " parameters. If they are non persistent buffers make sure to instantiate them with" " `persistent=False`", ) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif isinstance(tuple_object, Dict): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values(), dict_object.values() ): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence( model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True} ) # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _make_attention_mask_non_null(self, inputs_dict): """Make sure no sequence has all zeros as attention mask""" for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]: if k in inputs_dict: attention_mask = inputs_dict[k] # Make sure no all 0s attention masks - to avoid failure at this moment. # Put `1` at the beginning of sequences to make it still work when combining causal attention masks. # TODO: remove this line once a fix regarding large negative values for attention mask is done. attention_mask = torch.cat( [torch.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], dim=-1 ) # Here we make the first sequence with all 0s as attention mask. # Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative # values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks. # TODO: enable this block once the large negative values thing is cleaned up. # (see https://github.com/huggingface/transformers/issues/14859) # attention_mask = torch.cat( # [torch.zeros_like(attention_mask[:1], dtype=attention_mask.dtype), attention_mask[1:]], # dim=0 # ) inputs_dict[k] = attention_mask # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class): """For temporarily ignoring some failed test cases (issues to be fixed)""" tf_keys = {k for k, v in tf_outputs.items() if v is not None} pt_keys = {k for k, v in pt_outputs.items() if v is not None} key_differences = tf_keys.symmetric_difference(pt_keys) if model_class.__name__ in [ "FlaubertWithLMHeadModel", "FunnelForPreTraining", "ElectraForPreTraining", "XLMWithLMHeadModel", ]: for k in key_differences: if k in ["loss", "losses"]: tf_keys.discard(k) pt_keys.discard(k) elif model_class.__name__.startswith("GPT2"): # `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple. tf_keys.discard("past_key_values") pt_keys.discard("past_key_values") # create new outputs from the remaining fields new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys}) new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys}) return new_tf_outputs, new_pt_outputs # Copied from tests.test_modeling_tf_common.TFModelTesterMixin.check_pt_tf_outputs def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way. Args: model_class: The class of the model that is currently testing. For example, `TFBertModel`, TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative error messages. name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc. attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element being a named field in the output. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(tf_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is", ) # Don't copy this block to model specific test file! # TODO: remove this method and this line after issues are fixed tf_outputs, pt_outputs = self._postprocessing_to_ignore_test_cases(tf_outputs, pt_outputs, model_class) tf_keys = [k for k, v in tf_outputs.items() if v is not None] pt_keys = [k for k, v in pt_outputs.items() if v is not None] self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `name` attributes = tuple([f"{name}.{k}" for k in tf_keys]) self.check_pt_tf_outputs( tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(tf_outputs) in [tuple, list]: self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch") self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch") if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(tf_outputs), f"{name}: The tuple `attributes` should have the same length as `tf_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name` attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))]) for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes): self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(tf_outputs, tf.Tensor): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is" ) tf_outputs = tf_outputs.numpy() pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(tf_outputs): tf_outputs = np.array([tf_outputs]) pt_outputs = np.array([pt_outputs]) tf_nans = np.isnan(tf_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[tf_nans] = 0 tf_outputs[tf_nans] = 0 pt_outputs[pt_nans] = 0 tf_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(tf_outputs - pt_outputs)) self.assertLessEqual(max_diff, tol, f"{name}: Difference between PyTorch and TF is {max_diff} (>= {tol}).") else: raise ValueError( "`tf_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `tf.Tensor`. Got" f" {type(tf_outputs)} instead." ) def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict): tf_inputs_dict = {} for key, tensor in pt_inputs_dict.items(): # skip key that does not exist in tf if isinstance(tensor, bool): tf_inputs_dict[key] = tensor elif key == "input_values": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) elif key == "pixel_values": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) elif key == "input_features": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) # other general float inputs elif tensor.is_floating_point(): tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) else: tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32) return tf_inputs_dict def check_pt_tf_models(self, tf_model, pt_model, pt_inputs_dict): tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict) # send pytorch inputs to the correct device pt_inputs_dict = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items() } # send pytorch model to the correct device pt_model.to(torch_device) # Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences pt_model.eval() with torch.no_grad(): pt_outputs = pt_model(**pt_inputs_dict) tf_outputs = tf_model(tf_inputs_dict) # tf models returned loss is usually a tensor rather than a scalar. # (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`) # Change it here to a scalar to match PyTorch models' loss tf_loss = getattr(tf_outputs, "loss", None) if tf_loss is not None: tf_outputs.loss = tf.math.reduce_mean(tf_loss) self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(pt_model)) @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self, allow_missing_keys=False): import transformers for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_model_class_name = "TF" + model_class.__name__ # Add the "TF" at the beginning if not hasattr(transformers, tf_model_class_name): # transformers does not have this model in TF version yet return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`. # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it. self._make_attention_mask_non_null(inputs_dict) tf_model_class = getattr(transformers, tf_model_class_name) pt_model = model_class(config) tf_model = tf_model_class(config) pt_inputs_dict = self._prepare_for_class(inputs_dict, model_class) pt_inputs_dict_with_labels = self._prepare_for_class( inputs_dict, model_class, # Not all models accept "labels" in the forward pass (yet :) ) return_labels=True if "labels" in inspect.signature(model_class.forward).parameters.keys() else False, ) # make sure only tf inputs are forward that actually exist in function args tf_input_keys = set(inspect.signature(tf_model.call).parameters.keys()) # remove all head masks tf_input_keys.discard("head_mask") tf_input_keys.discard("cross_attn_head_mask") tf_input_keys.discard("decoder_head_mask") pt_inputs_dict = {k: v for k, v in pt_inputs_dict.items() if k in tf_input_keys} pt_inputs_dict_with_labels = {k: v for k, v in pt_inputs_dict_with_labels.items() if k in tf_input_keys} # For some models (e.g. base models), there is no label returned. # Set the input dict to `None` to avoid check outputs twice for the same input dicts. if not set(pt_inputs_dict_with_labels.keys()).symmetric_difference(pt_inputs_dict.keys()): pt_inputs_dict_with_labels = None # Check we can load pt model in tf and vice-versa with model => model functions # Here requires `tf_inputs_dict` to build `tf_model` tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict) tf_model = transformers.load_pytorch_model_in_tf2_model( tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys ) pt_model = transformers.load_tf2_model_in_pytorch_model( pt_model, tf_model, allow_missing_keys=allow_missing_keys ) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) # check with `labels` if pt_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels) # Check we can load pt model in tf and vice-versa with checkpoint => model functions with tempfile.TemporaryDirectory() as tmpdirname: pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin") torch.save(pt_model.state_dict(), pt_checkpoint_path) tf_model = transformers.load_pytorch_checkpoint_in_tf2_model( tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys ) tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5") tf_model.save_weights(tf_checkpoint_path) pt_model = transformers.load_tf2_checkpoint_in_pytorch_model( pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys ) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) # check with `labels` if pt_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels) def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float): diff = np.abs((a - b)).max() self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).") def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """ Args: model_class: The class of the model that is currently testing. For example, ..., etc. Currently unused, but it could make debugging easier and faster. names: A string, or a list of strings. These specify what fx_outputs/pt_outputs represent in the model outputs. Currently unused, but in the future, we could use this information to make the error message clearer by giving the name(s) of the output tensor(s) with large difference(s) between PT and Flax. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(fx_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `fx_outputs` is", ) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys, f"{name}: Output keys differ between Flax and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `name` attributes = tuple([f"{name}.{k}" for k in fx_keys]) self.check_pt_flax_outputs( fx_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(fx_outputs) in [tuple, list]: self.assertEqual( type(fx_outputs), type(pt_outputs), f"{name}: Output types differ between Flax and PyTorch" ) self.assertEqual( len(fx_outputs), len(pt_outputs), f"{name}: Output lengths differ between Flax and PyTorch" ) if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(fx_outputs), f"{name}: The tuple `attributes` should have the same length as `fx_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name` attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))]) for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes): self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(fx_outputs, jnp.ndarray): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `fx_outputs` is" ) # Using `np.asarray` gives `ValueError: assignment destination is read-only` at the line `fx_outputs[fx_nans] = 0`. fx_outputs = np.array(fx_outputs) pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( fx_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between Flax and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(fx_outputs): fx_outputs = np.array([fx_outputs]) pt_outputs = np.array([pt_outputs]) fx_nans = np.isnan(fx_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[fx_nans] = 0 fx_outputs[fx_nans] = 0 pt_outputs[pt_nans] = 0 fx_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(fx_outputs - pt_outputs)) self.assertLessEqual( max_diff, tol, f"{name}: Difference between PyTorch and Flax is {max_diff} (>= {tol})." ) else: raise ValueError( "`fx_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `jnp.ndarray`. Got" f" {type(fx_outputs)} instead." ) @is_pt_flax_cross_test def test_equivalence_pt_to_flax(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): fx_model_class_name = "Flax" + model_class.__name__ if not hasattr(transformers, fx_model_class_name): # no flax model exists for this class return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions fx_model_class = getattr(transformers, fx_model_class_name) # load PyTorch class pt_model = model_class(config).eval() # Flax models don't use the `use_cache` option and cache is not returned as a default. # So we disable `use_cache` here for PyTorch model. pt_model.config.use_cache = False # load Flax class fx_model = fx_model_class(config, dtype=jnp.float32) # make sure only flax inputs are forward that actually exist in function args fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys() # prepare inputs pt_inputs = self._prepare_for_class(inputs_dict, model_class) # remove function args that don't exist in Flax pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys} # send pytorch inputs to the correct device pt_inputs = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items() } # convert inputs to Flax fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)} fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state # send pytorch model to the correct device pt_model.to(torch_device) with torch.no_grad(): pt_outputs = pt_model(**pt_inputs) fx_outputs = fx_model(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class) @is_pt_flax_cross_test def test_equivalence_flax_to_pt(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): fx_model_class_name = "Flax" + model_class.__name__ if not hasattr(transformers, fx_model_class_name): # no flax model exists for this class return # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions fx_model_class = getattr(transformers, fx_model_class_name) # load PyTorch class pt_model = model_class(config).eval() # Flax models don't use the `use_cache` option and cache is not returned as a default. # So we disable `use_cache` here for PyTorch model. pt_model.config.use_cache = False # load Flax class fx_model = fx_model_class(config, dtype=jnp.float32) # make sure only flax inputs are forward that actually exist in function args fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys() # prepare inputs pt_inputs = self._prepare_for_class(inputs_dict, model_class) # remove function args that don't exist in Flax pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys} # send pytorch inputs to the correct device pt_inputs = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items() } # convert inputs to Flax fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)} pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) # make sure weights are tied in PyTorch pt_model.tie_weights() # send pytorch model to the correct device pt_model.to(torch_device) with torch.no_grad(): pt_outputs = pt_model(**pt_inputs) fx_outputs = fx_model(**fx_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class) with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True) # send pytorch model to the correct device pt_model_loaded.to(torch_device) pt_model_loaded.eval() with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs) fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None]) pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None]) self.assertEqual(fx_keys, pt_keys) self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) wte = model.get_input_embeddings() if not self.is_encoder_decoder: inputs["inputs_embeds"] = wte(input_ids) else: inputs["inputs_embeds"] = wte(encoder_input_ids) inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] @require_torch_multi_gpu def test_multi_gpu_data_parallel_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # some params shouldn't be scattered by nn.DataParallel # so just remove them if they are present. blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"] for k in blacklist_non_batched_params: inputs_dict.pop(k, None) # move input tensors to cuda:O for k, v in inputs_dict.items(): if torch.is_tensor(v): inputs_dict[k] = v.to(0) for model_class in self.all_model_classes: model = model_class(config=config) model.to(0) model.eval() # Wrap model in nn.DataParallel model = nn.DataParallel(model) with torch.no_grad(): _ = model(**self._prepare_for_class(inputs_dict, model_class)) @require_torch_multi_gpu def test_model_parallelization(self): if not self.test_model_parallel: return # a candidate for testing_utils def get_current_gpu_memory_use(): """returns a list of cuda memory allocations per GPU in MBs""" per_device_memory = [] for id in range(torch.cuda.device_count()): with torch.cuda.device(id): per_device_memory.append(torch.cuda.memory_allocated() >> 20) return per_device_memory # Needs a large model to see the difference. config = self.model_tester.get_large_model_config() for model_class in self.all_parallelizable_model_classes: torch.cuda.empty_cache() # 1. single gpu memory load + unload + memory measurements # Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests) memory_at_start = get_current_gpu_memory_use() # Put model on device 0 and take a memory snapshot model = model_class(config) model.to("cuda:0") memory_after_model_load = get_current_gpu_memory_use() # The memory use on device 0 should be higher than it was initially. self.assertGreater(memory_after_model_load[0], memory_at_start[0]) del model gc.collect() torch.cuda.empty_cache() # 2. MP test # it's essential to re-calibrate the usage before the next stage memory_at_start = get_current_gpu_memory_use() # Spread model layers over multiple devices model = model_class(config) model.parallelize() memory_after_parallelization = get_current_gpu_memory_use() # Assert that the memory use on all devices is higher than it was when loaded only on CPU for n in range(len(model.device_map.keys())): self.assertGreater(memory_after_parallelization[n], memory_at_start[n]) # Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it self.assertLess(memory_after_parallelization[0], memory_after_model_load[0]) # Assert that the memory use of device 1 is higher than it was when the entire model was loaded # on device 0 and device 1 wasn't used at all self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1]) del model gc.collect() torch.cuda.empty_cache() @require_torch_multi_gpu def test_model_parallel_equal_results(self): if not self.test_model_parallel: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_parallelizable_model_classes: inputs_dict = self._prepare_for_class(inputs_dict, model_class) def cast_to_device(dictionary, device): output = {} for k, v in dictionary.items(): if isinstance(v, torch.Tensor): output[k] = v.to(device) else: output[k] = v return output model = model_class(config) output = model(**cast_to_device(inputs_dict, "cpu")) model.parallelize() parallel_output = model(**cast_to_device(inputs_dict, "cuda:0")) for value, parallel_value in zip(output, parallel_output): if isinstance(value, torch.Tensor): self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7)) elif isinstance(value, (Tuple, List)): for value_, parallel_value_ in zip(value, parallel_value): self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7)) def check_device_map_is_respected(self, model, device_map): for param_name, param in model.named_parameters(): # Find device in device_map while len(param_name) > 0 and param_name not in device_map: param_name = ".".join(param_name.split(".")[:-1]) if param_name not in device_map: raise ValueError("device map is incomplete, it does not contain any device for `param_name`.") param_device = device_map[param_name] if param_device in ["cpu", "disk"]: self.assertEqual(param.device, torch.device("meta")) else: self.assertEqual(param.device, torch.device(param_device)) @require_accelerate @mark.accelerate_tests @require_torch_gpu def test_disk_offload_bin(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir, safe_serialization=False) with self.assertRaises(ValueError): max_size = int(self.model_split_percents[0] * model_size) max_memory = {0: max_size, "cpu": max_size} # This errors out cause it's missing an offload folder new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) max_size = int(self.model_split_percents[1] * model_size) max_memory = {0: max_size, "cpu": max_size} new_model = model_class.from_pretrained( tmp_dir, device_map="auto", max_memory=max_memory, offload_folder=tmp_dir ) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5)) @require_accelerate @mark.accelerate_tests @require_torch_gpu def test_disk_offload_safetensors(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) max_size = int(self.model_split_percents[1] * model_size) max_memory = {0: max_size, "cpu": max_size} # This doesn't error out as it's in safetensors and doesn't need an offload folder new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5)) @require_accelerate @mark.accelerate_tests @require_torch_gpu def test_cpu_offload(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] # We test several splits of sizes to make sure it works. max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents[1:]] with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) for max_size in max_gpu_sizes: max_memory = {0: max_size, "cpu": model_size * 2} new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) # Making sure part of the model will actually end up offloaded self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"}) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5)) @require_accelerate @mark.accelerate_tests @require_torch_multi_gpu def test_model_parallelism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class._no_split_modules is None: continue inputs_dict_class = self._prepare_for_class(inputs_dict, model_class) model = model_class(config).eval() model = model.to(torch_device) torch.manual_seed(0) base_output = model(**inputs_dict_class) model_size = compute_module_sizes(model)[""] # We test several splits of sizes to make sure it works. max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents[1:]] with tempfile.TemporaryDirectory() as tmp_dir: model.cpu().save_pretrained(tmp_dir) for max_size in max_gpu_sizes: max_memory = {0: max_size, 1: model_size * 2, "cpu": model_size * 2} new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory) # Making sure part of the model will actually end up offloaded self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1}) self.check_device_map_is_respected(new_model, new_model.hf_device_map) torch.manual_seed(0) new_output = new_model(**inputs_dict_class) self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5)) def test_problem_types(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() problem_types = [ {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float}, {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long}, {"title": "regression", "num_labels": 1, "dtype": torch.float}, ] for model_class in self.all_model_classes: if model_class.__name__ not in [ *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES), *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES), ]: continue for problem_type in problem_types: with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"): config.problem_type = problem_type["title"] config.num_labels = problem_type["num_labels"] model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) if problem_type["num_labels"] > 1: inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"]) inputs["labels"] = inputs["labels"].to(problem_type["dtype"]) # This tests that we do not trigger the warning form PyTorch "Using a target size that is different # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure # they have the same size." which is a symptom something in wrong for the regression problem. # See https://github.com/huggingface/transformers/issues/11780 with warnings.catch_warnings(record=True) as warning_list: loss = model(**inputs).loss for w in warning_list: if "Using a target size that is different to the input size" in str(w.message): raise ValueError( f"Something is going wrong in the regression problem: intercepted {w.message}" ) loss.backward() def test_load_with_mismatched_shapes(self): if not self.test_mismatched_shapes: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class.