update new phi2 structure
Browse files- README.md +1 -1
- config.json +16 -12
- configuration_imp.py +155 -45
- generation_config.json +3 -3
- model-00001-of-00007.safetensors +2 -2
- model-00002-of-00007.safetensors +2 -2
- model-00003-of-00007.safetensors +2 -2
- model-00004-of-00007.safetensors +2 -2
- model-00005-of-00007.safetensors +2 -2
- model-00006-of-00007.safetensors +2 -2
- model-00007-of-00007.safetensors +2 -2
- model.safetensors.index.json +0 -0
- modeling_imp.py +810 -776
- special_tokens_map.json +21 -3
- tokenizer.json +0 -0
- tokenizer_config.json +19 -40
- vocab.json +0 -0
README.md
CHANGED
@@ -22,7 +22,7 @@ We release our model weights and provide an example below to run our model . Det
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**Install dependencies**
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```bash
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pip install transformers # latest version is ok, but we recommend v4.
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pip install -q pillow accelerate einops
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```
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**Install dependencies**
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```bash
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pip install transformers # latest version is ok, but we recommend v4.37.0
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pip install -q pillow accelerate einops
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```
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config.json
CHANGED
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"architectures": [
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"ImpForCausalLM"
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],
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"attn_pdrop": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_imp.ImpConfig",
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"AutoModelForCausalLM": "modeling_imp.ImpForCausalLM"
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},
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"embd_pdrop": 0.0,
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"eos_token_id": 50295,
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"flash_attn": false,
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"flash_rotary": false,
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"freeze_mm_mlp_adapter": false,
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"
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"image_aspect_ratio": "square",
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"image_token": "<image>",
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"image_token_index": 50296,
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"img_processor": null,
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"initializer_range": 0.02,
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"
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"mm_hidden_size": 1152,
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"mm_projector_lr": 2e-05,
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"mm_projector_type": "mlp2x_gelu",
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"mm_vision_select_layer": -2,
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"mm_vision_tower": "google/siglip-so400m-patch14-384",
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"model_type": "imp",
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"
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"
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"
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"n_inner": null,
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"n_layer": 32,
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"n_positions": 3072,
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"pad_token_id": 50256,
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"resid_pdrop": 0.1,
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-
"
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"tie_word_embeddings": false,
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"tokenizer_model_max_length": 3072,
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"tokenizer_padding_side": "right",
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"torch_dtype": "float16",
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-
"transformers_version": "4.
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"use_cache": true,
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"use_mm_proj": true,
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"vision_tower_config": {
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"attention_dropout": 0.0,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"image_size": 384,
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"architectures": [
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"ImpForCausalLM"
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],
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"attention_dropout": 0.0,
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"attn_pdrop": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_imp.ImpConfig",
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"AutoModelForCausalLM": "modeling_imp.ImpForCausalLM"
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},
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"bos_token_id": 1,
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"embd_pdrop": 0.0,
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"eos_token_id": 50295,
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"freeze_mm_mlp_adapter": false,
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"hidden_act": "gelu_new",
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"hidden_size": 2560,
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"image_aspect_ratio": "square",
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"image_token": "<image>",
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"image_token_index": 50296,
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"img_processor": null,
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"initializer_range": 0.02,
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"intermediate_size": 10240,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 3072,
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"mm_hidden_size": 1152,
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"mm_projector_lr": 2e-05,
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"mm_projector_type": "mlp2x_gelu",
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"mm_vision_select_layer": -2,
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"mm_vision_tower": "google/siglip-so400m-patch14-384",
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"model_type": "imp",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": 50256,
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"partial_rotary_factor": 0.4,
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"qk_layernorm": false,
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"resid_pdrop": 0.1,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"tokenizer_model_max_length": 3072,
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"tokenizer_padding_side": "right",
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"torch_dtype": "float16",
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"transformers_version": "4.37.0",
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"use_cache": true,
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"use_mm_proj": true,
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"vision_tower_config": {
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"attention_dropout": 0.0,
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"attn_implementation": null,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"image_size": 384,
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configuration_imp.py
CHANGED
@@ -56,59 +56,169 @@ logger = logging.get_logger(__name__)
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class PhiConfig(PretrainedConfig):
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"""
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def __init__(
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self,
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vocab_size
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self.
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self.
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self.attn_pdrop = attn_pdrop
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self.embd_pdrop = embd_pdrop
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self.resid_pdrop = resid_pdrop
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self.
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self.initializer_range = initializer_range
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super().__init__(
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class SiglipVisionConfig(PretrainedConfig):
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class PhiConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Phi
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[microsoft/phi-1](https://huggingface.co/microsoft/phi-1).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 51200):
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Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`PhiModel`].
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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resid_pdrop (`float`, *optional*, defaults to 0.0):
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Dropout probability for mlp outputs.
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embd_pdrop (`int`, *optional*, defaults to 0.0):
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The dropout ratio for the embeddings.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after computing the attention scores.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
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tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
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is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
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is an experimental feature, subject to breaking API changes in future versions.
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partial_rotary_factor (`float`, *optional*, defaults to 0.5):
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Percentage of the query and keys which will have rotary embedding.
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qk_layernorm (`bool`, *optional*, defaults to `False`):
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Whether or not to normalize the Queries and Keys after projecting the hidden states.
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bos_token_id (`int`, *optional*, defaults to 1):
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Denotes beginning of sequences token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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Denotes end of sequences token id.
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Example:
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```python
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>>> from transformers import PhiModel, PhiConfig
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>>> # Initializing a Phi-1 style configuration
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>>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
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>>> # Initializing a model from the configuration
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>>> model = PhiModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "phi"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=51200,
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hidden_size=2048,
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intermediate_size=8192,
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num_hidden_layers=32, #24
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num_attention_heads=32,
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num_key_value_heads=None,
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attention_dropout=0.0,
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hidden_act="gelu_new",
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max_position_embeddings=2048,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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partial_rotary_factor=0.5,
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qk_layernorm=False,
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bos_token_id=1,
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eos_token_id=2,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attention_dropout = attention_dropout
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.partial_rotary_factor = partial_rotary_factor
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self.qk_layernorm = qk_layernorm
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self._rope_scaling_validation()
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
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f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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class SiglipVisionConfig(PretrainedConfig):
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generation_config.json
CHANGED
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{
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"eos_token_id":50295,
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"pad_token_id":50256,
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"_from_model_config": true,
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"
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}
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{
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"_from_model_config": true,
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"eos_token_id": 50295,
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"pad_token_id": 50256,
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"transformers_version": "4.37.0"
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}
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model-00001-of-00007.safetensors
CHANGED
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version https://git-lfs.github.com/spec/v1
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-
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|
model.safetensors.index.json
CHANGED
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See raw diff
|
|
modeling_imp.py
CHANGED
@@ -4,7 +4,7 @@
|
|
4 |
# Some code here is copied from the project Phi-2 (https://huggingface.co/microsoft/phi-2),
|
5 |
# SigLIP@transformers==4.37.0.dev0 (https://huggingface.co/google/siglip-so400m-patch14-384),
|
6 |
# and Llava (https://github.com/haotian-liu/LLaVA), and modified by
|
7 |
-
# Zhenwei Shao ([email protected]) @ MILVLG. We thank them for their great works.
|
8 |
# And their original licenses and copyright should be inherited (see the statements
|
9 |
# in `configuration_imp.py` for more details).
|
10 |
|
@@ -16,13 +16,15 @@ from __future__ import annotations
|
|
16 |
import os
|
17 |
import math
|
18 |
import re
|
19 |
-
from dataclasses import dataclass, field
|
20 |
from typing import Any, Dict, Optional, Tuple, Union, List
|
21 |
from abc import ABC, abstractmethod
|
22 |
|
23 |
import torch
|
24 |
-
import torch.nn as
|
25 |
-
|
|
|
|
|
|
|
26 |
from transformers import (
|
27 |
PretrainedConfig,
|
28 |
PreTrainedModel,
|
@@ -30,854 +32,744 @@ from transformers import (
|
|
30 |
AutoModelForCausalLM
|
31 |
)
|
32 |
from transformers.activations import ACT2FN
|
33 |
-
from transformers.
|
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|
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|
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|
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|
34 |
import sys
|
35 |
from .configuration_imp import PhiConfig, ImpConfig
|
36 |
from .vision_encoder import VisionTower
|
37 |
|
38 |
try:
|
39 |
-
from flash_attn
|
40 |
-
from flash_attn.
|
41 |
-
from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
|
42 |
-
from flash_attn.ops.fused_dense import FusedDense
|
43 |
except:
|
44 |
-
|
45 |
-
FlashRotaryEmbedding = None
|
46 |
-
FlashSelfAttention, FlashCrossAttention = None, None
|
47 |
-
FusedDense = None
|
48 |
|
|
|
49 |
|
50 |
-
|
51 |
-
class
|
52 |
-
|
53 |
-
and store context during inference.
|
54 |
-
|
55 |
-
Reference:
|
56 |
-
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py.
|
57 |
-
|
58 |
-
Args:
|
59 |
-
max_seqlen: Maximum sequence length.
|
60 |
-
max_batch_size: Maximum batch size.
|
61 |
-
seqlen_offset: Sequence length offset.
|
62 |
-
batch_size_offset: Batch size offset.
|
63 |
-
key_value_memory_dict: Key value memory dictionary.
|
64 |
-
lengths_per_sample: Lengths per sample.
|
65 |
-
|
66 |
-
"""
|
67 |
-
|
68 |
-
max_seqlen: int = field(metadata={"help": "Maximum sequence length."})
|
69 |
-
|
70 |
-
max_batch_size: int = field(metadata={"help": "Maximum batch size."})
|
71 |
-
|
72 |
-
seqlen_offset: int = field(default=0, metadata={"help": "Sequence length offset."})
|
73 |
-
|
74 |
-
batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."})
|
75 |
-
|
76 |
-
key_value_memory_dict: Dict[str, Any] = field(
|
77 |
-
default_factory=dict, metadata={"help": "Key value memory dictionary."}
|
78 |
-
)
|
79 |
-
|
80 |
-
lengths_per_sample: torch.Tensor = field(default=None, metadata={"help": "Lengths per sample."})
|
81 |
-
|
82 |
-
|
83 |
-
class Embedding(nn.Module):
|
84 |
-
"""Token embedding with dropout."""
|
85 |
-
|
86 |
-
def __init__(self, config: PretrainedConfig) -> None:
|
87 |
super().__init__()
|
88 |
|
89 |
-
self.
|
90 |
-
self.
