Source code for fairseq.models.lstm

# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.

import torch
import torch.nn as nn
import torch.nn.functional as F

from fairseq import options, utils
from fairseq.models import (
    FairseqEncoder,
    FairseqIncrementalDecoder,
    FairseqEncoderDecoderModel,
    register_model,
    register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax


[docs]@register_model('lstm') class LSTMModel(FairseqEncoderDecoderModel): def __init__(self, encoder, decoder): super().__init__(encoder, decoder)
[docs] @staticmethod 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('--encoder-embed-dim', type=int, metavar='N', help='encoder embedding dimension') parser.add_argument('--encoder-embed-path', type=str, metavar='STR', help='path to pre-trained encoder embedding') parser.add_argument('--encoder-freeze-embed', action='store_true', help='freeze encoder embeddings') parser.add_argument('--encoder-hidden-size', type=int, metavar='N', help='encoder hidden size') parser.add_argument('--encoder-layers', type=int, metavar='N', help='number of encoder layers') parser.add_argument('--encoder-bidirectional', action='store_true', help='make all layers of encoder bidirectional') 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-freeze-embed', action='store_true', help='freeze decoder embeddings') 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('--share-decoder-input-output-embed', default=False, action='store_true', help='share decoder input and output embeddings') parser.add_argument('--share-all-embeddings', default=False, action='store_true', help='share encoder, decoder and output embeddings' ' (requires shared dictionary and embed dim)') # Granular dropout settings (if not specified these default to --dropout) parser.add_argument('--encoder-dropout-in', type=float, metavar='D', help='dropout probability for encoder input embedding') parser.add_argument('--encoder-dropout-out', type=float, metavar='D', help='dropout probability for encoder output') 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')
# fmt: on
[docs] @classmethod def build_model(cls, args, task): """Build a new model instance.""" # make sure that all args are properly defaulted (in case there are any new ones) base_architecture(args) if args.encoder_layers != args.decoder_layers: raise ValueError('--encoder-layers must match --decoder-layers') 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) if args.encoder_embed_path: pretrained_encoder_embed = load_pretrained_embedding_from_file( args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim) else: num_embeddings = len(task.source_dictionary) pretrained_encoder_embed = Embedding( num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad() ) if args.share_all_embeddings: # double check all parameters combinations are valid if task.source_dictionary != task.target_dictionary: raise ValueError('--share-all-embeddings requires a joint dictionary') if args.decoder_embed_path and ( args.decoder_embed_path != args.encoder_embed_path): raise ValueError( '--share-all-embed not compatible with --decoder-embed-path' ) if args.encoder_embed_dim != args.decoder_embed_dim: raise ValueError( '--share-all-embeddings requires --encoder-embed-dim to ' 'match --decoder-embed-dim' ) pretrained_decoder_embed = pretrained_encoder_embed args.share_decoder_input_output_embed = True else: # separate decoder input embeddings 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 ) # one last double check of parameter combinations if args.share_decoder_input_output_embed and ( 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' ) if args.encoder_freeze_embed: pretrained_encoder_embed.weight.requires_grad = False if args.decoder_freeze_embed: pretrained_decoder_embed.weight.requires_grad = False encoder = LSTMEncoder( dictionary=task.source_dictionary, embed_dim=args.encoder_embed_dim, hidden_size=args.encoder_hidden_size, num_layers=args.encoder_layers, dropout_in=args.encoder_dropout_in, dropout_out=args.encoder_dropout_out, bidirectional=args.encoder_bidirectional, pretrained_embed=pretrained_encoder_embed, ) decoder = LSTMDecoder( dictionary=task.target_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=options.eval_bool(args.decoder_attention), encoder_output_units=encoder.output_units, pretrained_embed=pretrained_decoder_embed, share_input_output_embed=args.share_decoder_input_output_embed, adaptive_softmax_cutoff=( options.eval_str_list(args.adaptive_softmax_cutoff, type=int) if args.criterion == 'adaptive_loss' else None ), ) return cls(encoder, decoder)
