未验证 提交 45313ff3 编写于 作者: T topduke 提交者: GitHub

Update multiheadAttention.py

上级 685394cb
......@@ -9,214 +9,6 @@ from paddle.nn.initializer import XavierNormal as xavier_normal_
zeros_ = constant_(value=0.)
ones_ = constant_(value=1.)
class MultiheadAttention(nn.Layer):
r"""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
Examples::
>>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
>>> attn_output, attn_output_weights = multihead_attn(query, key, value)
"""
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)
if add_bias_kv:
self.bias_k = self.create_parameter(
shape=(1, 1, embed_dim), default_initializer=zeros_)
self.add_parameter("bias_k", self.bias_k)
self.bias_v = self.create_parameter(
shape=(1, 1, embed_dim), default_initializer=zeros_)
self.add_parameter("bias_v", self.bias_v)
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
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 * 2, kernel_size=(1, 1))
self.conv3 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim * 3, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
if self.bias_k is not None:
xavier_normal_(self.bias_k)
if self.bias_v is not None:
xavier_normal_(self.bias_v)
def forward(self, query, key, value, key_padding_mask=None, incremental_state=None,
need_weights=True, static_kv=False, attn_mask=None, qkv_ = [False,False,False]):
"""
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]
"""
qkv_same = qkv_[0]
kv_same = qkv_[1]
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
if qkv_same:
# self-attention
q, k, v = self._in_proj_qkv(query)
elif kv_same:
# encoder-decoder attention
q = self._in_proj_q(query)
if key is None:
assert value is None
k = v = None
else:
k, v = self._in_proj_kv(key)
else:
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
if self.bias_k is not None:
assert self.bias_v is not None
self.bias_k = paddle.concat([self.bias_k for i in range(bsz)],axis=1)
self.bias_v = paddle.concat([self.bias_v for i in range(bsz)],axis=1)
k = paddle.concat([k, self.bias_k])
v = paddle.concat([v, self.bias_v])
if attn_mask is not None:
attn_mask = paddle.concat([attn_mask, paddle.zeros([attn_mask.shape[0], 1],dtype=attn_mask.dtype)], axis=1)
if key_padding_mask is not None:
key_padding_mask = paddle.concat(
[key_padding_mask,paddle.zeros([key_padding_mask.shape[0], 1],dtype=key_padding_mask.dtype)], axis=1)
q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
if k is not None:
k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
if v is not None:
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
if self.add_zero_attn:
src_len += 1
k = paddle.concat([k, paddle.zeros((k.shape[0], 1) + k.shape[2:],dtype=k.dtype)], axis=1)
v = paddle.concat([v, paddle.zeros((v.shape[0], 1) + v.shape[2:],dtype=v.dtype)], axis=1)
if attn_mask is not None:
attn_mask = paddle.concat([attn_mask, paddle.zeros([attn_mask.shape[0], 1],dtype=attn_mask.dtype)], axis=1)
if key_padding_mask is not None:
key_padding_mask = paddle.concat(
[key_padding_mask, paddle.zeros([key_padding_mask.shape[0], 1],dtype=key_padding_mask.dtype)], axis=1)
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_qkv(self, query):
query = query.transpose([1, 2, 0])
query = paddle.unsqueeze(query, axis=2)
res = self.conv3(query)
res = paddle.squeeze(res, axis=2)
res = res.transpose([2, 0, 1])
return res.chunk(3, axis=-1)
def _in_proj_kv(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.chunk(2, axis=-1)
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.conv1(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.conv1(value)
res = paddle.squeeze(res, axis=2)
res = res.transpose([2, 0, 1])
return res
class MultiheadAttentionOptim(nn.Layer):
r"""Allows the model to jointly attend to information
......@@ -362,4 +154,4 @@ class MultiheadAttentionOptim(nn.Layer):
res = self.conv3(value)
res = paddle.squeeze(res, axis=2)
res = res.transpose([2, 0, 1])
return res
\ No newline at end of file
return res
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册