|
91 |
-
|
92 |
-
|
93 |
-
|
94 |
-
input_ids = input_ids.view(-1, input_shape[-1])
|
95 |
-
|
96 |
-
hidden_states = self.wte(input_ids)
|
97 |
-
hidden_states = self.drop(hidden_states)
|
98 |
-
|
99 |
-
return hidden_states
|
100 |
|
|
|
|
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|
|
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|
101 |
|
|
|
|
|
|
|
102 |
|
103 |
-
|
104 |
-
|
105 |
-
|
106 |
-
|
107 |
-
)
|
108 |
-
_, seqlen, _, _ = x.shape
|
109 |
-
_, rotary_dim = cos.shape
|
110 |
-
rotary_dim *= 2
|
111 |
|
112 |
-
|
113 |
-
|
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|
114 |
|
115 |
-
|
116 |
-
|
117 |
-
|
|
|
118 |
|
119 |
-
x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], axis=-1).to(x.dtype)
|
120 |
|
121 |
-
|
|
|
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|
122 |
|
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|
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|
123 |
|
124 |
-
def
|
125 |
-
|
126 |
-
|
127 |
-
|
128 |
-
cos_k: Optional[torch.FloatTensor] = None,
|
129 |
-
sin_k: Optional[torch.FloatTensor] = None,
|
130 |
-
) -> torch.FloatTensor:
|
131 |
-
_, seqlen, _, _, _ = kv.shape
|
132 |
-
_, rotary_dim = cos.shape
|
133 |
-
rotary_dim *= 2
|
134 |
|
135 |
-
|
136 |
-
|
|
|
|
|
|
|
137 |
|
138 |
-
k1, k2 = k_rot.chunk(2, dim=-1)
|
139 |
-
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
|
140 |
-
k1, k2, c, s = [t.to(dtype=torch.float32) for t in [k1, k2, c, s]]
|
141 |
|
142 |
-
|
|
|
|
|
143 |
|
144 |
-
|
145 |
-
|
146 |
-
|
147 |
-
kv[:, :, 1:2, :, :],
|
148 |
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],
|
149 |
-
axis=2,
|
150 |
-
)
|
151 |
|
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|
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|
153 |
-
|
154 |
-
|
155 |
-
|
156 |
-
|
157 |
-
|
158 |
-
|
159 |
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) -> torch.FloatTensor:
|
160 |
-
_, seqlen, _, _, _ = qkv.shape
|
161 |
-
_, rotary_dim = cos.shape
|
162 |
-
rotary_dim *= 2
|
163 |
|
164 |
-
|
165 |
-
q_pass = qkv[:, :, 0, :, rotary_dim:]
|
166 |
|
167 |
-
|
168 |
-
|
|
|
|
|
|
|
169 |
|
170 |
-
q1, q2 = q_rot.chunk(2, dim=-1)
|
171 |
-
k1, k2 = k_rot.chunk(2, dim=-1)
|
172 |
-
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
|
173 |
-
q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
|
174 |
|
175 |
-
|
176 |
-
|
|
|
|
|
|
|
|
|
177 |
|
178 |
-
return torch.cat(
|
179 |
-
[
|
180 |
-
torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
|
181 |
-
torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
|
182 |
-
qkv[:, :, 2:3, :, :],
|
183 |
-
],
|
184 |
-
axis=2,
|
185 |
-
)
|
186 |
|
|
|
|
|
|
|
187 |
|
188 |
-
|
189 |
-
|
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|
|
|
190 |
|
191 |
-
Reference:
|
192 |
-
RoFormer: Enhanced Transformer with Rotary Position Embedding.
|
193 |
-
https://arxiv.org/pdf/2104.09864.pdf.
|
194 |
|
195 |
-
"""
|
196 |
|
197 |
-
|
198 |
-
|
199 |
-
|
200 |
-
base: int = 10000,
|
201 |
-
scale_base: Optional[float] = None,
|
202 |
-
pos_idx_in_fp32: bool = True,
|
203 |
-
max_position_embeddings: int = 2048,
|
204 |
-
device: Optional[str] = None,
|
205 |
-
**kwargs,
|
206 |
-
) -> None:
|
207 |
super().__init__()
|
|
|
|
|
|
|
|
|
208 |
|
209 |
-
|
210 |
-
|
|
|
|
|
|
|
211 |
|
212 |
-
self.dim = dim
|
213 |
-
self.base = float(base)
|
214 |
-
self.scale_base = scale_base
|
215 |
-
self.pos_idx_in_fp32 = pos_idx_in_fp32
|
216 |
-
self.max_position_embeddings = max_position_embeddings
|
217 |
-
self.device = device
|
218 |
|
219 |
-
|
220 |
-
|
221 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
222 |
|
223 |
-
# Generate and save the scale buffer (non-trainable)
|
224 |
-
scale = (
|
225 |
-
(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
|
226 |
-
if scale_base is not None
|
227 |
-
else None
|
228 |
-
)
|
229 |
-
self.register_buffer("scale", scale, persistent=False)
|
230 |
|
231 |
-
# Initialize cached attributes since ONNX can't rely on dynamic initialization
|
232 |
-
self._update_cos_sin_cache(max_position_embeddings, device=device, dtype=torch.float32)
|
233 |
|
234 |
-
|
235 |
-
|
236 |
|
237 |
-
def
|
238 |
-
|
239 |
-
|
240 |
-
|
241 |
-
|
242 |
-
|
243 |
-
self.
|
244 |
-
|
245 |
-
#
|
246 |
-
#
|
247 |
-
|
248 |
-
|
249 |
-
|
250 |
-
|
251 |
-
|
252 |
-
|
253 |
-
|
254 |
-
|
255 |
-
|
256 |
-
|
257 |
-
|
258 |
-
|
259 |
-
if self.
|
260 |
-
|
261 |
-
|
262 |
-
|
263 |
-
|
264 |
-
torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
|
265 |
-
) / self.scale_base
|
266 |
-
scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
|
267 |
|
268 |
-
|
269 |
-
|
270 |
-
|
271 |
-
|
272 |
-
self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
|
273 |
|
274 |
-
|
275 |
-
self
|
276 |
-
|
277 |
-
|
278 |
-
seqlen_offset: int = 0,
|
279 |
-
**kwargs,
|
280 |
-
) -> Tuple[torch.Tensor, torch.Tensor]:
|
281 |
-
if (
|
282 |
-
self._seq_len_cached < qkv.shape[1] + seqlen_offset
|
283 |
-
or self._cos_cached.device != qkv.device
|
284 |
-
or self._cos_cached.dtype != qkv.dtype
|
285 |
-
or (self.training and self._cos_cached.is_inference())
|
286 |
-
):
|
287 |
-
self._update_cos_sin_cache(qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype)
|
288 |
-
|
289 |
-
if kv is None:
|
290 |
-
return _apply_rotary_emb_qkv(
|
291 |
-
qkv,
|
292 |
-
self._cos_cached[seqlen_offset:],
|
293 |
-
self._sin_cached[seqlen_offset:],
|
294 |
-
)
|
295 |
-
else:
|
296 |
-
q = _apply_rotary_emb(
|
297 |
-
qkv,
|
298 |
-
self._cos_cached[seqlen_offset:],
|
299 |
-
self._sin_cached[seqlen_offset:],
|
300 |
)
|
301 |
-
|
302 |
-
|
303 |
-
self._cos_cached[seqlen_offset:],
|
304 |
-
self._sin_cached[seqlen_offset:],
|
305 |
)
|
306 |
|
307 |
-
|
308 |
-
|
309 |
-
|
310 |
-
class MLP(nn.Module):
|
311 |
-
"""Multi-Layer Perceptron.
|
312 |
-
|
313 |
-
Reference:
|
314 |
-
Attention Is All You Need.
|
315 |
-
https://arxiv.org/pdf/1706.03762.pdf.
|
316 |
-
|
317 |
-
"""
|
318 |
-
|
319 |
-
def __init__(
|
320 |
-
self,
|
321 |
-
config: PretrainedConfig,
|
322 |
-
n_inner: Optional[int] = None,
|
323 |
-
act_fn: Optional[str] = None,
|
324 |
-
) -> None:
|
325 |
-
super().__init__()
|
326 |
-
|
327 |
-
act_fn = config.activation_function if act_fn is None else act_fn
|
328 |
-
|
329 |
-
n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
|
330 |
-
n_inner = n_inner if n_inner is not None else 4 * config.n_embd
|
331 |
-
|
332 |
-
self.fc1 = nn.Linear(config.n_embd, n_inner)
|
333 |
-
self.fc2 = nn.Linear(n_inner, config.n_embd)
|
334 |
-
self.act = ACT2FN[act_fn]
|
335 |
|
336 |
-
def
|
337 |
-
|
338 |
-
|
339 |
-
|
340 |
-
|
341 |
-
|
342 |
-
|
343 |
-
|
344 |
-
|
345 |
-
|
346 |
-
|
347 |
-
|
348 |
-
|
349 |
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|
350 |
-
|
351 |
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|
352 |
-
|
353 |
-
|
354 |
-
|
355 |
-
|
356 |
-
|
357 |
-
|
358 |
-
|
359 |
-
|
360 |
-
|
361 |
-
|
362 |
-
self.drop = nn.Dropout(attention_dropout)
|
363 |
|
|
|
364 |
@torch.autocast("cpu", enabled=False)
|
365 |
@torch.autocast("cuda", enabled=False)
|
366 |
def forward(
|
367 |
self,
|
368 |
-
|
369 |
-
|
370 |
-
|
371 |
-
|
372 |
-
|
373 |
-
|
374 |
-
|
375 |
-
|
376 |
-
|
377 |
-
k = k.to(torch.float32)
|
378 |
|
379 |
-
|
380 |
-
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|
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|
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#
|
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|
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-
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|
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|
385 |
|
386 |
-
if
|
387 |
-
|
388 |
-
|
|
|
|
|
|
|
389 |
|
390 |
-
|
|
|
|
|
391 |
|
392 |
-
|
393 |
-
causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
|
394 |
-
scores = scores + causal_mask.to(dtype=scores.dtype)
|
395 |
|
396 |
-
|
397 |
-
|
|
|
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|
|
|
398 |
|
399 |
-
|
|
|
400 |
|
401 |
-
return output
|
402 |
|
|
|
403 |
|
404 |
-
|
405 |
-
|
406 |
|
407 |
-
|
408 |
-
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
|
409 |
|
|
|
|
|
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|
|
|
|
|
410 |
"""
|
411 |
|
412 |
-
|
413 |
-
|
414 |
-
|
415 |
-
softmax_scale: Optional[float] = None,
|
416 |
-
attention_dropout: float = 0.0,
|
417 |
-
) -> None:
|
418 |
-
super().__init__()
|
419 |
|
420 |
-
|
421 |
-
|
422 |
-
|
|
|
423 |
|
424 |
-
@torch.autocast("cpu", enabled=False)
|
425 |
-
@torch.autocast("cuda", enabled=False)
|
426 |
def forward(
|
427 |
self,
|
428 |
-
|
429 |
-
|
430 |
-
|
431 |
-
|
|
|
|
|
432 |
**kwargs,
|
433 |
-
) -> torch.