[docs]class LSTMEncoder(FairseqEncoder): """LSTM encoder.""" def __init__( self, dictionary, embed_dim=512, hidden_size=512, num_layers=1, dropout_in=0.1, dropout_out=0.1, bidirectional=False, left_pad=True, pretrained_embed=None, padding_value=0., ): super().__init__(dictionary) self.num_layers = num_layers self.dropout_in = dropout_in self.dropout_out = dropout_out self.bidirectional = bidirectional self.hidden_size = hidden_size num_embeddings = len(dictionary) self.padding_idx = dictionary.pad() if pretrained_embed is None: self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) else: self.embed_tokens = pretrained_embed self.lstm = LSTM( input_size=embed_dim, hidden_size=hidden_size, num_layers=num_layers, dropout=self.dropout_out if num_layers > 1 else 0., bidirectional=bidirectional, ) self.left_pad = left_pad self.padding_value = padding_value self.output_units = hidden_size if bidirectional: self.output_units *= 2
[docs] def forward(self, src_tokens, src_lengths): if self.left_pad: # nn.utils.rnn.pack_padded_sequence requires right-padding; # convert left-padding to right-padding src_tokens = utils.convert_padding_direction( src_tokens, self.padding_idx, left_to_right=True, ) bsz, seqlen = src_tokens.size() # embed tokens x = self.embed_tokens(src_tokens) x = F.dropout(x, p=self.dropout_in, training=self.training) # B x T x C -> T x B x C x = x.transpose(0, 1) # pack embedded source tokens into a PackedSequence packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data.tolist()) # apply LSTM if self.bidirectional: state_size = 2 * self.num_layers, bsz, self.hidden_size else: state_size = self.num_layers, bsz, self.hidden_size h0 = x.new_zeros(*state_size) c0 = x.new_zeros(*state_size) packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0)) # unpack outputs and apply dropout x, _ = nn.utils.rnn.pad_packed_sequence(packed_outs, padding_value=self.padding_value) x = F.dropout(x, p=self.dropout_out, training=self.training) assert list(x.size()) == [seqlen, bsz, self.output_units] if self.bidirectional: def combine_bidir(outs): out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous() return out.view(self.num_layers, bsz, -1) final_hiddens = combine_bidir(final_hiddens) final_cells = combine_bidir(final_cells) encoder_padding_mask = src_tokens.eq(self.padding_idx).t() return { 'encoder_out': (x, final_hiddens, final_cells), 'encoder_padding_mask': encoder_padding_mask if encoder_padding_mask.any() else None }
[docs] def reorder_encoder_out(self, encoder_out, new_order): encoder_out['encoder_out'] = tuple( eo.index_select(1, new_order) for eo in encoder_out['encoder_out'] ) if encoder_out['encoder_padding_mask'] is not None: encoder_out['encoder_padding_mask'] = \ encoder_out['encoder_padding_mask'].index_select(1, new_order) return encoder_out
[docs] def max_positions(self): """Maximum input length supported by the encoder.""" return int(1e5) # an arbitrary large number
class AttentionLayer(nn.Module): def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False): super().__init__() self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias) self.output_proj = Linear(input_embed_dim + source_embed_dim, output_embed_dim, bias=bias) def forward(self, input, source_hids, encoder_padding_mask): # input: bsz x input_embed_dim # source_hids: srclen x bsz x output_embed_dim # x: bsz x output_embed_dim x = self.input_proj(input) # compute attention attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2) # don't attend over padding if encoder_padding_mask is not None: attn_scores = attn_scores.float().masked_fill_( encoder_padding_mask, float('-inf') ).type_as(attn_scores) # FP16 support: cast to float and back attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz # sum weighted sources x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0) x = torch.tanh(self.output_proj(torch.cat((x, input), dim=1))) return x, attn_scores