__name__ not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES): continue with self.subTest(msg=f"Testing {model_class}"): with tempfile.TemporaryDirectory() as tmp_dir: model = model_class(config) model.save_pretrained(tmp_dir) # Fails when we don't set ignore_mismatched_sizes=True with self.assertRaises(RuntimeError): new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42) with self.assertRaises(RuntimeError): new_model_without_prefix = AutoModel.from_pretrained(tmp_dir, vocab_size=10) logger = logging.get_logger("transformers.modeling_utils") with CaptureLogger(logger) as cl: new_model = AutoModelForSequenceClassification.from_pretrained( tmp_dir, num_labels=42, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) new_model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) logits = new_model(**inputs).logits self.assertEqual(logits.shape[1], 42) with CaptureLogger(logger) as cl: new_model_without_prefix = AutoModel.from_pretrained( tmp_dir, vocab_size=10, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) input_ids = ids_tensor((2, 8), 10) new_model_without_prefix.to(torch_device) if self.is_encoder_decoder: new_model_without_prefix(input_ids, decoder_input_ids=input_ids) else: new_model_without_prefix(input_ids) def test_mismatched_shapes_have_properly_initialized_weights(self): if not self.test_mismatched_shapes: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: if model_class.__name__ not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES): continue with self.subTest(msg=f"Testing {model_class}"): with tempfile.TemporaryDirectory() as tmp_dir: model = model_class(configs_no_init) model.save_pretrained(tmp_dir) # Fails when we don't set ignore_mismatched_sizes=True with self.assertRaises(RuntimeError): new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42) logger = logging.get_logger("transformers.modeling_utils") with CaptureLogger(logger) as cl: new_model = AutoModelForSequenceClassification.from_pretrained( tmp_dir, num_labels=42, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) for name, param in new_model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist(self): # 1. Create a dummy class. Should have buffers as well? To make sure we test __init__ class MyClass(PreTrainedModel): config_class = PretrainedConfig def __init__(self, config=None): super().__init__(config if config is not None else PretrainedConfig()) self.linear = nn.Linear(10, config.num_labels, bias=True) self.embedding = nn.Embedding(10, 10) self.std = 1 def _init_weights(self, module): if isinstance(module, nn.Linear): module.weight.data = nn.init.kaiming_uniform_(module.weight.data, np.sqrt(5)) if module.bias is not None: module.bias.data = module.bias.data.normal_(mean=0.0, std=self.std) # Used to make sure the weights with matched shape are loaded correctly config = PretrainedConfig() config.num_labels = 3 model = MyClass(config=config) # Used to make sure the weights with mismatched shape are properly initialized set_seed(0) config = PretrainedConfig() config.num_labels = 4 # not to init. the weights during the creation: to match the logic in `from_pretrained`, so we can keep the # same sequence of random ops in the execution path to allow us to compare `target_model` and `new_model` below # for `linear` part. with ContextManagers([no_init_weights(True)]): target_model = MyClass(config=config) target_model.apply(target_model._initialize_weights) with tempfile.TemporaryDirectory() as tmpdirname: state_dict = model.state_dict() del state_dict["linear.weight"] model.config.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) set_seed(0) new_model = MyClass.from_pretrained(tmpdirname, num_labels=4, ignore_mismatched_sizes=True) for key in new_model.state_dict().keys(): # check weight values for weights with matched shapes are identical # (i.e. correctly loaded from the checkpoint) if key not in ["linear.weight", "linear.bias"]: max_diff = torch.max(torch.abs(model.state_dict()[key] - new_model.state_dict()[key])) self.assertLessEqual( max_diff.item(), 1e-6, msg=f"the weight values for `{key}` in `new_model` and `model` are not identical", ) else: # check we have some mismatched shapes self.assertNotEqual( model.state_dict()[key].shape, new_model.state_dict()[key].shape, msg=f"the weight shapes for {key} in `model` and `new_model` should differ", ) # check the weights with mismatched shape are properly initialized max_diff = torch.max(torch.abs(new_model.state_dict()[key] - target_model.state_dict()[key])) self.assertLessEqual( max_diff.item(), 1e-6, msg=f"the weight values for `{key}` in `new_model` and `target_model` are not identical", ) def test_model_is_small(self): # Just a consistency check to make sure we are not running tests on 80M parameter models. config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) num_params = model.num_parameters() assert ( num_params < 1000000 ), f"{model_class} is too big for the common tests ({num_params})! It should have 1M max." @require_flash_attn @require_torch_gpu @mark.flash_attn_test @slow def test_flash_attn_2_conversion(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2" ).to(torch_device) for _, module in model.named_modules(): if "FlashAttention" in module.__class__.__name__: return self.assertTrue(False, "FlashAttention2 modules not found in model") @require_flash_attn @require_torch_gpu @mark.flash_attn_test @slow def test_flash_attn_2_inference(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model.to(torch_device) dummy_input = inputs_dict[model.main_input_name][:1] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is not None: dummy_attention_mask = dummy_attention_mask[:1] dummy_attention_mask[:, 1:] = 1 dummy_attention_mask[:, :1] = 0 if model.config.is_encoder_decoder: decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)[:1] outputs = model(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True) outputs_fa = model_fa(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True) else: outputs = model(dummy_input, output_hidden_states=True) outputs_fa = model_fa(dummy_input, output_hidden_states=True) logits = ( outputs.hidden_states[-1] if not model.config.is_encoder_decoder else outputs.decoder_hidden_states[-1] ) logits_fa = ( outputs_fa.hidden_states[-1] if not model.config.is_encoder_decoder else outputs_fa.decoder_hidden_states[-1] ) assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2) if model.config.is_encoder_decoder: other_inputs = { "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": dummy_attention_mask, "output_hidden_states": True, } if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(dummy_input, **other_inputs) outputs_fa = model_fa(dummy_input, **other_inputs) else: other_inputs = { "output_hidden_states": True, } if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(dummy_input, **other_inputs) outputs_fa = model_fa(dummy_input, **other_inputs) logits = ( outputs.hidden_states[-1] if not model.config.is_encoder_decoder else outputs.decoder_hidden_states[-1] ) logits_fa = ( outputs_fa.hidden_states[-1] if not model.config.is_encoder_decoder else outputs_fa.decoder_hidden_states[-1] ) assert torch.allclose(logits_fa[1:], logits[1:], atol=4e-2, rtol=4e-2) # check with inference + dropout model.train() _ = model_fa(dummy_input, **other_inputs) @require_flash_attn @require_torch_gpu @mark.flash_attn_test @slow def test_flash_attn_2_inference_padding_right(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model.to(torch_device) dummy_input = inputs_dict[model.main_input_name][:1] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is not None: dummy_attention_mask = dummy_attention_mask[:1] dummy_attention_mask[:, :-1] = 1 dummy_attention_mask[:, -1:] = 0 if model.config.is_encoder_decoder: decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)[:1] outputs = model(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True) outputs_fa = model_fa(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True) else: outputs = model(dummy_input, output_hidden_states=True) outputs_fa = model_fa(dummy_input, output_hidden_states=True) logits = ( outputs.hidden_states[-1] if not model.config.is_encoder_decoder else outputs.decoder_hidden_states[-1] ) logits_fa = ( outputs_fa.hidden_states[-1] if not model.config.is_encoder_decoder else outputs_fa.decoder_hidden_states[-1] ) assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2) if model.config.is_encoder_decoder: other_inputs = { "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": dummy_attention_mask, "output_hidden_states": True, } if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(dummy_input, **other_inputs) outputs_fa = model_fa(dummy_input, **other_inputs) else: other_inputs = { "output_hidden_states": True, } if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(dummy_input, **other_inputs) outputs_fa = model_fa(dummy_input, **other_inputs) logits = ( outputs.hidden_states[-1] if not model.config.is_encoder_decoder else outputs.decoder_hidden_states[-1] ) logits_fa = ( outputs_fa.hidden_states[-1] if not model.config.is_encoder_decoder else outputs_fa.decoder_hidden_states[-1] ) assert torch.allclose(logits_fa[:-1], logits[:-1], atol=4e-2, rtol=4e-2) @require_flash_attn @require_torch_gpu @mark.flash_attn_test @slow def test_flash_attn_2_generate_left_padding(self): for model_class in self.all_generative_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to( torch_device ) dummy_input = inputs_dict[model.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) # make sure we do left padding dummy_attention_mask[:, :-1] = 0 dummy_attention_mask[:, -1:] = 1 out = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False ) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) out_fa = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False ) self.assertTrue(torch.allclose(out, out_fa)) @require_flash_attn @require_torch_gpu @mark.flash_attn_test @slow def test_flash_attn_2_generate_padding_right(self): for model_class in self.all_generative_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to( torch_device ) dummy_input = inputs_dict[model.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) # make sure we do right padding dummy_attention_mask[:, :-1] = 1 dummy_attention_mask[:, -1:] = 0 out = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False ) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) out_fa = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False ) self.assertTrue(torch.allclose(out, out_fa)) @parameterized.expand([("float16",), ("bfloat16",), ("float32",)]) @require_torch_sdpa @slow def test_eager_matches_sdpa_inference(self, torch_dtype: str): if not self.all_model_classes[0]._supports_sdpa: self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA") if torch_dtype == "float16" and not is_torch_fp16_available_on_device(torch_device): self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)") if torch_dtype == "bfloat16" and not is_torch_bf16_available_on_device(torch_device): self.skipTest( f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)" ) # Not sure whether it's fine to put torch.XXX in a decorator if torch is not available so hacking it here instead. if torch_dtype == "float16": torch_dtype = torch.float16 elif torch_dtype == "bfloat16": torch_dtype = torch.bfloat16 elif torch_dtype == "float32": torch_dtype = torch.float32 atols = { ("cpu", False, torch.float32): 1e-6, ("cpu", False, torch.bfloat16): 1e-2, ("cpu", True, torch.float32): 1e-6, ("cpu", True, torch.bfloat16): 1e-2, ("cuda", False, torch.float32): 1e-6, ("cuda", False, torch.bfloat16): 1e-2, ("cuda", False, torch.float16): 5e-3, ("cuda", True, torch.float32): 1e-6, ("cuda", True, torch.bfloat16): 1e-2, ("cuda", True, torch.float16): 5e-3, } rtols = { ("cpu", False, torch.float32): 1e-4, ("cpu", False, torch.bfloat16): 1e-2, ("cpu", True, torch.float32): 1e-4, ("cpu", True, torch.bfloat16): 1e-2, ("cuda", False, torch.float32): 1e-4, ("cuda", False, torch.bfloat16): 1e-2, ("cuda", False, torch.float16): 5e-3, ("cuda", True, torch.float32): 1e-4, ("cuda", True, torch.bfloat16): 3e-2, ("cuda", True, torch.float16): 5e-3, } def get_mean_reldiff(failcase, x, ref, atol, rtol): return f"{failcase}: mean relative difference: {((x - ref).abs() / (ref.abs() + 1e-12)).mean():.3e}, torch atol = {atol}, torch rtol = {rtol}" for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) is_encoder_decoder = model.config.is_encoder_decoder with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_sdpa = model_class.from_pretrained(tmpdirname, torch_dtype=torch_dtype) model_sdpa = model_sdpa.eval().to(torch_device) self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") model_eager = model_class.from_pretrained( tmpdirname, torch_dtype=torch_dtype, attn_implementation="eager", ) model_eager = model_eager.eval().to(torch_device) self.assertTrue(model_eager.config._attn_implementation == "eager") for name, submodule in model_eager.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: raise ValueError("The eager model should not have SDPA attention layers") has_sdpa = False for name, submodule in model_sdpa.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: has_sdpa = True break if not has_sdpa and model_sdpa.config.model_type != "falcon": raise ValueError("The SDPA model should have SDPA attention layers") # We use these for loops instead of parameterized.expand just for the interest of avoiding loading/saving 8 times the model, # but it would be nicer to have an efficient way to use parameterized.expand fail_cases = [] for padding_side in ["left", "right"]: for use_mask in [False, True]: for batch_size in [1, 5]: dummy_input = inputs_dict[model.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16, torch.float16]: dummy_input = dummy_input.to(torch_dtype) dummy_input = dummy_input[:batch_size] if dummy_input.shape[0] != batch_size: if dummy_input.dtype in [torch.float32, torch.bfloat16, torch.float16]: extension = torch.rand( batch_size - dummy_input.shape[0], *dummy_input.shape[1:], dtype=torch_dtype, device=torch_device, ) dummy_input = torch.cat((dummy_input, extension), dim=0).to(torch_device) else: extension = torch.randint( high=5, size=(batch_size - dummy_input.shape[0], *dummy_input.shape[1:]), dtype=dummy_input.dtype, device=torch_device, ) dummy_input = torch.cat((dummy_input, extension), dim=0).to(torch_device) if not use_mask: dummy_attention_mask = None else: dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is None: if is_encoder_decoder: seqlen = inputs_dict.get("decoder_input_ids", dummy_input).shape[-1] else: seqlen = dummy_input.shape[-1] dummy_attention_mask = ( torch.ones(batch_size, seqlen).to(torch.int64).to(torch_device) ) dummy_attention_mask = dummy_attention_mask[:batch_size] if dummy_attention_mask.shape[0] != batch_size: extension = torch.ones( batch_size - dummy_attention_mask.shape[0], *dummy_attention_mask.shape[1:], dtype=dummy_attention_mask.dtype, device=torch_device, ) dummy_attention_mask = torch.cat((dummy_attention_mask, extension), dim=0) dummy_attention_mask = dummy_attention_mask.to(torch_device) dummy_attention_mask[:] = 1 if padding_side == "left": dummy_attention_mask[-1, :-1] = 1 dummy_attention_mask[-1, -4:] = 0 elif padding_side == "right": dummy_attention_mask[-1, 1:] = 1 dummy_attention_mask[-1, :3] = 0 for enable_kernels in [False, True]: failcase = f"padding_side={padding_side}, use_mask={use_mask}, batch_size={batch_size}, enable_kernels={enable_kernels}" if is_encoder_decoder: decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)[:batch_size] if decoder_input_ids.shape[0] != batch_size: extension = torch.ones( batch_size - decoder_input_ids.shape[0], *decoder_input_ids.shape[1:], dtype=decoder_input_ids.dtype, device=torch_device, ) decoder_input_ids = torch.cat((decoder_input_ids, extension), dim=0) decoder_input_ids = decoder_input_ids.to(torch_device) # TODO: never an `attention_mask` arg here? other_inputs = { "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": dummy_attention_mask, "output_hidden_states": True, } else: other_inputs = { "output_hidden_states": True, } # Otherwise fails for e.g. WhisperEncoderModel if "attention_mask" in inspect.signature(model_eager.forward).parameters: other_inputs["attention_mask"] = dummy_attention_mask # TODO: test gradients as well (& for FA2 as well!) with torch.no_grad(): with torch.backends.cuda.sdp_kernel( enable_flash=enable_kernels, enable_math=True, enable_mem_efficient=enable_kernels, ): outputs_eager = model_eager(dummy_input, **other_inputs) outputs_sdpa = model_sdpa(dummy_input, **other_inputs) logits_eager = ( outputs_eager.hidden_states[-1] if not is_encoder_decoder else outputs_eager.decoder_hidden_states[-1] ) logits_sdpa = ( outputs_sdpa.hidden_states[-1] if not is_encoder_decoder else outputs_sdpa.decoder_hidden_states[-1] ) if torch_device in ["cpu", "cuda"]: atol = atols[torch_device, enable_kernels, torch_dtype] rtol = rtols[torch_device, enable_kernels, torch_dtype] else: atol = 1e-7 rtol = 1e-4 # Masked tokens output slightly deviates - we don't mind that. if use_mask: if padding_side == "left": sub_sdpa = logits_sdpa[:-1] sub_eager = logits_eager[:-1] if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol): fail_cases.append( get_mean_reldiff(failcase, sub_sdpa, sub_eager, atol, rtol) ) sub_sdpa = logits_sdpa[-1, :-4] sub_eager = logits_eager[-1, :-4] if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol): fail_cases.append( get_mean_reldiff(failcase, sub_sdpa, sub_eager, atol, rtol) ) # Testing the padding tokens is not really meaningful but anyway # sub_sdpa = logits_sdpa[-1, -4:] # sub_eager = logits_eager[-1, -4:] # if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol): # fail_cases.append(get_mean_reldiff(failcase, sub_sdpa, sub_eager, 4e-2, 4e-2)) elif padding_side == "right": sub_sdpa = logits_sdpa[:-1] sub_eager = logits_eager[:-1] if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol): fail_cases.append( get_mean_reldiff(failcase, sub_sdpa, sub_eager, atol, rtol) ) sub_sdpa = logits_sdpa[-1, 3:] sub_eager = logits_eager[-1, 3:] if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol): fail_cases.append( get_mean_reldiff(failcase, sub_sdpa, sub_eager, atol, rtol) ) # Testing the padding tokens is not really meaningful but anyway # sub_sdpa = logits_sdpa[-1, :3] # sub_eager = logits_eager[-1, :3] # if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol): # fail_cases.append(get_mean_reldiff(failcase, sub_sdpa, sub_eager, 4e-2, 4e-2)) else: if not torch.allclose(logits_sdpa, logits_eager, atol=atol, rtol=rtol): fail_cases.append( get_mean_reldiff(failcase, logits_sdpa, logits_eager, atol, rtol) ) self.assertTrue(len(fail_cases) == 0, "\n".join(fail_cases)) @require_torch_sdpa @slow def test_eager_matches_sdpa_generate(self): max_new_tokens = 30 if len(self.all_generative_model_classes) == 0: self.skipTest(f"{self.__class__.__name__} tests a model that does support generate: skipping this test") for model_class in self.all_generative_model_classes: if not model_class._supports_sdpa: self.skipTest(f"{model_class.__name__} does not support SDPA") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) model_sdpa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(torch_device) self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") model_eager = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True, attn_implementation="eager", ).to(torch_device) self.assertTrue(model_eager.config._attn_implementation == "eager") for name, submodule in model_eager.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: raise ValueError("The eager model should not have SDPA attention layers") has_sdpa = False for name, submodule in model_sdpa.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: has_sdpa = True break if not has_sdpa: raise ValueError("The SDPA model should have SDPA attention layers") # Just test that a large cache works as expected res_eager = model_eager.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False ) res_sdpa = model_sdpa.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False ) self.assertTrue(torch.allclose(res_eager, res_sdpa)) @require_flash_attn @require_torch_gpu @mark.flash_attn_test @slow def test_flash_attn_2_generate_use_cache(self): max_new_tokens = 30 for model_class in self.all_generative_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) # Just test that a large cache works as expected _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False, use_cache=True, ) @require_flash_attn @require_torch_gpu @require_bitsandbytes @mark.flash_attn_test @slow def test_flash_attn_2_fp32_ln(self): for model_class in self.all_generative_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_input = inputs_dict[model.main_input_name] dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) if model.config.is_encoder_decoder: dummy_decoder_input_ids = inputs_dict["decoder_input_ids"] dummy_decoder_attention_mask = inputs_dict["decoder_attention_mask"] model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, load_in_4bit=True, ) for _, param in model.named_parameters(): # upcast only layer norms if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16): param.data = param.data.to(torch.float32) if model.config.is_encoder_decoder: _ = model(dummy_input, decoder_input_ids=dummy_decoder_input_ids) # with attention mask _ = model( dummy_input, attention_mask=dummy_attention_mask, decoder_input_ids=dummy_decoder_input_ids, decoder_attention_mask=dummy_decoder_attention_mask, ) else: _ = model(dummy_input) # with attention mask _ = model(dummy_input, attention_mask=dummy_attention_mask) @is_pt_tf_cross_test def test_tf_from_pt_safetensors(self): for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_model_class_name = "TF" + model_class.__name__ # Add the "TF" at the beginning if not hasattr(transformers, tf_model_class_name): # transformers does not have this model in TF version yet return tf_model_class = getattr(transformers, tf_model_class_name) pt_model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname, safe_serialization=True) tf_model_1 = tf_model_class.from_pretrained(tmpdirname, from_pt=True) pt_model.save_pretrained(tmpdirname, safe_serialization=False) tf_model_2 = tf_model_class.from_pretrained(tmpdirname, from_pt=True) # Check models are equal for p1, p2 in zip(tf_model_1.weights, tf_model_2.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) @is_pt_flax_cross_test def test_flax_from_pt_safetensors(self): for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() flax_model_class_name = "Flax" + model_class.__name__ # Add the "Flax at the beginning if not hasattr(transformers, flax_model_class_name): # transformers does not have this model in Flax version yet return flax_model_class = getattr(transformers, flax_model_class_name) pt_model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname, safe_serialization=True) flax_model_1 = flax_model_class.from_pretrained(tmpdirname, from_pt=True) pt_model.save_pretrained(tmpdirname, safe_serialization=False) flax_model_2 = flax_model_class.from_pretrained(tmpdirname, from_pt=True) # Check models are equal self.assertTrue(check_models_equal(flax_model_1, flax_model_2)) @require_flash_attn @require_torch_gpu @mark.flash_attn_test @slow def test_flash_attn_2_from_config(self): for model_class in self.all_generative_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") config, _ = self.model_tester.prepare_config_and_inputs_for_common() # TODO: to change it in the future with other relevant auto classes fa2_model = AutoModelForCausalLM.from_config( config, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16 ).to(torch_device) dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device) dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [0, 1, 1, 1]]).to(torch_device) fa2_correctly_converted = False for _, module in fa2_model.named_modules(): if "FlashAttention" in module.__class__.__name__: fa2_correctly_converted = True break self.assertTrue(fa2_correctly_converted) _ = fa2_model(input_ids=dummy_input, attention_mask=dummy_attention_mask) with tempfile.TemporaryDirectory() as tmpdirname: fa2_model.save_pretrained(tmpdirname) model_from_pretrained = AutoModelForCausalLM.from_pretrained(tmpdirname) self.assertTrue(model_from_pretrained.config._attn_implementation != "flash_attention_2") fa2_correctly_converted = False for _, module in model_from_pretrained.named_modules(): if "FlashAttention" in module.__class__.__name__: fa2_correctly_converted = True break self.assertFalse(fa2_correctly_converted) global_rng = random.Random() def ids_tensor(shape, vocab_size, rng=None, name=None): # Creates a random int32 tensor of the shape within the vocab size if rng is None: rng = global_rng total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous() def random_attention_mask(shape, rng=None, name=None): attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None) # make sure that at least one token is attended to for each batch # we choose the 1st token so this property of `at least one being non-zero` still holds after applying causal mask attn_mask[:, 0] = 1 return attn_mask def floats_tensor(shape, scale=1.0, rng=None, name=None): """Creates a random float32 tensor""" if rng is None: rng = global_rng total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.random() * scale) return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()