|
434 |
-
|
435 |
-
seqlen_k = kv.shape[1]
|
436 |
-
|
437 |
-
if kv.shape[3] != q.shape[2]:
|
438 |
-
kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
|
439 |
-
k, v = kv.unbind(dim=2)
|
440 |
-
|
441 |
-
q = q.to(torch.float32)
|
442 |
-
k = k.to(torch.float32)
|
443 |
-
|
444 |
-
causal = self.causal if causal is None else causal
|
445 |
-
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
|
446 |
-
|
447 |
-
# Autocast is manually disabled to avoid `torch.einsum` performing the operation
|
448 |
-
# using float16, which might lead to overflow
|
449 |
-
scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
|
450 |
-
|
451 |
-
if key_padding_mask is not None:
|
452 |
-
padding_mask = torch.full(
|
453 |
-
(batch_size, seqlen_k),
|
454 |
-
-10000.0,
|
455 |
-
dtype=scores.dtype,
|
456 |
-
device=scores.device,
|
457 |
-
)
|
458 |
-
padding_mask.masked_fill_(key_padding_mask, 0.0)
|
459 |
-
|
460 |
-
scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
|
461 |
-
|
462 |
-
if causal:
|
463 |
-
rows = rearrange(torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1")
|
464 |
-
cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
|
465 |
-
causal_mask = cols > rows + seqlen_k - seqlen_q
|
466 |
|
467 |
-
|
468 |
|
469 |
-
|
470 |
-
attention = self.drop(attention)
|
471 |
|
472 |
-
|
|
|
|
|
473 |
|
474 |
-
|
|
|
|
|
475 |
|
|
|
|
|
|
|
|
|
|
|
|
|
476 |
|
477 |
-
|
478 |
-
|
479 |
-
|
480 |
-
|
481 |
-
head_dim: Optional[int] = None,
|
482 |
-
) -> Tuple[int, int]:
|
483 |
-
if n_head is None and head_dim is None:
|
484 |
-
head_dim = config.n_embd // config.n_head
|
485 |
-
n_head = config.n_head
|
486 |
-
elif n_head is None or head_dim is None:
|
487 |
-
raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
|
488 |
|
489 |
-
|
490 |
-
|
491 |
-
|
492 |
-
|
493 |
-
|
494 |
-
|
495 |
-
def _update_kv_cache(kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int) -> torch.FloatTensor:
|
496 |
-
num_heads, head_dim = kv.shape[-2:]
|
497 |
-
|
498 |
-
if layer_idx not in inference_params.key_value_memory_dict:
|
499 |
-
inference_params.key_value_memory_dict[layer_idx] = torch.empty(
|
500 |
-
inference_params.max_batch_size,
|
501 |
-
inference_params.max_seqlen,
|
502 |
-
2,
|
503 |
-
num_heads,
|
504 |
-
head_dim,
|
505 |
-
dtype=kv.dtype,
|
506 |
-
device=kv.device,
|
507 |
)
|
508 |
-
|
509 |
-
|
510 |
-
|
511 |
-
|
512 |
-
sequence_start = inference_params.seqlen_offset
|
513 |
-
sequence_end = sequence_start + kv.shape[1]
|
514 |
-
|
515 |
-
# When the current sequence length is equal to or larger than the maximum sequence length,
|
516 |
-
# we need to concatenate the current `kv` with the cached `kv` to expand its length
|
517 |
-
if sequence_end >= inference_params.max_seqlen:
|
518 |
-
inference_params.key_value_memory_dict[layer_idx] = torch.concatenate((inference_params.key_value_memory_dict[layer_idx], kv), dim=1)
|
519 |
-
|
520 |
-
inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, sequence_start:sequence_end, ...] = kv
|
521 |
-
kv = inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, :sequence_end, ...]
|
522 |
-
|
523 |
-
return kv
|
524 |
-
|
525 |
-
|
526 |
-
class MHA(nn.Module):
|
527 |
-
"""Multi-head attention layer."""
|
528 |
-
|
529 |
-
def __init__(
|
530 |
-
self,
|
531 |
-
config: PretrainedConfig,
|
532 |
-
dtype: Optional[torch.dtype] = None,
|
533 |
-
device: Optional[str] = None,
|
534 |
-
rotary_dim: Optional[int] = None,
|
535 |
-
rotary_base: float = 10000.0,
|
536 |
-
rotary_scale_base: Optional[float] = None,
|
537 |
-
n_head: Optional[int] = None,
|
538 |
-
n_head_kv: Optional[int] = None,
|
539 |
-
head_dim: Optional[int] = None,
|
540 |
-
bias: bool = True,
|
541 |
-
causal: bool = True,
|
542 |
-
softmax_scale: Optional[float] = None,
|
543 |
-
layer_idx: Optional[int] = None,
|
544 |
-
return_residual: bool = False,
|
545 |
-
checkpointing: bool = False,
|
546 |
-
) -> None:
|
547 |
-
super().__init__()
|
548 |
-
|
549 |
-
# Rotary embedding
|
550 |
-
self.rotary_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
|
551 |
-
if self.rotary_dim > 0:
|
552 |
-
rotary_cls = FlashRotaryEmbedding if config.flash_rotary else RotaryEmbedding
|
553 |
-
if rotary_cls is None:
|
554 |
-
rotary_cls = RotaryEmbedding
|
555 |
-
|
556 |
-
rotary_kwargs = {}
|
557 |
-
if rotary_cls is RotaryEmbedding:
|
558 |
-
rotary_kwargs["max_position_embeddings"] = config.n_positions
|
559 |
-
|
560 |
-
self.rotary_emb = rotary_cls(
|
561 |
-
self.rotary_dim,
|
562 |
-
base=rotary_base,
|
563 |
-
scale_base=rotary_scale_base,
|
564 |
-
device=device,
|
565 |
-
**rotary_kwargs,
|
566 |
-
)
|
567 |
-
|
568 |
-
# MLP
|
569 |
-
self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(
|
570 |
-
config, n_head=n_head, n_head_kv=n_head_kv, head_dim=head_dim
|
571 |
)
|
572 |
-
|
573 |
-
|
574 |
-
|
575 |
-
|
576 |
-
|
577 |
-
|
578 |
-
|
579 |
-
|
580 |
-
|
581 |
-
|
582 |
-
|
583 |
-
|
584 |
-
|
585 |
-
|
586 |
-
|
587 |
-
|
588 |
-
|
589 |
-
|
590 |
-
|
591 |
-
|
592 |
-
|
593 |
-
|
594 |
-
|
595 |
-
|
596 |
-
|
597 |
-
|
598 |
-
|
599 |
-
|
600 |
-
|
601 |
-
|
602 |
-
|
603 |
-
self.layer_idx = layer_idx
|
604 |
-
self.return_residual = return_residual
|
605 |
-
self.checkpointing = checkpointing
|
606 |
-
|
607 |
-
def _forward_self_attn(
|
608 |
-
self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
|
609 |
-
) -> torch.FloatTensor:
|
610 |
-
qkv = self.Wqkv(x)
|
611 |
-
qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
|
612 |
-
|
613 |
-
if self.rotary_dim > 0:
|
614 |
-
qkv = self.rotary_emb(qkv)
|
615 |
-
|
616 |
-
if self.flash_attn:
|
617 |
-
batch_size, seqlen = qkv.shape[0], qkv.shape[1]
|
618 |
-
|
619 |
-
cu_seqlens, max_seqlen = None, None
|
620 |
-
if key_padding_mask is not None:
|
621 |
-
# If `key_padding_mask` is supplied, we need to unpad the input and retrieve
|
622 |
-
# the `cu_seqlens` and `max_seqlen` to be used by `flash-attn`
|
623 |
-
qkv, indices, cu_seqlens, max_seqlen = unpad_input(qkv, key_padding_mask)
|
624 |
-
|
625 |
-
if self.checkpointing:
|
626 |
-
attn_output = torch.utils.checkpoint.checkpoint(
|
627 |
-
self.inner_attn, qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
|
628 |
-
)
|
629 |
else:
|
630 |
-
|
631 |
-
|
632 |
-
# If `key_padding_mask` is supplied, we need to pad the output back to the original shape
|
633 |
-
return pad_input(attn_output, indices, batch_size, seqlen) if key_padding_mask is not None else attn_output
|
634 |
-
|
635 |
-
if self.checkpointing:
|
636 |
-
return torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, key_padding_mask=key_padding_mask)
|
637 |
-
|
638 |
-
return self.inner_attn(qkv, key_padding_mask=key_padding_mask)
|
639 |
|
640 |
-
|
641 |
-
|
642 |
-
|
643 |
-
|
644 |
-
|
645 |
-
) -> torch.FloatTensor:
|
646 |
-
batch_size = x.shape[0]
|
647 |
-
|
648 |
-
qkv = self.Wqkv(x)
|
649 |
|
650 |
-
|
651 |
-
|
|
|
652 |
|
653 |
-
|
654 |
-
|
|
|
655 |
|
656 |
-
|
657 |
-
|
658 |
-
if self.rotary_dim > 0:
|
659 |
-
q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset)
|
660 |
|
661 |
-
if
|
662 |
-
|
663 |
|
664 |
-
|
665 |
-
batch_size, seqlen_q = q.shape[0], q.shape[1]
|
666 |
-
seqlen_k = kv.shape[1]
|
667 |
|
668 |
-
|
669 |
-
|
670 |
-
|
671 |
-
|
672 |
-
|
673 |
-
|
674 |
-
|
675 |
-
|
676 |
-
|
677 |
-
|
678 |
-
|
679 |
-
|
680 |
-
|
681 |
-
|
682 |
-
|
683 |
-
|
684 |
-
|
685 |
-
|
686 |
-
|
687 |
-
|
688 |
-
|
689 |
-
|
690 |
-
|
691 |
-
|
692 |
-
|
693 |
-
|
694 |
-
|
695 |
-
|
696 |
-
attn_output = self.inner_cross_attn(
|
697 |
-
q,
|
698 |
-
kv,
|
699 |
-
causal=causal,
|
700 |
-
cu_seqlens=cu_seqlens_q,
|
701 |
-
max_seqlen=max_seqlen_q,
|
702 |
-
cu_seqlens_k=cu_seqlens_k,
|
703 |
-
max_seqlen_k=max_seqlen_k,
|
704 |
-
)
|
705 |
|
706 |
-
|
707 |
-
|
708 |
-
|
709 |
-
|
|
|
710 |
)
|
711 |
|
712 |
-
|
713 |
-
|
714 |
-
|
715 |
-
|
716 |
-
|
717 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
718 |
causal=causal,
|
719 |
)
|
720 |
|
721 |
-
|
722 |
-
|
723 |
-
def forward(
|
724 |
-
self,
|
725 |
-
x: torch.FloatTensor,
|
726 |
-
past_key_values: Optional[InferenceParams] = None,
|
727 |
-
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
728 |
-
**kwargs,
|
729 |
-
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
730 |
-
if attention_mask is not None:
|
731 |
-
attention_mask = attention_mask.bool()
|
732 |
else:
|
733 |
-
|
|
|
|
|
734 |
|
735 |
-
|
736 |
-
if self.n_head == self.n_head_kv:
|
737 |
-
if past_key_values is None:
|
738 |
-
# If `past_key_values` are not supplied, we run self-attention
|
739 |
-
attn_output = self._forward_self_attn(x, attention_mask)
|
740 |
-
else:
|
741 |
-
# If `past_key_values` are supplied, it means that we might have cached values and
|
742 |
-
# could take advantage of cross-attention
|
743 |
-
attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
|
744 |
-
# MQA / GQA
|
745 |
-
else:
|
746 |
-
# Regardless of `past_key_values` being supplied or not, it always use cross-attention
|
747 |
-
# because `q` and `kv` lengths might be different
|
748 |
-
attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
|
749 |
|
750 |
-
|
751 |
-
|
|
|
|
|
752 |
|
753 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
754 |
|
755 |
|
756 |
-
class ParallelBlock(nn.Module):
|
757 |
-
"""Parallel block.
