# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension """ import logging from typing import Optional import torch import torch.nn as nn from fairseq import utils from fairseq.models import register_model, register_model_architecture from fairseq.models.transformer import TransformerModel from fairseq.modules.transformer_sentence_encoder import init_bert_params from .hub_interface import BARTHubInterface logger = logging.getLogger(__name__) @register_model("bart") class BARTModel(TransformerModel): __jit_unused_properties__ = ["supported_targets"] @classmethod def hub_models(cls): return { "bart.base": "http://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz", "bart.large": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz", "bart.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz", "bart.large.cnn": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz", "bart.large.xsum": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz", } def __init__(self, args, encoder, decoder): super().__init__(args, encoder, decoder) # We follow BERT's random weight initialization self.apply(init_bert_params) self.classification_heads = nn.ModuleDict() if hasattr(self.encoder, "dictionary"): self.eos: int = self.encoder.dictionary.eos() @staticmethod def add_args(parser): super(BARTModel, BARTModel).add_args(parser) parser.add_argument( "--pooler-dropout", type=float, metavar="D", help="dropout probability in the masked_lm pooler layers", ) parser.add_argument( "--pooler-activation-fn", choices=utils.get_available_activation_fns(), help="activation function to use for pooler layer", ) parser.add_argument( "--spectral-norm-classification-head", action="store_true", help="Apply spectral normalization on the classification head", ) @property def supported_targets(self): return {"self"} def forward( self, src_tokens, src_lengths, prev_output_tokens, features_only: bool = False, classification_head_name: Optional[str] = None, token_embeddings: Optional[torch.Tensor] = None, return_all_hiddens: bool = True, alignment_layer: Optional[int] = None, alignment_heads: Optional[int] = None, ): if classification_head_name is not None: features_only = True encoder_out = self.encoder( src_tokens, src_lengths=src_lengths, token_embeddings=token_embeddings, return_all_hiddens=return_all_hiddens, ) x, extra = self.decoder( prev_output_tokens, encoder_out=encoder_out, features_only=features_only, alignment_layer=alignment_layer, alignment_heads=alignment_heads, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens, ) eos: int = self.eos if classification_head_name is not None: sentence_representation = x[src_tokens.eq(eos), :].view( x.size(0), -1, x.size(-1) )[:, -1, :] for k, head in self.classification_heads.items(): # for torch script only supports iteration if k == classification_head_name: x = head(sentence_representation) break return x, extra @classmethod def from_pretrained( cls, model_name_or_path, checkpoint_file="model.pt", data_name_or_path=".", bpe="gpt2", sample_break_mode="eos", **kwargs, ): from fairseq import hub_utils x = hub_utils.from_pretrained( model_name_or_path, checkpoint_file, data_name_or_path, archive_map=cls.hub_models(), bpe=bpe, load_checkpoint_heads=True, sample_break_mode=sample_break_mode, **kwargs, ) return BARTHubInterface(x["args"], x["task"], x["models"][0]) def register_classification_head( self, name, num_classes=None, inner_dim=None, **kwargs ): """Register a classification head.""" logger.info("Registering classification head: {0}".format(name)) if name in self.classification_heads: prev_num_classes = self.classification_heads[name].out_proj.out_features prev_inner_dim = self.classification_heads[name].dense.out_features if num_classes != prev_num_classes or inner_dim != prev_inner_dim: logger.warning( 're-registering head "{}" with num_classes {} (prev: {}) ' "and inner_dim {} (prev: {})".format( name, num_classes, prev_num_classes, inner_dim, prev_inner_dim ) ) self.classification_heads[name] = BARTClassificationHead( input_dim=self.args.encoder_embed_dim, inner_dim=inner_dim or self.args.encoder_embed_dim, num_classes=num_classes, activation_fn=self.args.pooler_activation_fn, pooler_dropout=self.args.pooler_dropout, do_spectral_norm=getattr( self.args, "spectral_norm_classification_head", False ), ) def upgrade_state_dict_named(self, state_dict, name): super().upgrade_state_dict_named(state_dict, name) prefix = name + "." if name != "" else "" current_head_names = ( [] if not hasattr(self, "classification_heads") else self.classification_heads.keys() ) # Handle new classification heads present in the state dict. keys_to_delete = [] for k in state_dict.keys(): if not k.startswith(prefix + "classification_heads."): continue head_name = k[len(prefix + "classification_heads.") :].split(".")[0] num_classes = state_dict[ prefix + "classification_heads." + head_name + ".out_proj.weight" ].size(0) inner_dim = state_dict[ prefix + "classification_heads." + head_name + ".dense.weight" ].size(0) if getattr(self.args, "load_checkpoint_heads", False): if head_name not in current_head_names: self.register_classification_head(head_name, num_classes, inner_dim) else: if head_name not in current_head_names: logger.warning( "deleting classification head ({}) from checkpoint " "not present in current model: {}".format(head_name, k) ) keys_to_delete.append(k) elif ( num_classes != self.classification_heads[head_name].out_proj.out_features or inner_dim != self.classification_heads[head_name].dense.out_features ): logger.warning( "deleting classification head ({}) from checkpoint " "with different dimensions than current model: {}".format( head_name, k ) ) keys_to_delete.append(k) for k in keys_to_delete: del state_dict[k] def truncate_emb(key): if