"""A simple, flexible implementation of a GPT model. Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py """ import math import warnings from typing import Any, Dict, List, Mapping, MutableMapping, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from transformers import PreTrainedModel, PreTrainedTokenizerBase from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, ) from .attention import ( MultiheadAttention, MultiQueryAttention, attn_bias_shape, build_attn_bias, ) from .blocks import MPTBlock from .custom_embedding import SharedEmbedding from .fc import FC_CLASS_REGISTRY as FC_CLASS_REGISTRY from .ffn import FFN_CLASS_REGISTRY as FFN_CLASS_REGISTRY from .ffn import MPTMLP as MPTMLP from .ffn import build_ffn as build_ffn from .norm import NORM_CLASS_REGISTRY from .configuration_mpt import MPTConfig from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising from .hf_prefixlm_converter import ( add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm, ) from .meta_init_context import init_empty_weights from .param_init_fns import generic_param_init_fn_, MODEL_INIT_REGISTRY try: from .flash_attn_triton import flash_attn_func as flash_attn_func except: pass import logging log = logging.getLogger(__name__) class MPTPreTrainedModel(PreTrainedModel): config_class = MPTConfig base_model_prefix = "model" _no_split_modules = ["MPTBlock"] supports_gradient_checkpointing = True def _set_gradient_checkpointing(self, module: nn.Module, value=False) -> None: if ( isinstance(module, MPTModel) or isinstance(module, MultiheadAttention) or isinstance(module, MultiQueryAttention) ): module.gradient_checkpointing = value class MPTModel(MPTPreTrainedModel): def __init__(self, config: MPTConfig): config._validate_config() super().__init__(config) self.gradient_checkpointing = False self.attn_impl = config.attn_config["attn_impl"] self.prefix_lm = config.attn_config["prefix_lm"] self.attn_uses_sequence_id = config.attn_config["attn_uses_sequence_id"] self.alibi = config.attn_config["alibi"] self.alibi_bias_max = config.attn_config["alibi_bias_max"] self.learned_pos_emb = config.learned_pos_emb if config.init_device == "mixed": if dist.get_local_rank() == 0: config.init_device = "cpu" else: config.init_device = "meta" if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys(): norm_options = " | ".join(NORM_CLASS_REGISTRY.keys()) raise NotImplementedError( f"Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options})." ) norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()] self.embedding_fraction = config.embedding_fraction self.wte = SharedEmbedding( config.vocab_size, config.d_model, device=config.init_device ) if self.learned_pos_emb: self.wpe = torch.nn.Embedding( config.max_seq_len, config.d_model, device=config.init_device ) self.emb_drop = nn.Dropout(config.emb_pdrop) self.blocks = nn.ModuleList( [ MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers) ] ) self.norm_f = norm_class(config.d_model, device=config.init_device) if config.init_device != "meta": log.info( f'We recommend using config.init_device="meta" with Composer + FSDP for faster initialization.' ) self.apply(self.param_init_fn) self.is_causal = not self.prefix_lm self._attn_bias_initialized = False self.attn_bias = None self.attn_bias_shape = attn_bias_shape( self.attn_impl, config.n_heads, config.max_seq_len, self.alibi, prefix_lm=self.prefix_lm, causal=self.is_causal, use_sequence_id=self.attn_uses_sequence_id, ) if config.no_bias: for module in self.modules(): if hasattr(module, "bias") and isinstance(module.bias, nn.Parameter): log.info(f"Removing bias ({module.bias}) from {module}.") module.register_parameter("bias", None) if hasattr(module, "use_bias"): log.info(f"Setting use_bias=False for {module}.") module.use_bias = False log.debug(self) log.debug(f"Using {self.config.init_config['name']} initialization.") def get_input_embeddings(self) -> nn.Embedding: return self.wte def set_input_embeddings(self, value: nn.Embedding) -> None: self.wte = value @torch.no_grad() def _attn_bias( self, device: torch.device, dtype: torch.dtype, attention_mask: Optional[torch.ByteTensor] = None, prefix_mask: Optional[torch.ByteTensor] = None, sequence_id: Optional[torch.LongTensor] = None, ) -> Tuple[Optional[torch.Tensor], Optional[torch.ByteTensor]]: if not self._attn_bias_initialized: if self.attn_bias_shape: self.attn_bias = torch.zeros( self.attn_bias_shape, device=device, dtype=dtype ) self.attn_bias = build_attn_bias( self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max, ) self._attn_bias_initialized = True if self.attn_impl == "flash": return (self.attn_bias, attention_mask) if self.attn_bias is not None: self.attn_bias = self.attn_bias.to(dtype=dtype, device=device) attn_bias = self.attn_bias if self.prefix_lm: assert isinstance(attn_bias, torch.Tensor) assert isinstance(prefix_mask, torch.Tensor) attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask) if self.attn_uses_sequence_id and sequence_id is not None: assert isinstance(attn_bias, torch.Tensor) attn_bias = self._apply_sequence_id(attn_bias, sequence_id) if attention_mask is not None: s_k = attention_mask.shape[-1] if attn_bias is None: attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype) else: _s_k = max(0, attn_bias.size(-1) - s_k) attn_bias = attn_bias[:, :, :, _s_k:] if prefix_mask is not None and attention_mask.shape != prefix_mask.shape: raise ValueError( f"attention_mask shape={attention_mask.shape} " + f"and prefix_mask shape={prefix_mask.shape} are not equal." ) min_val = torch.finfo(attn_bias.dtype).min attn_bias = attn_bias.masked_fill( ~attention_mask.view(-1, 1, 1, s_k), min_val ) return (attn_bias, None) def _apply_prefix_mask( self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor ) -> torch.Tensor: (s_k, s_q) = attn_bias.shape[-2:] if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len: raise ValueError( "attn_bias does not match the expected shape. " + f"The last two dimensions should both be {self.config.max_length} " + f"but are {s_k} and {s_q}." ) seq_len = prefix_mask.shape[-1] if seq_len > self.config.max_seq_len: raise ValueError( f"prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}" ) attn_bias = attn_bias[..., :seq_len, :seq_len] causal = torch.tril( torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device) ).view(1, 1, seq_len, seq_len) prefix = prefix_mask.view(-1, 1, 1, seq_len) cannot_attend = ~torch.logical_or(causal, prefix.bool()) min_val = torch.finfo(attn_bias.dtype).min attn_bias = attn_bias.masked_fill(cannot_attend, min_val) return attn_bias def _apply_sequence_id( self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor ) -> torch.Tensor: seq_len = sequence_id.shape[-1] if seq_len > self.config.max_seq_len: raise ValueError( f"sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}" ) attn_bias = attn_bias[..., :seq_len, :seq_len] cannot_attend = torch.logical_not( torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len)) ).unsqueeze(1) min_val = torch.finfo(attn_bias.dtype).min attn_bias = attn_bias.masked_fill(cannot_attend, min_val) return attn_bias def forward( self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.ByteTensor] = None, prefix_mask: Optional[torch.ByteTensor] = None, sequence_id: Optional[torch.LongTensor] = None, return_dict: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, use_cache: Optional[bool] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> BaseModelOutputWithPast: return_dict = ( return_dict if return_dict is not None else self.config.return_dict ) use_cache = use_cache if use_cache is not None else self.config.use_cache if self.gradient_checkpointing and self.training: if use_cache: use_cache = False if attention_mask is not None: attention_mask = attention_mask.bool() if prefix_mask is not None: prefix_mask = prefix_mask.bool() if not return_dict: raise NotImplementedError( "return_dict False is not implemented yet for MPT" ) if output_attentions: if self.attn_impl != "torch": raise NotImplementedError( "output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`." ) if ( self.training and attention_mask is not None and (attention_mask[:, 0].sum() != attention_mask.shape[0]) ): raise NotImplementedError( "MPT does not support training with left padding." ) if self.prefix_lm and prefix_mask is None: raise ValueError( "prefix_mask is