|
758 |
|
759 |
-
|
|
|
|
|
|
|
760 |
|
761 |
-
"""
|
762 |
|
763 |
-
|
764 |
-
|
765 |
-
config: PretrainedConfig,
|
766 |
-
block_idx: Optional[int] = None,
|
767 |
-
) -> None:
|
768 |
super().__init__()
|
769 |
-
|
770 |
-
self.
|
|
|
771 |
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
772 |
-
self.block_idx = block_idx
|
773 |
-
|
774 |
-
self.mixer = MHA(config, layer_idx=block_idx)
|
775 |
-
self.mlp = MLP(config)
|
776 |
|
777 |
def forward(
|
778 |
self,
|
779 |
-
hidden_states: torch.
|
780 |
-
|
781 |
-
|
782 |
-
|
783 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
784 |
residual = hidden_states
|
785 |
-
hidden_states = self.ln(hidden_states)
|
786 |
|
787 |
-
|
788 |
-
|
789 |
-
|
|
|
|
|
790 |
attention_mask=attention_mask,
|
|
|
|
|
|
|
|
|
791 |
)
|
792 |
-
if isinstance(attn_outputs, tuple):
|
793 |
-
attn_outputs = attn_outputs[0]
|
794 |
-
|
795 |
attn_outputs = self.resid_dropout(attn_outputs)
|
796 |
-
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
797 |
|
|
|
798 |
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
|
|
799 |
|
800 |
-
|
801 |
-
|
802 |
-
|
803 |
-
class CausalLMHead(nn.Module):
|
804 |
-
"""Causal Language Modeling head.
|
805 |
-
|
806 |
-
Reference:
|
807 |
-
Improving Language Understanding by Generative Pre-Training.
|
808 |
-
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
|
809 |
-
|
810 |
-
"""
|
811 |
-
|
812 |
-
def __init__(self, config: PretrainedConfig) -> None:
|
813 |
-
super().__init__()
|
814 |
-
|
815 |
-
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
816 |
-
self.linear = nn.Linear(config.n_embd, config.vocab_size)
|
817 |
|
818 |
-
|
819 |
-
|
820 |
-
logits = self.linear(hidden_states).to(torch.float32)
|
821 |
|
822 |
-
return
|
823 |
|
824 |
|
825 |
class PhiPreTrainedModel(PreTrainedModel):
|
826 |
"""Phi pre-trained model."""
|
827 |
|
828 |
config_class = PhiConfig
|
829 |
-
base_model_prefix = "
|
830 |
supports_gradient_checkpointing = True
|
831 |
-
_no_split_modules = ["
|
|
|
|
|
|
|
832 |
|
833 |
def __init__(self, *inputs, **kwargs) -> None:
|
834 |
super().__init__(*inputs, **kwargs)
|
835 |
|
836 |
-
def _init_weights(self, module
|
837 |
-
|
838 |
-
|
|
|
839 |
if module.bias is not None:
|
840 |
module.bias.data.zero_()
|
841 |
elif isinstance(module, nn.Embedding):
|
842 |
-
module.weight.data.normal_(mean=0.0, std=
|
843 |
if module.padding_idx is not None:
|
844 |
module.weight.data[module.padding_idx].zero_()
|
845 |
-
elif isinstance(module, nn.LayerNorm):
|
846 |
-
if module.bias is not None:
|
847 |
-
module.bias.data.zero_()
|
848 |
-
module.weight.data.fill_(1.0)
|
849 |
|
850 |
def prepare_inputs_for_generation(
|
851 |
self,
|
852 |
input_ids: torch.LongTensor,
|
853 |
-
past_key_values: Optional[
|
|
|
854 |
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
855 |
**kwargs,
|
856 |
) -> Dict[str, Any]:
|
857 |
-
if past_key_values is
|
858 |
-
past_key_values
|
859 |
-
|
860 |
-
|
861 |
-
|
862 |
-
|
863 |
-
|
864 |
-
|
865 |
-
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|
866 |
else:
|
867 |
-
|
868 |
-
|
869 |
-
|
870 |
-
|
871 |
-
|
872 |
-
|
873 |
-
|
874 |
-
|
875 |
-
|
876 |
-
|
877 |
-
|
878 |
-
"past_key_values": past_key_values,
|
879 |
-
"attention_mask": attention_mask,
|
880 |
-
}
|
881 |
|
882 |
|
883 |
class LlavaMetaModel(ABC):
|
@@ -922,15 +814,20 @@ class LlavaMetaModel(ABC):
|
|
922 |
class ImpModel(PhiPreTrainedModel, LlavaMetaModel):
|
923 |
"""Imp model. This implementation is modified from the implementation of Phi-2"""
|
924 |
|
925 |
-
config_class = ImpConfig
|
926 |
-
# _keys_to_ignore_on_load_missing = [""]
|
927 |
-
# _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
|
928 |
|
929 |
def __init__(self, config: ImpConfig) -> None:
|
930 |
super().__init__(config)
|
|
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|
931 |
|
932 |
-
self.embd = Embedding(config)
|
933 |
-
self.h = nn.ModuleList([ParallelBlock(config, block_idx=i) for i in range(config.n_layer)])
|
934 |
self.gradient_checkpointing = False
|
935 |
|
936 |
if hasattr(config, "mm_vision_tower"):
|
@@ -939,57 +836,139 @@ class ImpModel(PhiPreTrainedModel, LlavaMetaModel):
|
|
939 |
|
940 |
self.post_init()
|
941 |
|
942 |
-
def embed_tokens(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
|
943 |
-
|
944 |
|
945 |
def get_input_embeddings(self) -> nn.Embedding:
|
946 |
-
return self.embd.wte
|
|
|
947 |
|
948 |
-
def set_input_embeddings(self,
|
949 |
-
self.
|
950 |
|
951 |
def forward(
|
952 |
self,
|
953 |
input_ids: torch.LongTensor,
|
954 |
-
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
955 |
attention_mask: Optional[torch.BoolTensor] = None,
|
956 |
-
|
957 |
-
|
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|
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|
|
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|
958 |
|
959 |
-
if
|
960 |
-
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|
961 |
else:
|
962 |
-
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|
963 |
|
964 |
-
|
965 |
-
if
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|
966 |
|
967 |
-
|
968 |
-
|
969 |
-
|
970 |
-
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|
971 |
|
972 |
-
|
973 |
|
974 |
-
|
975 |
-
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|
976 |
hidden_states,
|
977 |
-
None,
|
978 |
attention_mask,
|
|
|
|
|
|
|
979 |
)
|
980 |
else:
|
981 |
-
|
982 |
hidden_states,
|
983 |
-
past_key_values=past_key_values,
|
984 |
attention_mask=attention_mask,
|
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|
985 |
)
|
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|
986 |
|
987 |
-
# I change the way of updating `past_key_values.seqlen_offset` to make the inference of imp work.
|
988 |
-
# [Edited by zhenwei - 2024-01-20 21:15]
|
989 |
-
if past_key_values is not None: # FIXME: when multi-batch inference, it is a bug
|
990 |
-
past_key_values.seqlen_offset += hidden_states.shape[1]
|
991 |
-
|
992 |
-
return hidden_states
|
993 |
|
994 |
|
995 |
class LlavaMetaForCausalLM(ABC):
|
@@ -1016,18 +995,40 @@ class LlavaMetaForCausalLM(ABC):
|
|
1016 |
self, input_ids, position_ids, attention_mask, past_key_values, labels, images
|
1017 |
):
|
1018 |
vision_tower = self.get_vision_tower()
|
1019 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1020 |
if vision_tower is None or images is None or input_ids.shape[1] == 1:
|
1021 |
-
if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
|
1022 |
-
|
1023 |
-
|
1024 |
-
|
1025 |
-
|
1026 |
-
|
1027 |
-
|
1028 |
-
|
1029 |
-
|
1030 |
-
return input_ids, position_ids, attention_mask, past_key_values, None, labels
|
|
|
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|
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|
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|
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|
|
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|
|
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|
|
|
|
|
|
1031 |
|
1032 |
if type(images) is list or images.ndim == 5:
|
1033 |
concat_images = torch.cat([image for image in images], dim=0)
|
@@ -1159,6 +1160,7 @@ class LlavaMetaForCausalLM(ABC):
|
|
1159 |
position_ids = None
|
1160 |
|
1161 |
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
|
|
|
1162 |
|
1163 |
|
1164 |
class ImpForCausalLM(PhiPreTrainedModel, LlavaMetaForCausalLM):
|
@@ -1171,37 +1173,36 @@ class ImpForCausalLM(PhiPreTrainedModel, LlavaMetaForCausalLM):
|
|
1171 |
def __init__(self, config: ImpConfig) -> None:
|
1172 |
super().__init__(config)
|
1173 |
|
1174 |
-
self.
|
1175 |
-
self.
|
|
|
1176 |
|
1177 |
self.post_init()
|
1178 |
self.init_constants(config)
|
1179 |
|
|
|
|
|
|
|
|
|
|
|
|
|
1180 |
def get_output_embeddings(self) -> nn.Linear:
|
1181 |
-
return self.lm_head
|
1182 |
|
1183 |
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
|
1184 |
-
self.lm_head
|
1185 |
|
1186 |
def get_model(self):
|
1187 |
-
return self.
|
|
|
|
|
|
|
|
|
|
|
|
|
1188 |
|
1189 |
def image_preprocess(self, images):
|
1190 |
return self.get_vision_tower().image_processor(images)['pixel_values']
|
1191 |
-
|
1192 |
-
def backbone_forward(
|
1193 |
-
self,
|
1194 |
-
input_ids: torch.LongTensor,
|
1195 |
-
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
1196 |
-
attention_mask: Optional[torch.BoolTensor] = None,
|
1197 |
-
labels: Optional[torch.LongTensor] = None,
|
1198 |
-
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1199 |
-
**kwargs,
|
1200 |
-
) -> CausalLMOutputWithPast:
|
1201 |
-
hidden_states = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds)
|
1202 |
-
lm_logits = self.lm_head(hidden_states)
|
1203 |
-
|
1204 |
-
return CausalLMOutputWithPast(loss=None, logits=lm_logits, past_key_values=past_key_values)
|
1205 |
|
1206 |
def forward(
|
1207 |
self,
|
@@ -1217,6 +1218,12 @@ class ImpForCausalLM(PhiPreTrainedModel, LlavaMetaForCausalLM):
|
|
1217 |
images: Optional[torch.FloatTensor] = None,
|
1218 |
return_dict: Optional[bool] = None,
|
1219 |
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
|
|
|
|
|
|
|
|
|
|
|
1220 |
|
1221 |
if inputs_embeds is None:
|
1222 |
(
|
@@ -1235,17 +1242,44 @@ class ImpForCausalLM(PhiPreTrainedModel, LlavaMetaForCausalLM):
|
|
1235 |
images
|
1236 |
)
|
1237 |
|
1238 |
-
|
1239 |
input_ids=input_ids,
|
|
|
1240 |
attention_mask=attention_mask,
|
1241 |
-
position_ids=position_ids,
|
1242 |
-
past_key_values=past_key_values,
|
1243 |
inputs_embeds=inputs_embeds,
|
1244 |
-
labels=labels,
|
1245 |
use_cache=use_cache,
|
1246 |
output_attentions=output_attentions,
|
1247 |
output_hidden_states=output_hidden_states,
|
1248 |
return_dict=return_dict
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
1249 |
)
|
1250 |
|
1251 |
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
|
|
|
4 |
# Some code here is copied from the project Phi-2 (https://huggingface.co/microsoft/phi-2),
|
5 |
# SigLIP@transformers==4.37.0.dev0 (https://huggingface.co/google/siglip-so400m-patch14-384),
|
6 |
# and Llava (https://github.com/haotian-liu/LLaVA), and modified by
|
7 |
+
# Zhenwei Shao ([email protected]) and Xuecheng Ouyang (ouyangxc@hdu.edu.cn) @ MILVLG. We thank them for their great works.