[docs]class LSTMDecoder(FairseqIncrementalDecoder): """LSTM decoder.""" def __init__( self, dictionary, embed_dim=512, hidden_size=512, out_embed_dim=512, num_layers=1, dropout_in=0.1, dropout_out=0.1, attention=True, encoder_output_units=512, pretrained_embed=None, share_input_output_embed=False, adaptive_softmax_cutoff=None, ): super().__init__(dictionary) self.dropout_in = dropout_in self.dropout_out = dropout_out self.hidden_size = hidden_size self.share_input_output_embed = share_input_output_embed self.need_attn = True self.adaptive_softmax = None num_embeddings = len(dictionary) padding_idx = dictionary.pad() if pretrained_embed is None: self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) else: self.embed_tokens = pretrained_embed self.encoder_output_units = encoder_output_units if encoder_output_units != hidden_size: self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size) self.encoder_cell_proj = Linear(encoder_output_units, hidden_size) else: self.encoder_hidden_proj = self.encoder_cell_proj = None self.layers = nn.ModuleList([ LSTMCell( input_size=hidden_size + embed_dim if layer == 0 else hidden_size, hidden_size=hidden_size, ) for layer in range(num_layers) ]) if attention: # TODO make bias configurable self.attention = AttentionLayer(hidden_size, encoder_output_units, hidden_size, bias=False) else: self.attention = None if hidden_size != out_embed_dim: self.additional_fc = Linear(hidden_size, out_embed_dim) if adaptive_softmax_cutoff is not None: # setting adaptive_softmax dropout to dropout_out for now but can be redefined self.adaptive_softmax = AdaptiveSoftmax(num_embeddings, hidden_size, adaptive_softmax_cutoff, dropout=dropout_out) elif not self.share_input_output_embed: self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)
[docs] def forward(self, prev_output_tokens, encoder_out, incremental_state=None): encoder_padding_mask = encoder_out['encoder_padding_mask'] encoder_out = encoder_out['encoder_out'] if incremental_state is not None: prev_output_tokens = prev_output_tokens[:, -1:] bsz, seqlen = prev_output_tokens.size() # get outputs from encoder encoder_outs, encoder_hiddens, encoder_cells = encoder_out[:3] srclen = encoder_outs.size(0) # embed tokens x = self.embed_tokens(prev_output_tokens) x = F.dropout(x, p=self.dropout_in, training=self.training) # B x T x C -> T x B x C x = x.transpose(0, 1) # initialize previous states (or get from cache during incremental generation) cached_state = utils.get_incremental_state(self, incremental_state, 'cached_state') if cached_state is not None: prev_hiddens, prev_cells, input_feed = cached_state else: num_layers = len(self.layers) prev_hiddens = [encoder_hiddens[i] for i in range(num_layers)] prev_cells = [encoder_cells[i] for i in range(num_layers)] if self.encoder_hidden_proj is not None: prev_hiddens = [self.encoder_hidden_proj(x) for x in prev_hiddens] prev_cells = [self.encoder_cell_proj(x) for x in prev_cells] input_feed = x.new_zeros(bsz, self.hidden_size) attn_scores = x.new_zeros(srclen, seqlen, bsz) outs = [] for j in range(seqlen): # input feeding: concatenate context vector from previous time step input = torch.cat((x[j, :, :], input_feed), dim=1) for i, rnn in enumerate(self.layers): # recurrent cell hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i])) # hidden state becomes the input to the next layer input = F.dropout(hidden, p=self.dropout_out, training=self.training) # save state for next time step prev_hiddens[i] = hidden prev_cells[i] = cell # apply attention using the last layer's hidden state if self.attention is not None: out, attn_scores[:, j, :] = self.attention(hidden, encoder_outs, encoder_padding_mask) else: out = hidden out = F.dropout(out, p=self.dropout_out, training=self.training) # input feeding input_feed = out # save final output outs.append(out) # cache previous states (no-op except during incremental generation) utils.set_incremental_state( self, incremental_state, 'cached_state', (prev_hiddens, prev_cells, input_feed), ) # collect outputs across time steps x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) # T x B x C -> B x T x C x = x.transpose(1, 0) # srclen x tgtlen x bsz -> bsz x tgtlen x srclen if not self.training and self.need_attn: attn_scores = attn_scores.transpose(0, 2) else: attn_scores = None # project back to size of vocabulary if self.adaptive_softmax is None: if hasattr(self, 'additional_fc'): x = self.additional_fc(x) x = F.dropout(x, p=self.dropout_out, training=self.training) if self.share_input_output_embed: x = F.linear(x, self.embed_tokens.weight) else: x = self.fc_out(x) return x, attn_scores
