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347 lines
13 KiB
347 lines
13 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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"""Multi-Head Attention layer definition."""
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import math
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import numpy
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import paddle
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from paddle import nn
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from paddlespeech.t2s.modules.masked_fill import masked_fill
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class MultiHeadedAttention(nn.Layer):
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"""Multi-Head Attention layer.
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Args:
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n_head (int):
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The number of heads.
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n_feat (int):
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The number of features.
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dropout_rate (float):
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Dropout rate.
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"""
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def __init__(self, n_head, n_feat, dropout_rate):
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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, bias_attr=True)
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self.linear_k = nn.Linear(n_feat, n_feat, bias_attr=True)
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self.linear_v = nn.Linear(n_feat, n_feat, bias_attr=True)
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self.linear_out = nn.Linear(n_feat, n_feat, bias_attr=True)
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self.attn = None
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self.dropout = nn.Dropout(p=dropout_rate)
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def forward_qkv(self, query, key, value):
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"""Transform query, key and value.
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Args:
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query(Tensor):
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query tensor (#batch, time1, size).
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key(Tensor):
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Key tensor (#batch, time2, size).
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value(Tensor):
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Value tensor (#batch, time2, size).
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Returns:
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Tensor:
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Transformed query tensor (#batch, n_head, time1, d_k).
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Tensor:
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Transformed key tensor (#batch, n_head, time2, d_k).
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Tensor:
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Transformed value tensor (#batch, n_head, time2, d_k).
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"""
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n_batch = paddle.shape(query)[0]
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q = paddle.reshape(
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self.linear_q(query), [n_batch, -1, self.h, self.d_k])
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k = paddle.reshape(self.linear_k(key), [n_batch, -1, self.h, self.d_k])
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v = paddle.reshape(
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self.linear_v(value), [n_batch, -1, self.h, self.d_k])
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# (batch, head, time1, d_k)
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q = q.transpose((0, 2, 1, 3))
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# (batch, head, time2, d_k)
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k = k.transpose((0, 2, 1, 3))
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# (batch, head, time2, d_k)
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v = v.transpose((0, 2, 1, 3))
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return q, k, v
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def forward_attention(self, value, scores, mask=None):
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"""Compute attention context vector.
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Args:
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value(Tensor):
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Transformed value (#batch, n_head, time2, d_k).
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scores(Tensor):
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Attention score (#batch, n_head, time1, time2).
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mask(Tensor, optional):
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Mask (#batch, 1, time2) or (#batch, time1, time2). (Default value = None)
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Returns:
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Tensor: Transformed value (#batch, time1, d_model) weighted by the attention score (#batch, time1, time2).
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"""
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n_batch = paddle.shape(value)[0]
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softmax = paddle.nn.Softmax(axis=-1)
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if mask is not None:
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mask = mask.unsqueeze(1)
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mask = paddle.logical_not(mask)
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# assume scores.dtype==paddle.float32, we only use "float32" here
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dtype = str(scores.dtype).split(".")[-1]
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min_value = float(numpy.finfo(dtype).min)
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scores = masked_fill(scores, mask, min_value)
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# (batch, head, time1, time2)
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self.attn = softmax(scores)
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self.attn = masked_fill(self.attn, mask, 0.0)
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else:
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# (batch, head, time1, time2)
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self.attn = softmax(scores)
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# (batch, head, time1, time2)
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p_attn = self.dropout(self.attn)
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# (batch, head, time1, time2) * (batch, head, time2, d_k) -> # (batch, head, time1, d_k)
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x = paddle.matmul(p_attn, value)
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# (batch, time1, d_model)
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x = (paddle.reshape(
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x.transpose((0, 2, 1, 3)), (n_batch, -1, self.h * self.d_k)))
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# (batch, time1, d_model)
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return self.linear_out(x)
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def forward(self, query, key, value, mask=None):
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"""Compute scaled dot product attention.
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Args:
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query(Tensor):
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Query tensor (#batch, time1, size).
