cosyvoice/transformer/attention.py

# Copyright (c) 2019 Shigeki Karita
#               2020 Mobvoi Inc (Binbin Zhang)
#               2022 Xingchen Song ([email protected])
#               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.
"""Multi-Head Attention layer definition."""

import math
from typing import Tuple

import torch
from torch import nn


class MultiHeadedAttention(nn.Module):
    """Multi-Head Attention layer.

    Args:
        n_head (int): The number of heads.
        n_feat (int): The number of features.
        dropout_rate (float): Dropout rate.

    """

    def __init__(self,
                 n_head: int,
                 n_feat: int,
                 dropout_rate: float,
                 key_bias: bool = True):
        """Construct an MultiHeadedAttention object."""
        super().__init__()
        assert n_feat % n_head == 0
        # We assume d_v always equals d_k
        self.d_k = n_feat // n_head
        self.h = n_head
        self.linear_q = nn.Linear(n_feat, n_feat)
        self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias)
        self.linear_v = nn.Linear(n_feat, n_feat)
        self.linear_out = nn.Linear(n_feat, n_feat)
        self.dropout = nn.Dropout(p=dropout_rate)

    def forward_qkv(
        self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Transform query, key and value.

        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).

        Returns:
            torch.Tensor: Transformed query tensor, size
                (#batch, n_head, time1, d_k).
            torch.Tensor: Transformed key tensor, size
                (#batch, n_head, time2, d_k).
            torch.Tensor: Transformed value tensor, size
                (#batch, n_head, time2, d_k).

        """
        n_batch = query.size(0)
        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
        q = q.transpose(1, 2)  # (batch, head, time1, d_k)
        k = k.transpose(1, 2)  # (batch, head, time2, d_k)
        v = v.transpose(1, 2)  # (batch, head, time2, d_k)

        return q, k, v

    def forward_attention(
        self,
        value: torch.Tensor,
        scores: torch.Tensor,
        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)
    ) -> torch.Tensor:
        """Compute attention context vector.

        Args:
            value (torch.Tensor): Transformed value, size
                (#batch, n_head, time2, d_k).
            scores (torch.Tensor): Attention score, size
                (#batch, n_head, time1, time2).
            mask (torch.Tensor): Mask, size (#batch, 1, time2) or
                (#batch, time1, time2), (0, 0, 0) means fake mask.

        Returns:
            torch.Tensor: Transformed value (#batch, time1, d_model)
                weighted by the attention score (#batch, time1, time2).

        """
        n_batch = value.size(0)
        # NOTE(xcsong): When will `if mask.size(2) > 0` be True?
        #   1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
        #           1st chunk to ease the onnx export.]
        #   2. pytorch training
        if mask.size(2) > 0:  # time2 > 0
            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
            # For last chunk, time2 might be larger than scores.size(-1)
            mask = mask[:, :, :, :scores.size(-1)]  # (batch, 1, *, time2)
            scores = scores.masked_fill(mask, -float('inf'))
            attn = torch.softmax(scores, dim=-1).masked_fill(
                mask, 0.0)  # (batch, head, time1, time2)
        # NOTE(xcsong): When will `if mask.size(2) > 0` be False?
        #   1. onnx(16/-1, -1/-1, 16/0)
        #   2. jit (16/-1, -1/-1, 16/0, 16/4)
        else:
            attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)

        p_attn = self.dropout(attn)
        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
        x = (x.transpose(1, 2).contiguous().view(n_batch, -1,
                                                 self.h * self.d_k)
             )  # (batch, time1, d_model)

        return self.linear_out(x)  # (batch, time1, d_model)

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
        pos_emb: torch.Tensor = torch.empty(0),
        cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Compute scaled dot product attention.

        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).
            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
                (#batch, time1, time2).
                1.When applying cross attention between decoder and encoder,
                the batch padding mask for input is in (#batch, 1, T) shape.
                2.When applying self attention of encoder,
                the mask is in (#batch, T, T)  shape.
                3.When applying self attention of decoder,
                the mask is in (#batch, L, L)  shape.
                4.If the different position in decoder see different block
                of the encoder, such as Mocha, the passed in mask could be
                in (#batch, L, T) shape. But there is no such case in current
                Wenet.
            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
                where `cache_t == chunk_size * num_decoding_left_chunks`
                and `head * d_k == size`


        Returns:
            torch.Tensor: Output tensor (#batch, time1, d_model).
            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
                where `cache_t == chunk_size * num_decoding_left_chunks`
                and `head * d_k == size`

        """
        q, k, v = self.forward_qkv(query, key, value)

