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https://github.com/FunAudioLLM/CosyVoice.git
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add cosyvoice code
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lyuxiang.lx
396
cosyvoice/transformer/decoder.py
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396
cosyvoice/transformer/decoder.py
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# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
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# 2024 Alibaba Inc (Xiang Lyu)
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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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# Modified from ESPnet(https://github.com/espnet/espnet)
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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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import torch.utils.checkpoint as ckpt
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import logging
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from cosyvoice.transformer.decoder_layer import DecoderLayer
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from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward
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from cosyvoice.utils.class_utils import (
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COSYVOICE_EMB_CLASSES,
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COSYVOICE_ATTENTION_CLASSES,
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COSYVOICE_ACTIVATION_CLASSES,
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)
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from cosyvoice.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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src_attention: if false, encoder-decoder cross attention is not
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applied, such as CIF model
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key_bias: whether use bias in attention.linear_k, False for whisper models.
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gradient_checkpointing: rerunning a forward-pass segment for each
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checkpointed segment during backward.
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tie_word_embedding: Tie or clone module weights depending of whether we are
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using TorchScript or not
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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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src_attention: bool = True,
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key_bias: bool = True,
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activation_type: str = "relu",
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gradient_checkpointing: bool = False,
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tie_word_embedding: bool = False,
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):
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super().__init__()
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attention_dim = encoder_output_size
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activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
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self.embed = torch.nn.Sequential(
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torch.nn.Identity() if input_layer == "no_pos" else
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torch.nn.Embedding(vocab_size, attention_dim),
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COSYVOICE_EMB_CLASSES[input_layer](attention_dim,
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positional_dropout_rate),
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)
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self.normalize_before = normalize_before
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self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5)
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self.use_output_layer = use_output_layer
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if use_output_layer:
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self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
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else:
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self.output_layer = torch.nn.Identity()
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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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COSYVOICE_ATTENTION_CLASSES["selfattn"](
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attention_heads, attention_dim,
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self_attention_dropout_rate, key_bias),
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COSYVOICE_ATTENTION_CLASSES["selfattn"](
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attention_heads, attention_dim, src_attention_dropout_rate,
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key_bias) if src_attention else None,
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PositionwiseFeedForward(attention_dim, linear_units,
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dropout_rate, activation),
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dropout_rate,
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normalize_before,
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) for _ in range(self.num_blocks)
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])
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self.gradient_checkpointing = gradient_checkpointing
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self.tie_word_embedding = tie_word_embedding
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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 = torch.empty(0),
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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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NOTE(xcsong):
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We pass the `__call__` method of the modules instead of `forward` to the
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checkpointing API because `__call__` attaches all the hooks of the module.
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https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
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"""
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tgt = ys_in_pad
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maxlen = tgt.size(1)
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# tgt_mask: (B, 1, L)
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tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1)
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tgt_mask = tgt_mask.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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if self.gradient_checkpointing and self.training:
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x = self.forward_layers_checkpointed(x, tgt_mask, memory,
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memory_mask)
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else:
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x = self.forward_layers(x, tgt_mask, memory, 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_layers(self, x: torch.Tensor, tgt_mask: torch.Tensor,
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memory: torch.Tensor,
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memory_mask: torch.Tensor) -> torch.Tensor:
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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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return x
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@torch.jit.ignore(drop=True)
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def forward_layers_checkpointed(self, x: torch.Tensor,
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tgt_mask: torch.Tensor,
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memory: torch.Tensor,
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memory_mask: torch.Tensor) -> torch.Tensor:
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for layer in self.decoders:
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x, tgt_mask, memory, memory_mask = ckpt.checkpoint(
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layer.__call__, x, tgt_mask, memory, memory_mask)
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return x
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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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def tie_or_clone_weights(self, jit_mode: bool = True):
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"""Tie or clone module weights (between word_emb and output_layer)
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depending of whether we are using TorchScript or not"""
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if not self.use_output_layer:
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return
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if jit_mode:
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logging.info("clone emb.weight to output.weight")
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self.output_layer.weight = torch.nn.Parameter(
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self.embed[0].weight.clone())
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else:
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logging.info("tie emb.weight with output.weight")
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self.output_layer.weight = self.embed[0].weight
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if getattr(self.output_layer, "bias", None) is not None:
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self.output_layer.bias.data = torch.nn.functional.pad(
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self.output_layer.bias.data,
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(
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0,
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self.output_layer.weight.shape[0] -
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self.output_layer.bias.shape[0],
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),
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"constant",
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0,
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)
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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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key_bias: whether use bias in attention.linear_k, False for whisper models.
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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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key_bias: bool = True,
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gradient_checkpointing: bool = False,
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tie_word_embedding: bool = False,
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):
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super().__init__()
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self.tie_word_embedding = tie_word_embedding
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self.left_decoder = TransformerDecoder(
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vocab_size,
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encoder_output_size,
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attention_heads,
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linear_units,
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num_blocks,
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dropout_rate,
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positional_dropout_rate,
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self_attention_dropout_rate,
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src_attention_dropout_rate,
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input_layer,
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use_output_layer,
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normalize_before,
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key_bias=key_bias,
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gradient_checkpointing=gradient_checkpointing,
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tie_word_embedding=tie_word_embedding)
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self.right_decoder = TransformerDecoder(
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vocab_size,
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encoder_output_size,
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attention_heads,
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linear_units,
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r_num_blocks,
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dropout_rate,
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positional_dropout_rate,
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self_attention_dropout_rate,
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src_attention_dropout_rate,
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input_layer,
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use_output_layer,
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normalize_before,
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key_bias=key_bias,
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gradient_checkpointing=gradient_checkpointing,
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tie_word_embedding=tie_word_embedding)
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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,
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r_ys_in_pad, 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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def tie_or_clone_weights(self, jit_mode: bool = True):
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"""Tie or clone module weights (between word_emb and output_layer)
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depending of whether we are using TorchScript or not"""
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self.left_decoder.tie_or_clone_weights(jit_mode)
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self.right_decoder.tie_or_clone_weights(jit_mode)
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