|
8 |
# And their original licenses and copyright should be inherited (see the statements
|
9 |
# in `configuration_imp.py` for more details).
|
10 |
|
|
|
16 |
import os
|
17 |
import math
|
18 |
import re
|
|
|
19 |
from typing import Any, Dict, Optional, Tuple, Union, List
|
20 |
from abc import ABC, abstractmethod
|
21 |
|
22 |
import torch
|
23 |
+
import torch.nn.functional as F
|
24 |
+
import torch.utils.checkpoint
|
25 |
+
from torch import nn
|
26 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
27 |
+
import torch.utils.checkpoint
|
28 |
from transformers import (
|
29 |
PretrainedConfig,
|
30 |
PreTrainedModel,
|
|
|
32 |
AutoModelForCausalLM
|
33 |
)
|
34 |
from transformers.activations import ACT2FN
|
35 |
+
from transformers.cache_utils import Cache, DynamicCache
|
36 |
+
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
|
37 |
+
from transformers.modeling_outputs import (
|
38 |
+
BaseModelOutputWithPast,
|
39 |
+
CausalLMOutputWithPast,
|
40 |
+
SequenceClassifierOutputWithPast,
|
41 |
+
TokenClassifierOutput,
|
42 |
+
)
|
43 |
+
from transformers.modeling_utils import PreTrainedModel
|
44 |
+
from transformers.utils import (
|
45 |
+
add_code_sample_docstrings,
|
46 |
+
add_start_docstrings,
|
47 |
+
add_start_docstrings_to_model_forward,
|
48 |
+
is_flash_attn_2_available,
|
49 |
+
is_flash_attn_greater_or_equal_2_10,
|
50 |
+
logging,
|
51 |
+
replace_return_docstrings,
|
52 |
+
)
|
53 |
import sys
|
54 |
from .configuration_imp import PhiConfig, ImpConfig
|
55 |
from .vision_encoder import VisionTower
|
56 |
|
57 |
try:
|
58 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
59 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
|
|
|
|
60 |
except:
|
61 |
+
pass
|
|
|
|
|
|
|
62 |
|
63 |
+
logger = logging.get_logger(__name__)
|
64 |
|
65 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi
|
66 |
+
class PhiRotaryEmbedding(nn.Module):
|
67 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
68 |
super().__init__()
|
69 |
|
70 |
+
self.dim = dim
|
71 |
+
self.max_position_embeddings = max_position_embeddings
|
72 |
+
self.base = base
|
73 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
74 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
|
|
|
|
|
|
|
|
|
|
|
75 |
|
76 |
+
# Build here to make `torch.jit.trace` work.
|
77 |
+
self._set_cos_sin_cache(
|
78 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
79 |
+
)
|
80 |
|
81 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
82 |
+
self.max_seq_len_cached = seq_len
|
83 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
84 |
|
85 |
+
freqs = torch.outer(t, self.inv_freq)
|
86 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
87 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
88 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
89 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
|
|
|
|
|
|
90 |
|
91 |
+
def forward(self, x, seq_len=None):
|
92 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
93 |
+
if seq_len > self.max_seq_len_cached:
|
94 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
95 |
|
96 |
+
return (
|
97 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
98 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
99 |
+
)
|
100 |
|
|
|
101 |
|
102 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
|
103 |
+
class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
|
104 |
+
"""PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
105 |
|
106 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
107 |
+
self.scaling_factor = scaling_factor
|
108 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
109 |
|
110 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
111 |
+
self.max_seq_len_cached = seq_len
|
112 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
113 |
+
t = t / self.scaling_factor
|
|
|
|
|
|
|
|
|
|
|
|
|
114 |
|
115 |
+
freqs = torch.outer(t, self.inv_freq)
|
116 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
117 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
118 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
119 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
120 |
|
|
|
|
|
|
|
121 |
|
122 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
|
123 |
+
class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
|
124 |
+
"""PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
125 |
|
126 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
127 |
+
self.scaling_factor = scaling_factor
|
128 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
|
|
|
|
|
|
|
|
129 |
|
130 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
131 |
+
self.max_seq_len_cached = seq_len
|
132 |
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133 |
+
if seq_len > self.max_position_embeddings:
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+
base = self.base * (
|
135 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
136 |
+
) ** (self.dim / (self.dim - 2))
|
137 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
138 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
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139 |
|
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+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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141 |
|
142 |
+
freqs = torch.outer(t, self.inv_freq)
|
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+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
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+
emb = torch.cat((freqs, freqs), dim=-1)
|
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+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
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+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
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148 |
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+
# Copied from transformers.models.llama.modeling_llama.rotate_half
|
150 |
+
def rotate_half(x):
|
151 |
+
"""Rotates half the hidden dims of the input."""
|
152 |
+
x1 = x[..., : x.shape[-1] // 2]
|
153 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
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+
return torch.cat((-x2, x1), dim=-1)
|
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156 |
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157 |
+
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
|
158 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
159 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
160 |
|
161 |
+
Args:
|
162 |
+
q (`torch.Tensor`): The query tensor.
|
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+
k (`torch.Tensor`): The key tensor.
|
164 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
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+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
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+
position_ids (`torch.Tensor`):
|
167 |
+
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
168 |
+
used to pass offsetted position ids when working with a KV-cache.
|
169 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
170 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
171 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
172 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
173 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
174 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
175 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
176 |
+
Returns:
|
177 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
178 |
+
"""
|
179 |
+
temp_type=q.dtype#ouyang modified
|
180 |
+
q, k, cos, sin = [t.to(dtype=torch.float32) for t in [q, k, cos, sin]] #ouyang modified
|
181 |
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
182 |
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
183 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
184 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
185 |
+
q_embed,k_embed = q_embed.to(temp_type), k_embed.to(temp_type)#ouyang modified
|
186 |
+
return q_embed, k_embed
|
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188 |
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189 |
|
190 |
+
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
|
191 |
+
class PhiMLP(nn.Module):
|
192 |
+
def __init__(self, config):
|
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|
193 |
super().__init__()
|
194 |
+
self.config = config
|
195 |
+
self.activation_fn = ACT2FN[config.hidden_act]
|
196 |
+
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
197 |
+
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
198 |
|
199 |
+
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
200 |
+
hidden_states = self.fc1(hidden_states)
|
201 |
+
hidden_states = self.activation_fn(hidden_states)
|
202 |
+
hidden_states = self.fc2(hidden_states)
|
203 |
+
return hidden_states
|
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205 |
|
206 |
+
# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
|
207 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
208 |
+
"""
|
209 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
210 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
211 |
+
"""
|
212 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
213 |
+
if n_rep == 1:
|
214 |
+
return hidden_states
|
215 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
216 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
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|
218 |
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|
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|
220 |
+
class PhiAttention(nn.Module):
|
221 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
222 |
|
223 |
+
def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
|
224 |
+
super().__init__()
|
225 |
+
self.config = config
|
226 |
+
self.layer_idx = layer_idx
|
227 |
+
# if layer_idx is None:
|
228 |
+
# logger.warning_once(
|
229 |
+
# f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
230 |
+
# "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
231 |
+
# "when creating this class."
|
232 |
+
# )
|
233 |
+
|
234 |
+
self.attention_dropout = config.attention_dropout
|
235 |
+
self.hidden_size = config.hidden_size
|
236 |
+
self.num_heads = config.num_attention_heads
|
237 |
+
self.head_dim = self.hidden_size // self.num_heads
|
238 |
+
self.num_key_value_heads = config.num_key_value_heads
|
239 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
240 |
+
self.max_position_embeddings = config.max_position_embeddings
|
241 |
+
self.rope_theta = config.rope_theta
|
242 |
+
self.partial_rotary_factor = config.partial_rotary_factor
|
243 |
+
self.is_causal = True
|
244 |
+
|
245 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
246 |
+
raise ValueError(
|
247 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
248 |
+
f" and `num_heads`: {self.num_heads})."
|
249 |
+
)
|
|
|
|
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|
|
250 |
|
251 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
|
252 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
|
253 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
|
254 |
+
self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True)
|
|
|
255 |
|
256 |
+
self.qk_layernorm = config.qk_layernorm
|
257 |
+
if self.qk_layernorm:
|
258 |
+
self.q_layernorm = nn.LayerNorm(
|
259 |
+
config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
|
|
|
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|
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|
|
|
|
|
260 |
)
|
261 |
+
self.k_layernorm = nn.LayerNorm(
|
262 |
+
config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
|
|
|
|
|
263 |
)
|
264 |
|
265 |
+
self._init_rope()
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
266 |
|
267 |
+
def _init_rope(self):
|
268 |
+
if self.config.rope_scaling is None:
|
269 |
+
self.rotary_emb = PhiRotaryEmbedding(
|
270 |
+
int(self.partial_rotary_factor * self.head_dim),
|
271 |
+
max_position_embeddings=self.max_position_embeddings,
|
272 |
+
base=self.rope_theta,
|
273 |
+
)
|
274 |
+
else:
|
275 |
+
scaling_type = self.config.rope_scaling["type"]
|
276 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
277 |
+
if scaling_type == "linear":
|
278 |
+
self.rotary_emb = PhiLinearScalingRotaryEmbedding(
|
279 |
+
int(self.partial_rotary_factor * self.head_dim),
|
280 |
+
max_position_embeddings=self.max_position_embeddings,
|
281 |
+
scaling_factor=scaling_factor,
|
282 |
+
base=self.rope_theta,
|
283 |
+
)
|
284 |
+
elif scaling_type == "dynamic":
|
285 |
+
self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
|
286 |
+
int(self.partial_rotary_factor * self.head_dim),
|
287 |
+
max_position_embeddings=self.max_position_embeddings,
|
288 |
+
scaling_factor=scaling_factor,
|
289 |
+
base=self.rope_theta,
|
290 |
+
)
|
291 |
+
else:
|
292 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
|
|
293 |
|
294 |
+
# Phi-2 has an attention overflow issue (with FP16) and requires autocast to be disabled
|
295 |
@torch.autocast("cpu", enabled=False)
|
296 |
@torch.autocast("cuda", enabled=False)
|
297 |
def forward(
|
298 |
self,
|
299 |
+
hidden_states: torch.Tensor,
|
300 |
+
attention_mask: Optional[torch.Tensor] = None,
|
301 |
+
position_ids: Optional[torch.LongTensor] = None,
|
302 |
+
past_key_value: Optional[Cache] = None,
|
303 |
+
output_attentions: bool = False,
|
304 |
+
use_cache: bool = False,
|
305 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
306 |
+
bsz, q_len, _ = hidden_states.size()
|
307 |
+
|
|
|
308 |
|
309 |
+
query_states = self.q_proj(hidden_states)
|
310 |
+
key_states = self.k_proj(hidden_states)
|
311 |
+
value_states = self.v_proj(hidden_states)
|
312 |
+
|
313 |
+
if self.qk_layernorm:
|
314 |
+
query_states = self.q_layernorm(query_states)
|
315 |
+
key_states = self.k_layernorm(key_states)
|
316 |
+
|
317 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
318 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
319 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
320 |
+
|
321 |
+
kv_seq_len = key_states.shape[-2]
|
322 |
+
if past_key_value is not None:
|
323 |
+
if self.layer_idx is None:
|
324 |
+
raise ValueError(
|
325 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
326 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
327 |
+
"with a layer index."