key in state_dict: state_dict[key] = state_dict[key][:-1, :] # When finetuning on translation task, remove last row of # embedding matrix that corresponds to mask_idx token. loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0) if ( loaded_dict_size == len(self.encoder.dictionary) + 1 and "" not in self.encoder.dictionary ): truncate_emb("encoder.embed_tokens.weight") truncate_emb("decoder.embed_tokens.weight") truncate_emb("encoder.output_projection.weight") truncate_emb("decoder.output_projection.weight") # When continued pretraining on new set of languages for mbart, # add extra lang embeddings at the end of embed_tokens. # Note: newly added languages are assumed to have been added at the end. if self.args.task == "multilingual_denoising" and loaded_dict_size < len( self.encoder.dictionary ): logger.info( "Adding extra language embeddings not found in pretrained model for " "continued pretraining of MBART on new set of languages." ) loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][ -1, : ] num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size embed_dim = state_dict["encoder.embed_tokens.weight"].size(1) new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim) nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim**-0.5) new_lang_embed_to_add = new_lang_embed_to_add.to( dtype=state_dict["encoder.embed_tokens.weight"].dtype, ) state_dict["encoder.embed_tokens.weight"] = torch.cat( [ state_dict["encoder.embed_tokens.weight"][ : loaded_dict_size - 1, : ], new_lang_embed_to_add, loaded_mask_token_embedding.unsqueeze(0), ] ) state_dict["decoder.embed_tokens.weight"] = torch.cat( [ state_dict["decoder.embed_tokens.weight"][ : loaded_dict_size - 1, : ], new_lang_embed_to_add, loaded_mask_token_embedding.unsqueeze(0), ] ) # Copy any newly-added classification heads into the state dict # with their current weights. if hasattr(self, "classification_heads"): cur_state = self.classification_heads.state_dict() for k, v in cur_state.items(): if prefix + "classification_heads." + k not in state_dict: logger.info("Overwriting " + prefix + "classification_heads." + k) state_dict[prefix + "classification_heads." + k] = v def set_beam_size(self, beam): """Set beam size for efficient beamable enc-dec attention.""" beamable = False for layer in self.decoder.layers: if layer.encoder_attn is not None: if hasattr(layer.encoder_attn, "set_beam_size"): layer.encoder_attn.set_beam_size(beam) beamable = True if beamable: self.encoder.reorder_encoder_out = self.encoder._reorder_encoder_out class BARTClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__( self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout, do_spectral_norm=False, ): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.activation_fn = utils.get_activation_fn(activation_fn) self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) if do_spectral_norm: self.out_proj = torch.nn.utils.spectral_norm(self.out_proj) def forward(self, features, **kwargs): x = features x = self.dropout(x) x = self.dense(x) x = self.activation_fn(x) x = self.dropout(x) x = self.out_proj(x) return x @register_model_architecture("bart", "bart_large") def bart_large_architecture(args): args.encoder_embed_path = getattr(args, "encoder_embed_path", None) args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024) args.encoder_layers = getattr(args, "encoder_layers", 12) args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) args.decoder_embed_path = getattr(args, "decoder_embed_path", None) args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) args.decoder_ffn_embed_dim = getattr( args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim ) args.decoder_layers = getattr(args, "decoder_layers", 12) args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True) args.attention_dropout = getattr(args, "attention_dropout", 0.0) args.relu_dropout = getattr(args, "relu_dropout", 0.0) args.dropout = getattr(args, "dropout", 0.1) args.max_target_positions = getattr(args, "max_target_positions", 1024) args.max_source_positions = getattr(args, "max_source_positions", 1024) args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) args.share_decoder_input_output_embed = getattr( args, "share_decoder_input_output_embed", True ) args.share_all_embeddings = getattr(args, "share_all_embeddings", True) args.decoder_output_dim = getattr( args, "decoder_output_dim", args.decoder_embed_dim ) args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) args.no_scale_embedding = getattr(args, "no_scale_embedding", True) args.layernorm_embedding = getattr(args, "layernorm_embedding", True) args.activation_fn = getattr(args, "activation_fn", "gelu") args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") args.pooler_dropout = getattr(args, "pooler_dropout", 0.0) @register_model_architecture("bart", "bart_base") def bart_base_architecture(args): args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768) args.encoder_layers = getattr(args, "encoder_layers", 6) args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) args.decoder_layers = getattr(args, "decoder_layers", 6) args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12) bart_large_architecture(args) @register_model_architecture("bart", "mbart_large") def mbart_large_architecture(args): args.no_scale_embedding = getattr(args, "no_scale_embedding", False) bart_large_architecture(args) @register_model_architecture("bart", "mbart_base") def mbart_base_architecture(args): args.no_scale_embedding = getattr(args, "no_scale_embedding", False) bart_base_architecture(args) @register_model_architecture("bart", "mbart_base_wmt20") def mbart_base_wmt20_architecture(args): args.layernorm_embedding = getattr(args, "layernorm_embedding", False) mbart_base_architecture(args)