torch/nn/modules/transformer.py

import torch
import copy
from .. import functional as F
from .module import Module
from .activation import MultiheadAttention
from .container import ModuleList
from ..init import xavier_uniform_
from .dropout import Dropout
from .linear import Linear
from .normalization import LayerNorm

class Transformer(Module):
    r"""A transformer model. User is able to modify the attributes as needed. The architechture
    is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer,
    Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and
    Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information
    Processing Systems, pages 6000-6010.

    Args:
        d_model: the number of expected features in the encoder/decoder inputs (default=512).
        nhead: the number of heads in the multiheadattention models (default=8).
        num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).
        num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).
        custom_encoder: custom encoder (default=None).
        custom_decoder: custom decoder (default=None).

    Examples::
        >>> transformer_model = nn.Transformer(src_vocab, tgt_vocab)
        >>> transformer_model = nn.Transformer(src_vocab, tgt_vocab, nhead=16, num_encoder_layers=12)
    """

    def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
                 num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,
                 custom_encoder=None, custom_decoder=None):
        super(Transformer, self).__init__()

        if custom_encoder is not None:
            self.encoder = custom_encoder
        else:
            encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout)
            encoder_norm = LayerNorm(d_model)
            self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)

        if custom_decoder is not None:
            self.decoder = custom_decoder
        else:
            decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout)
            decoder_norm = LayerNorm(d_model)
            self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm)

        self._reset_parameters()

        self.d_model = d_model
        self.nhead = nhead

    def forward(self, src, tgt, src_mask=None, tgt_mask=None, memory_mask=None):
        r"""Take in and process masked source/target sequences.

        Args:
            src: the sequence to the encoder (required).
            tgt: the sequence to the decoder (required).
            src_mask: the mask for the src sequence (optional).
            tgt_mask: the mask for the tgt sequence (optional).
            memory_mask: the mask for the encoder output (optional).

        Shape:
            - src: :math:`(S, N, E)`.
            - tgt: :math:`(T, N, E)`.
            - src_mask: :math:`(S, S)`.
            - tgt_mask: :math:`(T, T)`.
            - memory_mask: :math:`(T, S)`.

            Note: The maksed positions are filled with float('-inf'). Unmasked positions
            are filled with float(0.0). Masks ensure that the predictions 
            for position i depend only on the information before position i.

            - output: :math:`(T, N, E)`.

            Note: Due to the multi-head attention architecture in the transformer model,
            the output sequence length of a transformer is same as the input sequence
            (i.e. target) length of the decode.

            where S is the source sequence length, T is the target sequence length, N is the
            batch size, E is the feature number

        Examples:
            >>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)
        """

        if src.size(1) != tgt.size(1):
            raise RuntimeError("the batch number of src and tgt must be equal")

        if src.size(2) != self.d_model or tgt.size(2) != self.d_model:
            raise RuntimeError("the feature number of src and tgt must be equal to d_model")

        memory = self.encoder(src, src_mask)
        output = self.decoder(tgt, memory, tgt_mask, memory_mask)
        return output

    def generate_square_subsequent_mask(self, sz):
        r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
            Unmasked positions are filled with float(0.0).
        """
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask

    def _reset_parameters(self):
        r"""Initiate parameters in the transformer model."""

        for p in self.parameters():
            if p.dim() > 1:
                xavier_uniform_(p)


class TransformerEncoder(Module):
    r"""TransformerEncoder is a stack of N encoder layers

    Args:
        encoder_layer: an instance of the TransformerEncoderLayer() class (required).
        num_layers: the number of sub-encoder-layers in the encoder (required).
        norm: the layer normalization component (optional).

    Examples::
        >>> encoder_layer = nn.TransformerEncoderLayer(d_model, nhead)
        >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)
    """

    def __init__(self, encoder_layer, num_layers, norm=None):
        super(TransformerEncoder, self).__init__()
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm

    def forward(self, src, mask=None):
        r"""Pass the input through the endocder layers in turn.

        Args:
            src: the sequnce to the encoder (required).
            src_mask: the mask for the src sequence (optional).

        Shape:
            see the docs in Transformer class.
        """
        output = src

        for i in range(self.num_layers):
            output = self.layers[i](output, mask)

        if self.norm:
            output = self.norm(output)

        return output


class TransformerDecoder(Module):
    r"""TransformerDecoder is a stack of N decoder layers

    Args:
        decoder_layer: an instance of the TransformerDecoderLayer() class (required).
        num_layers: the number of sub-decoder-layers in the decoder (required).
        norm: the layer normalization component (optional).

