fairseq/fairseq/models/fconv.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 math
import torch
import torch.nn as nn
import torch.nn.functional as F

from fairseq import utils
from fairseq.data import LanguagePairDataset
from fairseq.modules import BeamableMM, GradMultiply, LearnedPositionalEmbedding, LinearizedConvolution

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


@register_model('fconv')
class FConvModel(FairseqModel):
    def __init__(self, encoder, decoder):
        super().__init__(encoder, decoder)
        self.encoder.num_attention_layers = sum(layer is not None for layer in decoder.attention)

    @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-layers', type=str, metavar='EXPR',
                            help='encoder layers [(dim, kernel_size), ...]')
        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-layers', type=str, metavar='EXPR',
                            help='decoder layers [(dim, kernel_size), ...]')
        parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
                            help='decoder output embedding dimension')
        parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
                            help='decoder attention [True, ...]')
        parser.add_argument('--share-input-output-embed', action='store_true',
                            help='share input and output embeddings (requires'
                                 ' --decoder-out-embed-dim and --decoder-embed-dim'
                                 ' to be equal)')

    @classmethod
    def build_model(cls, args, src_dict, dst_dict):
        """Build a new model instance."""
        if not hasattr(args, 'max_source_positions'):
            args.max_source_positions = args.max_positions
            args.max_target_positions = args.max_positions
        if not hasattr(args, 'share_input_output_embed'):
            args.share_input_output_embed = False
        if not hasattr(args, 'encoder_embed_path'):
            args.encoder_embed_path = None
        if not hasattr(args, 'decoder_embed_path'):
            args.decoder_embed_path = None

        encoder_embed_dict = None
        if args.encoder_embed_path:
            encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
            utils.print_embed_overlap(encoder_embed_dict, src_dict)

        decoder_embed_dict = None
        if args.decoder_embed_path:
            decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
            utils.print_embed_overlap(decoder_embed_dict, dst_dict)

        encoder = FConvEncoder(
            src_dict,
            embed_dim=args.encoder_embed_dim,
            embed_dict=encoder_embed_dict,
            convolutions=eval(args.encoder_layers),
            dropout=args.dropout,
            max_positions=args.max_source_positions,
        )
        decoder = FConvDecoder(
            dst_dict,
            embed_dim=args.decoder_embed_dim,
            embed_dict=decoder_embed_dict,
            convolutions=eval(args.decoder_layers),
            out_embed_dim=args.decoder_out_embed_dim,
            attention=eval(args.decoder_attention),
            dropout=args.dropout,
            max_positions=args.max_target_positions,
            share_embed=args.share_input_output_embed
        )
        return FConvModel(encoder, decoder)


class FConvEncoder(FairseqEncoder):
    """Convolutional encoder"""
    def __init__(self, dictionary, embed_dim=512, embed_dict=None,
                 max_positions=1024, convolutions=((512, 3),) * 20, dropout=0.1):
        super().__init__(dictionary)
        self.dropout = dropout
        self.num_attention_layers = None

        num_embeddings = len(dictionary)
        self.padding_idx = dictionary.pad()
        self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
        self.embed_positions = PositionalEmbedding(
            max_positions,
            embed_dim,
            self.padding_idx,
            left_pad=LanguagePairDataset.LEFT_PAD_SOURCE,
        )

        in_channels = convolutions[0][0]
        self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
        self.projections = nn.ModuleList()
        self.convolutions = nn.ModuleList()
        for (out_channels, kernel_size) in convolutions:
            self.projections.append(Linear(in_channels, out_channels)
                                    if in_channels != out_channels else None)
            if kernel_size % 2 == 1:
                padding = kernel_size // 2
            else:
                padding = 0
            self.convolutions.append(
                ConvTBC(in_channels, out_channels * 2, kernel_size,
                        dropout=dropout, padding=padding)
            )
            in_channels = out_channels
        self.fc2 = Linear(in_channels, embed_dim)

    def forward(self, src_tokens, src_lengths):
        # embed tokens and positions
        x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens)
        x = F.dropout(x, p=self.dropout, training=self.training)
        input_embedding = x

        # project to size of convolution
        x = self.fc1(x)

        # used to mask padding in input
        encoder_padding_mask = src_tokens.eq(self.padding_idx).t()  # -> T x B
        if not encoder_padding_mask.any():
            encoder_padding_mask = None

