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| # coding=utf-8 | |
| # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. | |
| # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ OpenAI GPT-2 configuration""" | |
| import os | |
| from typing import Tuple, Union | |
| from transformers import AutoConfig | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json", | |
| "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json", | |
| "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json", | |
| "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json", | |
| "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json", | |
| } | |
| class VGPT2Config(PretrainedConfig): | |
| """ | |
| This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to | |
| instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a | |
| configuration with the defaults will yield a similar configuration to that of the GPT-2 | |
| [gpt2](https://huggingface.co/gpt2) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| TODO: this doc is completely out of sync with the actual args | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 50257): | |
| Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`]. | |
| additional_vocab_size (`int`, *optional`, defaults to 0): | |
| Additional vocabulary size of the model, typically for the special "<img>" token. Additional vocab tokens | |
| are always trainable whereas regular vocab tokens can be frozen or not. | |
| n_positions (`int`, *optional*, defaults to 1024): | |
| The maximum sequence length that this model might ever be used with. Typically set this to something large | |
| just in case (e.g., 512 or 1024 or 2048). | |
| n_embd (`int`, *optional*, defaults to 768): | |
| Dimensionality of the embeddings and hidden states. | |
| n_layer (`int`, *optional*, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| n_head (`int`, *optional*, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| n_inner (`int`, *optional*, defaults to None): | |
| Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd | |
| activation_function (`str`, *optional*, defaults to `"gelu"`): | |
| Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. | |
| resid_pdrop (`float`, *optional*, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| embd_pdrop (`int`, *optional*, defaults to 0.1): | |
| The dropout ratio for the embeddings. | |
| attn_pdrop (`float`, *optional*, defaults to 0.1): | |
| The dropout ratio for the attention. | |
| layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): | |
| The epsilon to use in the layer normalization layers. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| alpha_initializer (`str`, *optional*, defaults to `"ones"`): | |
| Initialization type for the alphas. | |
| alphas_initializer_range (`float`, *optional*, defaults to 0.0): | |
| The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross Attention. | |
| alpha_type (`str`, *optional*, defaults to `"vector"`): | |
| Whether the gating alphas should be vectors or single floats. | |
| summary_type (`string`, *optional*, defaults to `"cls_index"`): | |
| Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and | |
| [`TFGPT2DoubleHeadsModel`]. | |
| Has to be one of the following options: | |
| - `"last"`: Take the last token hidden state (like XLNet). | |
| - `"first"`: Take the first token hidden state (like BERT). | |
| - `"mean"`: Take the mean of all tokens hidden states. | |
| - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). | |
| - `"attn"`: Not implemented now, use multi-head attention. | |
| summary_use_proj (`bool`, *optional*, defaults to `True`): | |
| Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and | |
| [`TFGPT2DoubleHeadsModel`]. | |
| Whether or not to add a projection after the vector extraction. | |
| summary_activation (`str`, *optional*): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| [`GPT2DoubleHeadsModel`]. | |
| Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. | |
| summary_proj_to_labels (`bool`, *optional*, defaults to `True`): | |
| Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and | |
| [`TFGPT2DoubleHeadsModel`]. | |
| Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. | |
| summary_first_dropout (`float`, *optional*, defaults to 0.1): | |
| Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and | |
| [`TFGPT2DoubleHeadsModel`]. | |
| The dropout ratio to be used after the projection and activation. | |
| scale_attn_weights (`bool`, *optional*, defaults to `True`): | |
| Scale attention weights by dividing by sqrt(hidden_size).. | |
| use_cache (`bool`, *optional*, defaults to `True`): | |
| Whether or not the model should return the last key/values attentions (not used by all models). | |
| scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): | |
| Whether to additionally scale attention weights by `1 / layer_idx + 1`. | |
| reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): | |
| Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention | |
| dot-product/softmax to float() when training with mixed precision. | |
| cross_layer_interval (`int`, *optional*, default to 1) | |
| Interval for cross attention (from text to image) layers. | |
| Example: | |
| ```python | |
| >>> from transformers import GPT2Model, GPT2Config | |
| >>> # Initializing a GPT2 configuration | |
| >>> configuration = GPT2Config() | |
| >>> # Initializing a model from the configuration | |
