Source code for onmt.decoders.transformer

Implementation of "Attention is All You Need"

import torch
import torch.nn as nn

from onmt.decoders.decoder import DecoderBase
from onmt.modules import MultiHeadedAttention, AverageAttention
from onmt.modules.position_ffn import PositionwiseFeedForward
from onmt.utils.misc import sequence_mask

class TransformerDecoderLayer(nn.Module):
      d_model (int): the dimension of keys/values/queries in
          :class:`MultiHeadedAttention`, also the input size of
          the first-layer of the :class:`PositionwiseFeedForward`.
      heads (int): the number of heads for MultiHeadedAttention.
      d_ff (int): the second-layer of the :class:`PositionwiseFeedForward`.
      dropout (float): dropout probability.
      self_attn_type (string): type of self-attention scaled-dot, average

    def __init__(self, d_model, heads, d_ff, dropout, attention_dropout,
                 self_attn_type="scaled-dot", max_relative_positions=0,
        super(TransformerDecoderLayer, self).__init__()

        if self_attn_type == "scaled-dot":
            self.self_attn = MultiHeadedAttention(
                heads, d_model, dropout=dropout,
        elif self_attn_type == "average":
            self.self_attn = AverageAttention(d_model,

        self.context_attn = MultiHeadedAttention(
            heads, d_model, dropout=attention_dropout)
        self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)
        self.layer_norm_1 = nn.LayerNorm(d_model, eps=1e-6)
        self.layer_norm_2 = nn.LayerNorm(d_model, eps=1e-6)
        self.drop = nn.Dropout(dropout)

    def forward(self, inputs, memory_bank, src_pad_mask, tgt_pad_mask,
                layer_cache=None, step=None):
            inputs (FloatTensor): ``(batch_size, 1, model_dim)``
            memory_bank (FloatTensor): ``(batch_size, src_len, model_dim)``
            src_pad_mask (LongTensor): ``(batch_size, 1, src_len)``
            tgt_pad_mask (LongTensor): ``(batch_size, 1, 1)``

            (FloatTensor, FloatTensor):

            * output ``(batch_size, 1, model_dim)``
            * attn ``(batch_size, 1, src_len)``

        dec_mask = None
        if step is None:
            tgt_len = tgt_pad_mask.size(-1)
            future_mask = torch.ones(
                [tgt_len, tgt_len],
            future_mask = future_mask.triu_(1).view(1, tgt_len, tgt_len)
            # BoolTensor was introduced in pytorch 1.2
                future_mask = future_mask.bool()
            except AttributeError:
            dec_mask = + future_mask, 0)

        input_norm = self.layer_norm_1(inputs)

        if isinstance(self.self_attn, MultiHeadedAttention):
            query, attn = self.self_attn(input_norm, input_norm, input_norm,
        elif isinstance(self.self_attn, AverageAttention):
            query, attn = self.self_attn(input_norm, mask=dec_mask,
                                         layer_cache=layer_cache, step=step)

        query = self.drop(query) + inputs

        query_norm = self.layer_norm_2(query)
        mid, attn = self.context_attn(memory_bank, memory_bank, query_norm,
        output = self.feed_forward(self.drop(mid) + query)

        return output, attn

    def update_dropout(self, dropout, attention_dropout):
        self.drop.p = dropout

