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# 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,
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]
"""
q_shape = paddle.shape(query)
src_shape = paddle.shape(key)
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
q = paddle.transpose(
paddle.reshape(
q, [q_shape[0], q_shape[1], self.num_heads, self.head_dim]),
[1, 2, 0, 3])
k = paddle.transpose(
paddle.reshape(
k, [src_shape[0], q_shape[1], self.num_heads, self.head_dim]),
[1, 2, 0, 3])
v = paddle.transpose(
paddle.reshape(
v, [src_shape[0], q_shape[1], self.num_heads, self.head_dim]),
[1, 2, 0, 3])
if key_padding_mask is not None:
assert key_padding_mask.shape[0] == q_shape[1]
assert key_padding_mask.shape[1] == src_shape[0]
attn_output_weights = paddle.matmul(q,
paddle.transpose(k, [0, 1, 3, 2]))
if attn_mask is not None:
attn_mask = paddle.unsqueeze(paddle.unsqueeze(attn_mask, 0), 0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = paddle.reshape(
attn_output_weights,
[q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
key = paddle.unsqueeze(paddle.unsqueeze(key_padding_mask, 1), 2)
key = paddle.cast(key, 'float32')
y = paddle.full(
shape=paddle.shape(key), dtype='float32', fill_value='-inf')
y = paddle.where(key == 0., key, y)
attn_output_weights += y
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.matmul(attn_output_weights, v)
attn_output = paddle.reshape(
paddle.transpose(attn_output, [2, 0, 1, 3]),
[q_shape[0], q_shape[1], self.embed_dim])
attn_output = self.out_proj(attn_output)
return attn_output
def _in_proj_q(self, query):
query = paddle.transpose(query, [1, 2, 0])
query = paddle.unsqueeze(query, axis=2)
res = self.conv1(query)
res = paddle.squeeze(res, axis=2)
res = paddle.transpose(res, [2, 0, 1])
return res
def _in_proj_k(self, key):
key = paddle.transpose(key, [1, 2, 0])
key = paddle.unsqueeze(key, axis=2)
res = self.conv2(key)
res = paddle.squeeze(res, axis=2)
res = paddle.transpose(res, [2, 0, 1])
return res
def _in_proj_v(self, value):
value = paddle.transpose(value, [1, 2, 0]) #(1, 2, 0)
value = paddle.unsqueeze(value, axis=2)
res = self.conv3(value)
res = paddle.squeeze(res, axis=2)
res = paddle.transpose(res, [2, 0, 1])
return res
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