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| # 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 | |
| from .utils import PretrainedFromWandbMixin | |
| logger = logging.get_logger(__name__) | |
| class DalleBartConfig(PretrainedFromWandbMixin, PretrainedConfig): | |
| 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, | |
| activation_function="gelu", | |
| d_model=1024, | |
| dropout=0.1, | |
| attention_dropout=0.0, | |
| activation_dropout=0.0, | |
| init_std=0.02, | |
| scale_embedding=False, | |
| gradient_checkpointing=False, | |
| use_cache=True, | |
| is_encoder_decoder=True, | |
| forced_eos_token_id=None, | |
| tie_word_embeddings=False, # different modalities and sizes | |
| do_sample=True, | |
| # transformer variants | |
| use_bias=False, # use bias in attention and dense layers (except for lm_head) | |
| ln_type="layernorm", # layer normalization type, "rmsnorm", "layernorm" | |
| ln_positions="normformer", # layer normalization positions, "normformer", "swinv2", "cogview", "postln", "preln", "deepnet" (same as postln) | |
| use_head_scale=False, # used in NormFormer | |
| use_cosine_attention=False, # used in Swin v2 | |
| tau_init=0.05, # used only in cosine attention (Swin v2) | |
| use_absolute_position_embeddings=True, # default | |
| use_swin_position_embeddings=False, # used in Swin v1/v2 | |
| use_deepnet_scaling=False, # used in Deepnet | |
| use_glu=False, # "GLU Variants Improve Transformer" | |
| use_alibi=False, # Not implemented yet - from "Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation" | |
| sinkhorn_iters=1, # used in SinkFormers | |
| use_final_ln_encoder=True, # final layer normalization in encoder | |
| use_final_ln_decoder=True, # final layer normalization in decoder | |
| # parameters that should not be necessary but could affect results | |
| force_ln_scale=False, # force scale in layernorm even when followed by dense layers | |
| **kwargs, | |
| ): | |
| # text normalizer | |
| self.normalize_text = normalize_text | |
| # transformer variants | |
| self.use_bias = use_bias | |
| assert ln_type in [ | |
| "rmsnorm", | |
| "layernorm", | |
| ], "ln_type must be 'rmsnorm' or 'layernorm'" | |
| self.ln_type = ln_type | |
| if ln_positions == "deepnet": | |
| ln_positions = "postln" | |
| assert ln_positions in [ | |
| "normformer", | |
| "swinv2", | |
| "cogview", | |
| "postln", | |
| "preln", | |
| ], "ln_positions must be 'normformer', 'swinv2', 'cogview', 'postln', 'preln'" | |
| self.use_head_scale = use_head_scale | |
| assert use_alibi is False, "use_alibi is not supported yet" | |
| self.ln_positions = ln_positions | |
| self.use_cosine_attention = use_cosine_attention | |
| self.tau_init = tau_init | |
| self.use_absolute_position_embeddings = use_absolute_position_embeddings | |
| self.use_swin_position_embeddings = use_swin_position_embeddings | |
| self.use_deepnet_scaling = use_deepnet_scaling | |
| self.use_glu = use_glu | |
| self.use_alibi = use_alibi | |
| self.sinkhorn_iters = sinkhorn_iters | |
| if ln_positions == "postln": | |
| assert ( | |
| use_final_ln_encoder | |
| ), "use_final_ln_encoder must be True when ln_positions is 'postln'" | |
| assert ( | |
| use_final_ln_decoder | |
| ), "use_final_ln_decoder must be True when ln_positions is 'postln'" | |
| self.use_final_ln_encoder = use_final_ln_encoder | |
| self.use_final_ln_decoder = use_final_ln_decoder | |
| self.force_ln_scale = force_ln_scale | |
| # common parameters | |
| 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.use_cache = use_cache | |
| self.gradient_checkpointing = gradient_checkpointing | |
| self.scale_embedding = ( | |
| scale_embedding # scale factor will be sqrt(d_model) if True | |
| ) | |
| # special token id's are appended to vocab if not provided | |
| decoder_start_token_id = kwargs.pop("decoder_start_token_id", image_vocab_size) | |
| bos_token_id = kwargs.pop("bos_token_id", image_vocab_size) | |
| pad_token_id = kwargs.pop("pad_token_id", image_vocab_size) | |
| eos_token_id = kwargs.pop("eos_token_id", image_vocab_size) | |
| # we generate to image_length + 1 (for bos) by default | |
| min_length = kwargs.pop("min_length", image_length + 1) | |
| max_length = kwargs.pop("max_length", image_length + 1) | |
| super().__init__( | |
| # args required in parent class | |
| is_encoder_decoder=is_encoder_decoder, | |
| tie_word_embeddings=tie_word_embeddings, | |
| forced_eos_token_id=forced_eos_token_id, | |
| decoder_start_token_id=decoder_start_token_id, | |
| bos_token_id=bos_token_id, | |
| pad_token_id=pad_token_id, | |
| eos_token_id=eos_token_id, | |
| min_length=min_length, | |
| max_length=max_length, | |
| do_sample=do_sample, | |
| **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." | |
| ) | |