transformers/tests/test_modeling_common.py/0

# coding=utf-8 # Copyright 2023 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from datasets import load_dataset from transformers import load_tool from .test_tools_common import ToolTesterMixin class DocumentQuestionAnsweringToolTester(unittest.TestCase, ToolTesterMixin): def setUp(self): self.tool = load_tool("document-question-answering") self.tool.setup() self.remote_tool = load_tool("document-question-answering", remote=True) def test_exact_match_arg(self): dataset = load_dataset("hf-internal-testing/example-documents", split="test") document = dataset[0]["image"] result = self.tool(document, "When is the coffee break?") self.assertEqual(result, "11-14 to 11:39 a.m.") def test_exact_match_arg_remote(self): dataset = load_dataset("hf-internal-testing/example-documents", split="test") document = dataset[0]["image"] result = self.remote_tool(document, "When is the coffee break?") self.assertEqual(result, "11-14 to 11:39 a.m.") def test_exact_match_kwarg(self): dataset = load_dataset("hf-internal-testing/example-documents", split="test") document = dataset[0]["image"] self.tool(document=document, question="When is the coffee break?") def test_exact_match_kwarg_remote(self): dataset = load_dataset("hf-internal-testing/example-documents", split="test") document = dataset[0]["image"] result = self.remote_tool(document=document, question="When is the coffee break?") self.assertEqual(result, "11-14 to 11:39 a.m.")

transformers/tests/tools/test_document_question_answering.py/0

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path from typing import Dict import numpy as np from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available from transformers.testing_utils import ( TestCasePlus, execute_subprocess_async, get_torch_dist_unique_port, require_torch_multi_gpu, require_torch_multi_xpu, require_torch_neuroncore, require_torch_npu, ) from transformers.training_args import ParallelMode from transformers.utils import logging logger = logging.get_logger(__name__) if is_torch_available(): import torch from torch import nn from torch.utils.data import Dataset, IterableDataset from transformers import Trainer class DummyDataset(Dataset): def __init__(self, length: int = 101): self.length = length def __len__(self): return self.length def __getitem__(self, i) -> int: return i class DummyDataCollator: def __call__(self, features): return {"input_ids": torch.tensor(features), "labels": torch.tensor(features)} class DummyModel(nn.Module): def __init__(self): super().__init__() # Add some (unused) params otherwise DDP will complain. self.fc = nn.Linear(120, 80) def forward(self, input_ids, labels=None): if labels is not None: return torch.tensor(0.0, device=input_ids.device), input_ids else: return input_ids class RegressionModel(nn.Module): def __init__(self, a=0, b=0, double_output=False): super().__init__() self.a = nn.Parameter(torch.tensor(a).float()) self.b = nn.Parameter(torch.tensor(b).float()) self.double_output = double_output self.config = None def forward(self, input_x, labels=None, **kwargs): y = input_x * self.a + self.b if labels is None: return (y, y) if self.double_output else (y,) loss = nn.functional.mse_loss(y, labels) return (loss, y, y) if self.double_output else (loss, y) class SampleIterableDataset(IterableDataset): def __init__(self, a=2, b=3, length=64, seed=42, label_names=None): self.dataset = RegressionDataset(a=a, b=b, length=length, seed=seed, label_names=label_names) def __iter__(self): for i in range(len(self.dataset)): yield self.dataset[i] class FiniteIterableDataset(SampleIterableDataset): def __init__(self, a=2, b=3, length=64, seed=42, label_names=None): super().__init__(a, b, length, seed, label_names) self.current_sample = 0 def __iter__(self): while self.current_sample < len(self.dataset): yield self.dataset[self.current_sample] self.current_sample += 1 class RegressionDataset: def __init__(self, a=2, b=3, length=64, seed=42, label_names=None): np.random.seed(seed) self.label_names = ["labels"] if label_names is None else label_names self.length = length self.x = np.random.normal(size=(length,)).astype(np.float32) self.ys = [a * self.x + b + np.random.normal(scale=0.1, size=(length,)) for _ in self.label_names] self.ys = [y.astype(np.float32) for y in self.ys] def __len__(self): return self.length def __getitem__(self, i): result = {name: y[i] for name, y in zip(self.label_names, self.ys)} result["input_x"] = self.x[i] return result class TestTrainerDistributedNeuronCore(TestCasePlus): @require_torch_neuroncore def test_trainer(self): distributed_args = f"""--nproc_per_node=2 --master_port={get_torch_dist_unique_port()} {self.test_file_dir}/test_trainer_distributed.py """.split() output_dir = self.get_auto_remove_tmp_dir() args = f"--output_dir {output_dir}".split() cmd = ["torchrun"] + distributed_args + args execute_subprocess_async(cmd, env=self.get_env()) # successful return here == success - any errors would have caused an error in the sub-call class TestTrainerDistributedNPU(TestCasePlus): @require_torch_npu def test_trainer(self): distributed_args = f"""--nproc_per_node=2 --master_port={get_torch_dist_unique_port()} {self.test_file_dir}/test_trainer_distributed.py """.split() output_dir = self.get_auto_remove_tmp_dir() args = f"--output_dir {output_dir}".split() cmd = ["torchrun"] + distributed_args + args execute_subprocess_async(cmd, env=self.get_env()) # successful return here == success - any errors would have caused an error in the sub-call class TestTrainerDistributed(TestCasePlus): @require_torch_multi_gpu def test_trainer(self): distributed_args = f"""--nproc_per_node={torch.cuda.device_count()} --master_port={get_torch_dist_unique_port()} {self.test_file_dir}/test_trainer_distributed.py """.split() output_dir = self.get_auto_remove_tmp_dir() args = f"--output_dir {output_dir}".split() cmd = ["torchrun"] + distributed_args + args execute_subprocess_async(cmd, env=self.get_env()) # successful return here == success - any errors would have caused an error in the sub-call @require_torch_multi_xpu class TestTrainerDistributedXPU(TestCasePlus): def test_trainer(self): distributed_args = f"""--nproc_per_node={torch.xpu.device_count()} --master_port={get_torch_dist_unique_port()} {self.test_file_dir}/test_trainer_distributed.py """.split() output_dir = self.get_auto_remove_tmp_dir() args = f"--output_dir {output_dir}".split() cmd = ["torchrun"] + distributed_args + args execute_subprocess_async(cmd, env=self.get_env()) # successful return here == success - any errors would have caused an error in the sub-call if __name__ == "__main__": # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs: # # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py parser = HfArgumentParser((TrainingArguments,)) training_args = parser.parse_args_into_dataclasses()[0] logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, " f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}" ) # Essentially, what we want to verify in the distributed case is that we get all samples back, # in the right order. (this is crucial for prediction for instance) for dataset_length in [101, 40, 7]: dataset = DummyDataset(dataset_length) def compute_metrics(p: EvalPrediction) -> Dict: sequential = list(range(len(dataset))) success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential if not success and training_args.local_rank == 0: logger.warning( "Predictions and/or labels do not match expected results:\n - predictions: " f"{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}" ) return {"success": success} trainer = Trainer( model=DummyModel(), args=training_args, data_collator=DummyDataCollator(), eval_dataset=dataset, compute_metrics=compute_metrics, ) metrics = trainer.evaluate() logger.info(metrics) if metrics["eval_success"] is not True: logger.error(metrics) exit(1) p = trainer.predict(dataset) logger.info(p.metrics) if p.metrics["test_success"] is not True: logger.error(p.metrics) exit(1) trainer.args.eval_accumulation_steps = 2 metrics = trainer.evaluate() logger.info(metrics) if metrics["eval_success"] is not True: logger.error(metrics) exit(1) p = trainer.predict(dataset) logger.info(p.metrics) if p.metrics["test_success"] is not True: logger.error(p.metrics) exit(1) trainer.args.eval_accumulation_steps = None # Check that saving does indeed work with temp dir rotation # If this fails, will see a FileNotFoundError model = RegressionModel() training_args.max_steps = 1 opt = torch.optim.Adam(model.parameters(), lr=1e-3) sched = torch.optim.lr_scheduler.LambdaLR(opt, lambda x: 1) trainer = Trainer( model, training_args, optimizers=(opt, sched), data_collator=DummyDataCollator(), eval_dataset=dataset ) trainer._save_checkpoint(model=None, trial=None) # Check that the temp folder does not exist assert not (Path(training_args.output_dir) / "tmp-checkpoint-0").exists() assert (Path(training_args.output_dir) / "checkpoint-0").exists() # Check that `dispatch_batches=False` will work on a finite iterable dataset train_dataset = FiniteIterableDataset(label_names=["labels", "extra"], length=1) model = RegressionModel() training_args.per_device_train_batch_size = 1 training_args.max_steps = 1 training_args.dispatch_batches = False trainer = Trainer(model, training_args, train_dataset=train_dataset) trainer.train()

