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add cosyvoice code
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lyuxiang.lx
326
cosyvoice/transformer/attention.py
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326
cosyvoice/transformer/attention.py
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# Copyright (c) 2019 Shigeki Karita
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# 2020 Mobvoi Inc (Binbin Zhang)
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# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
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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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"""Multi-Head Attention layer definition."""
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import math
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from typing import Tuple
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import torch
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from torch import nn
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class MultiHeadedAttention(nn.Module):
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"""Multi-Head Attention layer.
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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"""
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def __init__(self,
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n_head: int,
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n_feat: int,
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dropout_rate: float,
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key_bias: bool = True):
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"""Construct an MultiHeadedAttention object."""
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super().__init__()
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assert n_feat % n_head == 0
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# We assume d_v always equals d_k
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self.d_k = n_feat // n_head
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self.h = n_head
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self.linear_q = nn.Linear(n_feat, n_feat)
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self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias)
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self.linear_v = nn.Linear(n_feat, n_feat)
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self.linear_out = nn.Linear(n_feat, n_feat)
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self.dropout = nn.Dropout(p=dropout_rate)
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def forward_qkv(
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self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Transform query, key and value.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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Returns:
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torch.Tensor: Transformed query tensor, size
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(#batch, n_head, time1, d_k).
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torch.Tensor: Transformed key tensor, size
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(#batch, n_head, time2, d_k).
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torch.Tensor: Transformed value tensor, size
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(#batch, n_head, time2, d_k).
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"""
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n_batch = query.size(0)
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q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
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k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
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v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
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q = q.transpose(1, 2) # (batch, head, time1, d_k)
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k = k.transpose(1, 2) # (batch, head, time2, d_k)
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v = v.transpose(1, 2) # (batch, head, time2, d_k)
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return q, k, v
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def forward_attention(
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self,
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value: torch.Tensor,
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scores: torch.Tensor,
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mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)
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) -> torch.Tensor:
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"""Compute attention context vector.
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Args:
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value (torch.Tensor): Transformed value, size
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(#batch, n_head, time2, d_k).
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scores (torch.Tensor): Attention score, size
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(#batch, n_head, time1, time2).
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mask (torch.Tensor): Mask, size (#batch, 1, time2) or
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(#batch, time1, time2), (0, 0, 0) means fake mask.
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Returns:
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torch.Tensor: Transformed value (#batch, time1, d_model)
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weighted by the attention score (#batch, time1, time2).
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"""
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n_batch = value.size(0)
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# NOTE(xcsong): When will `if mask.size(2) > 0` be True?
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# 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
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# 1st chunk to ease the onnx export.]
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# 2. pytorch training
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if mask.size(2) > 0: # time2 > 0
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mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
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# For last chunk, time2 might be larger than scores.size(-1)
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mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2)
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scores = scores.masked_fill(mask, -float('inf'))
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attn = torch.softmax(scores, dim=-1).masked_fill(
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mask, 0.0) # (batch, head, time1, time2)
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# NOTE(xcsong): When will `if mask.size(2) > 0` be False?
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# 1. onnx(16/-1, -1/-1, 16/0)
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# 2. jit (16/-1, -1/-1, 16/0, 16/4)
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else:
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attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
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p_attn = self.dropout(attn)
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x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
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x = (x.transpose(1, 2).contiguous().view(n_batch, -1,
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self.h * self.d_k)
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) # (batch, time1, d_model)
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return self.linear_out(x) # (batch, time1, d_model)
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
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pos_emb: torch.Tensor = torch.empty(0),
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cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute scaled dot product attention.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
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(#batch, time1, time2).
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1.When applying cross attention between decoder and encoder,
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the batch padding mask for input is in (#batch, 1, T) shape.
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2.When applying self attention of encoder,
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the mask is in (#batch, T, T) shape.
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3.When applying self attention of decoder,
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the mask is in (#batch, L, L) shape.
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4.If the different position in decoder see different block
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of the encoder, such as Mocha, the passed in mask could be
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in (#batch, L, T) shape. But there is no such case in current
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Wenet.
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cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
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where `cache_t == chunk_size * num_decoding_left_chunks`
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and `head * d_k == size`
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Returns:
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torch.Tensor: Output tensor (#batch, time1, d_model).
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torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
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where `cache_t == chunk_size * num_decoding_left_chunks`
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and `head * d_k == size`
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"""
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q, k, v = self.forward_qkv(query, key, value)
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# NOTE(xcsong):
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# when export onnx model, for 1st chunk, we feed
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# cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
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# or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
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# In all modes, `if cache.size(0) > 0` will alwayse be `True`
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# and we will always do splitting and
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# concatnation(this will simplify onnx export). Note that
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# it's OK to concat & split zero-shaped tensors(see code below).
