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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2019 Mobvoi Inc. 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 wenet(https://github.com/wenet-e2e/wenet)
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"""Multi-Head Attention layer definition."""
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import math
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from typing import List
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from typing import Tuple
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import paddle
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from paddle import nn
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from paddle.nn import initializer as I
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from paddlespeech.s2t.modules.align import Linear
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from paddlespeech.s2t.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = [
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"MultiHeadedAttention", "RelPositionMultiHeadedAttention",
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"RoPERelPositionMultiHeadedAttention"
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]
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# Relative Positional Encodings
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# https://www.jianshu.com/p/c0608efcc26f
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# https://zhuanlan.zhihu.com/p/344604604
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class MultiHeadedAttention(nn.Layer):
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"""Multi-Head Attention layer."""
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def __init__(self, n_head: int, n_feat: int, dropout_rate: float):
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"""Construct an MultiHeadedAttention object.
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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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super().__init__()
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assert n_feat % n_head == 0
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self.n_feat = n_feat
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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 = Linear(n_feat, n_feat)
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self.linear_k = Linear(n_feat, n_feat)
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self.linear_v = Linear(n_feat, n_feat)
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self.linear_out = Linear(n_feat, n_feat)
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self.dropout = nn.Dropout(p=dropout_rate)
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def forward_qkv(self,
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query: paddle.Tensor,
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key: paddle.Tensor,
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value: paddle.Tensor
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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"""Transform query, key and value.
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Args:
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query (paddle.Tensor): Query tensor (#batch, time1, size).
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key (paddle.Tensor): Key tensor (#batch, time2, size).
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value (paddle.Tensor): Value tensor (#batch, time2, size).
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Returns:
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paddle.Tensor: Transformed query tensor, size
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(#batch, n_head, time1, d_k).
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paddle.Tensor: Transformed key tensor, size
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(#batch, n_head, time2, d_k).
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paddle.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.shape[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([0, 2, 1, 3]) # (batch, head, time1, d_k)
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k = k.transpose([0, 2, 1, 3]) # (batch, head, time2, d_k)
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v = v.transpose([0, 2, 1, 3]) # (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: paddle.Tensor,
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scores: paddle.Tensor,
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mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool)
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) -> paddle.Tensor:
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"""Compute attention context vector.
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Args:
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value (paddle.Tensor): Transformed value, size
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(#batch, n_head, time2, d_k).
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scores (paddle.Tensor): Attention score, size
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(#batch, n_head, time1, time2).
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mask (paddle.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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paddle.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.shape[0]
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# When `if mask.size(2) > 0` be True:
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# 1. training.
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# 2. oonx(16/4, chunk_size/history_size), feed real cache and real mask for the 1st chunk.
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# 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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if mask.shape[2] > 0: # time2 > 0
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mask = mask.unsqueeze(1).equal(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.shape[-1]]
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scores = scores.masked_fill(mask, -float('inf'))
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attn = paddle.softmax(
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scores, axis=-1).masked_fill(mask,
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0.0) # (batch, head, time1, time2)
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else:
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attn = paddle.softmax(
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scores, axis=-1) # (batch, head, time1, time2)
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p_attn = self.dropout(attn)
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x = paddle.matmul(p_attn, value) # (batch, head, time1, d_k)
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x = x.transpose([0, 2, 1, 3]).view(n_batch, -1, self.h *
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self.d_k) # (batch, time1, d_model)
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return self.linear_out(x) # (batch, time1, d_model)
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def forward(self,
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query: paddle.Tensor,
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key: paddle.Tensor,
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value: paddle.Tensor,
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mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
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pos_emb: paddle.Tensor=paddle.empty([0]),
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cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0])
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) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""Compute scaled dot product attention.
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Args:
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query (paddle.Tensor): Query tensor (#batch, time1, size).
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key (paddle.Tensor): Key tensor (#batch, time2, size).
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value (paddle.Tensor): Value tensor (#batch, time2, size).
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mask (paddle.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 (paddle.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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paddle.Tensor: Output tensor (#batch, time1, d_model).
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paddle.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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# (B,T,D) -> (B,T,H,D/H)
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q, k, v = self.forward_qkv(query, key, value)
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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.shape[0] > 0:
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# last dim `d_k * 2` for (key, val)
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key_cache, value_cache = paddle.split(cache, 2, axis=-1)
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k = paddle.concat([key_cache, k], axis=2)
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v = paddle.concat([value_cache, v], axis=2)
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# 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 = paddle.concat((k, v), axis=-1)
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# scores = paddle.matmul(q,
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# k.transpose([0, 1, 3, 2])) / math.sqrt(self.d_k)
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scores = paddle.matmul(q, k, transpose_y=True) / 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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def __init__(self,
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n_head,
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n_feat,
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dropout_rate,
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adaptive_scale=False,
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init_weights=False):
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"""Construct an RelPositionMultiHeadedAttention object.
