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| # 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. | |
| from fairseq import utils | |
| from fairseq.models import ( | |
| FairseqLanguageModel, | |
| register_model, | |
| register_model_architecture, | |
| ) | |
| from fairseq.models.lstm import Embedding, LSTMDecoder | |
| DEFAULT_MAX_TARGET_POSITIONS = 1e5 | |
| class LSTMLanguageModel(FairseqLanguageModel): | |
| def __init__(self, decoder): | |
| super().__init__(decoder) | |
| def add_args(parser): | |
| """Add model-specific arguments to the parser.""" | |
| # fmt: off | |
| parser.add_argument('--dropout', type=float, metavar='D', | |
| help='dropout probability') | |
| parser.add_argument('--decoder-embed-dim', type=int, metavar='N', | |
| help='decoder embedding dimension') | |
| parser.add_argument('--decoder-embed-path', type=str, metavar='STR', | |
| help='path to pre-trained decoder embedding') | |
| parser.add_argument('--decoder-hidden-size', type=int, metavar='N', | |
| help='decoder hidden size') | |
| parser.add_argument('--decoder-layers', type=int, metavar='N', | |
| help='number of decoder layers') | |
| parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', | |
| help='decoder output embedding dimension') | |
| parser.add_argument('--decoder-attention', type=str, metavar='BOOL', | |
| help='decoder attention') | |
| parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', | |
| help='comma separated list of adaptive softmax cutoff points. ' | |
| 'Must be used with adaptive_loss criterion') | |
| parser.add_argument('--residuals', default=False, | |
| action='store_true', | |
| help='applying residuals between LSTM layers') | |
| # Granular dropout settings (if not specified these default to --dropout) | |
| parser.add_argument('--decoder-dropout-in', type=float, metavar='D', | |
| help='dropout probability for decoder input embedding') | |
| parser.add_argument('--decoder-dropout-out', type=float, metavar='D', | |
| help='dropout probability for decoder output') | |
| parser.add_argument('--share-decoder-input-output-embed', default=False, | |
| action='store_true', | |
| help='share decoder input and output embeddings') | |
| # fmt: on | |
| def build_model(cls, args, task): | |
| """Build a new model instance.""" | |
| # make sure all arguments are present in older models | |
| base_architecture(args) | |
| if getattr(args, "max_target_positions", None) is not None: | |
| max_target_positions = args.max_target_positions | |
| else: | |
| max_target_positions = getattr( | |
| args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS | |
| ) | |
| def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): | |
| num_embeddings = len(dictionary) | |
| padding_idx = dictionary.pad() | |
| embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) | |
| embed_dict = utils.parse_embedding(embed_path) | |
| utils.print_embed_overlap(embed_dict, dictionary) | |
| return utils.load_embedding(embed_dict, dictionary, embed_tokens) | |
| pretrained_decoder_embed = None | |
| if args.decoder_embed_path: | |
| pretrained_decoder_embed = load_pretrained_embedding_from_file( | |
| args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim | |
| ) | |
| if args.share_decoder_input_output_embed: | |
| # double check all parameters combinations are valid | |
| if task.source_dictionary != task.target_dictionary: | |
| raise ValueError( | |
| "--share-decoder-input-output-embeddings requires a joint dictionary" | |
| ) | |
| if args.decoder_embed_dim != args.decoder_out_embed_dim: | |
| raise ValueError( | |
| "--share-decoder-input-output-embeddings requires " | |
| "--decoder-embed-dim to match --decoder-out-embed-dim" | |
| ) | |
| decoder = LSTMDecoder( | |
| dictionary=task.dictionary, | |
| embed_dim=args.decoder_embed_dim, | |
| hidden_size=args.decoder_hidden_size, | |
| out_embed_dim=args.decoder_out_embed_dim, | |
| num_layers=args.decoder_layers, | |
| dropout_in=args.decoder_dropout_in, | |
| dropout_out=args.decoder_dropout_out, | |
| attention=False, # decoder-only language model doesn't support attention | |
| encoder_output_units=0, | |
| pretrained_embed=pretrained_decoder_embed, | |
| share_input_output_embed=args.share_decoder_input_output_embed, | |
| adaptive_softmax_cutoff=( | |
| utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) | |
| if args.criterion == "adaptive_loss" | |
| else None | |
| ), | |
| max_target_positions=max_target_positions, | |
| residuals=args.residuals, | |
| ) | |
| return cls(decoder) | |
| def base_architecture(args): | |
| args.dropout = getattr(args, "dropout", 0.1) | |
| args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) | |
| args.decoder_embed_path = getattr(args, "decoder_embed_path", None) | |
| args.decoder_hidden_size = getattr( | |
| args, "decoder_hidden_size", args.decoder_embed_dim | |
| ) | |
| args.decoder_layers = getattr(args, "decoder_layers", 1) | |
| args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) | |
| args.decoder_attention = getattr(args, "decoder_attention", "0") | |
| args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) | |
| args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) | |
| args.share_decoder_input_output_embed = getattr( | |
| args, "share_decoder_input_output_embed", False | |
| ) | |
| args.adaptive_softmax_cutoff = getattr( | |
| args, "adaptive_softmax_cutoff", "10000,50000,200000" | |
| ) | |
| args.residuals = getattr(args, "residuals", False) | |