|
328 |
+
)
|
329 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
330 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
331 |
|
332 |
+
# Partial rotary embedding
|
333 |
+
query_rot, query_pass = (
|
334 |
+
query_states[..., : self.rotary_emb.dim],
|
335 |
+
query_states[..., self.rotary_emb.dim :],
|
336 |
+
)
|
337 |
+
key_rot, key_pass = (
|
338 |
+
key_states[..., : self.rotary_emb.dim],
|
339 |
+
key_states[..., self.rotary_emb.dim :],
|
340 |
+
)
|
341 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
342 |
+
query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
343 |
+
|
344 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
345 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
346 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
347 |
+
|
348 |
+
if past_key_value is not None:
|
349 |
+
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
|
350 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
351 |
+
|
352 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
353 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
354 |
+
|
355 |
+
# Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
|
356 |
+
# attn_weights = torch.matmul(
|
357 |
+
# query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
|
358 |
+
# ) / math.sqrt(self.head_dim)
|
359 |
+
|
360 |
+
softmax_scale = 1.0 / math.sqrt(query_states.shape[-1])
|
361 |
+
attn_weights = torch.matmul(
|
362 |
+
query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)*softmax_scale
|
363 |
+
)#ouyang modified
|
364 |
+
|
365 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
366 |
+
raise ValueError(
|
367 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
368 |
+
f" {attn_weights.size()}"
|
369 |
+
)
|
370 |
|
371 |
+
if attention_mask is not None:
|
372 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
373 |
+
raise ValueError(
|
374 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
375 |
+
)
|
376 |
+
attn_weights = attn_weights + attention_mask
|
377 |
|
378 |
+
# upcast attention to fp32
|
379 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
|
380 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
381 |
|
382 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
|
|
|
|
383 |
|
384 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
385 |
+
raise ValueError(
|
386 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
387 |
+
f" {attn_output.size()}"
|
388 |
+
)
|
389 |
|
390 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
391 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
392 |
|
|
|
393 |
|
394 |
+
attn_output = self.dense(attn_output)
|
395 |
|
396 |
+
if not output_attentions:
|
397 |
+
attn_weights = None
|
398 |
|
399 |
+
return attn_output, attn_weights, past_key_value
|
|
|
400 |
|
401 |
+
class PhiFlashAttention2(PhiAttention):
|
402 |
+
"""
|
403 |
+
Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
|
404 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
405 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
406 |
"""
|
407 |
|
408 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
409 |
+
def __init__(self, *args, **kwargs):
|
410 |
+
super().__init__(*args, **kwargs)
|
|
|
|
|
|
|
|
|
411 |
|
412 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
413 |
+
# 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.
|
414 |
+
# 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).
|
415 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
416 |
|
|
|
|
|
417 |
def forward(
|
418 |
self,
|
419 |
+
hidden_states: torch.Tensor,
|
420 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
421 |
+
position_ids: Optional[torch.LongTensor] = None,
|
422 |
+
past_key_value: Optional[Cache] = None,
|
423 |
+
output_attentions: bool = False,
|
424 |
+
use_cache: bool = False,
|
425 |
**kwargs,
|
426 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
427 |
+
# PhiFlashAttention2 attention does not support output_attentions
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
428 |
|
429 |
+
output_attentions = False
|
430 |
|
431 |
+
bsz, q_len, _ = hidden_states.size()
|
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|
432 |
|
433 |
+
query_states = self.q_proj(hidden_states)
|
434 |
+
key_states = self.k_proj(hidden_states)
|
435 |
+
value_states = self.v_proj(hidden_states)
|
436 |
|
437 |
+
if self.qk_layernorm:
|
438 |
+
query_states = self.q_layernorm(query_states)
|
439 |
+
key_states = self.k_layernorm(key_states)
|
440 |
|
441 |
+
# Flash attention requires the input to have the shape
|
442 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
443 |
+
# therefore we just need to keep the original shape
|
444 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
445 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
446 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
447 |
|
448 |
+
kv_seq_len = key_states.shape[-2]
|
449 |
+
if past_key_value is not None:
|
450 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
451 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
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|
452 |
|
453 |
+
# Partial rotary embedding
|
454 |
+
query_rot, query_pass = (
|
455 |
+
query_states[..., : self.rotary_emb.dim],
|
456 |
+
query_states[..., self.rotary_emb.dim :],
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|
457 |
)
|
458 |
+
key_rot, key_pass = (
|
459 |
+
key_states[..., : self.rotary_emb.dim],
|
460 |
+
key_states[..., self.rotary_emb.dim :],
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|
461 |
)
|
462 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
463 |
+
query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
464 |
+
|
465 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
466 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
467 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
468 |
+
|
469 |
+
if past_key_value is not None:
|
470 |
+
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
|
471 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
472 |
+
|
473 |
+
# 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
|
474 |
+
# to be able to avoid many of these transpose/reshape/view.
|
475 |
+
query_states = query_states.transpose(1, 2)
|
476 |
+
key_states = key_states.transpose(1, 2)
|
477 |
+
value_states = value_states.transpose(1, 2)
|
478 |
+
|
479 |
+
attn_dropout = self.attention_dropout if self.training else 0.0
|
480 |
+
|
481 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
482 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
483 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
484 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
485 |
+
# in fp32.
|
486 |
+
|
487 |
+
if query_states.dtype == torch.float32:
|
488 |
+
if torch.is_autocast_enabled():
|
489 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
490 |
+
# Handle the case where the model is quantized
|
491 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
492 |
+
target_dtype = self.config._pre_quantization_dtype
|
|
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|
|
493 |
else:
|
494 |
+
target_dtype = self.q_proj.weight.dtype
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
495 |
|
496 |
+
logger.warning_once(
|
497 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
498 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
499 |
+
f" {target_dtype}."
|
500 |
+
)
|
|
|
|
|
|
|
|
|
501 |
|
502 |
+
query_states = query_states.to(target_dtype)
|
503 |
+
key_states = key_states.to(target_dtype)
|
504 |
+
value_states = value_states.to(target_dtype)
|
505 |
|
506 |
+
attn_output = self._flash_attention_forward(
|
507 |
+
query_states, key_states, value_states, attention_mask, q_len, dropout=attn_dropout, softmax_scale=None
|
508 |
+
)
|
509 |
|
510 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
511 |
+
attn_output = self.dense(attn_output)
|
|
|
|
|
512 |
|
513 |
+
if not output_attentions:
|
514 |
+
attn_weights = None
|
515 |
|
516 |
+
return attn_output, attn_weights, past_key_value
|
|
|
|
|
517 |
|
518 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
|
519 |
+
def _flash_attention_forward(
|
520 |
+
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
521 |
+
):
|
522 |
+
"""
|
523 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
524 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
525 |
+
|
526 |
+
Args:
|
527 |
+
query_states (`torch.Tensor`):
|
528 |
+
Input query states to be passed to Flash Attention API
|
529 |
+
key_states (`torch.Tensor`):
|
530 |
+
Input key states to be passed to Flash Attention API
|
531 |
+
value_states (`torch.Tensor`):
|
532 |
+
Input value states to be passed to Flash Attention API
|
533 |
+
attention_mask (`torch.Tensor`):
|
534 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
535 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
536 |
+
dropout (`int`, *optional*):
|
537 |
+
Attention dropout
|
538 |
+
softmax_scale (`float`, *optional*):
|
539 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
540 |
+
"""
|
541 |
+
if not self._flash_attn_uses_top_left_mask:
|
542 |
+
causal = self.is_causal
|
543 |
+
else:
|
544 |
+
# 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__.
|
545 |
+
causal = self.is_causal and query_length != 1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
546 |
|
547 |
+
# Contains at least one padding token in the sequence
|
548 |
+
if attention_mask is not None:
|
549 |
+
batch_size = query_states.shape[0]
|
550 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
551 |
+
query_states, key_states, value_states, attention_mask, query_length
|
552 |
)
|
553 |
|
554 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
555 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
556 |
+
|
557 |
+
attn_output_unpad = flash_attn_varlen_func(
|
558 |
+
query_states,
|
559 |
+
key_states,
|
560 |
+
value_states,
|
561 |
+
cu_seqlens_q=cu_seqlens_q,
|
562 |
+
cu_seqlens_k=cu_seqlens_k,
|
563 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
564 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
565 |
+
dropout_p=dropout,
|
566 |
+
softmax_scale=softmax_scale,
|
567 |
causal=causal,
|
568 |
)
|
569 |
|
570 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
571 |
else:
|
572 |
+
attn_output = flash_attn_func(
|
573 |
+
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
574 |
+
)
|
575 |
|
576 |
+
return attn_output
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
577 |
|
578 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
|
579 |
+
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
580 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
581 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
582 |
|
583 |
+
key_layer = index_first_axis(
|
584 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
585 |
+
)
|
586 |
+
value_layer = index_first_axis(
|
587 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
588 |
+
)
|
589 |
+
if query_length == kv_seq_len:
|
590 |
+
query_layer = index_first_axis(
|
591 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
592 |
+
)
|
593 |
+
cu_seqlens_q = cu_seqlens_k
|
594 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
595 |
+
indices_q = indices_k
|
596 |
+
elif query_length == 1:
|
597 |
+
max_seqlen_in_batch_q = 1
|
598 |
+
cu_seqlens_q = torch.arange(
|
599 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
600 |
+
) # There is a memcpy here, that is very bad.
|
601 |
+
indices_q = cu_seqlens_q[:-1]
|
602 |
+
query_layer = query_layer.squeeze(1)
|
603 |
+
else:
|
604 |
+
# The -q_len: slice assumes left padding.
|
605 |
+
attention_mask = attention_mask[:, -query_length:]
|
606 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
607 |
+
|
608 |
+
return (
|
609 |
+
query_layer,
|
610 |
+
key_layer,
|
611 |
+
value_layer,
|
612 |
+
indices_q,
|
613 |
+
(cu_seqlens_q, cu_seqlens_k),
|
614 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
615 |
+
)
|
616 |
|
617 |
|
|
|
|
|
618 |
|
619 |
+
PHI_ATTENTION_CLASSES = {
|
620 |
+
"eager": PhiAttention,
|
621 |
+
"flash_attention_2": PhiFlashAttention2,
|
622 |
+
}
|
623 |
|
|
|
624 |
|
625 |
+
class PhiDecoderLayer(nn.Module):
|
626 |
+
def __init__(self, config: PhiConfig, layer_idx: int):
|
|
|
|
|
|
|
627 |
super().__init__()
|
628 |
+
self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
|
629 |
+
self.mlp = PhiMLP(config)
|
630 |
+
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
631 |
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
|
|
|
|
|
|
|
|
632 |
|
633 |
def forward(
|
634 |
self,
|
635 |
+
hidden_states: torch.Tensor,
|
636 |
+
attention_mask: Optional[torch.Tensor] = None,
|
637 |
+
position_ids: Optional[torch.LongTensor] = None,
|
638 |
+
output_attentions: Optional[bool] = False,
|
639 |
+
use_cache: Optional[bool] = False,
|
640 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
641 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
642 |
+
"""
|
643 |
+
Args:
|
644 |
+
hidden_states (`torch.FloatTensor`):
|
645 |
+
input to the layer of shape `(batch, seq_len, embed_dim)`
|
646 |
+
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
647 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
648 |
+
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
649 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
|
650 |
+
`[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
651 |
+
output_attentions (`bool`, *optional*):
|
652 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
653 |
+
returned tensors for more detail.
