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# copyright (c) 2020 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import ParamAttr, nn
from paddle import nn, ParamAttr
from paddle.nn import functional as F
import numpy as np
gradient_clip = 10
class WrapEncoderForFeature(nn.Layer):
def __init__(self,
src_vocab_size,
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
bos_idx=0):
super(WrapEncoderForFeature, self).__init__()
self.prepare_encoder = PrepareEncoder(
src_vocab_size,
d_model,
max_length,
prepostprocess_dropout,
bos_idx=bos_idx,
word_emb_param_name="src_word_emb_table")
self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model,
d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd,
postprocess_cmd)
def forward(self, enc_inputs):
conv_features, src_pos, src_slf_attn_bias = enc_inputs
enc_input = self.prepare_encoder(conv_features, src_pos)
enc_output = self.encoder(enc_input, src_slf_attn_bias)
return enc_output
class WrapEncoder(nn.Layer):
"""
embedder + encoder
"""
def __init__(self,
src_vocab_size,
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
bos_idx=0):
super(WrapEncoder, self).__init__()
self.prepare_decoder = PrepareDecoder(
src_vocab_size,
d_model,
max_length,
prepostprocess_dropout,
bos_idx=bos_idx)
self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model,
d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd,
postprocess_cmd)
def forward(self, enc_inputs):
src_word, src_pos, src_slf_attn_bias = enc_inputs
enc_input = self.prepare_decoder(src_word, src_pos)
enc_output = self.encoder(enc_input, src_slf_attn_bias)
return enc_output
class Encoder(nn.Layer):
"""
encoder
"""
def __init__(self,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd="n",
postprocess_cmd="da"):
super(Encoder, self).__init__()
self.encoder_layers = list()
for i in range(n_layer):
self.encoder_layers.append(
self.add_sublayer(
"layer_%d" % i,
EncoderLayer(n_head, d_key, d_value, d_model, d_inner_hid,
prepostprocess_dropout, attention_dropout,
relu_dropout, preprocess_cmd,
postprocess_cmd)))
self.processer = PrePostProcessLayer(preprocess_cmd, d_model,
prepostprocess_dropout)
def forward(self, enc_input, attn_bias):
for encoder_layer in self.encoder_layers:
enc_output = encoder_layer(enc_input, attn_bias)
enc_input = enc_output
enc_output = self.processer(enc_output)
return enc_output
class EncoderLayer(nn.Layer):
"""
EncoderLayer
"""
def __init__(self,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd="n",
postprocess_cmd="da"):
super(EncoderLayer, self).__init__()
self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model,
prepostprocess_dropout)
self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head,
attention_dropout)
self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model,
prepostprocess_dropout)
self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model,
prepostprocess_dropout)
self.ffn = FFN(d_inner_hid, d_model, relu_dropout)
self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model,
prepostprocess_dropout)
def forward(self, enc_input, attn_bias):
attn_output = self.self_attn(
self.preprocesser1(enc_input), None, None, attn_bias)
attn_output = self.postprocesser1(attn_output, enc_input)
ffn_output = self.ffn(self.preprocesser2(attn_output))
ffn_output = self.postprocesser2(ffn_output, attn_output)
return ffn_output
class MultiHeadAttention(nn.Layer):
"""
Multi-Head Attention
"""
def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.):
super(MultiHeadAttention, self).__init__()
self.n_head = n_head
self.d_key = d_key
self.d_value = d_value
self.d_model = d_model
self.dropout_rate = dropout_rate
self.q_fc = paddle.nn.Linear(
in_features=d_model, out_features=d_key * n_head, bias_attr=False)
self.k_fc = paddle.nn.Linear(
in_features=d_model, out_features=d_key * n_head, bias_attr=False)
self.v_fc = paddle.nn.Linear(
in_features=d_model, out_features=d_value * n_head, bias_attr=False)
self.proj_fc = paddle.nn.Linear(
in_features=d_value * n_head, out_features=d_model, bias_attr=False)
def _prepare_qkv(self, queries, keys, values, cache=None):
if keys is None: # self-attention
keys, values = queries, queries
static_kv = False
else: # cross-attention
static_kv = True
q = self.q_fc(queries)
q = paddle.reshape(x=q, shape=[0, 0, self.n_head, self.d_key])
q = paddle.transpose(x=q, perm=[0, 2, 1, 3])
if cache is not None and static_kv and "static_k" in cache:
# for encoder-decoder attention in inference and has cached
k = cache["static_k"]
v = cache["static_v"]
else:
k = self.k_fc(keys)
v = self.v_fc(values)
k = paddle.reshape(x=k, shape=[0, 0, self.n_head, self.d_key])
k = paddle.transpose(x=k, perm=[0, 2, 1, 3])
v = paddle.reshape(x=v, shape=[0, 0, self.n_head, self.d_value])
v = paddle.transpose(x=v, perm=[0, 2, 1, 3])
if cache is not None:
if static_kv and not "static_k" in cache:
# for encoder-decoder attention in inference and has not cached
cache["static_k"], cache["static_v"] = k, v
elif not static_kv:
# for decoder self-attention in inference
cache_k, cache_v = cache["k"], cache["v"]
k = paddle.concat([cache_k, k], axis=2)
v = paddle.concat([cache_v, v], axis=2)
cache["k"], cache["v"] = k, v
return q, k, v
def forward(self, queries, keys, values, attn_bias, cache=None):
# compute q ,k ,v
keys = queries if keys is None else keys
values = keys if values is None else values
q, k, v = self._prepare_qkv(queries, keys, values, cache)
# scale dot product attention
product = paddle.matmul(x=q, y=k, transpose_y=True)
product = product * self.d_model**-0.5
if attn_bias is not None:
product += attn_bias
weights = F.softmax(product)
if self.dropout_rate:
weights = F.dropout(
weights, p=self.dropout_rate, mode="downscale_in_infer")
out = paddle.matmul(weights, v)
# combine heads
out = paddle.transpose(out, perm=[0, 2, 1, 3])
out = paddle.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
# project to output
out = self.proj_fc(out)
return out
class PrePostProcessLayer(nn.Layer):
"""
PrePostProcessLayer
"""
def __init__(self, process_cmd, d_model, dropout_rate):
super(PrePostProcessLayer, self).__init__()
self.process_cmd = process_cmd
self.functors = []
for cmd in self.process_cmd:
if cmd == "a": # add residual connection
self.functors.append(lambda x, y: x + y if y is not None else x)
elif cmd == "n": # add layer normalization
self.functors.append(
self.add_sublayer(
"layer_norm_%d" % len(self.sublayers()),
paddle.nn.LayerNorm(
normalized_shape=d_model,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.Constant(1.)),
bias_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.Constant(0.)))))