| >>> model = GPT2Model(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "vgpt2" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| attribute_map = { | |
| "hidden_size": "n_embd", | |
| "max_position_embeddings": "n_positions", | |
| "num_attention_heads": "n_head", | |
| "num_hidden_layers": "n_layer", | |
| } | |
| def __init__( | |
| self, | |
| vocab_size=50257, | |
| additional_vocab_size=0, | |
| n_positions=1024, | |
| n_embd=768, | |
| n_layer=12, | |
| n_head=12, | |
| n_inner=None, | |
| activation_function="gelu_new", | |
| resid_pdrop=0.1, | |
| embd_pdrop=0.1, | |
| attn_pdrop=0.1, | |
| layer_norm_epsilon=1e-5, | |
| initializer_range=0.02, | |
| alpha_initializer="ones", | |
| alphas_initializer_range=0.0, | |
| alpha_type="vector", | |
| summary_type="cls_index", | |
| summary_use_proj=True, | |
| summary_activation=None, | |
| summary_proj_to_labels=True, | |
| summary_first_dropout=0.1, | |
| scale_attn_weights=True, | |
| use_cache=True, | |
| bos_token_id=50256, | |
| eos_token_id=50256, | |
| scale_attn_by_inverse_layer_idx=False, | |
| reorder_and_upcast_attn=False, | |
| cross_layer_interval=1, | |
| tie_word_embeddings=False, | |
| freeze_text_layers=True, | |
| freeze_lm_head=False, | |
| freeze_vision_layers=True, | |
| vision_model_name="google/vit-base-patch16-224", | |
| vision_model_params="{}", | |
| vision_embed_dim=768, | |
| vision_image_size=224, | |
| image_token_index=50257, | |
| use_resampler=False, | |
| resampler_n_latents=64, | |
| resampler_depth=6, | |
| resampler_n_heads=16, | |
| resampler_head_dim=96, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.additional_vocab_size = additional_vocab_size | |
| self.n_positions = n_positions | |
| self.n_embd = n_embd | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.n_inner = n_inner | |
| self.activation_function = activation_function | |
| self.resid_pdrop = resid_pdrop | |
| self.embd_pdrop = embd_pdrop | |
| self.attn_pdrop = attn_pdrop | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.initializer_range = initializer_range | |
| self.alpha_initializer = alpha_initializer | |
| self.alphas_initializer_range = alphas_initializer_range | |
| self.alpha_type = alpha_type | |
| self.summary_type = summary_type | |
| self.summary_use_proj = summary_use_proj | |
| self.summary_activation = summary_activation | |
| self.summary_first_dropout = summary_first_dropout | |
| self.summary_proj_to_labels = summary_proj_to_labels | |
| self.scale_attn_weights = scale_attn_weights | |
| self.use_cache = use_cache | |
| self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx | |
| self.reorder_and_upcast_attn = reorder_and_upcast_attn | |
| self.bos_token_id = bos_token_id | |
| self.eos_token_id = eos_token_id | |
| self.cross_layer_interval = cross_layer_interval | |
| self.freeze_vision_layers = freeze_vision_layers | |
| self.vision_model_name = vision_model_name | |
| self.vision_model_params = vision_model_params | |
| self.tie_word_embeddings = tie_word_embeddings | |
| self.freeze_text_layers = freeze_text_layers | |
| self.freeze_lm_head = freeze_lm_head | |
| self.image_token_index = image_token_index | |
| self.vision_embed_dim = vision_embed_dim | |
| self.vision_image_size = vision_image_size | |
| # Resampler params | |
| self.use_resampler = use_resampler | |
| self.resampler_n_latents = resampler_n_latents | |
| self.resampler_depth = resampler_depth | |
| self.resampler_n_heads = resampler_n_heads | |
| self.resampler_head_dim = resampler_head_dim | |
| # IMPORTANT: Do not do any __init__ args-based checks in the constructor, since | |
| # PretrainedConfig.from_dict first instantiates the class with the config dict and only then | |
| # updates the config object with `kwargs` from from_pretrained, so during the instantiation | |
| # of this object many attributes have default values and haven't yet been overridden. | |
| # Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run. | |
| super().__init__( | |
| bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs | |
| ) | |
| def check_compatibilities(self): | |
| if self.tie_word_embeddings and (self.freeze_text_layers != self.freeze_lm_head): | |
| raise ValueError( | |
| "if `tie_word_embeddings` is True, then `freeze_lm_head` and `freeze_text_layers` must be equal." | |
| ) | |
| vision_model_params = eval(self.vision_model_params) | |
| config = AutoConfig.from_pretrained(self.vision_model_name, **vision_model_params) | |
| if hasattr(config, "vision_config"): | |
| vision_config = config.vision_config | |
| else: | |
| vision_config = config | |
| vision_embed_dim = vision_config.hidden_size | |
| if self.vision_embed_dim != vision_embed_dim: | |
| raise ValueError( | |
| f"vision_embed_dim ({self.vision_embed_dim}) must match the hidden size of the vision model" | |
| f" ({vision_embed_dim})" | |
| ) | |
| vision_image_size = vision_config.image_size | |
| if self.vision_image_size != vision_image_size: | |
| raise ValueError( | |
| f"vision_image_size ({self.vision_image_size}) must match the hidden size of the vision model" | |
| f" ({vision_image_size})" | |
| ) | |
| def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": | |
| outputs = super(VGPT2Config, cls).from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| if isinstance(outputs, Tuple): | |
| # When called with return_unused_kwargs=True, the first item will be the config | |
| outputs[0].check_compatibilities() | |
| else: | |
| outputs.check_compatibilities() | |
| return outputs | |