transformers/tests/trainer/test_trainer_distributed.py/0

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os import sys import tempfile import unittest from argparse import Namespace from dataclasses import dataclass, field from enum import Enum from pathlib import Path from typing import List, Literal, Optional import yaml from transformers import HfArgumentParser, TrainingArguments from transformers.hf_argparser import make_choice_type_function, string_to_bool # Since Python 3.10, we can use the builtin `|` operator for Union types # See PEP 604: https://peps.python.org/pep-0604 is_python_no_less_than_3_10 = sys.version_info >= (3, 10) def list_field(default=None, metadata=None): return field(default_factory=lambda: default, metadata=metadata) @dataclass class BasicExample: foo: int bar: float baz: str flag: bool @dataclass class WithDefaultExample: foo: int = 42 baz: str = field(default="toto", metadata={"help": "help message"}) @dataclass class WithDefaultBoolExample: foo: bool = False baz: bool = True opt: Optional[bool] = None class BasicEnum(Enum): titi = "titi" toto = "toto" class MixedTypeEnum(Enum): titi = "titi" toto = "toto" fourtytwo = 42 @dataclass class EnumExample: foo: BasicEnum = "toto" def __post_init__(self): self.foo = BasicEnum(self.foo) @dataclass class MixedTypeEnumExample: foo: MixedTypeEnum = "toto" def __post_init__(self): self.foo = MixedTypeEnum(self.foo) @dataclass class OptionalExample: foo: Optional[int] = None bar: Optional[float] = field(default=None, metadata={"help": "help message"}) baz: Optional[str] = None ces: Optional[List[str]] = list_field(default=[]) des: Optional[List[int]] = list_field(default=[]) @dataclass class ListExample: foo_int: List[int] = list_field(default=[]) bar_int: List[int] = list_field(default=[1, 2, 3]) foo_str: List[str] = list_field(default=["Hallo", "Bonjour", "Hello"]) foo_float: List[float] = list_field(default=[0.1, 0.2, 0.3]) @dataclass class RequiredExample: required_list: List[int] = field() required_str: str = field() required_enum: BasicEnum = field() def __post_init__(self): self.required_enum = BasicEnum(self.required_enum) @dataclass class StringLiteralAnnotationExample: foo: int required_enum: "BasicEnum" = field() opt: "Optional[bool]" = None baz: "str" = field(default="toto", metadata={"help": "help message"}) foo_str: "List[str]" = list_field(default=["Hallo", "Bonjour", "Hello"]) if is_python_no_less_than_3_10: @dataclass class WithDefaultBoolExamplePep604: foo: bool = False baz: bool = True opt: bool | None = None @dataclass class OptionalExamplePep604: foo: int | None = None bar: float | None = field(default=None, metadata={"help": "help message"}) baz: str | None = None ces: list[str] | None = list_field(default=[]) des: list[int] | None = list_field(default=[]) class HfArgumentParserTest(unittest.TestCase): def argparsersEqual(self, a: argparse.ArgumentParser, b: argparse.ArgumentParser): """ Small helper to check pseudo-equality of parsed arguments on `ArgumentParser` instances. """ self.assertEqual(len(a._actions), len(b._actions)) for x, y in zip(a._actions, b._actions): xx = {k: v for k, v in vars(x).items() if k != "container"} yy = {k: v for k, v in vars(y).items() if k != "container"} # Choices with mixed type have custom function as "type" # So we need to compare results directly for equality if xx.get("choices", None) and yy.get("choices", None): for expected_choice in yy["choices"] + xx["choices"]: self.assertEqual(xx["type"](expected_choice), yy["type"](expected_choice)) del xx["type"], yy["type"] self.assertEqual(xx, yy) def test_basic(self): parser = HfArgumentParser(BasicExample) expected = argparse.ArgumentParser() expected.add_argument("--foo", type=int, required=True) expected.add_argument("--bar", type=float, required=True) expected.add_argument("--baz", type=str, required=True) expected.add_argument("--flag", type=string_to_bool, default=False, const=True, nargs="?") self.argparsersEqual(parser, expected) args = ["--foo", "1", "--baz", "quux", "--bar", "0.5"] (example,) = parser.parse_args_into_dataclasses(args, look_for_args_file=False) self.assertFalse(example.flag) def test_with_default(self): parser = HfArgumentParser(WithDefaultExample) expected = argparse.ArgumentParser() expected.add_argument("--foo", default=42, type=int) expected.add_argument("--baz", default="toto", type=str, help="help message") self.argparsersEqual(parser, expected) def test_with_default_bool(self): expected = argparse.ArgumentParser() expected.add_argument("--foo", type=string_to_bool, default=False, const=True, nargs="?") expected.add_argument("--baz", type=string_to_bool, default=True, const=True, nargs="?") # A boolean no_* argument always has to come after its "default: True" regular counter-part # and its default must be set to False expected.add_argument("--no_baz", action="store_false", default=False, dest="baz") expected.add_argument("--opt", type=string_to_bool, default=None) dataclass_types = [WithDefaultBoolExample] if is_python_no_less_than_3_10: dataclass_types.append(WithDefaultBoolExamplePep604) for dataclass_type in dataclass_types: parser = HfArgumentParser(dataclass_type) self.argparsersEqual(parser, expected) args = parser.parse_args([]) self.assertEqual(args, Namespace(foo=False, baz=True, opt=None)) args = parser.parse_args(["--foo", "--no_baz"]) self.assertEqual(args, Namespace(foo=True, baz=False, opt=None)) args = parser.parse_args(["--foo", "--baz"]) self.assertEqual(args, Namespace(foo=True, baz=True, opt=None)) args = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"]) self.assertEqual(args, Namespace(foo=True, baz=True, opt=True)) args = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"]) self.assertEqual(args, Namespace(foo=False, baz=False, opt=False)) def test_with_enum(self): parser = HfArgumentParser(MixedTypeEnumExample) expected = argparse.ArgumentParser() expected.add_argument( "--foo", default="toto", choices=["titi", "toto", 42], type=make_choice_type_function(["titi", "toto", 42]), ) self.argparsersEqual(parser, expected) args = parser.parse_args([]) self.assertEqual(args.foo, "toto") enum_ex = parser.parse_args_into_dataclasses([])[0] self.assertEqual(enum_ex.foo, MixedTypeEnum.toto) args = parser.parse_args(["--foo", "titi"]) self.assertEqual(args.foo, "titi") enum_ex = parser.parse_args_into_dataclasses(["--foo", "titi"])[0] self.assertEqual(enum_ex.foo, MixedTypeEnum.titi) args = parser.parse_args(["--foo", "42"]) self.assertEqual(args.foo, 42) enum_ex = parser.parse_args_into_dataclasses(["--foo", "42"])[0] self.assertEqual(enum_ex.foo, MixedTypeEnum.fourtytwo) def test_with_literal(self): @dataclass class LiteralExample: foo: Literal["titi", "toto", 42] = "toto" parser = HfArgumentParser(LiteralExample) expected = argparse.ArgumentParser() expected.add_argument( "--foo", default="toto", choices=("titi", "toto", 42), type=make_choice_type_function(["titi", "toto", 42]), ) self.argparsersEqual(parser, expected) args = parser.parse_args([]) self.assertEqual(args.foo, "toto") args = parser.parse_args(["--foo", "titi"]) self.assertEqual(args.foo, "titi") args = parser.parse_args(["--foo", "42"]) self.assertEqual(args.foo, 42) def test_with_list(self): parser = HfArgumentParser(ListExample) expected = argparse.ArgumentParser() expected.add_argument("--foo_int", nargs="+", default=[], type=int) expected.add_argument("--bar_int", nargs="+", default=[1, 2, 3], type=int) expected.add_argument("--foo_str", nargs="+", default=["Hallo", "Bonjour", "Hello"], type=str) expected.add_argument("--foo_float", nargs="+", default=[0.1, 0.2, 0.3], type=float) self.argparsersEqual(parser, expected) args = parser.parse_args([]) self.assertEqual( args, Namespace(foo_int=[], bar_int=[1, 2, 3], foo_str=["Hallo", "Bonjour", "Hello"], foo_float=[0.1, 0.2, 0.3]), ) args = parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split()) self.assertEqual(args, Namespace(foo_int=[1], bar_int=[2, 3], foo_str=["a", "b", "c"], foo_float=[0.1, 0.7])) def test_with_optional(self): expected = argparse.ArgumentParser() expected.add_argument("--foo", default=None, type=int) expected.add_argument("--bar", default=None, type=float, help="help message") expected.add_argument("--baz", default=None, type=str) expected.add_argument("--ces", nargs="+", default=[], type=str) expected.add_argument("--des", nargs="+", default=[], type=int) dataclass_types = [OptionalExample] if is_python_no_less_than_3_10: dataclass_types.append(OptionalExamplePep604) for dataclass_type in dataclass_types: parser = HfArgumentParser(dataclass_type) self.argparsersEqual(parser, expected) args = parser.parse_args([]) self.assertEqual(args, Namespace(foo=None, bar=None, baz=None, ces=[], des=[])) args = parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split()) self.assertEqual(args, Namespace(foo=12, bar=3.14, baz="42", ces=["a", "b", "c"], des=[1, 2, 3])) def test_with_required(self): parser = HfArgumentParser(RequiredExample) expected = argparse.ArgumentParser() expected.add_argument("--required_list", nargs="+", type=int, required=True) expected.add_argument("--required_str", type=str, required=True) expected.add_argument( "--required_enum", type=make_choice_type_function(["titi", "toto"]), choices=["titi", "toto"], required=True, ) self.argparsersEqual(parser, expected) def test_with_string_literal_annotation(self): parser = HfArgumentParser(StringLiteralAnnotationExample) expected = argparse.ArgumentParser() expected.add_argument("--foo", type=int, required=True) expected.add_argument( "--required_enum", type=make_choice_type_function(["titi", "toto"]), choices=["titi", "toto"], required=True, ) expected.add_argument("--opt", type=string_to_bool, default=None) expected.add_argument("--baz", default="toto", type=str, help="help message") expected.add_argument("--foo_str", nargs="+", default=["Hallo", "Bonjour", "Hello"], type=str) self.argparsersEqual(parser, expected) def test_parse_dict(self): parser = HfArgumentParser(BasicExample) args_dict = { "foo": 12, "bar": 3.14, "baz": "42", "flag": True, } parsed_args = parser.parse_dict(args_dict)[0] args = BasicExample(**args_dict) self.assertEqual(parsed_args, args) def test_parse_dict_extra_key(self): parser = HfArgumentParser(BasicExample) args_dict = { "foo": 12, "bar": 3.14, "baz": "42", "flag": True, "extra": 42, } self.assertRaises(ValueError, parser.parse_dict, args_dict, allow_extra_keys=False) def test_parse_json(self): parser = HfArgumentParser(BasicExample) args_dict_for_json = { "foo": 12, "bar": 3.14, "baz": "42", "flag": True, } with tempfile.TemporaryDirectory() as tmp_dir: temp_local_path = os.path.join(tmp_dir, "temp_json") os.mkdir(temp_local_path) with open(temp_local_path + ".json", "w+") as f: json.dump(args_dict_for_json, f) parsed_args = parser.parse_yaml_file(Path(temp_local_path + ".json"))[0] args = BasicExample(**args_dict_for_json) self.assertEqual(parsed_args, args) def test_parse_yaml(self): parser = HfArgumentParser(BasicExample) args_dict_for_yaml = { "foo": 12, "bar": 3.14, "baz": "42", "flag": True, } with tempfile.TemporaryDirectory() as tmp_dir: temp_local_path = os.path.join(tmp_dir, "temp_yaml") os.mkdir(temp_local_path) with open(temp_local_path + ".yaml", "w+") as f: yaml.dump(args_dict_for_yaml, f) parsed_args = parser.parse_yaml_file(Path(temp_local_path + ".yaml"))[0] args = BasicExample(**args_dict_for_yaml) self.assertEqual(parsed_args, args) def test_integration_training_args(self): parser = HfArgumentParser(TrainingArguments) self.assertIsNotNone(parser)