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)

        # temporal convolutions
        for proj, conv in zip(self.projections, self.convolutions):
            residual = x if proj is None else proj(x)

            if encoder_padding_mask is not None:
                x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)

            x = F.dropout(x, p=self.dropout, training=self.training)
            if conv.kernel_size[0] % 2 == 1:
                # padding is implicit in the conv
                x = conv(x)
            else:
                padding_l = (conv.kernel_size[0] - 1) // 2
                padding_r = conv.kernel_size[0] // 2
                x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
                x = conv(x)
            x = F.glu(x, dim=2)
            x = (x + residual) * math.sqrt(0.5)

        # T x B x C -> B x T x C
        x = x.transpose(1, 0)

        # project back to size of embedding
        x = self.fc2(x)

        if encoder_padding_mask is not None:
            encoder_padding_mask = encoder_padding_mask.t()  # -> B x T
            x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)

        # scale gradients (this only affects backward, not forward)
        x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers))

        # add output to input embedding for attention
        y = (x + input_embedding) * math.sqrt(0.5)

        return {
            'encoder_out': (x, y),
            'encoder_padding_mask': encoder_padding_mask,  # B x T
        }

    def max_positions(self):
        """Maximum input length supported by the encoder."""
        return self.embed_positions.max_positions()


class AttentionLayer(nn.Module):
    def __init__(self, conv_channels, embed_dim, bmm=None):
        super().__init__()
        # projects from output of convolution to embedding dimension
        self.in_projection = Linear(conv_channels, embed_dim)
        # projects from embedding dimension to convolution size
        self.out_projection = Linear(embed_dim, conv_channels)

        self.bmm = bmm if bmm is not None else torch.bmm

    def forward(self, x, target_embedding, encoder_out, encoder_padding_mask):
        residual = x

        # attention
        x = (self.in_projection(x) + target_embedding) * math.sqrt(0.5)
        x = self.bmm(x, encoder_out[0])

        # don't attend over padding
        if encoder_padding_mask is not None:
            x = x.float().masked_fill(
                encoder_padding_mask.unsqueeze(1),
                float('-inf')
            ).type_as(x)  # FP16 support: cast to float and back

        # softmax over last dim
        sz = x.size()
        x = F.softmax(x.view(sz[0] * sz[1], sz[2]), dim=1)
        x = x.view(sz)
        attn_scores = x

        x = self.bmm(x, encoder_out[1])

        # scale attention output (respecting potentially different lengths)
        s = encoder_out[1].size(1)
        if encoder_padding_mask is None:
            x = x * (s * math.sqrt(1.0 / s))
        else:
            s = s - encoder_padding_mask.type_as(x).sum(dim=1, keepdim=True)  # exclude padding
            s = s.unsqueeze(-1)
            x = x * (s * s.rsqrt())

        # project back
        x = (self.out_projection(x) + residual) * math.sqrt(0.5)
        return x, attn_scores

    def make_generation_fast_(self, beamable_mm_beam_size=None, **kwargs):
        """Replace torch.bmm with BeamableMM."""
        if beamable_mm_beam_size is not None:
            del self.bmm
            self.add_module('bmm', BeamableMM(beamable_mm_beam_size))


class FConvDecoder(FairseqIncrementalDecoder):
    """Convolutional decoder"""
    def __init__(self, dictionary, embed_dim=512,
                 embed_dict=None, out_embed_dim=256,
                 max_positions=1024, convolutions=((512, 3),) * 20,
                 attention=True, dropout=0.1, share_embed=False):
        super().__init__(dictionary)
        self.register_buffer('version', torch.Tensor([2]))
        self.dropout = dropout

        in_channels = convolutions[0][0]
        if isinstance(attention, bool):
            # expand True into [True, True, ...] and do the same with False
            attention = [attention] * len(convolutions)
        if not isinstance(attention, list) or len(attention) != len(convolutions):
            raise ValueError('Attention is expected to be a list of booleans of '
                             'length equal to the number of layers.')