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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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super().__init__(n_head, n_feat, dropout_rate)
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# linear transformation for positional encoding
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self.linear_pos = 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 = 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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pos_bias_u = self.create_parameter(
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[self.h, self.d_k], default_initializer=I.XavierUniform())
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self.add_parameter('pos_bias_u', pos_bias_u)
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pos_bias_v = self.create_parameter(
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(self.h, self.d_k), default_initializer=I.XavierUniform())
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self.add_parameter('pos_bias_v', pos_bias_v)
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self.adaptive_scale = adaptive_scale
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if self.adaptive_scale:
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ada_scale = self.create_parameter(
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[1, 1, n_feat], default_initializer=I.Constant(1.0))
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self.add_parameter('ada_scale', ada_scale)
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ada_bias = self.create_parameter(
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[1, 1, n_feat], default_initializer=I.Constant(0.0))
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self.add_parameter('ada_bias', ada_bias)
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if init_weights:
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self.init_weights()
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def init_weights(self):
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input_max = (self.h * self.d_k)**-0.5
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self.linear_q._param_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_q._bias_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_k._param_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_k._bias_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_v._param_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_v._bias_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_pos._param_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_pos._bias_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_out._param_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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self.linear_out._bias_attr = paddle.nn.initializer.Uniform(
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low=-input_max, high=input_max)
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def rel_shift(self, x, zero_triu: bool=False):
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"""Compute relative positinal encoding.
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Args:
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x (paddle.Tensor): Input tensor (batch, head, time1, time1).
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zero_triu (bool): If true, return the lower triangular part of
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the matrix.
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Returns:
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paddle.Tensor: Output tensor. (batch, head, time1, time1)
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"""
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zero_pad = paddle.zeros(
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(x.shape[0], x.shape[1], x.shape[2], 1), dtype=x.dtype)
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x_padded = paddle.cat([zero_pad, x], dim=-1)
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x_padded = x_padded.view(x.shape[0], x.shape[1], x.shape[3] + 1,
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x.shape[2])
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x = x_padded[:, :, 1:].view_as(x) # [B, H, T1, T1]
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if zero_triu:
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ones = paddle.ones((x.shape[2], x.shape[3]))
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x = x * paddle.tril(ones, x.shape[3] - x.shape[2])[None, None, :, :]
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return x
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def forward(self,
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query: paddle.Tensor,
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key: paddle.Tensor,
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value: paddle.Tensor,
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mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
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pos_emb: paddle.Tensor=paddle.empty([0]),
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cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0])
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) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
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Args:
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query (paddle.Tensor): Query tensor (#batch, time1, size).
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key (paddle.Tensor): Key tensor (#batch, time2, size).
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value (paddle.Tensor): Value tensor (#batch, time2, size).
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mask (paddle.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 (paddle.Tensor): Positional embedding tensor
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(#batch, time2, size).
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cache (paddle.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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paddle.Tensor: Output tensor (#batch, time1, d_model).
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paddle.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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if self.adaptive_scale:
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query = self.ada_scale * query + self.ada_bias
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key = self.ada_scale * key + self.ada_bias
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value = self.ada_scale * value + self.ada_bias
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q, k, v = self.forward_qkv(query, key, value)
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# q = q.transpose([0, 2, 1, 3]) # (batch, time1, head, d_k)
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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.shape[0] > 0:
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# last dim `d_k * 2` for (key, val)
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key_cache, value_cache = paddle.split(cache, 2, axis=-1)
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k = paddle.concat([key_cache, k], axis=2)
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v = paddle.concat([value_cache, v], axis=2)
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# 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 = paddle.concat((k, v), axis=-1)
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n_batch_pos = pos_emb.shape[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([0, 2, 1, 3]) # (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([0, 2, 1, 3])
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q_with_bias_u = q + self.pos_bias_u.unsqueeze(1)
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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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q_with_bias_v = q + self.pos_bias_v.unsqueeze(1)
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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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matrix_ac = paddle.matmul(q_with_bias_u, k, transpose_y=True)
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# compute matrix b and matrix d
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# (batch, head, time1, time2)
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# matrix_bd = paddle.matmul(q_with_bias_v, p.transpose([0, 1, 3, 2]))
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matrix_bd = paddle.matmul(q_with_bias_v, p, transpose_y=True)
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# Remove rel_shift since it is useless in speech recognition,
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# and it requires special attention for streaming.
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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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class RoPERelPositionMultiHeadedAttention(MultiHeadedAttention):
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"""Multi-Head Attention layer with RoPE relative position encoding."""
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def __init__(self,
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n_head,
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n_feat,
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dropout_rate,
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adaptive_scale=False,
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init_weights=False):
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"""Construct an RelPositionMultiHeadedAttention object.
