# 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 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_deepnet_scaling=False, # used in Deepnet use_glu=False, # "GLU Variants Improve Transformer" use_alibi=False, # from "Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation" sinkhorn_iters=1, # used in SinkFormers use_final_ln_encoder=False, # final layer normalization in encoder use_final_ln_decoder=False, # 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_head_scale = use_head_scale # per Normformer 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'" 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_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." )