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key(Tensor):
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Key tensor (#batch, time2, size).
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value(Tensor):
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Value tensor (#batch, time2, size).
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mask(Tensor, optional):
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Mask tensor (#batch, 1, time2) or (#batch, time1, time2). (Default value = None)
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Returns:
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Tensor: Output tensor (#batch, time1, d_model).
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"""
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q, k, v = self.forward_qkv(query, key, value)
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scores = paddle.matmul(q, k.transpose(
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(0, 1, 3, 2))) / math.sqrt(self.d_k)
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return self.forward_attention(v, scores, mask)
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class RelPositionMultiHeadedAttention(MultiHeadedAttention):
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"""Multi-Head Attention layer with relative position encoding (new implementation).
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Details can be found in https://github.com/espnet/espnet/pull/2816.
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Paper: https://arxiv.org/abs/1901.02860
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Args:
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n_head (int):
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The number of heads.
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n_feat (int):
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The number of features.
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dropout_rate (float):
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Dropout rate.
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zero_triu (bool):
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Whether to zero the upper triangular part of attention matrix.
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"""
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def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
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"""Construct an RelPositionMultiHeadedAttention object."""
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super().__init__(n_head, n_feat, dropout_rate)
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self.zero_triu = zero_triu
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# linear transformation for positional encoding
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self.linear_pos = nn.Linear(n_feat, n_feat, bias_attr=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 = paddle.create_parameter(
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shape=(self.h, self.d_k),
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dtype='float32',
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default_initializer=paddle.nn.initializer.XavierUniform())
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self.pos_bias_v = paddle.create_parameter(
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shape=(self.h, self.d_k),
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dtype='float32',
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default_initializer=paddle.nn.initializer.XavierUniform())
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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(Tensor):
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Input tensor (batch, head, time1, 2*time1-1).
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Returns:
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Tensor: Output tensor.
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"""
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b, h, t1, t2 = paddle.shape(x)
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zero_pad = paddle.zeros((b, h, t1, 1))
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x_padded = paddle.concat([zero_pad, x], axis=-1)
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x_padded = x_padded.reshape([b, h, t2 + 1, t1])
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# only keep the positions from 0 to time2
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new_t = paddle.cast(paddle.floor(t2 / 2) + 1, dtype='int32')
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x = x_padded[:, :, 1:].reshape([b, h, t1, t2])[:, :, :, :new_t]
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if self.zero_triu:
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ones = paddle.ones((t1, t2))
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x = x * paddle.tril(ones, t2 - t1)[None, None, :, :]
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return x
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def forward(self, query, key, value, pos_emb, mask):
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"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
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Args:
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query(Tensor):
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Query tensor (#batch, time1, size).
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key(Tensor):
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Key tensor (#batch, time2, size).
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value(Tensor):
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Value tensor (#batch, time2, size).
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pos_emb(Tensor):
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Positional embedding tensor (#batch, 2*time1-1, size).
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mask(Tensor):
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Mask tensor (#batch, 1, time2) or (#batch, time1, time2).
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Returns:
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Tensor: Output tensor (#batch, time1, d_model).
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"""
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q, k, v = self.forward_qkv(query, key, value)
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# (batch, time1, head, d_k)
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q = q.transpose([0, 2, 1, 3])
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n_batch_pos = paddle.shape(pos_emb)[0]
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p = self.linear_pos(pos_emb).reshape(
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[n_batch_pos, -1, self.h, self.d_k])
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# (batch, head, 2*time1-1, d_k)
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p = p.transpose([0, 2, 1, 3])
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# (batch, head, time1, d_k)
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q_with_bias_u = (q + self.pos_bias_u).transpose([0, 2, 1, 3])
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# (batch, head, time1, d_k)
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q_with_bias_v = (q + self.pos_bias_v).transpose([0, 2, 1, 3])
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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 = paddle.matmul(q_with_bias_u, k.transpose([0, 1, 3, 2]))
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# compute matrix b and matrix d
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# (batch, head, time1, 2*time1-1)
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matrix_bd = paddle.matmul(q_with_bias_v, p.transpose([0, 1, 3, 2]))
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matrix_bd = self.rel_shift(matrix_bd)
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# (batch, head, time1, time2)
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scores = (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
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return self.forward_attention(v, scores, mask)
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class LegacyRelPositionMultiHeadedAttention(MultiHeadedAttention):
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"""Multi-Head Attention layer with relative position encoding (old version).