a required argument when MPT is configured with prefix_lm=True." ) if inputs_embeds is not None: raise NotImplementedError("inputs_embeds is not implemented for MPT.") if self.training: if self.attn_uses_sequence_id and sequence_id is None: raise ValueError( "sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True " + "and the model is in train mode." ) elif self.attn_uses_sequence_id is False and sequence_id is not None: warnings.warn( "MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. " + "This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True." ) S = input_ids.size(1) assert ( S <= self.config.max_seq_len ), f"Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}" tok_emb = self.wte(input_ids) if self.learned_pos_emb: past_position = 0 if past_key_values is not None: if len(past_key_values) != self.config.n_layers: raise ValueError( f"past_key_values must provide a past_key_value for each attention " + f"layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r})." ) past_position = past_key_values[0][0].size(1) if self.attn_impl == "torch": past_position = past_key_values[0][0].size(3) if S + past_position > self.config.max_seq_len: raise ValueError( f"Cannot forward input with past sequence length {past_position} and current sequence length " + f"{S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}." ) # print(past_position) # print(S + past_position) pos = torch.arange( past_position, S + past_position, dtype=torch.long, device=input_ids.device, ).unsqueeze(0) # print(pos) if attention_mask is not None: # print(torch.cumsum((~attention_mask).to(torch.int32), dim=1)) pos = torch.clamp( pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[ :, past_position: ], min=0, ) # print(pos) # print(attention_mask) pos_emb = self.wpe(pos) # print(pos_emb) x = tok_emb + pos_emb else: x = tok_emb if self.embedding_fraction == 1: x = self.emb_drop(x) else: x_shrunk = x * self.embedding_fraction + x.detach() * ( 1 - self.embedding_fraction ) assert isinstance(self.emb_drop, nn.Module) x = self.emb_drop(x_shrunk) (attn_bias, attention_mask) = self._attn_bias( device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, ) presents = () if use_cache else None if use_cache and past_key_values is None: past_key_values = [() for _ in range(self.config.n_layers)] all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for b_idx, block in enumerate(self.blocks): if output_hidden_states: assert all_hidden_states is not None all_hidden_states = all_hidden_states + (x,) past_key_value = ( past_key_values[b_idx] if past_key_values is not None else None ) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs) return custom_forward (x, attn_weights, present) = torch.utils.checkpoint.checkpoint( create_custom_forward(block), x, past_key_value, attn_bias, attention_mask, self.is_causal, bool(output_attentions), ) else: (x, attn_weights, present) = block( x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal, output_attentions=bool(output_attentions), ) if presents is not None: presents += (present,) if output_attentions: assert all_self_attns is not None all_self_attns = all_self_attns + (attn_weights,) x = self.norm_f(x) if output_hidden_states: assert all_hidden_states is not None all_hidden_states = all_hidden_states + (x,) return BaseModelOutputWithPast( last_hidden_state=x, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attns, ) def param_init_fn(self, module: nn.Module) -> None: init_fn_name = self.config.init_config["name"] MODEL_INIT_REGISTRY[init_fn_name]( module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config, ) def fsdp_wrap_fn(self, module: nn.Module) -> bool: return isinstance(module, MPTBlock) def activation_checkpointing_fn(self, module: nn.Module) -> bool: return isinstance(module, MPTBlock) class MPTForCausalLM(MPTPreTrainedModel): def __init__(self, config: MPTConfig): super().