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# when export jit model, for 1st chunk, we always feed
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# cache(0, 0, 0, 0) since jit supports dynamic if-branch.
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# >>> a = torch.ones((1, 2, 0, 4))
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# >>> b = torch.ones((1, 2, 3, 4))
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# >>> c = torch.cat((a, b), dim=2)
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# >>> torch.equal(b, c) # True
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# >>> d = torch.split(a, 2, dim=-1)
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# >>> torch.equal(d[0], d[1]) # True
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if cache.size(0) > 0:
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key_cache, value_cache = torch.split(cache,
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cache.size(-1) // 2,
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dim=-1)
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k = torch.cat([key_cache, k], dim=2)
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v = torch.cat([value_cache, v], dim=2)
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# NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
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# non-trivial to calculate `next_cache_start` here.
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new_cache = torch.cat((k, v), dim=-1)
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scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
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return self.forward_attention(v, scores, mask), new_cache
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class RelPositionMultiHeadedAttention(MultiHeadedAttention):
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"""Multi-Head Attention layer with relative position encoding.
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Paper: https://arxiv.org/abs/1901.02860
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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"""
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def __init__(self,
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n_head: int,
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n_feat: int,
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dropout_rate: float,
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key_bias: bool = True):
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"""Construct an RelPositionMultiHeadedAttention object."""
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super().__init__(n_head, n_feat, dropout_rate, key_bias)
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# linear transformation for positional encoding
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self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
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# these two learnable bias are used in matrix c and matrix d
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
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self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
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torch.nn.init.xavier_uniform_(self.pos_bias_u)
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torch.nn.init.xavier_uniform_(self.pos_bias_v)
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def rel_shift(self, x):
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"""Compute relative positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
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time1 means the length of query vector.
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Returns:
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torch.Tensor: Output tensor.
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"""
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zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
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x_padded = torch.cat([zero_pad, x], dim=-1)
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x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
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x = x_padded[:, :, 1:].view_as(x)[
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:, :, :, : x.size(-1) // 2 + 1
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] # only keep the positions from 0 to time2
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return x
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
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pos_emb: torch.Tensor = torch.empty(0),
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cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
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(#batch, time1, time2), (0, 0, 0) means fake mask.
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pos_emb (torch.Tensor): Positional embedding tensor
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(#batch, time2, size).
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cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
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where `cache_t == chunk_size * num_decoding_left_chunks`
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and `head * d_k == size`
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Returns:
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torch.Tensor: Output tensor (#batch, time1, d_model).
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torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
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where `cache_t == chunk_size * num_decoding_left_chunks`
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and `head * d_k == size`
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"""
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q, k, v = self.forward_qkv(query, key, value)
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q = q.transpose(1, 2) # (batch, time1, head, d_k)
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# NOTE(xcsong):
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# when export onnx model, for 1st chunk, we feed
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# cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
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# or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
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# In all modes, `if cache.size(0) > 0` will alwayse be `True`
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# and we will always do splitting and
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# concatnation(this will simplify onnx export). Note that
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# it's OK to concat & split zero-shaped tensors(see code below).
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# when export jit model, for 1st chunk, we always feed
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# cache(0, 0, 0, 0) since jit supports dynamic if-branch.
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# >>> a = torch.ones((1, 2, 0, 4))
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# >>> b = torch.ones((1, 2, 3, 4))
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# >>> c = torch.cat((a, b), dim=2)
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# >>> torch.equal(b, c) # True
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# >>> d = torch.split(a, 2, dim=-1)
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# >>> torch.equal(d[0], d[1]) # True
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if cache.size(0) > 0:
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key_cache, value_cache = torch.split(cache,
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cache.size(-1) // 2,
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dim=-1)
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k = torch.cat([key_cache, k], dim=2)
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v = torch.cat([value_cache, v], dim=2)
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# NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
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# non-trivial to calculate `next_cache_start` here.
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new_cache = torch.cat((k, v), dim=-1)
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n_batch_pos = pos_emb.size(0)
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p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
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p = p.transpose(1, 2) # (batch, head, time1, d_k)
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# (batch, head, time1, d_k)
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q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
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# (batch, head, time1, d_k)
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q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
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# compute attention score
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# first compute matrix a and matrix c
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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# (batch, head, time1, time2)
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matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
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# compute matrix b and matrix d
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# (batch, head, time1, time2)
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matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
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# NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used
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if matrix_ac.shape != matrix_bd.shape:
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matrix_bd = self.rel_shift(matrix_bd)
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scores = (matrix_ac + matrix_bd) / math.sqrt(
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self.d_k) # (batch, head, time1, time2)
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return self.forward_attention(v, scores, mask), new_cache
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