# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """OpenAI ImageGPT configuration""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging if TYPE_CHECKING: from ... import FeatureExtractionMixin, TensorType logger = logging.get_logger(__name__) class ImageGPTConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`ImageGPTModel`] or a [`TFImageGPTModel`]. 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 ImageGPT [openai/imagegpt-small](https://huggingface.co/openai/imagegpt-small) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 512): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ImageGPTModel`] or [`TFImageGPTModel`]. n_positions (`int`, *optional*, defaults to 32*32): 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 512): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 8): 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 `"quick_gelu"`): Activation function (can be one of the activation functions defined in src/transformers/activations.py). Defaults to "quick_gelu". 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. 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. Example: ```python >>> from transformers import ImageGPTConfig, ImageGPTModel >>> # Initializing a ImageGPT configuration >>> configuration = ImageGPTConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = ImageGPTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "imagegpt" 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=512 + 1, # add one for start of sentence (sos) token n_positions=32 * 32, n_embd=512, n_layer=24, n_head=8, n_inner=None, activation_function="quick_gelu", resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, scale_attn_weights=True, use_cache=True, tie_word_embeddings=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False, **kwargs, ): self.vocab_size = 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.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.tie_word_embeddings = tie_word_embeddings super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) class ImageGPTOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ] ) def generate_dummy_inputs( self, preprocessor: "FeatureExtractionMixin", batch_size: int = 1, seq_length: int = -1, is_pair: bool = False, framework: Optional["TensorType"] = None, num_channels: int = 3, image_width: int = 32, image_height: int = 32, ) -> Mapping[str, Any]: """ Generate inputs to provide to the ONNX exporter for the specific framework Args: preprocessor ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]): The preprocessor associated with this model configuration. batch_size (`int`, *optional*, defaults to -1): The batch size to export the model for (-1 means dynamic axis). num_choices (`int`, *optional*, defaults to -1): The number of candidate answers provided for multiple choice task (-1 means dynamic axis). seq_length (`int`, *optional*, defaults to -1): The sequence length to export the model for (-1 means dynamic axis). is_pair (`bool`, *optional*, defaults to `False`): Indicate if the input is a pair (sentence 1, sentence 2) framework (`TensorType`, *optional*, defaults to `None`): The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for. num_channels (`int`, *optional*, defaults to 3): The number of channels of the generated images. image_width (`int`, *optional*, defaults to 40): The width of the generated images. image_height (`int`, *optional*, defaults to 40): The height of the generated images. Returns: Mapping[str, Tensor] holding the kwargs to provide to the model's forward function """ input_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width) inputs = dict(preprocessor(images=input_image, return_tensors=framework)) return inputs __all__ = ["ImageGPTConfig", "ImageGPTOnnxConfig"]