|
654 |
+
use_cache (`bool`, *optional*):
|
655 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
656 |
+
(see `past_key_values`).
|
657 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
658 |
+
"""
|
659 |
+
|
660 |
residual = hidden_states
|
|
|
661 |
|
662 |
+
hidden_states = self.input_layernorm(hidden_states)
|
663 |
+
|
664 |
+
# Self Attention
|
665 |
+
attn_outputs, self_attn_weights, present_key_value = self.self_attn(
|
666 |
+
hidden_states=hidden_states,
|
667 |
attention_mask=attention_mask,
|
668 |
+
position_ids=position_ids,
|
669 |
+
past_key_value=past_key_value,
|
670 |
+
output_attentions=output_attentions,
|
671 |
+
use_cache=use_cache,
|
672 |
)
|
|
|
|
|
|
|
673 |
attn_outputs = self.resid_dropout(attn_outputs)
|
|
|
674 |
|
675 |
+
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
676 |
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
677 |
+
outputs = (hidden_states,)
|
678 |
|
679 |
+
if output_attentions:
|
680 |
+
outputs += (self_attn_weights,)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
681 |
|
682 |
+
if use_cache:
|
683 |
+
outputs += (present_key_value,)
|
|
|
684 |
|
685 |
+
return outputs
|
686 |
|
687 |
|
688 |
class PhiPreTrainedModel(PreTrainedModel):
|
689 |
"""Phi pre-trained model."""
|
690 |
|
691 |
config_class = PhiConfig
|
692 |
+
base_model_prefix = "model"
|
693 |
supports_gradient_checkpointing = True
|
694 |
+
_no_split_modules = ["PhiDecoderLayer"]
|
695 |
+
_skip_keys_device_placement = "past_key_values"
|
696 |
+
_supports_flash_attn_2 = True
|
697 |
+
_supports_cache_class = True
|
698 |
|
699 |
def __init__(self, *inputs, **kwargs) -> None:
|
700 |
super().__init__(*inputs, **kwargs)
|
701 |
|
702 |
+
def _init_weights(self, module):
|
703 |
+
std = self.config.initializer_range
|
704 |
+
if isinstance(module, nn.Linear):
|
705 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
706 |
if module.bias is not None:
|
707 |
module.bias.data.zero_()
|
708 |
elif isinstance(module, nn.Embedding):
|
709 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
710 |
if module.padding_idx is not None:
|
711 |
module.weight.data[module.padding_idx].zero_()
|
|
|
|
|
|
|
|
|
712 |
|
713 |
def prepare_inputs_for_generation(
|
714 |
self,
|
715 |
input_ids: torch.LongTensor,
|
716 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
717 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
718 |
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
719 |
**kwargs,
|
720 |
) -> Dict[str, Any]:
|
721 |
+
if past_key_values is not None:
|
722 |
+
if isinstance(past_key_values, Cache):
|
723 |
+
cache_length = past_key_values.get_seq_length()
|
724 |
+
past_length = past_key_values.seen_tokens
|
725 |
+
max_cache_length = past_key_values.get_max_length()
|
726 |
+
else:
|
727 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
728 |
+
max_cache_length = None
|
729 |
+
|
730 |
+
# Keep only the unprocessed tokens:
|
731 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
732 |
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
733 |
+
# input)
|
734 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
735 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
736 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
737 |
+
# input_ids based on the past_length.
|
738 |
+
elif past_length < input_ids.shape[1]:
|
739 |
+
input_ids = input_ids[:, past_length:]
|
740 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
741 |
+
|
742 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
743 |
+
if (
|
744 |
+
max_cache_length is not None
|
745 |
+
and attention_mask is not None
|
746 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
747 |
+
):
|
748 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
749 |
+
|
750 |
+
position_ids = kwargs.get("position_ids", None)
|
751 |
+
if attention_mask is not None and position_ids is None:
|
752 |
+
# create position_ids on the fly for batch generation
|
753 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
754 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
755 |
+
if past_key_values:
|
756 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
757 |
+
|
758 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
759 |
+
if inputs_embeds is not None and past_key_values is None:
|
760 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
761 |
else:
|
762 |
+
model_inputs = {"input_ids": input_ids}
|
763 |
+
|
764 |
+
model_inputs.update(
|
765 |
+
{
|
766 |
+
"position_ids": position_ids,
|
767 |
+
"past_key_values": past_key_values,
|
768 |
+
"use_cache": kwargs.get("use_cache"),
|
769 |
+
"attention_mask": attention_mask,
|
770 |
+
}
|
771 |
+
)
|
772 |
+
return model_inputs
|
|
|
|
|
|
|
773 |
|
774 |
|
775 |
class LlavaMetaModel(ABC):
|
|
|
814 |
class ImpModel(PhiPreTrainedModel, LlavaMetaModel):
|
815 |
"""Imp model. This implementation is modified from the implementation of Phi-2"""
|
816 |
|
|
|
|
|
|
|
817 |
|
818 |
def __init__(self, config: ImpConfig) -> None:
|
819 |
super().__init__(config)
|
820 |
+
self.padding_idx = config.pad_token_id
|
821 |
+
self.vocab_size = config.vocab_size
|
822 |
+
|
823 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
824 |
+
self.embed_dropout = nn.Dropout(config.embd_pdrop)
|
825 |
+
self.layers = nn.ModuleList(
|
826 |
+
[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
827 |
+
)
|
828 |
+
self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
829 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
830 |
|
|
|
|
|
831 |
self.gradient_checkpointing = False
|
832 |
|
833 |
if hasattr(config, "mm_vision_tower"):
|
|
|
836 |
|
837 |
self.post_init()
|
838 |
|
839 |
+
# def embed_tokens(self, input_ids: torch.LongTensor) -> torch.FloatTensor: #old
|
840 |
+
# return self.embd(input_ids)[0]
|
841 |
|
842 |
def get_input_embeddings(self) -> nn.Embedding:
|
843 |
+
# return self.embd.wte#old
|
844 |
+
return self.embed_tokens
|
845 |
|
846 |
+
def set_input_embeddings(self, value: nn.Embedding) -> None:
|
847 |
+
self.embed_tokens = value
|
848 |
|
849 |
def forward(
|
850 |
self,
|
851 |
input_ids: torch.LongTensor,
|
|
|
852 |
attention_mask: Optional[torch.BoolTensor] = None,
|
853 |
+
position_ids: Optional[torch.LongTensor] = None,
|
854 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
855 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
856 |
+
use_cache: Optional[bool] = None,
|
857 |
+
output_attentions: Optional[bool] = None,
|
858 |
+
output_hidden_states: Optional[bool] = None,
|
859 |
+
return_dict: Optional[bool] = None,
|
860 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
861 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
862 |
+
output_hidden_states = (
|
863 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
864 |
+
)
|
865 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
866 |
|
867 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
868 |
+
|
869 |
+
# retrieve input_ids and inputs_embeds
|
870 |
+
if input_ids is not None and inputs_embeds is not None:
|
871 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
872 |
+
elif input_ids is not None:
|
873 |
+
batch_size, seq_length = input_ids.shape
|
874 |
+
elif inputs_embeds is not None:
|
875 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
876 |
else:
|
877 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
878 |
+
|
879 |
+
past_key_values_length = 0
|
880 |
|
881 |
+
if self.gradient_checkpointing and self.training:
|
882 |
+
if use_cache:
|
883 |
+
logger.warning_once(
|
884 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
885 |
+
)
|
886 |
+
use_cache = False
|
887 |
+
if use_cache:
|
888 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
889 |
+
if use_legacy_cache:
|
890 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
891 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
892 |
+
|
893 |
+
|
894 |
|
895 |
+
if position_ids is None:
|
896 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
897 |
+
position_ids = torch.arange(
|
898 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
899 |
+
)
|
900 |
+
position_ids = position_ids.unsqueeze(0)
|
901 |
+
|
902 |
+
if inputs_embeds is None:
|
903 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
904 |
|
905 |
+
inputs_embeds = self.embed_dropout(inputs_embeds)
|
906 |
|
907 |
+
if self._use_flash_attention_2:
|
908 |
+
# 2d mask is passed through the layers
|
909 |
+
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
910 |
+
else:
|
911 |
+
# 4d mask is passed through the layers
|
912 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
913 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
914 |
+
)
|
915 |
+
hidden_states = inputs_embeds
|
916 |
+
# ok
|
917 |
+
|
918 |
+
# decoder layers
|
919 |
+
all_hidden_states = () if output_hidden_states else None
|
920 |
+
all_self_attns = () if output_attentions else None
|
921 |
+
next_decoder_cache = None
|
922 |
+
|
923 |
+
|
924 |
+
for nums,decoder_layer in enumerate(self.layers):
|
925 |
+
if output_hidden_states:
|
926 |
+
all_hidden_states += (hidden_states,)
|
927 |
+
|
928 |
+
if self.gradient_checkpointing and self.training:
|
929 |
+
layer_outputs = self._gradient_checkpointing_func(
|
930 |
+
decoder_layer.__call__,
|
931 |
hidden_states,
|
|
|
932 |
attention_mask,
|
933 |
+
position_ids,
|
934 |
+
past_key_values,
|
935 |
+
output_attentions,
|
936 |
)
|
937 |
else:
|
938 |
+
layer_outputs = decoder_layer(
|
939 |
hidden_states,
|
|
|
940 |
attention_mask=attention_mask,
|
941 |
+
position_ids=position_ids,
|
942 |
+
past_key_value=past_key_values,
|
943 |
+
output_attentions=output_attentions,
|
944 |
+
use_cache=use_cache,
|
945 |
)
|
946 |
+
hidden_states = layer_outputs[0]
|
947 |
+
|
948 |
+
if use_cache:
|
949 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
950 |
+
if output_attentions:
|
951 |
+
all_self_attns += (layer_outputs[1],)
|
952 |
+
|
953 |
+
|
954 |
+
hidden_states = self.final_layernorm(hidden_states) #final_new_phi
|
955 |
+
|
956 |
+
# add hidden states from the last decoder layer
|
957 |
+
if output_hidden_states:
|
958 |
+
all_hidden_states += (hidden_states,)
|
959 |
+
|
960 |
+
next_cache = None
|
961 |
+
if use_cache:
|
962 |
+
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
963 |
+
if not return_dict:
|
964 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
965 |
+
return BaseModelOutputWithPast(
|
966 |
+
last_hidden_state=hidden_states,
|
967 |
+
past_key_values=next_cache,
|
968 |
+
hidden_states=all_hidden_states,
|
969 |
+
attentions=all_self_attns,
|
970 |
+
)
|
971 |
|
|
|
|
|
|
|
|
|
|
|
|
|
972 |
|
973 |
|
974 |
class LlavaMetaForCausalLM(ABC):
|
|
|
995 |
self, input_ids, position_ids, attention_mask, past_key_values, labels, images
|
996 |
):
|
997 |
vision_tower = self.get_vision_tower()
|
998 |
+
if past_key_values is not None:
|
999 |
+
target_shape = past_key_values[0][0].shape[2] + 1
|
1000 |
+
attention_mask = torch.ones(
|
1001 |
+
(attention_mask.shape[0], target_shape),
|
1002 |
+
dtype=attention_mask.dtype,
|
1003 |
+
device=attention_mask.device
|
1004 |
+
)
|
1005 |
+
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
|
1006 |
+
# print(input_ids[:, -1:].item())
|
1007 |
+
return input_ids[:, -1:], position_ids, attention_mask, past_key_values, None, labels
|
1008 |
if vision_tower is None or images is None or input_ids.shape[1] == 1:
