179 lines
6.8 KiB
Python
Executable File
179 lines
6.8 KiB
Python
Executable File
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# 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.
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
import paddle.nn.functional as F
|
|
from paddle.nn import Linear
|
|
from paddle.nn.initializer import XavierUniform as xavier_uniform_
|
|
from paddle.nn.initializer import Constant as constant_
|
|
from paddle.nn.initializer import XavierNormal as xavier_normal_
|
|
|
|
zeros_ = constant_(value=0.)
|
|
ones_ = constant_(value=1.)
|
|
|
|
|
|
class MultiheadAttention(nn.Layer):
|
|
"""Allows the model to jointly attend to information
|
|
from different representation subspaces.
|
|
See reference: Attention Is All You Need
|
|
|
|
.. math::
|
|
\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
|
|
\text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
|
|
|
|
Args:
|
|
embed_dim: total dimension of the model
|
|
num_heads: parallel attention layers, or heads
|
|
|
|
"""
|
|
|
|
def __init__(self,
|
|
embed_dim,
|
|
num_heads,
|
|
dropout=0.,
|
|
bias=True,
|
|
add_bias_kv=False,
|
|
add_zero_attn=False):
|
|
super(MultiheadAttention, self).__init__()
|
|
self.embed_dim = embed_dim
|
|
self.num_heads = num_heads
|
|
self.dropout = dropout
|
|
self.head_dim = embed_dim // num_heads
|
|
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
|
|
self.scaling = self.head_dim**-0.5
|
|
self.out_proj = Linear(embed_dim, embed_dim, bias_attr=bias)
|
|
self._reset_parameters()
|
|
self.conv1 = paddle.nn.Conv2D(
|
|
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
|
|
self.conv2 = paddle.nn.Conv2D(
|
|
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
|
|
self.conv3 = paddle.nn.Conv2D(
|
|
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
|
|
|
|
def _reset_parameters(self):
|
|
xavier_uniform_(self.out_proj.weight)
|
|
|
|
def forward(self,
|
|
query,
|
|
key,
|
|
value,
|
|
key_padding_mask=None,
|
|
incremental_state=None,
|
|
need_weights=True,
|
|
static_kv=False,
|
|
attn_mask=None):
|
|
"""
|
|
Inputs of forward function
|
|
query: [target length, batch size, embed dim]
|
|
key: [sequence length, batch size, embed dim]
|
|
value: [sequence length, batch size, embed dim]
|
|
key_padding_mask: if True, mask padding based on batch size
|
|
incremental_state: if provided, previous time steps are cashed
|
|
need_weights: output attn_output_weights
|
|
static_kv: key and value are static
|
|
|
|
Outputs of forward function
|
|
attn_output: [target length, batch size, embed dim]
|
|
attn_output_weights: [batch size, target length, sequence length]
|
|
"""
|
|
tgt_len, bsz, embed_dim = query.shape
|
|
assert embed_dim == self.embed_dim
|
|
assert list(query.shape) == [tgt_len, bsz, embed_dim]
|
|
assert key.shape == value.shape
|
|
|
|
q = self._in_proj_q(query)
|
|
k = self._in_proj_k(key)
|
|
v = self._in_proj_v(value)
|
|
q *= self.scaling
|
|
|
|
q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose(
|
|
[1, 0, 2])
|
|
k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose(
|
|
[1, 0, 2])
|
|
v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose(
|
|
[1, 0, 2])
|
|
|
|
src_len = k.shape[1]
|
|
|
|
if key_padding_mask is not None:
|
|
assert key_padding_mask.shape[0] == bsz
|
|
assert key_padding_mask.shape[1] == src_len
|
|
|
|
attn_output_weights = paddle.bmm(q, k.transpose([0, 2, 1]))
|
|
assert list(attn_output_weights.
|
|
shape) == [bsz * self.num_heads, tgt_len, src_len]
|
|
|
|
if attn_mask is not None:
|
|
attn_mask = attn_mask.unsqueeze(0)
|
|
attn_output_weights += attn_mask
|
|
if key_padding_mask is not None:
|
|
attn_output_weights = attn_output_weights.reshape(
|
|
[bsz, self.num_heads, tgt_len, src_len])
|
|
key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32')
|
|
y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf')
|
|
y = paddle.where(key == 0., key, y)
|
|
attn_output_weights += y
|
|
attn_output_weights = attn_output_weights.reshape(
|
|
[bsz * self.num_heads, tgt_len, src_len])
|
|
|
|
attn_output_weights = F.softmax(
|
|
attn_output_weights.astype('float32'),
|
|
axis=-1,
|
|
dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16
|
|
else attn_output_weights.dtype)
|
|
attn_output_weights = F.dropout(
|
|
attn_output_weights, p=self.dropout, training=self.training)
|
|
|
|
attn_output = paddle.bmm(attn_output_weights, v)
|
|
assert list(attn_output.
|
|
shape) == [bsz * self.num_heads, tgt_len, self.head_dim]
|
|
attn_output = attn_output.transpose([1, 0, 2]).reshape(
|
|
[tgt_len, bsz, embed_dim])
|
|
attn_output = self.out_proj(attn_output)
|
|
|
|
if need_weights:
|
|
# average attention weights over heads
|
|
attn_output_weights = attn_output_weights.reshape(
|
|
[bsz, self.num_heads, tgt_len, src_len])
|
|
attn_output_weights = attn_output_weights.sum(
|
|
axis=1) / self.num_heads
|
|
else:
|
|
attn_output_weights = None
|
|
return attn_output, attn_output_weights
|
|
|
|
def _in_proj_q(self, query):
|
|
query = query.transpose([1, 2, 0])
|
|
query = paddle.unsqueeze(query, axis=2)
|
|
res = self.conv1(query)
|
|
res = paddle.squeeze(res, axis=2)
|
|
res = res.transpose([2, 0, 1])
|
|
return res
|
|
|
|
def _in_proj_k(self, key):
|
|
key = key.transpose([1, 2, 0])
|
|
key = paddle.unsqueeze(key, axis=2)
|
|
res = self.conv2(key)
|
|
res = paddle.squeeze(res, axis=2)
|
|
res = res.transpose([2, 0, 1])
|
|
return res
|
|
|
|
def _in_proj_v(self, value):
|
|
value = value.transpose([1, 2, 0]) #(1, 2, 0)
|
|
value = paddle.unsqueeze(value, axis=2)
|
|
res = self.conv3(value)
|
|
res = paddle.squeeze(res, axis=2)
|
|
res = res.transpose([2, 0, 1])
|
|
return res
|