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# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
# 2024 Alibaba Inc (Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from ESPnet(https://github.com/espnet/espnet)
"""Decoder definition."""
from typing import Tuple, List, Optional
import torch
import torch.utils.checkpoint as ckpt
import logging
from cosyvoice.transformer.decoder_layer import DecoderLayer
from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward
from cosyvoice.utils.class_utils import (
COSYVOICE_EMB_CLASSES,
COSYVOICE_ATTENTION_CLASSES,
COSYVOICE_ACTIVATION_CLASSES,
)
from cosyvoice.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.
src_attention: if false, encoder-decoder cross attention is not
applied, such as CIF model
key_bias: whether use bias in attention.linear_k, False for whisper models.
gradient_checkpointing: rerunning a forward-pass segment for each
checkpointed segment during backward.
tie_word_embedding: Tie or clone module weights depending of whether we are
using TorchScript or not
"""
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,
src_attention: bool = True,
key_bias: bool = True,
activation_type: str = "relu",
gradient_checkpointing: bool = False,
tie_word_embedding: bool = False,
):
super().__init__()
attention_dim = encoder_output_size
activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
self.embed = torch.nn.Sequential(
torch.nn.Identity() if input_layer == "no_pos" else
torch.nn.Embedding(vocab_size, attention_dim),
COSYVOICE_EMB_CLASSES[input_layer](attention_dim,
positional_dropout_rate),
)
self.normalize_before = normalize_before
self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5)
self.use_output_layer = use_output_layer
if use_output_layer:
self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
else:
self.output_layer = torch.nn.Identity()
self.num_blocks = num_blocks
self.decoders = torch.nn.ModuleList([
DecoderLayer(
attention_dim,
COSYVOICE_ATTENTION_CLASSES["selfattn"](
attention_heads, attention_dim,
self_attention_dropout_rate, key_bias),
COSYVOICE_ATTENTION_CLASSES["selfattn"](
attention_heads, attention_dim, src_attention_dropout_rate,
key_bias) if src_attention else None,
PositionwiseFeedForward(attention_dim, linear_units,
dropout_rate, activation),
dropout_rate,
normalize_before,
) for _ in range(self.num_blocks)
])
self.gradient_checkpointing = gradient_checkpointing
self.tie_word_embedding = tie_word_embedding
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 = torch.empty(0),
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, )
NOTE(xcsong):
We pass the `__call__` method of the modules instead of `forward` to the
checkpointing API because `__call__` attaches all the hooks of the module.
https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
"""
tgt = ys_in_pad
maxlen = tgt.size(1)
# tgt_mask: (B, 1, L)
tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1)
tgt_mask = tgt_mask.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)
if self.gradient_checkpointing and self.training:
x = self.forward_layers_checkpointed(x, tgt_mask, memory,
memory_mask)
else:
x = self.forward_layers(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_layers(self, x: torch.Tensor, tgt_mask: torch.Tensor,
memory: torch.Tensor,
memory_mask: torch.Tensor) -> torch.Tensor:
for layer in self.decoders:
x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory,
memory_mask)
return x
@torch.jit.ignore(drop=True)
def forward_layers_checkpointed(self, x: torch.Tensor,
tgt_mask: torch.Tensor,
memory: torch.Tensor,
memory_mask: torch.Tensor) -> torch.Tensor:
for layer in self.decoders:
x, tgt_mask, memory, memory_mask = ckpt.checkpoint(
layer.__call__, x, tgt_mask, memory, memory_mask)
return x
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
def tie_or_clone_weights(self, jit_mode: bool = True):
"""Tie or clone module weights (between word_emb and output_layer)
depending of whether we are using TorchScript or not"""
if not self.use_output_layer:
return
if jit_mode:
logging.info("clone emb.weight to output.weight")
self.output_layer.weight = torch.nn.Parameter(
self.embed[0].weight.clone())
else:
logging.info("tie emb.weight with output.weight")
self.output_layer.weight = self.embed[0].weight
if getattr(self.output_layer, "bias", None) is not None:
self.output_layer.bias.data = torch.nn.functional.pad(
self.output_layer.bias.data,
(
0,
self.output_layer.weight.shape[0] -
self.output_layer.bias.shape[0],
),
"constant",
0,
)
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.
key_bias: whether use bias in attention.linear_k, False for whisper models.
"""
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,
key_bias: bool = True,
gradient_checkpointing: bool = False,
tie_word_embedding: bool = False,
):
super().__init__()
self.tie_word_embedding = tie_word_embedding
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,
key_bias=key_bias,
gradient_checkpointing=gradient_checkpointing,
tie_word_embedding=tie_word_embedding)
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,
key_bias=key_bias,
gradient_checkpointing=gradient_checkpointing,
tie_word_embedding=tie_word_embedding)
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)
def tie_or_clone_weights(self, jit_mode: bool = True):
"""Tie or clone module weights (between word_emb and output_layer)
depending of whether we are using TorchScript or not"""
self.left_decoder.tie_or_clone_weights(jit_mode)
self.right_decoder.tie_or_clone_weights(jit_mode)
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