        num_embeddings = len(dictionary)
        padding_idx = dictionary.pad()
        self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
        if embed_dict:
            self.embed_tokens = utils.load_embedding(embed_dict, self.dictionary, self.embed_tokens)

        self.embed_positions = PositionalEmbedding(
            max_positions,
            embed_dim,
            padding_idx,
            left_pad=LanguagePairDataset.LEFT_PAD_TARGET,
        )

        self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
        self.projections = nn.ModuleList()
        self.convolutions = nn.ModuleList()
        self.attention = nn.ModuleList()
        for i, (out_channels, kernel_size) in enumerate(convolutions):
            self.projections.append(Linear(in_channels, out_channels)
                                    if in_channels != out_channels else None)
            self.convolutions.append(
                LinearizedConv1d(in_channels, out_channels * 2, kernel_size,
                                 padding=(kernel_size - 1), dropout=dropout)
            )
            self.attention.append(AttentionLayer(out_channels, embed_dim)
                                  if attention[i] else None)
            in_channels = out_channels
        self.fc2 = Linear(in_channels, out_embed_dim)
        if share_embed:
            assert out_embed_dim == embed_dim, \
                "Shared embed weights implies same dimensions " \
                " out_embed_dim={} vs embed_dim={}".format(out_embed_dim, embed_dim)
            self.fc3 = nn.Linear(out_embed_dim, num_embeddings)
            self.fc3.weight = self.embed_tokens.weight
        else:
            self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)

    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']

        # split and transpose encoder outputs
        encoder_a, encoder_b = self._split_encoder_out(encoder_out, incremental_state)

        # embed tokens and combine with positional embeddings
        pos_embed = self.embed_positions(prev_output_tokens, incremental_state)
        if incremental_state is not None:
            prev_output_tokens = prev_output_tokens[:, -1:]
        x = self._embed_tokens(prev_output_tokens, incremental_state)
        x += pos_embed
        x = F.dropout(x, p=self.dropout, training=self.training)
        target_embedding = x

        # project to size of convolution
        x = self.fc1(x)

        # B x T x C -> T x B x C
        x = self._transpose_if_training(x, incremental_state)

        # temporal convolutions
        avg_attn_scores = None
        num_attn_layers = len(self.attention)
        for proj, conv, attention in zip(self.projections, self.convolutions, self.attention):
            residual = x if proj is None else proj(x)

            x = F.dropout(x, p=self.dropout, training=self.training)
            x = conv(x, incremental_state)
            x = F.glu(x, dim=2)

            # attention
            if attention is not None:
                x = self._transpose_if_training(x, incremental_state)

                x, attn_scores = attention(x, target_embedding, (encoder_a, encoder_b), encoder_padding_mask)
                attn_scores = attn_scores / num_attn_layers
                if avg_attn_scores is None:
                    avg_attn_scores = attn_scores
                else:
                    avg_attn_scores.add_(attn_scores)

                x = self._transpose_if_training(x, incremental_state)

            # residual
            x = (x + residual) * math.sqrt(0.5)

        # T x B x C -> B x T x C
        x = self._transpose_if_training(x, incremental_state)

        # project back to size of vocabulary
        x = self.fc2(x)
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.fc3(x)

        return x, avg_attn_scores

    def reorder_incremental_state(self, incremental_state, new_order):
        super().reorder_incremental_state(incremental_state, new_order)
        encoder_out = utils.get_incremental_state(self, incremental_state, 'encoder_out')
        if encoder_out is not None:
            encoder_out = tuple(eo.index_select(0, new_order) for eo in encoder_out)
            utils.set_incremental_state(self, incremental_state, 'encoder_out', encoder_out)

    def reorder_encoder_out(self, encoder_out_dict, new_order):
        if encoder_out_dict['encoder_padding_mask'] is not None:
            encoder_out_dict['encoder_padding_mask'] = \
                encoder_out_dict['encoder_padding_mask'].index_select(0, new_order)
        return encoder_out_dict

    def max_positions(self):
        """Maximum output length supported by the decoder."""
        return self.embed_positions.max_positions()

    def upgrade_state_dict(self, state_dict):
        if state_dict.get('decoder.version', torch.Tensor([1]))[0] < 2:
            # old models use incorrect weight norm dimension
            for i, conv in enumerate(self.convolutions):
                # reconfigure weight norm
                nn.utils.remove_weight_norm(conv)
                self.convolutions[i] = nn.utils.weight_norm(conv, dim=0)
            state_dict['decoder.version'] = torch.Tensor([1])
        return state_dict

    def _embed_tokens(self, tokens, incremental_state):
        if incremental_state is not None:
            # keep only the last token for incremental forward pass
            tokens = tokens[:, -1:]
        return self.embed_tokens(tokens)

    def _split_encoder_out(self, encoder_out, incremental_state):
        """Split and transpose encoder outputs.