        # NOTE(xcsong):
        #   when export onnx model, for 1st chunk, we feed
        #       cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
        #       or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
        #       In all modes, `if cache.size(0) > 0` will alwayse be `True`
        #       and we will always do splitting and
        #       concatnation(this will simplify onnx export). Note that
        #       it's OK to concat & split zero-shaped tensors(see code below).
        #   when export jit  model, for 1st chunk, we always feed
        #       cache(0, 0, 0, 0) since jit supports dynamic if-branch.
        # >>> a = torch.ones((1, 2, 0, 4))
        # >>> b = torch.ones((1, 2, 3, 4))
        # >>> c = torch.cat((a, b), dim=2)
        # >>> torch.equal(b, c)        # True
        # >>> d = torch.split(a, 2, dim=-1)
        # >>> torch.equal(d[0], d[1])  # True
        if cache.size(0) > 0:
            key_cache, value_cache = torch.split(cache,
                                                 cache.size(-1) // 2,
                                                 dim=-1)
            k = torch.cat([key_cache, k], dim=2)
            v = torch.cat([value_cache, v], dim=2)
        # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
        #   non-trivial to calculate `next_cache_start` here.
        new_cache = torch.cat((k, v), dim=-1)

        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
        return self.forward_attention(v, scores, mask), new_cache


class RelPositionMultiHeadedAttention(MultiHeadedAttention):
    """Multi-Head Attention layer with relative position encoding.
    Paper: https://arxiv.org/abs/1901.02860
    Args:
        n_head (int): The number of heads.
        n_feat (int): The number of features.
        dropout_rate (float): Dropout rate.
    """

    def __init__(self,
                 n_head: int,
                 n_feat: int,
                 dropout_rate: float,
                 key_bias: bool = True):
        """Construct an RelPositionMultiHeadedAttention object."""
        super().__init__(n_head, n_feat, dropout_rate, key_bias)
        # linear transformation for positional encoding
        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
        # these two learnable bias are used in matrix c and matrix d
        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
        torch.nn.init.xavier_uniform_(self.pos_bias_u)
        torch.nn.init.xavier_uniform_(self.pos_bias_v)

    def rel_shift(self, x):
        """Compute relative positional encoding.

        Args:
            x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
            time1 means the length of query vector.

        Returns:
            torch.Tensor: Output tensor.

        """
        zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
        x_padded = torch.cat([zero_pad, x], dim=-1)

        x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
        x = x_padded[:, :, 1:].view_as(x)[
            :, :, :, : x.size(-1) // 2 + 1
        ]  # only keep the positions from 0 to time2
        return x

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
        pos_emb: torch.Tensor = torch.empty(0),
        cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).
            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
                (#batch, time1, time2), (0, 0, 0) means fake mask.
            pos_emb (torch.Tensor): Positional embedding tensor
                (#batch, time2, size).
            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
                where `cache_t == chunk_size * num_decoding_left_chunks`
                and `head * d_k == size`
        Returns:
            torch.Tensor: Output tensor (#batch, time1, d_model).
            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
                where `cache_t == chunk_size * num_decoding_left_chunks`
                and `head * d_k == size`
        """
        q, k, v = self.forward_qkv(query, key, value)
        q = q.transpose(1, 2)  # (batch, time1, head, d_k)

        # NOTE(xcsong):
        #   when export onnx model, for 1st chunk, we feed
        #       cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
        #       or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
        #       In all modes, `if cache.size(0) > 0` will alwayse be `True`
        #       and we will always do splitting and
        #       concatnation(this will simplify onnx export). Note that
        #       it's OK to concat & split zero-shaped tensors(see code below).
        #   when export jit  model, for 1st chunk, we always feed
        #       cache(0, 0, 0, 0) since jit supports dynamic if-branch.
        # >>> a = torch.ones((1, 2, 0, 4))
        # >>> b = torch.ones((1, 2, 3, 4))
        # >>> c = torch.cat((a, b), dim=2)
        # >>> torch.equal(b, c)        # True
        # >>> d = torch.split(a, 2, dim=-1)
        # >>> torch.equal(d[0], d[1])  # True
        if cache.size(0) > 0:
            key_cache, value_cache = torch.split(cache,
                                                 cache.size(-1) // 2,
                                                 dim=-1)
            k = torch.cat([key_cache, k], dim=2)
            v = torch.cat([value_cache, v], dim=2)
        # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
        #   non-trivial to calculate `next_cache_start` here.
        new_cache = torch.cat((k, v), dim=-1)

        n_batch_pos = pos_emb.size(0)
        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
        p = p.transpose(1, 2)  # (batch, head, time1, d_k)

        # (batch, head, time1, d_k)
        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
        # (batch, head, time1, d_k)
        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)

        # compute attention score
        # first compute matrix a and matrix c
        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
        # (batch, head, time1, time2)
        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))

        # compute matrix b and matrix d
        # (batch, head, time1, time2)
        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
        # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used
        if matrix_ac.shape != matrix_bd.shape:
            matrix_bd = self.rel_shift(matrix_bd)

        scores = (matrix_ac + matrix_bd) / math.sqrt(
            self.d_k)  # (batch, head, time1, time2)

        return self.forward_attention(v, scores, mask), new_cache