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247 lines
9.6 KiB
247 lines
9.6 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Decoder definition."""
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from typing import Any
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from typing import List
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from typing import Optional
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from typing import Tuple
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import paddle
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from paddle import nn
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from typeguard import check_argument_types
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from deepspeech.decoders.scorers.scorer_interface import BatchScorerInterface
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from deepspeech.modules.attention import MultiHeadedAttention
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from deepspeech.modules.decoder_layer import DecoderLayer
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from deepspeech.modules.embedding import PositionalEncoding
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from deepspeech.modules.mask import make_non_pad_mask
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from deepspeech.modules.mask import make_xs_mask
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from deepspeech.modules.mask import subsequent_mask
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from deepspeech.modules.positionwise_feed_forward import PositionwiseFeedForward
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ["TransformerDecoder"]
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class TransformerDecoder(BatchScorerInterface, nn.Layer):
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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, `embed`
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use_output_layer: whether to use output layer
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pos_enc_class: PositionalEncoding module
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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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assert check_argument_types()
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nn.Layer.__init__(self)
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self.selfattention_layer_type = 'selfattn'
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attention_dim = encoder_output_size
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if input_layer == "embed":
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self.embed = nn.Sequential(
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nn.Embedding(vocab_size, attention_dim),
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PositionalEncoding(attention_dim, positional_dropout_rate), )
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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 = nn.LayerNorm(attention_dim, epsilon=1e-12)
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self.use_output_layer = use_output_layer
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self.output_layer = nn.Linear(attention_dim, vocab_size)
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self.decoders = nn.LayerList([
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DecoderLayer(
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size=attention_dim,
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self_attn=MultiHeadedAttention(attention_heads, attention_dim,
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self_attention_dropout_rate),
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src_attn=MultiHeadedAttention(attention_heads, attention_dim,
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src_attention_dropout_rate),
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feed_forward=PositionwiseFeedForward(
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attention_dim, linear_units, dropout_rate),
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dropout_rate=dropout_rate,
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normalize_before=normalize_before,
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concat_after=concat_after, ) for _ in range(num_blocks)
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])
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def forward(
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self,
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memory: paddle.Tensor,
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memory_mask: paddle.Tensor,
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ys_in_pad: paddle.Tensor,
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ys_in_lens: paddle.Tensor, ) -> Tuple[paddle.Tensor, paddle.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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Returns:
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(tuple): tuple containing:
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x: decoded token score 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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tgt = ys_in_pad
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# tgt_mask: (B, 1, L)
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tgt_mask = (make_non_pad_mask(ys_in_lens).unsqueeze(1))
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# m: (1, L, L)
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m = subsequent_mask(tgt_mask.shape[-1]).unsqueeze(0)
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# tgt_mask: (B, L, L)
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# TODO(Hui Zhang): not support & for tensor
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# tgt_mask = tgt_mask & m
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tgt_mask = tgt_mask.logical_and(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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# TODO(Hui Zhang): reduce_sum not support bool type
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# olens = tgt_mask.sum(1)
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olens = tgt_mask.astype(paddle.int).sum(1)
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return x, olens
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def forward_one_step(
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self,
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memory: paddle.Tensor,
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memory_mask: paddle.Tensor,
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tgt: paddle.Tensor,
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tgt_mask: paddle.Tensor,
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cache: Optional[List[paddle.Tensor]]=None,
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) -> Tuple[paddle.Tensor, List[paddle.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, maxlen_out)
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dtype=paddle.bool
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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, 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(
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x, tgt_mask, memory, memory_mask, 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 = paddle.log_softmax(self.output_layer(y), axis=-1)
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return y, new_cache
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# beam search API (see ScorerInterface)
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def score(self, ys, state, x):
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"""Score.
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ys: (ylen,)
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x: (xlen, n_feat)
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"""
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ys_mask = subsequent_mask(len(ys)).unsqueeze(0) # (B,L,L)
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x_mask = make_xs_mask(x.unsqueeze(0)).unsqueeze(1) # (B,1,T)
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if self.selfattention_layer_type != "selfattn":
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# TODO(karita): implement cache
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logging.warning(
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f"{self.selfattention_layer_type} does not support cached decoding."
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)
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state = None
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logp, state = self.forward_one_step(
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x.unsqueeze(0), x_mask, ys.unsqueeze(0), ys_mask, cache=state)
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return logp.squeeze(0), state
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# batch beam search API (see BatchScorerInterface)
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def batch_score(self,
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ys: paddle.Tensor,
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states: List[Any],
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xs: paddle.Tensor) -> Tuple[paddle.Tensor, List[Any]]:
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"""Score new token batch (required).
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Args:
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ys (paddle.Tensor): paddle.int64 prefix tokens (n_batch, ylen).
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states (List[Any]): Scorer states for prefix tokens.
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xs (paddle.Tensor):
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The encoder feature that generates ys (n_batch, xlen, n_feat).
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Returns:
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tuple[paddle.Tensor, List[Any]]: Tuple of
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batchfied scores for next token with shape of `(n_batch, n_vocab)`
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and next state list for ys.
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"""
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# merge states
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n_batch = len(ys)
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n_layers = len(self.decoders)
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if states[0] is None:
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batch_state = None
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else:
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# transpose state of [batch, layer] into [layer, batch]
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batch_state = [
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paddle.stack([states[b][i] for b in range(n_batch)])
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for i in range(n_layers)
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]
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# batch decoding
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ys_mask = subsequent_mask(ys.size(-1)).unsqueeze(0) # (B,L,L)
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xs_mask = make_xs_mask(xs).unsqueeze(1) # (B,1,T)
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logp, states = self.forward_one_step(
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xs, xs_mask, ys, ys_mask, cache=batch_state)
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# transpose state of [layer, batch] into [batch, layer]
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state_list = [[states[i][b] for i in range(n_layers)]
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for b in range(n_batch)]
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return logp, state_list
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