Commit 9196c0b6 authored by Myle Ott's avatar Myle Ott Committed by Facebook Github Bot
Browse files

LSTM improvements (fixes #414) 

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/470

Differential Revision: D13803964

Pulled By: myleott

fbshipit-source-id: 91b66599e9a539833fcedea07c608b349ba3b449
parent d0ebcec4

fairseq/models/lstm.py

+43 −24
Original line number Diff line number Diff line
@@ -32,6 +32,8 @@ class LSTMModel(FairseqModel): 
                            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',
@@ -42,6 +44,8 @@ class LSTMModel(FairseqModel): 
                            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',
@@ -53,6 +57,12 @@ class LSTMModel(FairseqModel): 
        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',
@@ -63,12 +73,6 @@ class LSTMModel(FairseqModel): 
                            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')

        parser.add_argument('--share-all-embeddings', default=False, action='store_true',

                            help='share encoder, decoder and output embeddings'

                                 ' (requires shared dictionary and embed dim)')

        # fmt: on



    @classmethod
@@ -130,6 +134,11 @@ class LSTMModel(FairseqModel): 
                '--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,
@@ -149,7 +158,6 @@ class LSTMModel(FairseqModel): 
            dropout_in=args.decoder_dropout_in,

            dropout_out=args.decoder_dropout_out,

            attention=options.eval_bool(args.decoder_attention),

            encoder_embed_dim=args.encoder_embed_dim,

            encoder_output_units=encoder.output_units,

            pretrained_embed=pretrained_decoder_embed,

            share_input_output_embed=args.share_decoder_input_output_embed,
@@ -222,8 +230,8 @@ class LSTMEncoder(FairseqEncoder): 
            state_size = 2 * self.num_layers, bsz, self.hidden_size

        else:

            state_size = self.num_layers, bsz, self.hidden_size

        h0 = x.data.new(*state_size).zero_()

        c0 = x.data.new(*state_size).zero_()

        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
@@ -263,11 +271,11 @@ class LSTMEncoder(FairseqEncoder): 




class AttentionLayer(nn.Module):

    def __init__(self, input_embed_dim, output_embed_dim):

    def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False):

        super().__init__()



        self.input_proj = Linear(input_embed_dim, output_embed_dim, bias=False)

        self.output_proj = Linear(input_embed_dim + output_embed_dim, output_embed_dim, bias=False)

        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
@@ -300,7 +308,7 @@ class LSTMDecoder(FairseqIncrementalDecoder): 
    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_embed_dim=512, encoder_output_units=512, pretrained_embed=None,

        encoder_output_units=512, pretrained_embed=None,

        share_input_output_embed=False, adaptive_softmax_cutoff=None,

    ):

        super().__init__(dictionary)
@@ -319,18 +327,23 @@ class LSTMDecoder(FairseqIncrementalDecoder): 
            self.embed_tokens = pretrained_embed



        self.encoder_output_units = encoder_output_units

        assert encoder_output_units == hidden_size, \

            'encoder_output_units ({}) != hidden_size ({})'.format(encoder_output_units, hidden_size)

        # TODO another Linear layer if not equal



        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=encoder_output_units + embed_dim if layer == 0 else hidden_size,

                input_size=hidden_size + embed_dim if layer == 0 else hidden_size,

                hidden_size=hidden_size,

            )

            for layer in range(num_layers)

        ])

        self.attention = AttentionLayer(encoder_output_units, hidden_size) if attention else None

        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:
@@ -349,7 +362,7 @@ class LSTMDecoder(FairseqIncrementalDecoder): 
        bsz, seqlen = prev_output_tokens.size()



        # get outputs from encoder

        encoder_outs, _, _ = encoder_out[:3]

        encoder_outs, encoder_hiddens, encoder_cells = encoder_out[:3]

        srclen = encoder_outs.size(0)



        # embed tokens
@@ -364,13 +377,15 @@ class LSTMDecoder(FairseqIncrementalDecoder): 
        if cached_state is not None:

            prev_hiddens, prev_cells, input_feed = cached_state

        else:

            _, encoder_hiddens, encoder_cells = encoder_out[:3]

            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)]

            input_feed = x.data.new(bsz, self.encoder_output_units).zero_()

            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.data.new(srclen, seqlen, bsz).zero_()

        attn_scores = x.new_zeros(srclen, seqlen, bsz)

        outs = []

        for j in range(seqlen):

            # input feeding: concatenate context vector from previous time step
@@ -402,7 +417,9 @@ class LSTMDecoder(FairseqIncrementalDecoder): 


        # cache previous states (no-op except during incremental generation)

        utils.set_incremental_state(

            self, incremental_state, 'cached_state', (prev_hiddens, prev_cells, input_feed))

            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)
@@ -486,6 +503,7 @@ 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)
@@ -493,6 +511,7 @@ def base_architecture(args): 
    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)