transformers/tests/utils/test_hf_argparser.py/0

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utility that checks whether the copies defined in the library match the original or not. This includes: - All code commented with `# Copied from` comments, - The list of models in the main README.md matches the ones in the localized READMEs, - Files that are registered as full copies of one another in the `FULL_COPIES` constant of this script. This also checks the list of models in the README is complete (has all models) and add a line to complete if there is a model missing. Use from the root of the repo with: ```bash python utils/check_copies.py ``` for a check that will error in case of inconsistencies (used by `make repo-consistency`) or ```bash python utils/check_copies.py --fix_and_overwrite ``` for a check that will fix all inconsistencies automatically (used by `make fix-copies`). """ import argparse import glob import os import re import subprocess from collections import OrderedDict from typing import List, Optional, Tuple, Union from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_copies.py TRANSFORMERS_PATH = "src/transformers" MODEL_TEST_PATH = "tests/models" PATH_TO_DOCS = "docs/source/en" REPO_PATH = "." # Mapping for files that are full copies of others (keys are copies, values the file to keep them up to data with) FULL_COPIES = { "examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py", "examples/flax/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py", } LOCALIZED_READMES = { # If the introduction or the conclusion of the list change, the prompts may need to be updated. "README.md": { "start_prompt": "🤗 Transformers currently provides the following architectures", "end_prompt": "1. Want to contribute a new model?", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_zh-hans.md": { "start_prompt": "🤗 Transformers 目前支持如下的架构", "end_prompt": "1. 想要贡献新的模型？", "format_model_list": ( "**[{title}]({model_link})** (来自 {paper_affiliations}) 伴随论文 {paper_title_link} 由 {paper_authors}" " 发布。{supplements}" ), }, "README_zh-hant.md": { "start_prompt": "🤗 Transformers 目前支援以下的架構", "end_prompt": "1. 想要貢獻新的模型？", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_ko.md": { "start_prompt": "🤗 Transformers는 다음 모델들을 제공합니다", "end_prompt": "1. 새로운 모델을 올리고 싶나요?", "format_model_list": ( "**[{title}]({model_link})** ({paper_affiliations} 에서 제공)은 {paper_authors}.{supplements}의" " {paper_title_link}논문과 함께 발표했습니다." ), }, "README_es.md": { "start_prompt": "🤗 Transformers actualmente proporciona las siguientes arquitecturas", "end_prompt": "1. ¿Quieres aportar un nuevo modelo?", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_ja.md": { "start_prompt": "🤗Transformersは現在、以下のアーキテクチャを提供しています", "end_prompt": "1. 新しいモデルを投稿したいですか？", "format_model_list": ( "**[{title}]({model_link})** ({paper_affiliations} から) {paper_authors}.{supplements} から公開された研究論文" " {paper_title_link}" ), }, "README_hd.md": { "start_prompt": "🤗 ट्रांसफॉर्मर वर्तमान में निम्नलिखित आर्किटेक्चर का समर्थन करते हैं", "end_prompt": "1. एक नए मॉडल में योगदान देना चाहते हैं?", "format_model_list": ( "**[{title}]({model_link})** ({paper_affiliations} से) {paper_authors}.{supplements} द्वारा" "अनुसंधान पत्र {paper_title_link} के साथ जारी किया गया" ), }, "README_fr.md": { "start_prompt": "🤗 Transformers fournit actuellement les architectures suivantes", "end_prompt": "1. Vous souhaitez contribuer avec un nouveau modèle ?", "format_model_list": ( "**[{title}]({model_link})** (de {paper_affiliations}) publié dans l'article {paper_title_link} par" "{paper_authors}.{supplements}" ), }, } # This is to make sure the transformers module imported is the one in the repo. transformers_module = direct_transformers_import(TRANSFORMERS_PATH) def _is_definition_header_ending_line(line: str) -> bool: # Helper function. Returns `True` if `line` is the end parenthesis of a class/function definition return re.search(r"^\s*\)(\s*->.*:|:)\s*$", line) is not None def _should_continue(line: str, indent: str) -> bool: # Helper function. Returns `True` if `line` is empty, starts with the `indent` or is the end parenthesis of a # class/function definition return line.startswith(indent) or len(line.strip()) == 0 or _is_definition_header_ending_line(line) def _sanity_check_splits(splits_1, splits_2, is_class): """Check the two (inner) block structures of the corresponding code block given by `split_code_into_blocks` match. For the case of `class`, they must be of one of the following 3 cases: - a single block without name: class foo: a = 1 - a consecutive sequence of (1 or more) blocks with name class foo: def f(x): return x - a block without name, followed by a consecutive sequence of (1 or more) blocks with name class foo: a = 1 def f(x): return x def g(x): return None The 2 code snippets that give `splits_1` and `splits_2` have to be in the same case to pass this check, but the number of blocks with name in the consecutive sequence is not taken into account. For the case of `function or method`, we don't require it to be in one of the above 3 cases. However, the structure of`splits_1` and `splits_2` have to match exactly. In particular, the number of blocks with name in a consecutive sequence is taken into account. """ block_names_1 = [] block_names_2 = [] for block in splits_1[1:]: if block[0].startswith("_block_without_name_"): block_names_1.append("block_without_name") elif not block[0].startswith("_empty_block_") and ( not is_class or len(block_names_1) == 0 or block_names_1[-1].startswith("block_without_name") ): block_names_1.append("block_with_name") for block in splits_2[1:]: if block[0].startswith("_block_without_name_"): block_names_2.append("block_without_name") elif not block[0].startswith("_empty_block_") and ( not is_class or len(block_names_2) == 0 or block_names_2[-1].startswith("block_without_name") ): block_names_2.append("block_with_name") if is_class: if block_names_1 not in [ ["block_without_name"], ["block_with_name"], ["block_without_name", "block_with_name"], ]: raise ValueError( "For a class, it must have a specific structure. See the docstring of `_sanity_check_splits` in the file `utils/check_copies.py`" ) if block_names_1 != block_names_2: raise ValueError("The structures in the 2 code blocks differ.") def find_block_end(lines: List[str], start_index: int, indent: int) -> int: """ Find the end of the class/func block starting at `start_index` in a source code (defined by `lines`). Args: lines (`List[str]`): The source code, represented by a list of lines. start_index (`int`): The starting index of the target class/func block. indent (`int`): The indent of the class/func body. Returns: `int`: The index of the block's ending line plus by 1 (i.e. exclusive). """ indent = " " * indent # enter the block body line_index = start_index + 1 while line_index < len(lines) and _should_continue(lines[line_index], indent): line_index += 1 # Clean up empty lines at the end (if any). while len(lines[line_index - 1]) <= 1: line_index -= 1 return line_index def split_code_into_blocks( lines: List[str], start_index: int, end_index: int, indent: int, backtrace: bool = False ) -> List[Tuple[str, int, int]]: """ Split the class/func block starting at `start_index` in a source code (defined by `lines`) into *inner blocks*. The block's header is included as the first element. The contiguous regions (without empty lines) that are not inside any inner block are included as blocks. The contiguous regions of empty lines that are not inside any inner block are also included as (dummy) blocks. Args: lines (`List[str]`): The source code, represented by a list of lines. start_index (`int`): The starting index of the target class/func block. end_index (`int`): The ending index of the target class/func block. indent (`int`): The indent of the class/func body. backtrace (`bool`, *optional*, defaults to `False`): Whether or not to include the lines before the inner class/func block's header (e.g. comments, decorators, etc.) until an empty line is encountered. Returns: `List[Tuple[str, int, int]]`: A list of elements with the form `(block_name, start_index, end_index)`. """ splits = [] # `indent - 4` is the indent level of the target class/func header target_block_name = re.search(rf"^{' ' * (indent - 4)}((class|def)\s+\S+)(\(|\:)", lines[start_index]).groups()[0] # from now on, the `block` means inner blocks unless explicitly specified indent_str = " " * indent block_without_name_idx = 0 empty_block_idx = 0 # Find the lines for the definition header index = start_index if "(" in lines[start_index] and "):" not in lines[start_index] in lines[start_index]: while index < end_index: if _is_definition_header_ending_line(lines[index]): break index += 1 # the first line outside the definition header index += 1 splits.append((target_block_name, start_index, index)) block_start_index, prev_block_end_index = index, index while index < end_index: # if found, it will be an inner block block_found = re.search(rf"^{indent_str}((class|def)\s+\S+)(\(|\:)", lines[index]) if block_found: name = block_found.groups()[0] block_end_index = find_block_end(lines, index, indent + 4) # backtrace to include the lines before the found block's definition header (e.g. comments, decorators, # etc.) until an empty line is encountered. block_start_index = index if index > prev_block_end_index and backtrace: idx = index - 1 for idx in range(index - 1, prev_block_end_index - 2, -1): if not (len(lines[idx].strip()) > 0 and lines[idx].startswith(indent_str)): break idx += 1 if idx < index: block_start_index = idx # between the current found block and the previous found block if block_start_index > prev_block_end_index: # give it a dummy name if len("".join(lines[prev_block_end_index:block_start_index]).strip()) == 0: prev_block_name = f"_empty_block_{empty_block_idx}" empty_block_idx += 1 else: prev_block_name = f"_block_without_name_{block_without_name_idx}" block_without_name_idx += 1 # Add it as a block splits.append((prev_block_name, prev_block_end_index, block_start_index)) # Add the current found block splits.append((name, block_start_index, block_end_index)) prev_block_end_index = block_end_index index = block_end_index - 1 index += 1 if index > prev_block_end_index: if len("".join(lines[prev_block_end_index:index]).strip()) == 0: prev_block_name = f"_empty_block_{empty_block_idx}" else: prev_block_name = f"_block_without_name_{block_without_name_idx}" splits.append((prev_block_name, prev_block_end_index, index)) return splits def find_code_in_transformers( object_name: str, base_path: str = None, return_indices: bool = False ) -> Union[str, Tuple[List[str], int, int]]: """ Find and return the source code of an object. Args: object_name (`str`): The name of the object we want the source code of. base_path (`str`, *optional*): The path to the base folder where files are checked. If not set, it will be set to `TRANSFORMERS_PATH`. return_indices(`bool`, *optional*, defaults to `False`): If `False`, will only return the code (as a string), otherwise it will also return the whole lines of the file where the object specified by `object_name` is defined, together the start/end indices of the block in the file that defines the object. Returns: `Union[str, Tuple[List[str], int, int]]`: If `return_indices=False`, only the source code of the object will be returned. Otherwise, it also returns the whole lines of the file where the object specified by `object_name` is defined, together the start/end indices of the block in the file that defines the object. """ parts = object_name.split(".") i = 0 # We can't set this as the default value in the argument, otherwise `CopyCheckTester` will fail, as it uses a # patched temp directory. if base_path is None: base_path = TRANSFORMERS_PATH # Detail: the `Copied from` statement is originally designed to work with the last part of `TRANSFORMERS_PATH`, # (which is `transformers`). The same should be applied for `MODEL_TEST_PATH`. However, its last part is `models` # (to only check and search in it) which is a bit confusing. So we keep the copied statement staring with # `tests.models.` and change it to `tests` here. if base_path == MODEL_TEST_PATH: base_path = "tests" # First let's find the module where our object lives. module = parts[i] while i < len(parts) and not os.path.isfile(os.path.join(base_path, f"{module}.py")): i += 1 if i < len(parts): module = os.path.join(module, parts[i]) if i >= len(parts): raise ValueError( f"`object_name` should begin with the name of a module of transformers but got {object_name}." ) with open(os.path.join(base_path, f"{module}.py"), "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Now let's find the class / func in the code! indent = "" line_index = 0 for name in parts[i + 1 :]: while ( line_index < len(lines) and re.search(rf"^{indent}(class|def)\s+{name}(\(|\:)", lines[line_index]) is None ): line_index += 1 # find the target specified in the current level in `parts` -> increase `indent` so we can search the next indent += " " # the index of the first line in the (currently found) block *body* line_index += 1 if line_index >= len(lines): raise ValueError(f" {object_name} does not match any function or class in {module}.") # `indent` is already one level deeper than the (found) class/func block's definition header # We found the beginning of the class / func, now let's find the end (when the indent diminishes). # `start_index` is the index of the class/func block's definition header start_index = line_index - 1 end_index = find_block_end(lines, start_index, len(indent)) code = "".join(lines[start_index:end_index]) return (code, (lines, start_index, end_index)) if return_indices else code def replace_code(code: str, replace_pattern: str) -> str: """Replace `code` by a pattern of the form `with X1->X2,Y1->Y2,Z1->Z2`. Args: code (`str`): The code to be modified. replace_pattern (`str`): The pattern used to modify `code`. Returns: `str`: The modified code. """ if len(replace_pattern) > 0: patterns = replace_pattern.replace("with", "").split(",") patterns = [_re_replace_pattern.search(p) for p in patterns] for pattern in patterns: if pattern is None: continue obj1, obj2, option = pattern.groups() code = re.sub(obj1, obj2, code) if option.strip() == "all-casing": code = re.sub(obj1.lower(), obj2.lower(), code) code = re.sub(obj1.upper(), obj2.upper(), code) return code def find_code_and_splits(object_name: str, base_path: str, buffer: dict = None): """Find the code of an object (specified by `object_name`) and split it into blocks. Args: object_name (`str`): The name of the object, e.g. `transformers.models.bert.modeling_bert.BertAttention` or `tests.models.llama.test_modeling_llama.LlamaModelTest.test_config`. base_path (`str`): The path to the base directory within which the search will be performed. It could be either `TRANSFORMERS_PATH` or `MODEL_TEST_PATH`. buffer (`dict`, *optional*): The buffer used to store the previous results in order to speed up the process. Returns: lines (`List[str]`): The lines of the whole file where the object is defined. code (`str`): The object's code. code_splits (`List[Tuple[str, int, int]]`): `code` splitted into blocks. See `split_code_into_blocks`. """ if buffer is None: buffer = {} if (object_name, base_path) in buffer: lines, code, code_splits = buffer[(object_name, base_path)] else: code, (lines, target_start_index, target_end_index) = find_code_in_transformers( object_name, base_path=base_path, return_indices=True ) indent = get_indent(code) # Split the code into blocks # `indent` is the indent of the class/func definition header, but `code_splits` expects the indent level of the # block body. code_splits = split_code_into_blocks( lines, target_start_index, target_end_index, len(indent) + 4, backtrace=True ) buffer[(object_name, base_path)] = lines, code, code_splits return lines, code, code_splits _re_copy_warning = re.compile(r"^(\s*)#\s*Copied from\s+transformers\.(\S+\.\S+)\s*($|\S.*$)") _re_copy_warning_for_test_file = re.compile(r"^(\s*)#\s*Copied from\s+tests\.(\S+\.\S+)\s*($|\S.*$)") _re_replace_pattern = re.compile(r"^\s*(\S+)->(\S+)(\s+.*|$)") _re_fill_pattern = re.compile(r"<FILL\s+[^>]*>") def get_indent(code: str) -> str: """ Find the indent in the first non empty line in a code sample. Args: code (`str`): The code to inspect. Returns: `str`: The indent looked at (as string). """ lines = code.split("\n") idx = 0 while idx < len(lines) and len(lines[idx]) == 0: idx += 1 if idx < len(lines): return re.search(r"^(\s*)\S", lines[idx]).groups()[0] return "" def run_ruff(code): command = ["ruff", "format", "-", "--config", "pyproject.toml", "--silent"] process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) stdout, _ = process.communicate(input=code.encode()) return stdout.decode() def stylify(code: str) -> str: """ Applies the ruff part of our `make style` command to some code. This formats the code using `ruff format`. As `ruff` does not provide a python api this cannot be done on the fly. Args: code (`str`): The code to format. Returns: `str`: The formatted code. """ has_indent = len(get_indent(code)) > 0 if has_indent: code = f"class Bla:\n{code}" formatted_code = run_ruff(code) return formatted_code[len("class Bla:\n") :] if has_indent else formatted_code def check_codes_match(observed_code: str, theoretical_code: str) -> Optional[int]: """ Checks if two version of a code match with the exception of the class/function name. Args: observed_code (`str`): The code found. theoretical_code (`str`): The code to match. Returns: `Optional[int]`: The index of the first line where there is a difference (if any) and `None` if the codes match. """ observed_code_header = observed_code.split("\n")[0] theoretical_code_header = theoretical_code.split("\n")[0] # Catch the function/class name: it is expected that those do not match. _re_class_match = re.compile(r"class\s+([^\(:]+)(?:\(|:)") _re_func_match = re.compile(r"def\s+([^\(]+)\(") for re_pattern in [_re_class_match, _re_func_match]: if re_pattern.match(observed_code_header) is not None: observed_obj_name = re_pattern.search(observed_code_header).groups()[0] theoretical_name = re_pattern.search(theoretical_code_header).groups()[0] theoretical_code_header = theoretical_code_header.replace(theoretical_name, observed_obj_name) # Find the first diff. Line 0 is special since we need to compare with the function/class names ignored. diff_index = 0 if theoretical_code_header != observed_code_header: return 0 diff_index = 1 for observed_line, theoretical_line in zip(observed_code.split("\n")[1:], theoretical_code.split("\n")[1:]): if observed_line != theoretical_line: return diff_index diff_index += 1 def is_copy_consistent(filename: str, overwrite: bool = False, buffer: dict = None) -> Optional[List[Tuple[str, int]]]: """ Check if the code commented as a copy in a file matches the original. Args: filename (`str`): The name of the file to check. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the copies when they don't match. buffer (`dict`, *optional*): The buffer used to store the previous results in order to speed up the process. Returns: `Optional[List[Tuple[str, int]]]`: If `overwrite=False`, returns the list of differences as tuples `(str, int)` with the name of the object having a diff and the line number where theere is the first diff. """ base_path = TRANSFORMERS_PATH if not filename.startswith("tests") else MODEL_TEST_PATH with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() diffs = [] line_index = 0 # Not a for loop cause `lines` is going to change (if `overwrite=True`). while line_index < len(lines): search_re = _re_copy_warning if filename.startswith("tests"): search_re = _re_copy_warning_for_test_file search = search_re.search(lines[line_index]) if search is None: line_index += 1 continue # There is some copied code here, let's retrieve the original. indent, object_name, replace_pattern = search.groups() # Find the file lines, the object's code, and its blocks target_lines, theoretical_code, theoretical_code_splits = find_code_and_splits( object_name, base_path, buffer=buffer ) # code replaced by the patterns theoretical_code_blocks = OrderedDict() for name, start, end in theoretical_code_splits: name = replace_code(name, replace_pattern) code = "".join(target_lines[start:end]) code = replace_code(code, replace_pattern) theoretical_code_blocks[name] = code theoretical_indent = get_indent(theoretical_code) # `start_index` is the index of the first line (the definition header) after `# Copied from`. # (`indent != theoretical_indent` doesn't seem to occur so far, not sure what this case is for.) start_index = line_index + 1 if indent == theoretical_indent else line_index # enter the block body line_index = start_index + 1 subcode = "\n".join(theoretical_code.split("\n")[1:]) indent = get_indent(subcode) # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment. # We can't call `find_block_end` directly as there is sth. special `# End copy"` here. should_continue = True while line_index < len(lines) and should_continue: line_index += 1 if line_index >= len(lines): break line = lines[line_index] # There is a special pattern `# End copy` to stop early. It's not documented cause it shouldn't really be # used. should_continue = _should_continue(line, indent) and re.search(f"^{indent}# End copy", line) is None # `line_index` is outside the block # Clean up empty lines at the end (if any). while len(lines[line_index - 1]) <= 1: line_index -= 1 # Split the observed code into blocks observed_code_splits = split_code_into_blocks(lines, start_index, line_index, len(indent), backtrace=True) is_class = lines[start_index].startswith(f"{' ' * (len(indent) - 4)}class ") # sanity check _sanity_check_splits(theoretical_code_splits, observed_code_splits, is_class=is_class) # observed code in a structured way (a dict mapping block names to blocks' code) observed_code_blocks = OrderedDict() for name, start, end in observed_code_splits: code = "".join(lines[start:end]) observed_code_blocks[name] = code # Below, we change some names in `theoretical_code_blocks` and `observed_code_blocks`. These mappings map the # original names to the modified names: this is used to restore the original order of the code blocks. name_mappings_1 = {k: k for k in theoretical_code_blocks.keys()} name_mappings_2 = {k: k for k in observed_code_blocks.keys()} # Update code blocks' name and content: # If `"# Ignore copy"` is found in a block of the observed code: # 1. if it's a block only in the observed code --> add it to the theoretical code. # 2. if it's also in the theoretical code () --> put its content (body) to the corresponding block under the # same name in the theoretical code. # In both cases, we change the name to have a prefix `_ignored_` so we know if we can discard them during the # comparison. ignored_existing_block_index = 0 ignored_new_block_index = 0 for name in list(observed_code_blocks.keys()): code = observed_code_blocks[name] if "# Ignore copy" in code: if name in theoretical_code_blocks: # in the target --> just copy the content del theoretical_code_blocks[name] theoretical_code_blocks[f"_ignored_existing_block_{ignored_existing_block_index}"] = code name_mappings_1[name] = f"_ignored_existing_block_{ignored_existing_block_index}" del observed_code_blocks[name] observed_code_blocks[f"_ignored_existing_block_{ignored_existing_block_index}"] = code name_mappings_2[name] = f"_ignored_existing_block_{ignored_existing_block_index}" ignored_existing_block_index += 1 else: # not in the target --> add it theoretical_code_blocks[f"_ignored_new_block_{ignored_new_block_index}"] = code name_mappings_1[ f"_ignored_new_block_{ignored_new_block_index}" ] = f"_ignored_new_block_{ignored_new_block_index}" del observed_code_blocks[name] observed_code_blocks[f"_ignored_new_block_{ignored_new_block_index}"] = code name_mappings_2[name] = f"_ignored_new_block_{ignored_new_block_index}" ignored_new_block_index += 1 # Respect the original block order: # 1. in `theoretical_code_blocks`: the new blocks will follow the existing ones # 2. in `observed_code_blocks`: the original order are kept with names modified potentially. This is necessary # to compute the correct `diff_index` if `overwrite=True` and there is a diff. theoretical_code_blocks = { name_mappings_1[orig_name]: theoretical_code_blocks[name_mappings_1[orig_name]] for orig_name in name_mappings_1 } observed_code_blocks = { name_mappings_2[orig_name]: observed_code_blocks[name_mappings_2[orig_name]] for orig_name in name_mappings_2 } # Ignore the blocks specified to be ignored. This is the version used to check if there is a mismatch theoretical_code_blocks_clean = { k: v for k, v in theoretical_code_blocks.items() if not (k.startswith(("_ignored_existing_block_", "_ignored_new_block_"))) } theoretical_code = "".join(list(theoretical_code_blocks_clean.values())) # stylify `theoretical_code` before compare (this is needed only when `replace_pattern` is not empty) if replace_pattern: theoretical_code = stylify(theoretical_code) # Remove `\n\n` in `theoretical_code` before compare (so no empty line) while "\n\n" in theoretical_code: theoretical_code = theoretical_code.replace("\n\n", "\n") # Compute `observed_code` where we don't include any empty line + keep track the line index between the # original/processed `observed_code` so we can have the correct `diff_index`. idx_to_orig_idx_mapping_for_observed_code_lines = {} idx = -1 orig_idx = -1 observed_code = "" for name, code in observed_code_blocks.items(): if code.endswith("\n"): code = code[:-1] for code_line in code.split("\n"): orig_idx += 1 if code_line.strip() and not name.startswith(("_ignored_existing_block_", "_ignored_new_block_")): idx += 1 observed_code += code_line + "\n" idx_to_orig_idx_mapping_for_observed_code_lines[idx] = orig_idx # Test for a diff and act accordingly. diff_index = check_codes_match(observed_code, theoretical_code) if diff_index is not None: # switch to the index in the original `observed_code` (i.e. before removing empty lines) diff_index = idx_to_orig_idx_mapping_for_observed_code_lines[diff_index] diffs.append([object_name, diff_index + start_index + 1]) if overwrite: # `theoretical_code_to_write` is a single string but may have several lines. theoretical_code_to_write = stylify("".join(list(theoretical_code_blocks.values()))) lines = lines[:start_index] + [theoretical_code_to_write] + lines[line_index:] # Here we treat it as a single entry in `lines`. line_index = start_index + 1 if overwrite and len(diffs) > 0: # Warn the user a file has been modified. print(f"Detected changes, rewriting {filename}.") with open(filename, "w", encoding="utf-8", newline="\n") as f: f.writelines(lines) return diffs def check_copies(overwrite: bool = False, file: str = None): """ Check every file is copy-consistent with the original. Also check the model list in the main README and other READMEs are consistent. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the copies when they don't match. file (`bool`, *optional*): The path to a specific file to check and/or fix. """ buffer = {} if file is None: all_files = glob.glob(os.path.join(TRANSFORMERS_PATH, "**/*.py"), recursive=True) all_test_files = glob.glob(os.path.join(MODEL_TEST_PATH, "**/*.py"), recursive=True) all_files = list(all_files) + list(all_test_files) else: all_files = [file] diffs = [] for filename in all_files: new_diffs = is_copy_consistent(filename, overwrite, buffer) diffs += [f"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs] if not overwrite and len(diffs) > 0: diff = "\n".join(diffs) raise Exception( "Found the following copy inconsistencies:\n" + diff + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." ) check_model_list_copy(overwrite=overwrite) def check_full_copies(overwrite: bool = False): """ Check the files that are full copies of others (as indicated in `FULL_COPIES`) are copy-consistent. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the copies when they don't match. """ diffs = [] for target, source in FULL_COPIES.items(): with open(source, "r", encoding="utf-8") as f: source_code = f.read() with open(target, "r", encoding="utf-8") as f: target_code = f.read() if source_code != target_code: if overwrite: with open(target, "w", encoding="utf-8") as f: print(f"Replacing the content of {target} by the one of {source}.") f.write(source_code) else: diffs.append(f"- {target}: copy does not match {source}.") if not overwrite and len(diffs) > 0: diff = "\n".join(diffs) raise Exception( "Found the following copy inconsistencies:\n" + diff + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." ) def get_model_list(filename: str, start_prompt: str, end_prompt: str) -> str: """ Extracts the model list from a README. Args: filename (`str`): The name of the README file to check. start_prompt (`str`): The string to look for that introduces the model list. end_prompt (`str`): The string to look for that ends the model list. Returns: `str`: The model list. """ with open(os.path.join(REPO_PATH, filename), "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Find the start of the list. start_index = 0 while not lines[start_index].startswith(start_prompt): start_index += 1 start_index += 1 result = [] current_line = "" end_index = start_index # Keep going until the end of the list. while not lines[end_index].startswith(end_prompt): if lines[end_index].startswith("1."): if len(current_line) > 1: result.append(current_line) current_line = lines[end_index] elif len(lines[end_index]) > 1: current_line = f"{current_line[:-1]} {lines[end_index].lstrip()}" end_index += 1 if len(current_line) > 1: result.append(current_line) return "".join(result) def convert_to_localized_md(model_list: str, localized_model_list: str, format_str: str) -> Tuple[bool, str]: """ Compare the model list from the main README to the one in a localized README. Args: model_list (`str`): The model list in the main README. localized_model_list (`str`): The model list in one of the localized README. format_str (`str`): The template for a model entry in the localized README (look at the `format_model_list` in the entries of `LOCALIZED_READMES` for examples). Returns: `Tuple[bool, str]`: A tuple where the first value indicates if the READMEs match or not, and the second value is the correct localized README. """ def _rep(match): title, model_link, paper_affiliations, paper_title_link, paper_authors, supplements = match.groups() return format_str.format( title=title, model_link=model_link, paper_affiliations=paper_affiliations, paper_title_link=paper_title_link, paper_authors=paper_authors, supplements=" " + supplements.strip() if len(supplements) != 0 else "", ) # This regex captures metadata from an English model description, including model title, model link, # affiliations of the paper, title of the paper, authors of the paper, and supplemental data (see DistilBERT for # example). _re_capture_meta = re.compile( r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\* \(from ([^)]*)\)[^\[]*([^\)]*\)).*?by (.*?[A-Za-z\*]{2,}?)\. (.*)$" ) # This regex is used to synchronize link. _re_capture_title_link = re.compile(r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\*") if len(localized_model_list) == 0: localized_model_index = {} else: try: localized_model_index = { re.search(r"\*\*\[([^\]]*)", line).groups()[0]: line for line in localized_model_list.strip().split("\n") } except AttributeError: raise AttributeError("A model name in localized READMEs cannot be recognized.") model_keys = [re.search(r"\*\*\[([^\]]*)", line).groups()[0] for line in model_list.strip().split("\n")] # We exclude keys in localized README not in the main one. readmes_match = not any(k not in model_keys for k in localized_model_index) localized_model_index = {k: v for k, v in localized_model_index.items() if k in model_keys} for model in model_list.strip().split("\n"): title, model_link = _re_capture_title_link.search(model).groups() if title not in localized_model_index: readmes_match = False # Add an anchor white space behind a model description string for regex. # If metadata cannot be captured, the English version will be directly copied. localized_model_index[title] = _re_capture_meta.sub(_rep, model + " ") elif _re_fill_pattern.search(localized_model_index[title]) is not None: update = _re_capture_meta.sub(_rep, model + " ") if update != localized_model_index[title]: readmes_match = False localized_model_index[title] = update else: # Synchronize link localized_model_index[title] = _re_capture_title_link.sub( f"**[{title}]({model_link})**", localized_model_index[title], count=1 ) sorted_index = sorted(localized_model_index.items(), key=lambda x: x[0].lower()) return readmes_match, "\n".join((x[1] for x in sorted_index)) + "\n" def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> Tuple[str, int, int, List[str]]: """ Find the text in a file between two prompts. Args: filename (`str`): The name of the file to look into. start_prompt (`str`): The string to look for that introduces the content looked for. end_prompt (`str`): The string to look for that ends the content looked for. Returns: Tuple[str, int, int, List[str]]: The content between the two prompts, the index of the start line in the original file, the index of the end line in the original file and the list of lines of that file. """ with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Find the start prompt. start_index = 0 while not lines[start_index].startswith(start_prompt): start_index += 1 start_index += 1 end_index = start_index while not lines[end_index].startswith(end_prompt): end_index += 1 end_index -= 1 while len(lines[start_index]) <= 1: start_index += 1 while len(lines[end_index]) <= 1: end_index -= 1 end_index += 1 return "".join(lines[start_index:end_index]), start_index, end_index, lines def check_model_list_copy(overwrite: bool = False): """ Check the model lists in the README is consistent with the ones in the other READMES and also with `index.nmd`. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the copies when they don't match. """ # Fix potential doc links in the README with open(os.path.join(REPO_PATH, "README.md"), "r", encoding="utf-8", newline="\n") as f: readme = f.read() new_readme = readme.replace("https://huggingface.co/transformers", "https://huggingface.co/docs/transformers") new_readme = new_readme.replace( "https://huggingface.co/docs/main/transformers", "https://huggingface.co/docs/transformers/main" ) if new_readme != readme: if overwrite: with open(os.path.join(REPO_PATH, "README.md"), "w", encoding="utf-8", newline="\n") as f: f.write(new_readme) else: raise ValueError( "The main README contains wrong links to the documentation of Transformers. Run `make fix-copies` to " "automatically fix them." ) md_list = get_model_list( filename="README.md", start_prompt=LOCALIZED_READMES["README.md"]["start_prompt"], end_prompt=LOCALIZED_READMES["README.md"]["end_prompt"], ) # Build the converted Markdown. converted_md_lists = [] for filename, value in LOCALIZED_READMES.items(): _start_prompt = value["start_prompt"] _end_prompt = value["end_prompt"] _format_model_list = value["format_model_list"] localized_md_list = get_model_list(filename, _start_prompt, _end_prompt) readmes_match, converted_md_list = convert_to_localized_md(md_list, localized_md_list, _format_model_list) converted_md_lists.append((filename, readmes_match, converted_md_list, _start_prompt, _end_prompt)) # Compare the converted Markdowns for converted_md_list in converted_md_lists: filename, readmes_match, converted_md, _start_prompt, _end_prompt = converted_md_list if filename == "README.md": continue if overwrite: _, start_index, end_index, lines = _find_text_in_file( filename=os.path.join(REPO_PATH, filename), start_prompt=_start_prompt, end_prompt=_end_prompt ) with open(os.path.join(REPO_PATH, filename), "w", encoding="utf-8", newline="\n") as f: f.writelines(lines[:start_index] + [converted_md] + lines[end_index:]) elif not readmes_match: raise ValueError( f"The model list in the README changed and the list in `{filename}` has not been updated. Run " "`make fix-copies` to fix this." ) # Map a model name with the name it has in the README for the check_readme check SPECIAL_MODEL_NAMES = { "Bert Generation": "BERT For Sequence Generation", "BigBird": "BigBird-RoBERTa", "Data2VecAudio": "Data2Vec", "Data2VecText": "Data2Vec", "Data2VecVision": "Data2Vec", "DonutSwin": "Swin Transformer", "Marian": "MarianMT", "MaskFormerSwin": "Swin Transformer", "OpenAI GPT-2": "GPT-2", "OpenAI GPT": "GPT", "Perceiver": "Perceiver IO", "SAM": "Segment Anything", "ViT": "Vision Transformer (ViT)", } # Update this list with the models that shouldn't be in the README. This only concerns modular models or those who do # not have an associated paper. MODELS_NOT_IN_README = [ "BertJapanese", "Encoder decoder", "FairSeq Machine-Translation", "HerBERT", "RetriBERT", "Speech Encoder decoder", "Speech2Text", "Speech2Text2", "TimmBackbone", "Vision Encoder decoder", "VisionTextDualEncoder", "CLIPVisionModel", "SiglipVisionModel", "ChineseCLIPVisionModel", ] # Template for new entries to add in the main README when we have missing models. README_TEMPLATE = ( "1. **[{model_name}](https://huggingface.co/docs/main/transformers/model_doc/{model_type})** (from " "<FILL INSTITUTION>) released with the paper [<FILL PAPER TITLE>](<FILL ARKIV LINK>) by <FILL AUTHORS>." ) def check_readme(overwrite: bool = False): """ Check if the main README contains all the models in the library or not. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether or not to add an entry for the missing models using `README_TEMPLATE`. """ info = LOCALIZED_READMES["README.md"] models, start_index, end_index, lines = _find_text_in_file( os.path.join(REPO_PATH, "README.md"), info["start_prompt"], info["end_prompt"], ) models_in_readme = [re.search(r"\*\*\[([^\]]*)", line).groups()[0] for line in models.strip().split("\n")] model_names_mapping = transformers_module.models.auto.configuration_auto.MODEL_NAMES_MAPPING absents = [ (key, name) for key, name in model_names_mapping.items() if SPECIAL_MODEL_NAMES.get(name, name) not in models_in_readme ] # Remove exceptions absents = [(key, name) for key, name in absents if name not in MODELS_NOT_IN_README] if len(absents) > 0 and not overwrite: print(absents) raise ValueError( "The main README doesn't contain all models, run `make fix-copies` to fill it with the missing model(s)" " then complete the generated entries.\nIf the model is not supposed to be in the main README, add it to" " the list `MODELS_NOT_IN_README` in utils/check_copies.py.\nIf it has a different name in the repo than" " in the README, map the correspondence in `SPECIAL_MODEL_NAMES` in utils/check_copies.py." ) new_models = [README_TEMPLATE.format(model_name=name, model_type=key) for key, name in absents] all_models = models.strip().split("\n") + new_models all_models = sorted(all_models, key=lambda x: re.search(r"\*\*\[([^\]]*)", x).groups()[0].lower()) all_models = "\n".join(all_models) + "\n" if all_models != models: if overwrite: print("Fixing the main README.") with open(os.path.join(REPO_PATH, "README.md"), "w", encoding="utf-8", newline="\n") as f: f.writelines(lines[:start_index] + [all_models] + lines[end_index:]) else: raise ValueError("The main README model list is not properly sorted. Run `make fix-copies` to fix this.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--file", type=str, default=None, help="A specific file to check and/or fix") parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_readme(args.fix_and_overwrite) check_copies(args.fix_and_overwrite, args.file) check_full_copies(args.fix_and_overwrite)