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Details can be found in https://github.com/espnet/espnet/pull/2816.
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Paper: https://arxiv.org/abs/1901.02860
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Args:
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n_head (int):
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The number of heads.
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n_feat (int):
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The number of features.
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dropout_rate (float):
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Dropout rate.
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zero_triu (bool):
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Whether to zero the upper triangular part of attention matrix.
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"""
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def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
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"""Construct an RelPositionMultiHeadedAttention object."""
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super().__init__(n_head, n_feat, dropout_rate)
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self.zero_triu = zero_triu
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# linear transformation for positional encoding
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self.linear_pos = nn.Linear(n_feat, n_feat, bias_attr=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 = paddle.create_parameter(
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shape=(self.h, self.d_k),
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dtype='float32',
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default_initializer=paddle.nn.initializer.XavierUniform())
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self.pos_bias_v = paddle.create_parameter(
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shape=(self.h, self.d_k),
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dtype='float32',
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default_initializer=paddle.nn.initializer.XavierUniform())
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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(Tensor):
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Input tensor (batch, head, time1, time2).
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Returns:
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Tensor:Output tensor.
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"""
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b, h, t1, t2 = paddle.shape(x)
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zero_pad = paddle.zeros((b, h, t1, 1))
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x_padded = paddle.concat([zero_pad, x], axis=-1)
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x_padded = paddle.reshape(x_padded, [b, h, t2 + 1, t1])
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# only keep the positions from 0 to time2
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x = paddle.reshape(x_padded[:, :, 1:], [b, h, t1, t2])
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if self.zero_triu:
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ones = paddle.ones((t1, t2))
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x = x * paddle.tril(ones, t2 - t1)[None, None, :, :]
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return x
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def forward(self, query, key, value, pos_emb, mask):
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"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
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Args:
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query(Tensor): Query tensor (#batch, time1, size).
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key(Tensor): Key tensor (#batch, time2, size).
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value(Tensor): Value tensor (#batch, time2, size).
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pos_emb(Tensor): Positional embedding tensor (#batch, time1, size).
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mask(Tensor): Mask tensor (#batch, 1, time2) or (#batch, time1, time2).
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Returns:
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Tensor: Output tensor (#batch, time1, d_model).
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"""
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q, k, v = self.forward_qkv(query, key, value)
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# (batch, time1, head, d_k)
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q = paddle.transpose(q, [0, 2, 1, 3])
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n_batch_pos = paddle.shape(pos_emb)[0]
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p = paddle.reshape(
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self.linear_pos(pos_emb), [n_batch_pos, -1, self.h, self.d_k])
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# (batch, head, time1, d_k)
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p = paddle.transpose(p, [0, 2, 1, 3])
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# (batch, head, time1, d_k)
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q_with_bias_u = paddle.transpose((q + self.pos_bias_u), [0, 2, 1, 3])
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# (batch, head, time1, d_k)
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q_with_bias_v = paddle.transpose((q + self.pos_bias_v), [0, 2, 1, 3])
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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 = paddle.matmul(q_with_bias_u,
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paddle.transpose(k, [0, 1, 3, 2]))
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# compute matrix b and matrix d
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# (batch, head, time1, time1)
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matrix_bd = paddle.matmul(q_with_bias_v,
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paddle.transpose(p, [0, 1, 3, 2]))
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matrix_bd = self.rel_shift(matrix_bd)
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# (batch, head, time1, time2)
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scores = (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
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return self.forward_attention(v, scores, mask)
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