competition update
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nckcard
286
language_model/wenet/transformer/decoder.py
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286
language_model/wenet/transformer/decoder.py
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# Copyright 2021 Mobvoi Inc. All Rights Reserved.
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# Author: di.wu@mobvoi.com (DI WU)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Decoder definition."""
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from typing import Tuple, List, Optional
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import torch
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from typeguard import check_argument_types
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from wenet.transformer.attention import MultiHeadedAttention
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from wenet.transformer.decoder_layer import DecoderLayer
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from wenet.transformer.embedding import PositionalEncoding
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from wenet.transformer.positionwise_feed_forward import PositionwiseFeedForward
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from wenet.utils.mask import (subsequent_mask, make_pad_mask)
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class TransformerDecoder(torch.nn.Module):
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"""Base class of Transfomer decoder module.
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Args:
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vocab_size: output dim
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encoder_output_size: dimension of attention
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attention_heads: the number of heads of multi head attention
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linear_units: the hidden units number of position-wise feedforward
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num_blocks: the number of decoder blocks
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dropout_rate: dropout rate
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self_attention_dropout_rate: dropout rate for attention
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input_layer: input layer type
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use_output_layer: whether to use output layer
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pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
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normalize_before:
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True: use layer_norm before each sub-block of a layer.
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False: use layer_norm after each sub-block of a layer.
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concat_after: whether to concat attention layer's input and output
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True: x -> x + linear(concat(x, att(x)))
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False: x -> x + att(x)
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"""
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def __init__(
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self,
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vocab_size: int,
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encoder_output_size: int,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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self_attention_dropout_rate: float = 0.0,
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src_attention_dropout_rate: float = 0.0,
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input_layer: str = "embed",
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use_output_layer: bool = True,
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normalize_before: bool = True,
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concat_after: bool = False,
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):
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assert check_argument_types()
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super().__init__()
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attention_dim = encoder_output_size
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if input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(vocab_size, attention_dim),
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PositionalEncoding(attention_dim, positional_dropout_rate),
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)
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else:
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raise ValueError(f"only 'embed' is supported: {input_layer}")
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self.normalize_before = normalize_before
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self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-12)
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self.use_output_layer = use_output_layer
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self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
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self.num_blocks = num_blocks
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self.decoders = torch.nn.ModuleList([
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DecoderLayer(
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attention_dim,
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MultiHeadedAttention(attention_heads, attention_dim,
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self_attention_dropout_rate),
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MultiHeadedAttention(attention_heads, attention_dim,
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src_attention_dropout_rate),
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PositionwiseFeedForward(attention_dim, linear_units,
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dropout_rate),
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dropout_rate,
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normalize_before,
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concat_after,
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) for _ in range(self.num_blocks)
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])
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def forward(
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self,
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memory: torch.Tensor,
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memory_mask: torch.Tensor,
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ys_in_pad: torch.Tensor,
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ys_in_lens: torch.Tensor,
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r_ys_in_pad: Optional[torch.Tensor] = None,
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reverse_weight: float = 0.0,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Forward decoder.
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Args:
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memory: encoded memory, float32 (batch, maxlen_in, feat)
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memory_mask: encoder memory mask, (batch, 1, maxlen_in)
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ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
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ys_in_lens: input lengths of this batch (batch)
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r_ys_in_pad: not used in transformer decoder, in order to unify api
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with bidirectional decoder
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reverse_weight: not used in transformer decoder, in order to unify
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api with bidirectional decode
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Returns:
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(tuple): tuple containing:
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x: decoded token score before softmax (batch, maxlen_out,
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vocab_size) if use_output_layer is True,
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torch.tensor(0.0), in order to unify api with bidirectional decoder
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olens: (batch, )
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"""
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tgt = ys_in_pad
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# tgt_mask: (B, 1, L)
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tgt_mask = (~make_pad_mask(ys_in_lens).unsqueeze(1)).to(tgt.device)
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# m: (1, L, L)
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m = subsequent_mask(tgt_mask.size(-1),
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device=tgt_mask.device).unsqueeze(0)
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# tgt_mask: (B, L, L)
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tgt_mask = tgt_mask & m
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x, _ = self.embed(tgt)
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for layer in self.decoders:
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x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory,
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memory_mask)
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if self.normalize_before:
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x = self.after_norm(x)
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if self.use_output_layer:
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x = self.output_layer(x)
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olens = tgt_mask.sum(1)
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return x, torch.tensor(0.0), olens
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def forward_one_step(
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self,
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memory: torch.Tensor,
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memory_mask: torch.Tensor,
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tgt: torch.Tensor,
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tgt_mask: torch.Tensor,
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cache: Optional[List[torch.Tensor]] = None,
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) -> Tuple[torch.Tensor, List[torch.Tensor]]:
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"""Forward one step.
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This is only used for decoding.
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Args:
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memory: encoded memory, float32 (batch, maxlen_in, feat)
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memory_mask: encoded memory mask, (batch, 1, maxlen_in)
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tgt: input token ids, int64 (batch, maxlen_out)
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tgt_mask: input token mask, (batch, maxlen_out)
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dtype=torch.uint8 in PyTorch 1.2-
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dtype=torch.bool in PyTorch 1.2+ (include 1.2)
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cache: cached output list of (batch, max_time_out-1, size)
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Returns:
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y, cache: NN output value and cache per `self.decoders`.