|
1009 |
+
# if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
|
1010 |
+
# target_shape = past_key_values.seqlen_offset + 1
|
1011 |
+
# attention_mask = torch.cat((attention_mask, torch.ones(
|
1012 |
+
# (attention_mask.shape[0], target_shape - attention_mask.shape[1]),
|
1013 |
+
# dtype=attention_mask.dtype,
|
1014 |
+
# device=attention_mask.device
|
1015 |
+
# )), dim=1)
|
1016 |
+
# position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
|
1017 |
+
return input_ids, None, None, past_key_values, None, None
|
1018 |
+
# return input_ids, position_ids, attention_mask, past_key_values, None, labels
|
1019 |
+
|
1020 |
+
# if vision_tower is None or images is None or past_key_values.seqlen_offset != 0:
|
1021 |
+
# if vision_tower is None or images is None or input_ids.shape[1] == 1:
|
1022 |
+
# if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
|
1023 |
+
# target_shape = past_key_values.seqlen_offset + 1
|
1024 |
+
# # inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, sequence_start:sequence_end, ...]
|
1025 |
+
# attention_mask = torch.cat((attention_mask, torch.ones(
|
1026 |
+
# (attention_mask.shape[0], target_shape - attention_mask.shape[1]),
|
1027 |
+
# dtype=attention_mask.dtype,
|
1028 |
+
# device=attention_mask.device
|
1029 |
+
# )), dim=1)
|
1030 |
+
# position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
|
1031 |
+
# return input_ids, position_ids, attention_mask, past_key_values, None, labels
|
1032 |
|
1033 |
if type(images) is list or images.ndim == 5:
|
1034 |
concat_images = torch.cat([image for image in images], dim=0)
|
|
|
1160 |
position_ids = None
|
1161 |
|
1162 |
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
|
1163 |
+
#return input_ids, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
|
1164 |
|
1165 |
|
1166 |
class ImpForCausalLM(PhiPreTrainedModel, LlavaMetaForCausalLM):
|
|
|
1173 |
def __init__(self, config: ImpConfig) -> None:
|
1174 |
super().__init__(config)
|
1175 |
|
1176 |
+
self.model = ImpModel(config)
|
1177 |
+
self.vocab_size = config.vocab_size
|
1178 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
|
1179 |
|
1180 |
self.post_init()
|
1181 |
self.init_constants(config)
|
1182 |
|
1183 |
+
def get_input_embeddings(self):
|
1184 |
+
return self.model.embed_tokens
|
1185 |
+
|
1186 |
+
def set_input_embeddings(self, value):
|
1187 |
+
self.model.embed_tokens = value
|
1188 |
+
|
1189 |
def get_output_embeddings(self) -> nn.Linear:
|
1190 |
+
return self.lm_head
|
1191 |
|
1192 |
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
|
1193 |
+
self.lm_head = new_embeddings
|
1194 |
|
1195 |
def get_model(self):
|
1196 |
+
return self.model
|
1197 |
+
|
1198 |
+
def get_decoder(self):
|
1199 |
+
return self.model
|
1200 |
+
|
1201 |
+
def set_decoder(self, decoder):
|
1202 |
+
self.model = decoder
|
1203 |
|
1204 |
def image_preprocess(self, images):
|
1205 |
return self.get_vision_tower().image_processor(images)['pixel_values']
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1206 |
|
1207 |
def forward(
|
1208 |
self,
|
|
|
1218 |
images: Optional[torch.FloatTensor] = None,
|
1219 |
return_dict: Optional[bool] = None,
|
1220 |
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1221 |
+
|
1222 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1223 |
+
output_hidden_states = (
|
1224 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1225 |
+
)
|
1226 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1227 |
|
1228 |
if inputs_embeds is None:
|
1229 |
(
|
|
|
1242 |
images
|
1243 |
)
|
1244 |
|
1245 |
+
outputs = self.model(
|
1246 |
input_ids=input_ids,
|
1247 |
+
past_key_values=past_key_values,
|
1248 |
attention_mask=attention_mask,
|
1249 |
+
position_ids=position_ids,
|
|
|
1250 |
inputs_embeds=inputs_embeds,
|
|
|
1251 |
use_cache=use_cache,
|
1252 |
output_attentions=output_attentions,
|
1253 |
output_hidden_states=output_hidden_states,
|
1254 |
return_dict=return_dict
|
1255 |
+
)
|
1256 |
+
hidden_states = outputs[0]
|
1257 |
+
logits = self.lm_head(hidden_states)
|
1258 |
+
logits = logits.float()
|
1259 |
+
|
1260 |
+
loss = None
|
1261 |
+
if labels is not None:
|
1262 |
+
# Shift so that tokens < n predict n
|
1263 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
1264 |
+
shift_labels = labels[..., 1:].contiguous()
|
1265 |
+
# Flatten the tokens
|
1266 |
+
loss_fct = CrossEntropyLoss()
|
1267 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1268 |
+
shift_labels = shift_labels.view(-1)
|
1269 |
+
# Enable model parallelism
|
1270 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
1271 |
+
loss = loss_fct(shift_logits, shift_labels)
|
1272 |
+
if not return_dict:
|
1273 |
+
loss = None
|
1274 |
+
output = (logits,) + outputs[1:]
|
1275 |
+
return (loss,) + output if loss is not None else output
|
1276 |
+
|
1277 |
+
return CausalLMOutputWithPast(
|
1278 |
+
loss=loss,
|
1279 |
+
logits=logits,
|
1280 |
+
past_key_values=outputs.past_key_values,
|
1281 |
+
hidden_states=outputs.hidden_states,
|
1282 |
+
attentions=outputs.attentions,
|
1283 |
)
|
1284 |
|
1285 |
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
|
special_tokens_map.json
CHANGED
@@ -1,5 +1,23 @@
|
|
1 |
{
|
2 |
-
"bos_token":
|
3 |
-
|
4 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
5 |
}
|
|
|
1 |
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "<|endoftext|>",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"eos_token": {
|
10 |
+
"content": "<|endoftext|>",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"unk_token": {
|
17 |
+
"content": "<|endoftext|>",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
}
|
23 |
}
|
tokenizer.json
ADDED
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tokenizer_config.json
CHANGED
@@ -2,22 +2,6 @@
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2 |
"add_bos_token": false,
|
3 |
"add_prefix_space": false,
|
4 |
"added_tokens_decoder": {
|
5 |
-
"50296": {
|
6 |
-
"content": "<image>",
|
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-
"lstrip": false,
|
8 |
-
"normalized": false,
|
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-
"rstrip": false,
|
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-
"single_word": false,
|
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-
"special": true
|
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-
},
|
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-
"50295": {
|
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-
"content": "</s>",
|
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-
"lstrip": false,
|
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-
"normalized": false,
|
17 |
-
"rstrip": false,
|
18 |
-
"single_word": false,
|
19 |
-
"special": true
|
20 |
-
},
|
21 |
"50256": {
|
22 |
"content": "<|endoftext|>",
|
23 |
"lstrip": false,
|
@@ -329,35 +313,30 @@
|
|
329 |
"rstrip": false,
|
330 |
"single_word": false,
|
331 |
"special": false
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|
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|
332 |
}
|
333 |
},
|
334 |
-
"bos_token":
|
335 |
-
"__type": "AddedToken",
|
336 |
-
"content": "<|endoftext|>",
|
337 |
-
"lstrip": false,
|
338 |
-
"normalized": true,
|
339 |
-
"rstrip": false,
|
340 |
-
"single_word": false
|
341 |
-
},
|
342 |
"clean_up_tokenization_spaces": true,
|
343 |
-
"eos_token":
|
344 |
-
"__type": "AddedToken",
|
345 |
-
"content": "<|endoftext|>",
|
346 |
-
"lstrip": false,
|
347 |
-
"normalized": true,
|
348 |
-
"rstrip": false,
|
349 |
-
"single_word": false
|
350 |
-
},
|
351 |
"errors": "replace",
|
352 |
"model_max_length": 3072,
|
353 |
"pad_token": null,
|
354 |
"tokenizer_class": "CodeGenTokenizer",
|
355 |
-
"unk_token":
|
356 |
-
"__type": "AddedToken",
|
357 |
-
"content": "<|endoftext|>",
|
358 |
-
"lstrip": false,
|
359 |
-
"normalized": true,
|
360 |
-
"rstrip": false,
|
361 |
-
"single_word": false
|
362 |
-
}
|
363 |
}
|
|
|
2 |
"add_bos_token": false,
|
3 |
"add_prefix_space": false,
|
4 |
"added_tokens_decoder": {
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
5 |
"50256": {
|
6 |
"content": "<|endoftext|>",
|
7 |
"lstrip": false,
|
|
|
313 |
"rstrip": false,
|
314 |
"single_word": false,
|
315 |
"special": false
|
316 |
+
},
|
317 |
+
"50295": {
|
318 |
+
"content": "</s>",
|
319 |
+
"lstrip": false,
|
320 |
+
"normalized": false,
|
321 |
+
"rstrip": false,
|
322 |
+
"single_word": false,
|
323 |
+
"special": true
|
324 |
+
},
|
325 |
+
"50296": {
|
326 |
+
"content": "<image>",
|
327 |
+
"lstrip": false,
|
328 |
+
"normalized": false,
|
329 |
+
"rstrip": false,
|
330 |
+
"single_word": false,
|
331 |
+
"special": true
|
332 |
}
|
333 |
},
|
334 |
+
"bos_token": "<|endoftext|>",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
335 |
"clean_up_tokenization_spaces": true,
|
336 |
+
"eos_token": "<|endoftext|>",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
337 |
"errors": "replace",
|
338 |
"model_max_length": 3072,
|
339 |
"pad_token": null,
|
340 |
"tokenizer_class": "CodeGenTokenizer",
|
341 |
+
"unk_token": "<|endoftext|>"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
342 |
}
|
vocab.json
CHANGED
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|
|