[docs] def reorder_incremental_state(self, incremental_state, new_order): super().reorder_incremental_state(incremental_state, new_order) cached_state = utils.get_incremental_state(self, incremental_state, 'cached_state') if cached_state is None: return def reorder_state(state): if isinstance(state, list): return [reorder_state(state_i) for state_i in state] return state.index_select(0, new_order) new_state = tuple(map(reorder_state, cached_state)) utils.set_incremental_state(self, incremental_state, 'cached_state', new_state)
[docs] def max_positions(self): """Maximum output length supported by the decoder.""" return int(1e5) # an arbitrary large number
def make_generation_fast_(self, need_attn=False, **kwargs): self.need_attn = need_attn
def Embedding(num_embeddings, embedding_dim, padding_idx): m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) nn.init.uniform_(m.weight, -0.1, 0.1) nn.init.constant_(m.weight[padding_idx], 0) return m def LSTM(input_size, hidden_size, **kwargs): m = nn.LSTM(input_size, hidden_size, **kwargs) for name, param in m.named_parameters(): if 'weight' in name or 'bias' in name: param.data.uniform_(-0.1, 0.1) return m def LSTMCell(input_size, hidden_size, **kwargs): m = nn.LSTMCell(input_size, hidden_size, **kwargs) for name, param in m.named_parameters(): if 'weight' in name or 'bias' in name: param.data.uniform_(-0.1, 0.1) return m def Linear(in_features, out_features, bias=True, dropout=0): """Linear layer (input: N x T x C)""" m = nn.Linear(in_features, out_features, bias=bias) m.weight.data.uniform_(-0.1, 0.1) if bias: m.bias.data.uniform_(-0.1, 0.1) return m @register_model_architecture('lstm', 'lstm') def base_architecture(args): args.dropout = getattr(args, 'dropout', 0.1) args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512) args.encoder_embed_path = getattr(args, 'encoder_embed_path', None) args.encoder_freeze_embed = getattr(args, 'encoder_freeze_embed', False) args.encoder_hidden_size = getattr(args, 'encoder_hidden_size', args.encoder_embed_dim) args.encoder_layers = getattr(args, 'encoder_layers', 1) args.encoder_bidirectional = getattr(args, 'encoder_bidirectional', False) args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', args.dropout) args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', args.dropout) args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) args.decoder_embed_path = getattr(args, 'decoder_embed_path', None) args.decoder_freeze_embed = getattr(args, 'decoder_freeze_embed', False) 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', '1') 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.share_all_embeddings = getattr(args, 'share_all_embeddings', False) args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000') @register_model_architecture('lstm', 'lstm_wiseman_iwslt_de_en') def lstm_wiseman_iwslt_de_en(args): args.dropout = getattr(args, 'dropout', 0.1) args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256) args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', 0) args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', 0) args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256) args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256) args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', 0) args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout) base_architecture(args) @register_model_architecture('lstm', 'lstm_luong_wmt_en_de') def lstm_luong_wmt_en_de(args): args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1000) args.encoder_layers = getattr(args, 'encoder_layers', 4) args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', 0) args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1000) args.decoder_layers = getattr(args, 'decoder_layers', 4) args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 1000) args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', 0) base_architecture(args)