        This is cached when doing incremental inference.
        """
        cached_result = utils.get_incremental_state(self, incremental_state, 'encoder_out')
        if cached_result is not None:
            return cached_result

        # transpose only once to speed up attention layers
        encoder_a, encoder_b = encoder_out
        encoder_a = encoder_a.transpose(1, 2).contiguous()
        result = (encoder_a, encoder_b)

        if incremental_state is not None:
            utils.set_incremental_state(self, incremental_state, 'encoder_out', result)
        return result

    def _transpose_if_training(self, x, incremental_state):
        if incremental_state is None:
            x = x.transpose(0, 1)
        return x


def Embedding(num_embeddings, embedding_dim, padding_idx):
    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
    nn.init.normal(m.weight, 0, 0.1)
    nn.init.constant(m.weight[padding_idx], 0)
    return m


def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx, left_pad):
    m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx, left_pad)
    nn.init.normal(m.weight, 0, 0.1)
    nn.init.constant(m.weight[padding_idx], 0)
    return m


def Linear(in_features, out_features, dropout=0):
    """Weight-normalized Linear layer (input: N x T x C)"""
    m = nn.Linear(in_features, out_features)
    m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
    m.bias.data.zero_()
    return nn.utils.weight_norm(m)


def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
    """Weight-normalized Conv1d layer optimized for decoding"""
    m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
    std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
    m.weight.data.normal_(mean=0, std=std)
    m.bias.data.zero_()
    return nn.utils.weight_norm(m, dim=2)


def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
    """Weight-normalized Conv1d layer"""
    from fairseq.modules import ConvTBC
    m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
    std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
    m.weight.data.normal_(mean=0, std=std)
    m.bias.data.zero_()
    return nn.utils.weight_norm(m, dim=2)


@register_model_architecture('fconv', 'fconv')
def base_architecture(args):
    args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
    args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 20')
    args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
    args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 20')
    args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
    args.decoder_attention = getattr(args, 'decoder_attention', 'True')
    args.share_input_output_embed = getattr(args, 'share_input_output_embed', False)


@register_model_architecture('fconv', 'fconv_iwslt_de_en')
def fconv_iwslt_de_en(args):
    base_architecture(args)
    args.encoder_embed_dim = 256
    args.encoder_layers = '[(256, 3)] * 4'
    args.decoder_embed_dim = 256
    args.decoder_layers = '[(256, 3)] * 3'
    args.decoder_out_embed_dim = 256


@register_model_architecture('fconv', 'fconv_wmt_en_ro')
def fconv_wmt_en_ro(args):
    base_architecture(args)
    args.encoder_embed_dim = 512
    args.encoder_layers = '[(512, 3)] * 20'
    args.decoder_embed_dim = 512
    args.decoder_layers = '[(512, 3)] * 20'
    args.decoder_out_embed_dim = 512


@register_model_architecture('fconv', 'fconv_wmt_en_de')
def fconv_wmt_en_de(args):
    base_architecture(args)
    convs = '[(512, 3)] * 9'       # first 9 layers have 512 units
    convs += ' + [(1024, 3)] * 4'  # next 4 layers have 1024 units
    convs += ' + [(2048, 1)] * 2'  # final 2 layers use 1x1 convolutions
    args.encoder_embed_dim = 768
    args.encoder_layers = convs
    args.decoder_embed_dim = 768
    args.decoder_layers = convs
    args.decoder_out_embed_dim = 512


@register_model_architecture('fconv', 'fconv_wmt_en_fr')
def fconv_wmt_en_fr(args):
    base_architecture(args)
    convs = '[(512, 3)] * 6'       # first 6 layers have 512 units
    convs += ' + [(768, 3)] * 4'   # next 4 layers have 768 units
    convs += ' + [(1024, 3)] * 3'  # next 3 layers have 1024 units
    convs += ' + [(2048, 1)] * 1'  # next 1 layer uses 1x1 convolutions
    convs += ' + [(4096, 1)] * 1'  # final 1 layer uses 1x1 convolutions
    args.encoder_embed_dim = 768
    args.encoder_layers = convs
    args.decoder_embed_dim = 768
    args.decoder_layers = convs
    args.decoder_out_embed_dim = 512