[docs]class TransformerDecoder(DecoderBase): """The Transformer decoder from "Attention is All You Need". :cite:`DBLP:journals/corr/VaswaniSPUJGKP17` .. mermaid:: graph BT A[input] B[multi-head self-attn] BB[multi-head src-attn] C[feed forward] O[output] A --> B B --> BB BB --> C C --> O Args: num_layers (int): number of encoder layers. d_model (int): size of the model heads (int): number of heads d_ff (int): size of the inner FF layer copy_attn (bool): if using a separate copy attention self_attn_type (str): type of self-attention scaled-dot, average dropout (float): dropout parameters embeddings (onmt.modules.Embeddings): embeddings to use, should have positional encodings """ def __init__(self, num_layers, d_model, heads, d_ff, copy_attn, self_attn_type, dropout, attention_dropout, embeddings, max_relative_positions, aan_useffn): super(TransformerDecoder, self).__init__() self.embeddings = embeddings # Decoder State self.state = {} self.transformer_layers = nn.ModuleList( [TransformerDecoderLayer(d_model, heads, d_ff, dropout, attention_dropout, self_attn_type=self_attn_type, max_relative_positions=max_relative_positions, aan_useffn=aan_useffn) for i in range(num_layers)]) # previously, there was a GlobalAttention module here for copy # attention. But it was never actually used -- the "copy" attention # just reuses the context attention. self._copy = copy_attn self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
[docs] @classmethod def from_opt(cls, opt, embeddings): """Alternate constructor.""" return cls( opt.dec_layers, opt.dec_rnn_size, opt.heads, opt.transformer_ff, opt.copy_attn, opt.self_attn_type, opt.dropout[0] if type(opt.dropout) is list else opt.dropout, opt.attention_dropout[0] if type(opt.attention_dropout) is list else opt.dropout, embeddings, opt.max_relative_positions, opt.aan_useffn)
[docs] def init_state(self, src, memory_bank, enc_hidden): """Initialize decoder state.""" self.state["src"] = src self.state["cache"] = None
def map_state(self, fn): def _recursive_map(struct, batch_dim=0): for k, v in struct.items(): if v is not None: if isinstance(v, dict): _recursive_map(v) else: struct[k] = fn(v, batch_dim) self.state["src"] = fn(self.state["src"], 1) if self.state["cache"] is not None: _recursive_map(self.state["cache"]) def detach_state(self): self.state["src"] = self.state["src"].detach()
[docs] def forward(self, tgt, memory_bank, step=None, **kwargs): """Decode, possibly stepwise.""" if step == 0: self._init_cache(memory_bank) tgt_words = tgt[:, :, 0].transpose(0, 1) emb = self.embeddings(tgt, step=step) assert emb.dim() == 3 # len x batch x embedding_dim output = emb.transpose(0, 1).contiguous() src_memory_bank = memory_bank.transpose(0, 1).contiguous() pad_idx = self.embeddings.word_padding_idx src_lens = kwargs["memory_lengths"] src_max_len = self.state["src"].shape[0] src_pad_mask = ~sequence_mask(src_lens, src_max_len).unsqueeze(1) tgt_pad_mask = # [B, 1, T_tgt] for i, layer in enumerate(self.transformer_layers): layer_cache = self.state["cache"]["layer_{}".format(i)] \ if step is not None else None output, attn = layer( output, src_memory_bank, src_pad_mask, tgt_pad_mask, layer_cache=layer_cache, step=step) output = self.layer_norm(output) dec_outs = output.transpose(0, 1).contiguous() attn = attn.transpose(0, 1).contiguous() attns = {"std": attn} if self._copy: attns["copy"] = attn # TODO change the way attns is returned dict => list or tuple (onnx) return dec_outs, attns
def _init_cache(self, memory_bank): self.state["cache"] = {} batch_size = memory_bank.size(1) depth = memory_bank.size(-1) for i, layer in enumerate(self.transformer_layers): layer_cache = {"memory_keys": None, "memory_values": None} if isinstance(layer.self_attn, AverageAttention): layer_cache["prev_g"] = torch.zeros((batch_size, 1, depth), device=memory_bank.device) else: layer_cache["self_keys"] = None layer_cache["self_values"] = None self.state["cache"]["layer_{}".format(i)] = layer_cache def update_dropout(self, dropout, attention_dropout): self.embeddings.update_dropout(dropout) for layer in self.transformer_layers: layer.update_dropout(dropout, attention_dropout)