fairseq/fairseq/models/lstm.py

# 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
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F

from fairseq import utils
from fairseq.data import LanguagePairDataset

from . import FairseqEncoder, FairseqIncrementalDecoder, FairseqModel, register_model, register_model_architecture


@register_model('lstm')
class LSTMModel(FairseqModel):
    def __init__(self, encoder, decoder):
        super().__init__(encoder, decoder)

    @staticmethod
    def add_args(parser):
        """Add model-specific arguments to the parser."""
        parser.add_argument('--dropout', default=0.1, 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', default=None, type=str, metavar='STR',
                            help='path to pre-trained encoder embedding')
        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', default=None, 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')

        # 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')

    @classmethod
    def build_model(cls, args, src_dict, dst_dict):
        """Build a new model instance."""
        if not hasattr(args, 'encoder_embed_path'):
            args.encoder_embed_path = None
        if not hasattr(args, 'decoder_embed_path'):
            args.decoder_embed_path = None
        if not hasattr(args, 'encoder_hidden_size'):
            args.encoder_hidden_size = args.encoder_embed_dim
        if not hasattr(args, 'decoder_hidden_size'):
            args.decoder_hidden_size = args.decoder_embed_dim
        if not hasattr(args, 'encoder_bidirectional'):
            args.encoder_bidirectional = False

        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_encoder_embed = None
        if args.encoder_embed_path:
            pretrained_encoder_embed = load_pretrained_embedding_from_file(
                args.encoder_embed_path, src_dict, args.encoder_embed_dim)
        pretrained_decoder_embed = None
        if args.decoder_embed_path:
            pretrained_decoder_embed = load_pretrained_embedding_from_file(
                args.decoder_embed_path, dst_dict, args.decoder_embed_dim)

        encoder = LSTMEncoder(
            dictionary=src_dict,
            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,
        )
        try:
            attention = bool(eval(args.decoder_attention))
        except TypeError:
            attention = bool(args.decoder_attention)
        decoder = LSTMDecoder(
            dictionary=dst_dict,
            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=attention,
            encoder_embed_dim=args.encoder_embed_dim,
            encoder_output_units=encoder.output_units,
            pretrained_embed=pretrained_decoder_embed,
        )
        return cls(encoder, decoder)


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_source=LanguagePairDataset.LEFT_PAD_SOURCE,
            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,
            bidirectional=bidirectional,
        )
        self.left_pad_source = left_pad_source
        self.padding_value = padding_value

        self.output_units = hidden_size
        if bidirectional:
            self.output_units *= 2

    def forward(self, src_tokens, src_lengths):
        if self.left_pad_source:
            # 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 = Variable(x.data.new(*state_size).zero_())
        c0 = Variable(x.data.new(*state_size).zero_())
        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:
            bi_final_hiddens, bi_final_cells = [], []
            for i in range(self.num_layers):
                bi_final_hiddens.append(
                    torch.cat(
                        (final_hiddens[2 * i], final_hiddens[2 * i + 1]),
                        dim=0).view(bsz, self.output_units))
                bi_final_cells.append(
                    torch.cat(
                        (final_cells[2 * i], final_cells[2 * i + 1]),
                        dim=0).view(bsz, self.output_units))
            return x, bi_final_hiddens, bi_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
        }

    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, output_embed_dim):
        super().__init__()

        self.input_proj = Linear(input_embed_dim, output_embed_dim, bias=False)
        self.output_proj = Linear(2*output_embed_dim, output_embed_dim, bias=False)

    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 = F.tanh(self.output_proj(torch.cat((x, input), dim=1)))
        return x, attn_scores


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_embed_dim=512, encoder_output_units=512,
            pretrained_embed=None,
    ):
        super().__init__(dictionary)
        self.dropout_in = dropout_in
        self.dropout_out = dropout_out
        self.hidden_size = hidden_size

        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
        assert encoder_output_units == hidden_size, \
            '{} {}'.format(encoder_output_units, hidden_size)
        # TODO another Linear layer if not equal

        self.layers = nn.ModuleList([
            LSTMCell(
                input_size=encoder_output_units + 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 hidden_size != out_embed_dim:
            self.additional_fc = Linear(hidden_size, out_embed_dim)
        self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)

    def forward(self, prev_output_tokens, encoder_out_dict, incremental_state=None):
        encoder_out = encoder_out_dict['encoder_out']
        encoder_padding_mask = encoder_out_dict['encoder_padding_mask']

        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_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:
            _, 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 = Variable(x.data.new(bsz, self.encoder_output_units).zero_())

        attn_scores = Variable(x.data.new(srclen, seqlen, bsz).zero_())
        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
        attn_scores = attn_scores.transpose(0, 2)

        # project back to size of vocabulary
        if hasattr(self, 'additional_fc'):
            x = self.additional_fc(x)
            x = F.dropout(x, p=self.dropout_out, training=self.training)
        x = self.fc_out(x)

        return x, attn_scores

    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)

    def reorder_encoder_out(self, encoder_out_dict, new_order):
        encoder_out_dict['encoder_out'] = tuple(
            eo.index_select(1, new_order)
            for eo in encoder_out_dict['encoder_out']
        )
        if encoder_out_dict['encoder_padding_mask'] is not None:
            encoder_out_dict['encoder_padding_mask'] = \
                encoder_out_dict['encoder_padding_mask'].index_select(1, new_order)
        return encoder_out_dict

    def max_positions(self):
        """Maximum output length supported by the decoder."""
        return int(1e5)  # an arbitrary large number


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):
    """Weight-normalized 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.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
    args.encoder_hidden_size = getattr(args, 'encoder_hidden_size', 512)
    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_hidden_size = getattr(args, 'decoder_hidden_size', 512)
    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)


@register_model_architecture('lstm', 'lstm_wiseman_iwslt_de_en')
def lstm_wiseman_iwslt_de_en(args):
    base_architecture(args)
    args.encoder_embed_dim = 256
    args.encoder_hidden_size = 256
    args.encoder_layers = 1
    args.encoder_bidirectional = False
    args.encoder_dropout_in = 0
    args.encoder_dropout_out = 0
    args.decoder_embed_dim = 256
    args.decoder_hidden_size = 256
    args.decoder_layers = 1
    args.decoder_out_embed_dim = 256
    args.decoder_attention = '1'
    args.decoder_dropout_in = 0


@register_model_architecture('lstm', 'lstm_luong_wmt_en_de')
def lstm_luong_wmt_en_de(args):
    base_architecture(args)
    args.encoder_embed_dim = 1000
    args.encoder_hidden_size = 1000
    args.encoder_layers = 4
    args.encoder_dropout_out = 0
    args.encoder_bidirectional = False
    args.decoder_embed_dim = 1000
    args.decoder_hidden_size = 1000
    args.decoder_layers = 4
    args.decoder_out_embed_dim = 1000
    args.decoder_attention = '1'
    args.decoder_dropout_out = 0