# coding=utf-8 # Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ DalleBart model configuration """ import warnings from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class DalleBartConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a `DalleBartModel`. It is used to instantiate a DalleBart 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 BART `facebook/bart-large `__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: encoder_vocab_size (:obj:`int`, `optional`, defaults to 50265): Vocabulary size of the BART model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.BartModel` or :class:`~transformers.TFBartModel`. d_model (:obj:`int`, `optional`, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (:obj:`int`, `optional`, defaults to 12): Number of encoder layers. decoder_layers (:obj:`int`, `optional`, defaults to 12): Number of decoder layers. encoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (:obj:`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). init_std (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the encoder. See the `LayerDrop paper `__ for more details. decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the decoder. See the `LayerDrop paper `__ for more details. gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. scale_embedding (:obj:`bool`, `optional`, defaults to :obj:`False`): Scale embeddings by diving by sqrt(d_model). use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). num_labels: (:obj:`int`, `optional`, defaults to 3): The number of labels to use in :class:`~transformers.BartForSequenceClassification`. forced_eos_token_id (:obj:`int`, `optional`, defaults to 2): The id of the token to force as the last generated token when :obj:`max_length` is reached. Usually set to :obj:`eos_token_id`. """ model_type = "dallebart" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, normalize_text=False, encoder_vocab_size=50264, image_vocab_size=16384, # encoded image token space image_length=256, # number of encoded tokens max_text_length=64, # max number of text tokens encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, activation_function="gelu", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, classifier_dropout=0.0, scale_embedding=False, gradient_checkpointing=False, use_cache=True, num_labels=3, is_encoder_decoder=True, forced_eos_token_id=None, tie_word_embeddings=False, # don't tie for scaling reasons and due to different modalities and sizes **kwargs, ): self.normalize_text = normalize_text self.encoder_vocab_size = encoder_vocab_size self.image_vocab_size = image_vocab_size self.image_length = image_length self.max_text_length = max_text_length self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.gradient_checkpointing = gradient_checkpointing self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.decoder_start_token_id = image_vocab_size # BOS appended to vocab self.min_length = image_length + 1 self.max_length = image_length + 1 # remove keys we are about to set to prevent errors for k in ['bos_token_id', 'eos_token_id', 'pad_token_id', 'decoder_start_token_id', 'forced_eos_token_id']: kwargs.pop(k, None) super().__init__( num_labels=num_labels, pad_token_id=image_vocab_size + 1, # needed to avoid errors during generation (converted to jnp.array) bos_token_id=image_vocab_size + 1, # set to unreachable values eos_token_id=image_vocab_size + 1, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=self.decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) # ensure backward compatibility for BART CNN models if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): self.forced_bos_token_id = self.bos_token_id warnings.warn( f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions." "The config can simply be saved and uploaded again to be fixed." )