elif cmd == "d": # add dropout
self.functors.append(lambda x: F.dropout(
x, p=dropout_rate, mode="downscale_in_infer")
if dropout_rate else x)
def forward(self, x, residual=None):
for i, cmd in enumerate(self.process_cmd):
if cmd == "a":
x = self.functors[i](x, residual)
else:
x = self.functors[i](x)
return x
class PrepareEncoder(nn.Layer):
def __init__(self,
src_vocab_size,
src_emb_dim,
src_max_len,
dropout_rate=0,
bos_idx=0,
word_emb_param_name=None,
pos_enc_param_name=None):
super(PrepareEncoder, self).__init__()
self.src_emb_dim = src_emb_dim
self.src_max_len = src_max_len
self.emb = paddle.nn.Embedding(
num_embeddings=self.src_max_len, embedding_dim=self.src_emb_dim)
self.dropout_rate = dropout_rate
def forward(self, src_word, src_pos):
src_word_emb = src_word
src_word_emb = paddle.cast(src_word_emb, 'float32')
src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)
src_pos = paddle.squeeze(src_pos, axis=-1)
src_pos_enc = self.emb(src_pos)
src_pos_enc.stop_gradient = True
enc_input = src_word_emb + src_pos_enc
if self.dropout_rate:
out = F.dropout(
x=enc_input, p=self.dropout_rate, mode="downscale_in_infer")
else:
out = enc_input
return out
class PrepareDecoder(nn.Layer):
def __init__(self,
src_vocab_size,
src_emb_dim,
src_max_len,
dropout_rate=0,
bos_idx=0,
word_emb_param_name=None,
pos_enc_param_name=None):
super(PrepareDecoder, self).__init__()
self.src_emb_dim = src_emb_dim
"""
self.emb0 = Embedding(num_embeddings=src_vocab_size,
embedding_dim=src_emb_dim)
"""
self.emb0 = paddle.nn.Embedding(
num_embeddings=src_vocab_size,
embedding_dim=self.src_emb_dim,
padding_idx=bos_idx,
weight_attr=paddle.ParamAttr(
name=word_emb_param_name,
initializer=nn.initializer.Normal(0., src_emb_dim**-0.5)))
self.emb1 = paddle.nn.Embedding(
num_embeddings=src_max_len,
embedding_dim=self.src_emb_dim,
weight_attr=paddle.ParamAttr(name=pos_enc_param_name))
self.dropout_rate = dropout_rate
def forward(self, src_word, src_pos):
src_word = paddle.cast(src_word, 'int64')
src_word = paddle.squeeze(src_word, axis=-1)
src_word_emb = self.emb0(src_word)
src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)
src_pos = paddle.squeeze(src_pos, axis=-1)
src_pos_enc = self.emb1(src_pos)
src_pos_enc.stop_gradient = True
enc_input = src_word_emb + src_pos_enc
if self.dropout_rate:
out = F.dropout(
x=enc_input, p=self.dropout_rate, mode="downscale_in_infer")
else:
out = enc_input
return out
class FFN(nn.Layer):
"""
Feed-Forward Network
"""
def __init__(self, d_inner_hid, d_model, dropout_rate):
super(FFN, self).__init__()
self.dropout_rate = dropout_rate
self.fc1 = paddle.nn.Linear(
in_features=d_model, out_features=d_inner_hid)
self.fc2 = paddle.nn.Linear(
in_features=d_inner_hid, out_features=d_model)
def forward(self, x):
hidden = self.fc1(x)
hidden = F.relu(hidden)
if self.dropout_rate:
hidden = F.dropout(
hidden, p=self.dropout_rate, mode="downscale_in_infer")
out = self.fc2(hidden)
return out
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