transformers/utils/check_copies.py/0

import argparse import json import os import time import zipfile from get_ci_error_statistics import download_artifact, get_artifacts_links from transformers import logging logger = logging.get_logger(__name__) def extract_warnings_from_single_artifact(artifact_path, targets): """Extract warnings from a downloaded artifact (in .zip format)""" selected_warnings = set() buffer = [] def parse_line(fp): for line in fp: if isinstance(line, bytes): line = line.decode("UTF-8") if "warnings summary (final)" in line: continue # This means we are outside the body of a warning elif not line.startswith(" "): # process a single warning and move it to `selected_warnings`. if len(buffer) > 0: warning = "\n".join(buffer) # Only keep the warnings specified in `targets` if any(f": {x}: " in warning for x in targets): selected_warnings.add(warning) buffer.clear() continue else: line = line.strip() buffer.append(line) if from_gh: for filename in os.listdir(artifact_path): file_path = os.path.join(artifact_path, filename) if not os.path.isdir(file_path): # read the file if filename != "warnings.txt": continue with open(file_path) as fp: parse_line(fp) else: try: with zipfile.ZipFile(artifact_path) as z: for filename in z.namelist(): if not os.path.isdir(filename): # read the file if filename != "warnings.txt": continue with z.open(filename) as fp: parse_line(fp) except Exception: logger.warning( f"{artifact_path} is either an invalid zip file or something else wrong. This file is skipped." ) return selected_warnings def extract_warnings(artifact_dir, targets): """Extract warnings from all artifact files""" selected_warnings = set() paths = [os.path.join(artifact_dir, p) for p in os.listdir(artifact_dir) if (p.endswith(".zip") or from_gh)] for p in paths: selected_warnings.update(extract_warnings_from_single_artifact(p, targets)) return selected_warnings if __name__ == "__main__": def list_str(values): return values.split(",") parser = argparse.ArgumentParser() # Required parameters parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.") parser.add_argument( "--output_dir", type=str, required=True, help="Where to store the downloaded artifacts and other result files.", ) parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.") # optional parameters parser.add_argument( "--targets", default="DeprecationWarning,UserWarning,FutureWarning", type=list_str, help="Comma-separated list of target warning(s) which we want to extract.", ) parser.add_argument( "--from_gh", action="store_true", help="If running from a GitHub action workflow and collecting warnings from its artifacts.", ) args = parser.parse_args() from_gh = args.from_gh if from_gh: # The artifacts have to be downloaded using `actions/download-artifact@v3` pass else: os.makedirs(args.output_dir, exist_ok=True) # get download links artifacts = get_artifacts_links(args.workflow_run_id, token=args.token) with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp: json.dump(artifacts, fp, ensure_ascii=False, indent=4) # download artifacts for idx, (name, url) in enumerate(artifacts.items()): print(name) print(url) print("=" * 80) download_artifact(name, url, args.output_dir, args.token) # Be gentle to GitHub time.sleep(1) # extract warnings from artifacts selected_warnings = extract_warnings(args.output_dir, args.targets) selected_warnings = sorted(selected_warnings) with open(os.path.join(args.output_dir, "selected_warnings.json"), "w", encoding="UTF-8") as fp: json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

transformers/utils/extract_warnings.py/0

from transformers import PretrainedConfig class CustomConfig(PretrainedConfig): model_type = "custom" def __init__(self, attribute=1, **kwargs): self.attribute = attribute super().__init__(**kwargs) class NoSuperInitConfig(PretrainedConfig): model_type = "custom" def __init__(self, attribute=1, **kwargs): self.attribute = attribute

transformers/utils/test_module/custom_configuration.py/0

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transformers/CODE_OF_CONDUCT.md/0

#!/bin/bash source ~/.bashrc echo "running docker-entrypoint.sh" conda activate container echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS echo "printed TPU info" export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}" exec "$@"#!/bin/bash

transformers/docker/transformers-pytorch-tpu/docker-entrypoint.sh/0

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. --> # Ein Modell teilen Die letzten beiden Tutorials haben gezeigt, wie man ein Modell mit PyTorch, Keras und 🤗 Accelerate für verteilte Setups feinabstimmen kann. Der nächste Schritt besteht darin, Ihr Modell mit der Community zu teilen! Bei Hugging Face glauben wir an den offenen Austausch von Wissen und Ressourcen, um künstliche Intelligenz für alle zu demokratisieren. Wir ermutigen Sie, Ihr Modell mit der Community zu teilen, um anderen zu helfen, Zeit und Ressourcen zu sparen. In diesem Tutorial lernen Sie zwei Methoden kennen, wie Sie ein trainiertes oder verfeinertes Modell auf dem [Model Hub](https://huggingface.co/models) teilen können: - Programmgesteuertes Übertragen Ihrer Dateien auf den Hub. - Ziehen Sie Ihre Dateien per Drag-and-Drop über die Weboberfläche in den Hub. <iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <Tip> Um ein Modell mit der Öffentlichkeit zu teilen, benötigen Sie ein Konto auf [huggingface.co](https://huggingface.co/join). Sie können auch einer bestehenden Organisation beitreten oder eine neue Organisation gründen. </Tip> ## Repository-Funktionen Jedes Repository im Model Hub verhält sich wie ein typisches GitHub-Repository. Unsere Repositorys bieten Versionierung, Commit-Historie und die Möglichkeit, Unterschiede zu visualisieren. Die integrierte Versionierung des Model Hub basiert auf Git und [git-lfs](https://git-lfs.github.com/). Mit anderen Worten: Sie können ein Modell als ein Repository behandeln, was eine bessere Zugriffskontrolle und Skalierbarkeit ermöglicht. Die Versionskontrolle ermöglicht *Revisionen*, eine Methode zum Anheften einer bestimmten Version eines Modells mit einem Commit-Hash, Tag oder Branch. Folglich können Sie eine bestimmte Modellversion mit dem Parameter "Revision" laden: ```py >>> model = AutoModel.from_pretrained( ... "julien-c/EsperBERTo-small", revision="v2.0.1" # tag name, or branch name, or commit hash ... ) ``` Dateien lassen sich auch in einem Repository leicht bearbeiten, und Sie können die Commit-Historie sowie die Unterschiede einsehen:  ## Einrichtung Bevor Sie ein Modell für den Hub freigeben, benötigen Sie Ihre Hugging Face-Anmeldedaten. Wenn Sie Zugang zu einem Terminal haben, führen Sie den folgenden Befehl in der virtuellen Umgebung aus, in der 🤗 Transformers installiert ist. Dadurch werden Ihre Zugangsdaten in Ihrem Hugging Face-Cache-Ordner (standardmäßig `~/.cache/`) gespeichert: ```bash huggingface-cli login ``` Wenn Sie ein Notebook wie Jupyter oder Colaboratory verwenden, stellen Sie sicher, dass Sie die [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) Bibliothek installiert haben. Diese Bibliothek ermöglicht Ihnen die programmatische Interaktion mit dem Hub. ```bash pip install huggingface_hub ``` Verwenden Sie dann `notebook_login`, um sich beim Hub anzumelden, und folgen Sie dem Link [hier](https://huggingface.co/settings/token), um ein Token für die Anmeldung zu generieren: ```py >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## Ein Modell für alle Frameworks konvertieren Um sicherzustellen, dass Ihr Modell von jemandem verwendet werden kann, der mit einem anderen Framework arbeitet, empfehlen wir Ihnen, Ihr Modell sowohl mit PyTorch- als auch mit TensorFlow-Checkpoints zu konvertieren und hochzuladen. Während Benutzer immer noch in der Lage sind, Ihr Modell von einem anderen Framework zu laden, wenn Sie diesen Schritt überspringen, wird es langsamer sein, weil 🤗 Transformers den Checkpoint on-the-fly konvertieren müssen. Die Konvertierung eines Checkpoints für ein anderes Framework ist einfach. Stellen Sie sicher, dass Sie PyTorch und TensorFlow installiert haben (siehe [hier](installation) für Installationsanweisungen), und finden Sie dann das spezifische Modell für Ihre Aufgabe in dem anderen Framework. <frameworkcontent> <pt> Geben Sie `from_tf=True` an, um einen Prüfpunkt von TensorFlow nach PyTorch zu konvertieren: ```py >>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True) >>> pt_model.save_pretrained("path/to/awesome-name-you-picked") ``` </pt> <tf> Geben Sie `from_pt=True` an, um einen Prüfpunkt von PyTorch nach TensorFlow zu konvertieren: ```py >>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True) ``` Dann können Sie Ihr neues TensorFlow-Modell mit seinem neuen Checkpoint speichern: ```py >>> tf_model.save_pretrained("path/to/awesome-name-you-picked") ``` </tf> <jax> Wenn ein Modell in Flax verfügbar ist, können Sie auch einen Kontrollpunkt von PyTorch nach Flax konvertieren: ```py >>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained( ... "path/to/awesome-name-you-picked", from_pt=True ... ) ``` </jax> </frameworkcontent> ## Ein Modell während des Trainings hochladen <frameworkcontent> <pt> <Youtube id="Z1-XMy-GNLQ"/> Die Weitergabe eines Modells an den Hub ist so einfach wie das Hinzufügen eines zusätzlichen Parameters oder Rückrufs. Erinnern Sie sich an das [Feinabstimmungs-Tutorial](training), in der Klasse [`TrainingArguments`] geben Sie Hyperparameter und zusätzliche Trainingsoptionen an. Eine dieser Trainingsoptionen beinhaltet die Möglichkeit, ein Modell direkt an den Hub zu pushen. Setzen Sie `push_to_hub=True` in Ihrer [`TrainingArguments`]: ```py >>> training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True) ``` Übergeben Sie Ihre Trainingsargumente wie gewohnt an [`Trainer`]: ```py >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=small_train_dataset, ... eval_dataset=small_eval_dataset, ... compute_metrics=compute_metrics, ... ) ``` Nach der Feinabstimmung Ihres Modells rufen Sie [`~transformers.Trainer.push_to_hub`] auf [`Trainer`] auf, um das trainierte Modell an den Hub zu übertragen. Transformers fügt sogar automatisch Trainings-Hyperparameter, Trainingsergebnisse und Framework-Versionen zu Ihrer Modellkarte hinzu! ```py >>> trainer.push_to_hub() ``` </pt> <tf> Geben Sie ein Modell mit [`PushToHubCallback`] an den Hub weiter. In der [`PushToHubCallback`] Funktion, fügen Sie hinzu: - Ein Ausgabeverzeichnis für Ihr Modell. - Einen Tokenizer. - Die `hub_model_id`, die Ihr Hub-Benutzername und Modellname ist. ```py >>> from transformers import PushToHubCallback >>> push_to_hub_callback = PushToHubCallback( ... output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model" ... ) ``` Fügen Sie den Callback zu [`fit`](https://keras.io/api/models/model_training_apis/) hinzu, und 🤗 Transformers wird das trainierte Modell an den Hub weiterleiten: ```py >>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback) ``` </tf> </frameworkcontent> ## Verwenden Sie die Funktion `push_to_hub`. Sie können `push_to_hub` auch direkt für Ihr Modell aufrufen, um es in den Hub hochzuladen. Geben Sie den Namen Ihres Modells in "push_to_hub" an: ```py >>> pt_model.push_to_hub("my-awesome-model") ``` Dadurch wird ein Repository unter Ihrem Benutzernamen mit dem Modellnamen `my-awesome-model` erstellt. Benutzer können nun Ihr Modell mit der Funktion `from_pretrained` laden: ```py >>> from transformers import AutoModel >>> model = AutoModel.from_pretrained("your_username/my-awesome-model") ``` Wenn Sie zu einer Organisation gehören und Ihr Modell stattdessen unter dem Namen der Organisation pushen wollen, fügen Sie diesen einfach zur `repo_id` hinzu: ```py >>> pt_model.push_to_hub("my-awesome-org/my-awesome-model") ``` Die Funktion "push_to_hub" kann auch verwendet werden, um andere Dateien zu einem Modell-Repository hinzuzufügen. Zum Beispiel kann man einen Tokenizer zu einem Modell-Repository hinzufügen: ```py >>> tokenizer.push_to_hub("my-awesome-model") ``` Oder vielleicht möchten Sie die TensorFlow-Version Ihres fein abgestimmten PyTorch-Modells hinzufügen: ```py >>> tf_model.push_to_hub("my-awesome-model") ``` Wenn Sie nun zu Ihrem Hugging Face-Profil navigieren, sollten Sie Ihr neu erstelltes Modell-Repository sehen. Wenn Sie auf die Registerkarte **Dateien** klicken, werden alle Dateien angezeigt, die Sie in das Repository hochgeladen haben. Weitere Einzelheiten zum Erstellen und Hochladen von Dateien in ein Repository finden Sie in der Hub-Dokumentation [hier](https://huggingface.co/docs/hub/how-to-upstream). ## Hochladen mit der Weboberfläche Benutzer, die einen no-code Ansatz bevorzugen, können ein Modell über das Webinterface des Hubs hochladen. Besuchen Sie [huggingface.co/new](https://huggingface.co/new) um ein neues Repository zu erstellen:  Fügen Sie von hier aus einige Informationen über Ihr Modell hinzu: - Wählen Sie den **Besitzer** des Repositorys. Dies können Sie selbst oder eine der Organisationen sein, denen Sie angehören. - Wählen Sie einen Namen für Ihr Modell, der auch der Name des Repositorys sein wird. - Wählen Sie, ob Ihr Modell öffentlich oder privat ist. - Geben Sie die Lizenzverwendung für Ihr Modell an. Klicken Sie nun auf die Registerkarte **Dateien** und klicken Sie auf die Schaltfläche **Datei hinzufügen**, um eine neue Datei in Ihr Repository hochzuladen. Ziehen Sie dann eine Datei per Drag-and-Drop hoch und fügen Sie eine Übergabemeldung hinzu.  ## Hinzufügen einer Modellkarte Um sicherzustellen, dass die Benutzer die Fähigkeiten, Grenzen, möglichen Verzerrungen und ethischen Aspekte Ihres Modells verstehen, fügen Sie bitte eine Modellkarte zu Ihrem Repository hinzu. Die Modellkarte wird in der Datei `README.md` definiert. Sie können eine Modellkarte hinzufügen, indem Sie: * Manuelles Erstellen und Hochladen einer "README.md"-Datei. * Klicken Sie auf die Schaltfläche **Modellkarte bearbeiten** in Ihrem Modell-Repository. Werfen Sie einen Blick auf die DistilBert [model card](https://huggingface.co/distilbert/distilbert-base-uncased) als gutes Beispiel für die Art von Informationen, die eine Modellkarte enthalten sollte. Weitere Details über andere Optionen, die Sie in der Datei "README.md" einstellen können, wie z.B. den Kohlenstoff-Fußabdruck eines Modells oder Beispiele für Widgets, finden Sie in der Dokumentation [hier](https://huggingface.co/docs/hub/models-cards).