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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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super().__init__(n_head, n_feat, dropout_rate)
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def align(self, tensor: paddle.Tensor, axes: List[int], ndim=None):
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"""重新对齐tensor（批量版expand_dims）
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axes：原来的第i维对齐新tensor的第axes[i]维；
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ndim：新tensor的维度。
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"""
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assert len(axes) == tensor.dim()
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assert ndim or min(axes) >= 0
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ndim = ndim or max(axes) + 1
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# a[0, None, 1] = a[0, np.newaxis, 1]
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indices = [None] * ndim
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for i in axes:
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# slice nothing, a[0, slice(None), 1] = a[0, :, 1]
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indices[i] = slice(None)
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return tensor[indices]
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def apply_rotary_position_embeddings(self, sinusoidal, *tensors):
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|
"""应用RoPE到tensors中
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|
其中，sinusoidal.shape=[B, T, D]，tensors为tensor的列表，而
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tensor.shape=[B, T, ..., D], or (B,H,T,D/H)
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"""
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assert len(tensors) > 0, 'at least one input tensor'
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assert all(
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[tensor.shape == tensors[0].shape
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|
for tensor in tensors[1:]]), 'all tensors must have the same shape'
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|
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|
# (B,H,T,D)
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ndim = tensors[0].dim()
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_, H, T, D = tensors[0].shape
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|
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|
# sinusoidal shape same with tensors[0]
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|
# [B,T,D] -> [B,T,H,D/H] -> (B,H,T,D/H)
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|
# sinusoidal = self.align(sinusoidal, [0, 1, -1], ndim)
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|
sinusoidal = sinusoidal.reshape((1, T, H, D)).transpose([0, 2, 1, 3])
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|
|
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|
# http://man.hubwiz.com/docset/TensorFlow.docset/Contents/Resources/Documents/api_docs/python/tf/keras/backend/repeat_elements.html
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|
# like np.repeat, x (s1, s2, s3), axis 1, (s1, s2*rep, s3)
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|
# [b,T, ..., d/2] -> [b,T, ..., d]
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|
cos_pos = paddle.repeat_interleave(sinusoidal[..., 1::2], 2, axis=-1)
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|
sin_pos = paddle.repeat_interleave(sinusoidal[..., 0::2], 2, axis=-1)
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|
|
outputs = []
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|
|
for tensor in tensors:
|
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|
# x2 = [-x2, x1, -x4, x3, ..., -x_d, x_{d-1}]
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|
tensor2 = paddle.stack([-tensor[..., 1::2], tensor[..., ::2]], ndim)
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|
|
tensor2 = paddle.reshape(tensor2, paddle.shape(tensor))
|
|
|
|
|
|
# 公式 34, out = x * cos_pos + x2 * sin_pos
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|
|
outputs.append(tensor * cos_pos + tensor2 * sin_pos)
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|
|
return outputs[0] if len(outputs) == 1 else outputs
|
|
|
|
|
|
def forward(self,
|
|
|
query: paddle.Tensor,
|
|
|
key: paddle.Tensor,
|
|
|
value: paddle.Tensor,
|
|
|
mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
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|
|
pos_emb: paddle.Tensor=paddle.empty([0]),
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|
|
cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0])
|
|
|
) -> Tuple[paddle.Tensor, paddle.Tensor]:
|
|
|
"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
|
|
|
Args:
|
|
|
query (paddle.Tensor): Query tensor (#batch, time1, size).
|
|
|
key (paddle.Tensor): Key tensor (#batch, time2, size).
|
|
|
value (paddle.Tensor): Value tensor (#batch, time2, size).
|
|
|
mask (paddle.Tensor): Mask tensor (#batch, 1, time2) or
|
|
|
(#batch, time1, time2), (0, 0, 0) means fake mask.
|
|
|
pos_emb (paddle.Tensor): Positional embedding tensor
|
|
|
(#batch, time2, size).
|
|
|
cache (paddle.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:
|
|
|
paddle.Tensor: Output tensor (#batch, time1, d_model).
|
|
|
paddle.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([0, 2, 1, 3]) # (batch, time1, head, d_k)
|
|
|
|
|
|
# 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.shape[0] > 0:
|
|
|
# last dim `d_k * 2` for (key, val)
|
|
|
key_cache, value_cache = paddle.split(cache, 2, axis=-1)
|
|
|
k = paddle.concat([key_cache, k], axis=2)
|
|
|
v = paddle.concat([value_cache, v], axis=2)
|
|
|
# We do cache slicing in encoder.forward_chunk, since it's
|
|
|
# non-trivial to calculate `next_cache_start` here.
|
|
|
new_cache = paddle.concat((k, v), axis=-1)
|
|
|
|
|
|
# f{q,k}(x_m, m) = R^d_{\theta, m} W_{q,k} x_m, m is position index
|
|
|
# q_t always is chunk_size
|
|
|
q_t = q.shape[2]
|
|
|
q = self.apply_rotary_position_embeddings(pos_emb[:, -q_t:, :], q)
|
|
|
# k will increase when in streaming decoding.
|
|
|
k = self.apply_rotary_position_embeddings(pos_emb, k)
|
|
|
|
|
|
# dot(q, k)
|
|
|
scores = paddle.matmul(q, k, transpose_y=True) / math.sqrt(self.d_k)
|
|
|
return self.forward_attention(v, scores, mask), new_cache
|