__init__(config) if not config.tie_word_embeddings: raise ValueError("MPTForCausalLM only supports tied word embeddings") log.info(f"Instantiating an MPTForCausalLM model from {__file__}") self.transformer: MPTModel = MPTModel(config) for child in self.transformer.children(): if isinstance(child, torch.nn.ModuleList): continue if isinstance(child, torch.nn.Module): child._fsdp_wrap = True self.logit_scale = None if config.logit_scale is not None: logit_scale = config.logit_scale if isinstance(logit_scale, str): if logit_scale == "inv_sqrt_d_model": logit_scale = 1 / math.sqrt(config.d_model) else: raise ValueError( f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'." ) self.logit_scale = logit_scale def get_input_embeddings(self) -> nn.Embedding: return self.transformer.wte def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None: self.transformer.wte = value def get_output_embeddings(self) -> nn.Embedding: return self.transformer.wte def set_output_embeddings( self, new_embeddings: Union[SharedEmbedding, nn.Embedding] ) -> None: self.transformer.wte = new_embeddings def set_decoder(self, decoder: MPTModel) -> None: self.transformer = decoder def get_decoder(self) -> MPTModel: return self.transformer def forward( self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.ByteTensor] = None, prefix_mask: Optional[torch.ByteTensor] = None, sequence_id: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, return_dict: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, use_cache: Optional[bool] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> CausalLMOutputWithPast: return_dict = ( return_dict if return_dict is not None else self.config.return_dict ) use_cache = use_cache if use_cache is not None else self.config.use_cache if inputs_embeds is not None: raise NotImplementedError( "inputs_embeds has to be None (for hf/peft support)." ) outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache, ) logits = self.transformer.wte( outputs.last_hidden_state.to(self.transformer.wte.weight.device), True ) if self.logit_scale is not None: if self.logit_scale == 0: warnings.warn( f"Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs." ) logits *= self.logit_scale loss = None if labels is not None: _labels = torch.roll(labels, shifts=-1) _labels[:, -1] = -100 loss = F.cross_entropy( logits.view(-1, logits.size(-1)), _labels.to(logits.device).view(-1) ) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def param_init_fn(self, module: nn.Module) -> None: init_fn_name = self.config.init_config["name"] MODEL_INIT_REGISTRY[init_fn_name]( module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config, ) def fsdp_wrap_fn(self, module: nn.Module) -> bool: return isinstance(module, MPTBlock) def activation_checkpointing_fn(self, module: nn.Module) -> bool: return isinstance(module, MPTBlock) def prepare_inputs_for_generation( self, input_ids: torch.Tensor, past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, inputs_embeds: Optional[torch.Tensor] = None, **kwargs: Any, ) -> Dict[str, Any]: if inputs_embeds is not None: raise NotImplementedError("inputs_embeds is not implemented for MPT yet") attention_mask = kwargs["attention_mask"].bool() if attention_mask[:, -1].sum() != attention_mask.shape[0]: raise NotImplementedError( "MPT does not support generation with right padding." ) if self.transformer.attn_uses_sequence_id and self.training: sequence_id = torch.zeros_like(input_ids[:1]) else: sequence_id = None if past_key_values is not None: input_ids = input_ids[:, -1].unsqueeze(-1) if self.transformer.prefix_lm: prefix_mask = torch.ones_like(attention_mask) if kwargs.get("use_cache") == False: raise NotImplementedError( "MPT with prefix_lm=True does not support use_cache=False." ) else: prefix_mask = None return { "input_ids": input_ids, "attention_mask": attention_mask, "prefix_mask": prefix_mask, "sequence_id": sequence_id, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache", True), } @staticmethod def _reorder_cache( past_key_values: List[Tuple[torch.Tensor, torch.Tensor]], beam_idx: torch.LongTensor, ) -> List[Tuple[torch.Tensor, ...]]: """Used by HuggingFace generate when using beam search with kv-caching. See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133 for an example in transformers. """ reordered_past = [] for layer_past in past_key_values: reordered_past += [ tuple( (past_state.index_select(0, beam_idx) for past_state in layer_past) ) ] return reordered_past