transformers/docs/source/de/model_sharing.md/0

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. --> # How to convert a 🤗 Transformers model to TensorFlow? Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that adding TensorFlow compatibility to an existing model is simpler than [adding a new model from scratch](add_new_model)! Whether you wish to have a deeper understanding of large TensorFlow models, make a major open-source contribution, or enable TensorFlow for your model of choice, this guide is for you. This guide empowers you, a member of our community, to contribute TensorFlow model weights and/or architectures to be used in 🤗 Transformers, with minimal supervision from the Hugging Face team. Writing a new model is no small feat, but hopefully this guide will make it less of a rollercoaster 🎢 and more of a walk in the park 🚶. Harnessing our collective experiences is absolutely critical to make this process increasingly easier, and thus we highly encourage that you suggest improvements to this guide! Before you dive deeper, it is recommended that you check the following resources if you're new to 🤗 Transformers: - [General overview of 🤗 Transformers](add_new_model#general-overview-of-transformers) - [Hugging Face's TensorFlow Philosophy](https://huggingface.co/blog/tensorflow-philosophy) In the remainder of this guide, you will learn what's needed to add a new TensorFlow model architecture, the procedure to convert PyTorch into TensorFlow model weights, and how to efficiently debug mismatches across ML frameworks. Let's get started! <Tip> Are you unsure whether the model you wish to use already has a corresponding TensorFlow architecture? &nbsp; Check the `model_type` field of the `config.json` of your model of choice ([example](https://huggingface.co/google-bert/bert-base-uncased/blob/main/config.json#L14)). If the corresponding model folder in 🤗 Transformers has a file whose name starts with "modeling_tf", it means that it has a corresponding TensorFlow architecture ([example](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert)). </Tip> ## Step-by-step guide to add TensorFlow model architecture code There are many ways to design a large model architecture, and multiple ways of implementing said design. However, you might recall from our [general overview of 🤗 Transformers](add_new_model#general-overview-of-transformers) that we are an opinionated bunch - the ease of use of 🤗 Transformers relies on consistent design choices. From experience, we can tell you a few important things about adding TensorFlow models: - Don't reinvent the wheel! More often than not, there are at least two reference implementations you should check: the PyTorch equivalent of the model you are implementing and other TensorFlow models for the same class of problems. - Great model implementations survive the test of time. This doesn't happen because the code is pretty, but rather because the code is clear, easy to debug and build upon. If you make the life of the maintainers easy with your TensorFlow implementation, by replicating the same patterns as in other TensorFlow models and minimizing the mismatch to the PyTorch implementation, you ensure your contribution will be long lived. - Ask for help when you're stuck! The 🤗 Transformers team is here to help, and we've probably found solutions to the same problems you're facing. Here's an overview of the steps needed to add a TensorFlow model architecture: 1. Select the model you wish to convert 2. Prepare transformers dev environment 3. (Optional) Understand theoretical aspects and the existing implementation 4. Implement the model architecture 5. Implement model tests 6. Submit the pull request 7. (Optional) Build demos and share with the world ### 1.-3. Prepare your model contribution **1. Select the model you wish to convert** Let's start off with the basics: the first thing you need to know is the architecture you want to convert. If you don't have your eyes set on a specific architecture, asking the 🤗 Transformers team for suggestions is a great way to maximize your impact - we will guide you towards the most prominent architectures that are missing on the TensorFlow side. If the specific model you want to use with TensorFlow already has a TensorFlow architecture implementation in 🤗 Transformers but is lacking weights, feel free to jump straight into the [weight conversion section](#adding-tensorflow-weights-to--hub) of this page. For simplicity, the remainder of this guide assumes you've decided to contribute with the TensorFlow version of *BrandNewBert* (the same example as in the [guide](add_new_model) to add a new model from scratch). <Tip> Before starting the work on a TensorFlow model architecture, double-check that there is no ongoing effort to do so. You can search for `BrandNewBert` on the [pull request GitHub page](https://github.com/huggingface/transformers/pulls?q=is%3Apr) to confirm that there is no TensorFlow-related pull request. </Tip> **2. Prepare transformers dev environment** Having selected the model architecture, open a draft PR to signal your intention to work on it. Follow the instructions below to set up your environment and open a draft PR. 1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the 'Fork' button on the repository's page. This creates a copy of the code under your GitHub user account. 2. Clone your `transformers` fork to your local disk, and add the base repository as a remote: ```bash git clone https://github.com/[your Github handle]/transformers.git cd transformers git remote add upstream https://github.com/huggingface/transformers.git ``` 3. Set up a development environment, for instance by running the following command: ```bash python -m venv .env source .env/bin/activate pip install -e ".[dev]" ``` Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a failure with this command. If that's the case make sure to install TensorFlow then do: ```bash pip install -e ".[quality]" ``` **Note:** You don't need to have CUDA installed. Making the new model work on CPU is sufficient. 4. Create a branch with a descriptive name from your main branch ```bash git checkout -b add_tf_brand_new_bert ``` 5. Fetch and rebase to current main ```bash git fetch upstream git rebase upstream/main ``` 6. Add an empty `.py` file in `transformers/src/models/brandnewbert/` named `modeling_tf_brandnewbert.py`. This will be your TensorFlow model file. 7. Push the changes to your account using: ```bash git add . git commit -m "initial commit" git push -u origin add_tf_brand_new_bert ``` 8. Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for future changes. 9. Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page. Now you have set up a development environment to port *BrandNewBert* to TensorFlow in 🤗 Transformers. **3. (Optional) Understand theoretical aspects and the existing implementation** You should take some time to read *BrandNewBert's* paper, if such descriptive work exists. There might be large sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is not to get a deep theoretical understanding of the paper, but to extract the necessary information required to effectively re-implement the model in 🤗 Transformers using TensorFlow. That being said, you don't have to spend too much time on the theoretical aspects, but rather focus on the practical ones, namely the existing model documentation page (e.g. [model docs for BERT](model_doc/bert)). After you've grasped the basics of the models you are about to implement, it's important to understand the existing implementation. This is a great chance to confirm that a working implementation matches your expectations for the model, as well as to foresee technical challenges on the TensorFlow side. It's perfectly natural that you feel overwhelmed with the amount of information that you've just absorbed. It is definitely not a requirement that you understand all facets of the model at this stage. Nevertheless, we highly encourage you to clear any pressing questions in our [forum](https://discuss.huggingface.co/). ### 4. Model implementation Now it's time to finally start coding. Our suggested starting point is the PyTorch file itself: copy the contents of `modeling_brand_new_bert.py` inside `src/transformers/models/brand_new_bert/` into `modeling_tf_brand_new_bert.py`. The goal of this section is to modify the file and update the import structure of 🤗 Transformers such that you can import `TFBrandNewBert` and `TFBrandNewBert.from_pretrained(model_repo, from_pt=True)` successfully loads a working TensorFlow *BrandNewBert* model. Sadly, there is no prescription to convert a PyTorch model into TensorFlow. You can, however, follow our selection of tips to make the process as smooth as possible: - Prepend `TF` to the name of all classes (e.g. `BrandNewBert` becomes `TFBrandNewBert`). - Most PyTorch operations have a direct TensorFlow replacement. For example, `torch.nn.Linear` corresponds to `tf.keras.layers.Dense`, `torch.nn.Dropout` corresponds to `tf.keras.layers.Dropout`, etc. If you're not sure about a specific operation, you can use the [TensorFlow documentation](https://www.tensorflow.org/api_docs/python/tf) or the [PyTorch documentation](https://pytorch.org/docs/stable/). - Look for patterns in the 🤗 Transformers codebase. If you come across a certain operation that doesn't have a direct replacement, the odds are that someone else already had the same problem. - By default, keep the same variable names and structure as in PyTorch. This will make it easier to debug, track issues, and add fixes down the line. - Some layers have different default values in each framework. A notable example is the batch normalization layer's epsilon (`1e-5` in [PyTorch](https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d) and `1e-3` in [TensorFlow](https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization)). Double-check the documentation! - PyTorch's `nn.Parameter` variables typically need to be initialized within TF Layer's `build()`. See the following example: [PyTorch](https://github.com/huggingface/transformers/blob/655f72a6896c0533b1bdee519ed65a059c2425ac/src/transformers/models/vit_mae/modeling_vit_mae.py#L212) / [TensorFlow](https://github.com/huggingface/transformers/blob/655f72a6896c0533b1bdee519ed65a059c2425ac/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L220) - If the PyTorch model has a `#copied from ...` on top of a function, the odds are that your TensorFlow model can also borrow that function from the architecture it was copied from, assuming it has a TensorFlow architecture. - Assigning the `name` attribute correctly in TensorFlow functions is critical to do the `from_pt=True` weight cross-loading. `name` is almost always the name of the corresponding variable in the PyTorch code. If `name` is not properly set, you will see it in the error message when loading the model weights. - The logic of the base model class, `BrandNewBertModel`, will actually reside in `TFBrandNewBertMainLayer`, a Keras layer subclass ([example](https://github.com/huggingface/transformers/blob/4fd32a1f499e45f009c2c0dea4d81c321cba7e02/src/transformers/models/bert/modeling_tf_bert.py#L719)). `TFBrandNewBertModel` will simply be a wrapper around this layer. - Keras models need to be built in order to load pretrained weights. For that reason, `TFBrandNewBertPreTrainedModel` will need to hold an example of inputs to the model, the `dummy_inputs` ([example](https://github.com/huggingface/transformers/blob/4fd32a1f499e45f009c2c0dea4d81c321cba7e02/src/transformers/models/bert/modeling_tf_bert.py#L916)). - If you get stuck, ask for help - we're here to help you! 🤗 In addition to the model file itself, you will also need to add the pointers to the model classes and related documentation pages. You can complete this part entirely following the patterns in other PRs ([example](https://github.com/huggingface/transformers/pull/18020/files)). Here's a list of the needed manual changes: - Include all public classes of *BrandNewBert* in `src/transformers/__init__.py` - Add *BrandNewBert* classes to the corresponding Auto classes in `src/transformers/models/auto/modeling_tf_auto.py` - Add the lazy loading classes related to *BrandNewBert* in `src/transformers/utils/dummy_tf_objects.py` - Update the import structures for the public classes in `src/transformers/models/brand_new_bert/__init__.py` - Add the documentation pointers to the public methods of *BrandNewBert* in `docs/source/en/model_doc/brand_new_bert.md` - Add yourself to the list of contributors to *BrandNewBert* in `docs/source/en/model_doc/brand_new_bert.md` - Finally, add a green tick ✅ to the TensorFlow column of *BrandNewBert* in `docs/source/en/index.md` When you're happy with your implementation, run the following checklist to confirm that your model architecture is ready: 1. All layers that behave differently at train time (e.g. Dropout) are called with a `training` argument, which is propagated all the way from the top-level classes 2. You have used `#copied from ...` whenever possible 3. `TFBrandNewBertMainLayer` and all classes that use it have their `call` function decorated with `@unpack_inputs` 4. `TFBrandNewBertMainLayer` is decorated with `@keras_serializable` 5. A TensorFlow model can be loaded from PyTorch weights using `TFBrandNewBert.from_pretrained(model_repo, from_pt=True)` 6. You can call the TensorFlow model using the expected input format ### 5. Add model tests Hurray, you've implemented a TensorFlow model! Now it's time to add tests to make sure that your model behaves as expected. As in the previous section, we suggest you start by copying the `test_modeling_brand_new_bert.py` file in `tests/models/brand_new_bert/` into `test_modeling_tf_brand_new_bert.py`, and continue by making the necessary TensorFlow replacements. For now, in all `.from_pretrained()` calls, you should use the `from_pt=True` flag to load the existing PyTorch weights. After you're done, it's time for the moment of truth: run the tests! 😬 ```bash NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \ py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py ``` The most likely outcome is that you'll see a bunch of errors. Don't worry, this is expected! Debugging ML models is notoriously hard, and the key ingredient to success is patience (and `breakpoint()`). In our experience, the hardest problems arise from subtle mismatches between ML frameworks, for which we have a few pointers at the end of this guide. In other cases, a general test might not be directly applicable to your model, in which case we suggest an override at the model test class level. Regardless of the issue, don't hesitate to ask for help in your draft pull request if you're stuck. When all tests pass, congratulations, your model is nearly ready to be added to the 🤗 Transformers library! 🎉 ### 6.-7. Ensure everyone can use your model **6. Submit the pull request** Once you're done with the implementation and the tests, it's time to submit a pull request. Before pushing your code, run our code formatting utility, `make fixup` 🪄. This will automatically fix any formatting issues, which would cause our automatic checks to fail. It's now time to convert your draft pull request into a real pull request. To do so, click on the "Ready for review" button and add Joao (`@gante`) and Matt (`@Rocketknight1`) as reviewers. A model pull request will need at least 3 reviewers, but they will take care of finding appropriate additional reviewers for your model. After all reviewers are happy with the state of your PR, the final action point is to remove the `from_pt=True` flag in `.from_pretrained()` calls. Since there are no TensorFlow weights, you will have to add them! Check the section below for instructions on how to do it. Finally, when the TensorFlow weights get merged, you have at least 3 reviewer approvals, and all CI checks are green, double-check the tests locally one last time ```bash NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \ py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py ``` and we will merge your PR! Congratulations on the milestone 🎉 **7. (Optional) Build demos and share with the world** One of the hardest parts about open-source is discovery. How can the other users learn about the existence of your fabulous TensorFlow contribution? With proper communication, of course! 📣 There are two main ways to share your model with the community: - Build demos. These include Gradio demos, notebooks, and other fun ways to show off your model. We highly encourage you to add a notebook to our [community-driven demos](https://huggingface.co/docs/transformers/community). - Share stories on social media like Twitter and LinkedIn. You should be proud of your work and share your achievement with the community - your model can now be used by thousands of engineers and researchers around the world 🌍! We will be happy to retweet your posts and help you share your work with the community. ## Adding TensorFlow weights to 🤗 Hub Assuming that the TensorFlow model architecture is available in 🤗 Transformers, converting PyTorch weights into TensorFlow weights is a breeze! Here's how to do it: 1. Make sure you are logged into your Hugging Face account in your terminal. You can log in using the command `huggingface-cli login` (you can find your access tokens [here](https://huggingface.co/settings/tokens)) 2. Run `transformers-cli pt-to-tf --model-name foo/bar`, where `foo/bar` is the name of the model repository containing the PyTorch weights you want to convert 3. Tag `@joaogante` and `@Rocketknight1` in the 🤗 Hub PR the command above has just created That's it! 🎉 ## Debugging mismatches across ML frameworks 🐛 At some point, when adding a new architecture or when creating TensorFlow weights for an existing architecture, you might come across errors complaining about mismatches between PyTorch and TensorFlow. You might even decide to open the model architecture code for the two frameworks, and find that they look identical. What's going on? 🤔 First of all, let's talk about why understanding these mismatches matters. Many community members will use 🤗 Transformers models out of the box, and trust that our models behave as expected. When there is a large mismatch between the two frameworks, it implies that the model is not following the reference implementation for at least one of the frameworks. This might lead to silent failures, in which the model runs but has poor performance. This is arguably worse than a model that fails to run at all! To that end, we aim at having a framework mismatch smaller than `1e-5` at all stages of the model. As in other numerical problems, the devil is in the details. And as in any detail-oriented craft, the secret ingredient here is patience. Here is our suggested workflow for when you come across this type of issues: 1. Locate the source of mismatches. The model you're converting probably has near identical inner variables up to a certain point. Place `breakpoint()` statements in the two frameworks' architectures, and compare the values of the numerical variables in a top-down fashion until you find the source of the problems. 2. Now that you've pinpointed the source of the issue, get in touch with the 🤗 Transformers team. It is possible that we've seen a similar problem before and can promptly provide a solution. As a fallback, scan popular pages like StackOverflow and GitHub issues. 3. If there is no solution in sight, it means you'll have to go deeper. The good news is that you've located the issue, so you can focus on the problematic instruction, abstracting away the rest of the model! The bad news is that you'll have to venture into the source implementation of said instruction. In some cases, you might find an issue with a reference implementation - don't abstain from opening an issue in the upstream repository. In some cases, in discussion with the 🤗 Transformers team, we might find that fixing the mismatch is infeasible. When the mismatch is very small in the output layers of the model (but potentially large in the hidden states), we might decide to ignore it in favor of distributing the model. The `pt-to-tf` CLI mentioned above has a `--max-error` flag to override the error message at weight conversion time.

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