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