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y.shape` is (batch, maxlen_out, token)
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"""
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x, _ = self.embed(tgt)
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new_cache = []
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for i, decoder in enumerate(self.decoders):
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if cache is None:
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c = None
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else:
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c = cache[i]
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x, tgt_mask, memory, memory_mask = decoder(x,
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tgt_mask,
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memory,
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memory_mask,
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cache=c)
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new_cache.append(x)
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if self.normalize_before:
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y = self.after_norm(x[:, -1])
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else:
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y = x[:, -1]
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if self.use_output_layer:
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y = torch.log_softmax(self.output_layer(y), dim=-1)
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return y, new_cache
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class BiTransformerDecoder(torch.nn.Module):
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"""Base class of Transfomer decoder module.
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Args:
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vocab_size: output dim
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encoder_output_size: dimension of attention
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attention_heads: the number of heads of multi head attention
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linear_units: the hidden units number of position-wise feedforward
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num_blocks: the number of decoder blocks
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r_num_blocks: the number of right to left decoder blocks
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dropout_rate: dropout rate
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self_attention_dropout_rate: dropout rate for attention
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input_layer: input layer type
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use_output_layer: whether to use output layer
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pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
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normalize_before:
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True: use layer_norm before each sub-block of a layer.
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False: use layer_norm after each sub-block of a layer.
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concat_after: whether to concat attention layer's input and output
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True: x -> x + linear(concat(x, att(x)))
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False: x -> x + att(x)
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"""
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def __init__(
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self,
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vocab_size: int,
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encoder_output_size: int,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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r_num_blocks: int = 0,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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self_attention_dropout_rate: float = 0.0,
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src_attention_dropout_rate: float = 0.0,
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input_layer: str = "embed",
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use_output_layer: bool = True,
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normalize_before: bool = True,
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concat_after: bool = False,
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):
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assert check_argument_types()
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super().__init__()
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self.left_decoder = TransformerDecoder(
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vocab_size, encoder_output_size, attention_heads, linear_units,
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num_blocks, dropout_rate, positional_dropout_rate,
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self_attention_dropout_rate, src_attention_dropout_rate,
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input_layer, use_output_layer, normalize_before, concat_after)
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self.right_decoder = TransformerDecoder(
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vocab_size, encoder_output_size, attention_heads, linear_units,
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r_num_blocks, dropout_rate, positional_dropout_rate,
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self_attention_dropout_rate, src_attention_dropout_rate,
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input_layer, use_output_layer, normalize_before, concat_after)
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def forward(
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self,
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memory: torch.Tensor,
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memory_mask: torch.Tensor,
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ys_in_pad: torch.Tensor,
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ys_in_lens: torch.Tensor,
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r_ys_in_pad: torch.Tensor,
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reverse_weight: float = 0.0,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Forward decoder.
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Args:
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memory: encoded memory, float32 (batch, maxlen_in, feat)
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memory_mask: encoder memory mask, (batch, 1, maxlen_in)
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ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
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ys_in_lens: input lengths of this batch (batch)
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r_ys_in_pad: padded input token ids, int64 (batch, maxlen_out),
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used for right to left decoder
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reverse_weight: used for right to left decoder
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Returns:
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(tuple): tuple containing:
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x: decoded token score before softmax (batch, maxlen_out,
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vocab_size) if use_output_layer is True,
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r_x: x: decoded token score (right to left decoder)
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before softmax (batch, maxlen_out, vocab_size)
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if use_output_layer is True,
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olens: (batch, )
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"""
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l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad,
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ys_in_lens)
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r_x = torch.tensor(0.0)
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if reverse_weight > 0.0:
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r_x, _, olens = self.right_decoder(memory, memory_mask, r_ys_in_pad,
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ys_in_lens)
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return l_x, r_x, olens
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def forward_one_step(
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self,
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memory: torch.Tensor,
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memory_mask: torch.Tensor,
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tgt: torch.Tensor,
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tgt_mask: torch.Tensor,
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cache: Optional[List[torch.Tensor]] = None,
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) -> Tuple[torch.Tensor, List[torch.Tensor]]:
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"""Forward one step.
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This is only used for decoding.
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Args:
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memory: encoded memory, float32 (batch, maxlen_in, feat)
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memory_mask: encoded memory mask, (batch, 1, maxlen_in)
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tgt: input token ids, int64 (batch, maxlen_out)
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tgt_mask: input token mask, (batch, maxlen_out)
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dtype=torch.uint8 in PyTorch 1.2-
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dtype=torch.bool in PyTorch 1.2+ (include 1.2)
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cache: cached output list of (batch, max_time_out-1, size)
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Returns:
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y, cache: NN output value and cache per `self.decoders`.
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y.shape` is (batch, maxlen_out, token)
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"""
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return self.left_decoder.forward_one_step(memory, memory_mask, tgt,
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tgt_mask, cache)
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