pull/2425/head
Hui Zhang 2 years ago
parent 2a75405e9a
commit 925abcca23

@ -19,8 +19,8 @@ from typing import Tuple
import paddle
from paddle import nn
from paddle.nn import initializer as I
from paddle.nn import functional as F
from paddle.nn import initializer as I
from paddlespeech.s2t.modules.align import Linear
from paddlespeech.s2t.utils.log import Log
@ -56,12 +56,12 @@ class MultiHeadedAttention(nn.Layer):
self.linear_out = Linear(n_feat, n_feat)
self.dropout = nn.Dropout(p=dropout_rate)
def _build_once(self, *args, **kwargs):
super()._build_once(*args, **kwargs)
# if self.self_att:
# self.linear_kv = Linear(self.n_feat, self.n_feat*2)
self.weight = paddle.concat([self.linear_k.weight, self.linear_v.weight], axis=-1)
self.weight = paddle.concat(
[self.linear_k.weight, self.linear_v.weight], axis=-1)
self.bias = paddle.concat([self.linear_k.bias, self.linear_v.bias])
self._built = True
@ -84,12 +84,14 @@ class MultiHeadedAttention(nn.Layer):
(#batch, n_head, time2, d_k).
"""
n_batch = query.shape[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)
k, v = F.linear(key, self.weight, self.bias).view(n_batch, -1, 2 * self.h, self.d_k).split(2, axis=2)
k, v = F.linear(key, self.weight, self.bias).view(
n_batch, -1, 2 * self.h, self.d_k).split(
2, axis=2)
q = q.transpose([0, 2, 1, 3]) # (batch, head, time1, d_k)
k = k.transpose([0, 2, 1, 3]) # (batch, head, time2, d_k)
v = v.transpose([0, 2, 1, 3]) # (batch, head, time2, d_k)
@ -203,7 +205,7 @@ class MultiHeadedAttention(nn.Layer):
new_cache = paddle.concat((k, v), axis=-1)
# scores = paddle.matmul(q,
# k.transpose([0, 1, 3, 2])) / math.sqrt(self.d_k)
# k.transpose([0, 1, 3, 2])) / math.sqrt(self.d_k)
scores = paddle.matmul(q, k, transpose_y=True) / math.sqrt(self.d_k)
return self.forward_attention(v, scores, mask), new_cache

@ -221,7 +221,7 @@ class BaseEncoder(nn.Layer):
xs, pos_emb, _ = self.embed(xs, tmp_masks, offset=offset)
# after embed, xs=(B=1, chunk_size, hidden-dim)
elayers, _, cache_t1, _ = att_cache.shape
elayers, _, cache_t1, _ = att_cache.shape
chunk_size = xs.shape[1]
attention_key_size = cache_t1 + chunk_size

@ -110,7 +110,7 @@ def subsequent_mask(size: int) -> paddle.Tensor:
"""
ret = paddle.ones([size, size], dtype=paddle.bool)
return paddle.tril(ret)
def subsequent_chunk_mask(
size: int,

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