    Examples::
        >>> decoder_layer = nn.TransformerDecoderLayer(d_model, nhead)
        >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers)
    """

    def __init__(self, decoder_layer, num_layers, norm=None):
        super(TransformerDecoder, self).__init__()
        self.layers = _get_clones(decoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm

    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None):
        r"""Pass the inputs (and mask) through the decoder layer in turn.

        Args:
            tgt: the sequence to the decoder (required).
            memory: the sequnce from the last layer of the encoder (required).
            tgt_mask: the mask for the tgt sequence (optional).
            memory_mask: the mask for the memory sequence (optional).

        Shape:
            see the docs in Transformer class.
        """
        output = tgt

        for i in range(self.num_layers):
            output = self.layers[i](output, memory, tgt_mask, memory_mask)

        if self.norm:
            output = self.norm(output)

        return output

class TransformerEncoderLayer(Module):
    r"""TransformerEncoderLayer is made up of self-attn and feedforward network.
    This standard encoder layer is based on the paper "Attention Is All You Need".
    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, 
    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in 
    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement 
    in a different way during application.

    Args:
        d_model: the number of expected features in the input (required).
        nhead: the number of heads in the multiheadattention models (required).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).

    Examples::
        >>> encoder_layer = nn.TransformerEncoderLayer(d_model, nhead)
    """

    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1):
        super(TransformerEncoderLayer, self).__init__()
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
        # Implementation of Feedforward model
        self.linear1 = Linear(d_model, dim_feedforward)
        self.dropout = Dropout(dropout)
        self.linear2 = Linear(dim_feedforward, d_model)

        self.norm1 = LayerNorm(d_model)
        self.norm2 = LayerNorm(d_model)
        self.dropout1 = Dropout(dropout)
        self.dropout2 = Dropout(dropout)

    def forward(self, src, src_mask=None):
        r"""Pass the input through the endocder layer.

        Args:
            src: the sequnce to the encoder layer (required).
            src_mask: the mask for the src sequence (optional).

        Shape:
            see the docs in Transformer class.
        """
        src2 = self.self_attn(src, src, src, attn_mask=src_mask)[0]
        src = src + self.dropout1(src2)
        src = self.norm1(src)
        src2 = self.linear2(self.dropout(F.relu(self.linear1(src))))
        src = src + self.dropout2(src2)
        src = self.norm2(src)
        return src


class TransformerDecoderLayer(Module):
    r"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
    This standard decoder layer is based on the paper "Attention Is All You Need".
    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, 
    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in 
    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement 
    in a different way during application.

    Args:
        d_model: the number of expected features in the input (required).
        nhead: the number of heads in the multiheadattention models (required).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).

    Examples::
        >>> decoder_layer = nn.TransformerDecoderLayer(d_model, nhead)
    """

    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1):
        super(TransformerDecoderLayer, self).__init__()
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
        self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
        # Implementation of Feedforward model
        self.linear1 = Linear(d_model, dim_feedforward)
        self.dropout = Dropout(dropout)
        self.linear2 = Linear(dim_feedforward, d_model)

        self.norm1 = LayerNorm(d_model)
        self.norm2 = LayerNorm(d_model)
        self.norm3 = LayerNorm(d_model)
        self.dropout1 = Dropout(dropout)
        self.dropout2 = Dropout(dropout)
        self.dropout3 = Dropout(dropout)

    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None):
        r"""Pass the inputs (and mask) through the decoder layer.

        Args:
            tgt: the sequence to the decoder layer (required).
            memory: the sequnce from the last layer of the encoder (required).
            tgt_mask: the mask for the tgt sequence (optional).
            memory_mask: the mask for the memory sequence (optional).

        Shape:
            see the docs in Transformer class.
        """
        tgt2 = self.self_attn(tgt, tgt, tgt, attn_mask=tgt_mask)[0]
        tgt = tgt + self.dropout1(tgt2)
        tgt = self.norm1(tgt)
        tgt2 = self.multihead_attn(tgt, memory, memory, attn_mask=memory_mask)[0]
        tgt = tgt + self.dropout2(tgt2)
        tgt = self.norm2(tgt)
        tgt2 = self.linear2(self.dropout(F.relu(self.linear1(tgt))))
        tgt = tgt + self.dropout3(tgt2)
        tgt = self.norm3(tgt)
        return tgt


def _get_clones(module, N):
    return ModuleList([copy.deepcopy(module) for i in range(N)])