Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
wzpei9
Paddle
提交
e9553612
P
Paddle
项目概览
wzpei9
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
e9553612
编写于
8月 20, 2018
作者:
T
tensor-tang
提交者:
GitHub
8月 20, 2018
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #12737 from tensor-tang/feature/op/fusion_lstm
add fusion lstm
上级
99f74be5
674b719b
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
624 addition
and
31 deletion
+624
-31
paddle/fluid/operators/fc_op.cc
paddle/fluid/operators/fc_op.cc
+7
-24
paddle/fluid/operators/fusion_lstm_op.cc
paddle/fluid/operators/fusion_lstm_op.cc
+354
-0
paddle/fluid/operators/fusion_lstm_op.h
paddle/fluid/operators/fusion_lstm_op.h
+42
-0
paddle/fluid/operators/math/fc_compute.h
paddle/fluid/operators/math/fc_compute.h
+43
-0
python/paddle/fluid/tests/unittests/test_fc_op.py
python/paddle/fluid/tests/unittests/test_fc_op.py
+27
-7
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
+151
-0
未找到文件。
paddle/fluid/operators/fc_op.cc
浏览文件 @
e9553612
...
...
@@ -15,8 +15,7 @@ limitations under the License. */
#include "paddle/fluid/operators/fc_op.h"
#include <vector>
#include "paddle/fluid/operators/math/blas.h"
DECLARE_int32
(
paddle_num_threads
);
#include "paddle/fluid/operators/math/fc_compute.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -110,13 +109,8 @@ void FCOpMaker::Make() {
AddComment
(
R"DOC(
Fully Connected Operator.
The fully connected operation calculates the output based on the input, weights and bias
attribute
.
The fully connected operation calculates the output based on the input, weights and bias.
The size of each dimension of the parameters checked in the infer-shape.
The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
Additional parametrs are use_mkldnn and bias_attr.
The input(X) size and output(Out) size may be diffrent.
The fully connected layer only supports MKLDNN version
)DOC"
);
}
...
...
@@ -133,26 +127,15 @@ class FCOpKernel : public framework::OpKernel<T> {
auto
in_dims
=
input
->
dims
();
auto
w_dims
=
w
->
dims
();
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>();
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
dev_ctx
);
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
w_data
=
w
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
ctx
);
math
::
FCCompute
<
platform
::
CPUDeviceContext
,
T
>
(
blas
,
in_dims
[
0
],
w_dims
[
1
],
w_dims
[
0
],
input_data
,
w_data
,
output_data
,
bias
?
bias
->
data
<
T
>
()
:
NULL
);
blas
.
GEMM
(
CblasNoTrans
,
CblasNoTrans
,
in_dims
[
0
],
w_dims
[
1
],
w_dims
[
0
],
static_cast
<
T
>
(
1
),
input_data
,
w_data
,
static_cast
<
T
>
(
0
),
output_data
);
if
(
bias
)
{
const
T
*
bias_data
=
bias
->
data
<
T
>
();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for
(
int
bs
=
0
;
bs
<
in_dims
[
0
];
bs
++
)
{
blas
.
AXPY
(
w_dims
[
1
],
static_cast
<
T
>
(
1
),
bias_data
,
output_data
+
bs
*
w_dims
[
1
]);
}
}
// TODO(TJ): fuse act
}
};
...
...
paddle/fluid/operators/fusion_lstm_op.cc
0 → 100644
浏览文件 @
e9553612
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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. */
#include "paddle/fluid/operators/fusion_lstm_op.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
namespace
paddle
{
namespace
operators
{
void
FusionLSTMOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"WeightX"
),
"Input(WeightX) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"WeightH"
),
"Input(WeightH) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Bias"
),
"Input(Bias) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"XX"
),
"Output(XX) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Hidden"
),
"Output(Hidden) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Cell"
),
"Output(Cell) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BatchedGate"
),
"Output(BatchedGate) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BatchCellPreAct"
),
"Output(BatchedGate) of LSTM should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank must be 2."
);
if
(
ctx
->
HasInput
(
"H0"
))
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"C0"
),
"Input(Cell) and Input(Hidden) of LSTM should not "
"be null at the same time."
);
auto
h_dims
=
ctx
->
GetInputDim
(
"H0"
);
auto
c_dims
=
ctx
->
GetInputDim
(
"C0"
);
PADDLE_ENFORCE
(
h_dims
==
c_dims
,
"The dimension of Input(H0) and Input(C0) "
"should be the same."
);
}
auto
wx_dims
=
ctx
->
GetInputDim
(
"WeightX"
);
PADDLE_ENFORCE_EQ
(
wx_dims
.
size
(),
2
,
"The rank of Input(WeightX) should be 2."
);
PADDLE_ENFORCE_EQ
(
wx_dims
[
0
],
x_dims
[
1
],
"The first dimension of Input(WeightX) "
"should be %d."
,
x_dims
[
1
]);
int
frame_size
=
wx_dims
[
1
]
/
4
;
auto
wh_dims
=
ctx
->
GetInputDim
(
"WeightH"
);
PADDLE_ENFORCE_EQ
(
wh_dims
.
size
(),
2
,
"The rank of Input(WeightH) should be 2."
);
PADDLE_ENFORCE_EQ
(
wh_dims
[
0
],
frame_size
,
"The first dimension of Input(WeightH) "
"should be %d."
,
frame_size
);
PADDLE_ENFORCE_EQ
(
wh_dims
[
1
],
4
*
frame_size
,
"The second dimension of Input(WeightH) "
"should be 4 * %d."
,
frame_size
);
auto
b_dims
=
ctx
->
GetInputDim
(
"Bias"
);
PADDLE_ENFORCE_EQ
(
b_dims
.
size
(),
2
,
"The rank of Input(Bias) should be 2."
);
PADDLE_ENFORCE_EQ
(
b_dims
[
0
],
1
,
"The first dimension of Input(Bias) should be 1."
);
PADDLE_ENFORCE
(
!
ctx
->
Attrs
().
Get
<
bool
>
(
"use_peepholes"
),
"Do not support peephole yet."
);
PADDLE_ENFORCE_EQ
(
b_dims
[
1
],
4
*
frame_size
,
"The second dimension of Input(Bias) should be "
"4 * %d if disable peepholes connection"
,
frame_size
);
framework
::
DDim
out_dims
({
x_dims
[
0
],
frame_size
});
ctx
->
SetOutputDim
(
"Hidden"
,
out_dims
);
ctx
->
SetOutputDim
(
"Cell"
,
out_dims
);
ctx
->
SetOutputDim
(
"BatchedGate"
,
{
x_dims
[
0
],
wx_dims
[
1
]});
ctx
->
SetOutputDim
(
"BatchCellPreAct"
,
out_dims
);
ctx
->
ShareLoD
(
"X"
,
"Hidden"
);
ctx
->
ShareLoD
(
"X"
,
"Cell"
);
int
xx_width
=
x_dims
[
1
]
>
wx_dims
[
1
]
?
wx_dims
[
1
]
:
x_dims
[
1
];
ctx
->
SetOutputDim
(
"XX"
,
{
x_dims
[
0
],
xx_width
});
ctx
->
ShareLoD
(
"X"
,
"XX"
);
}
framework
::
OpKernelType
FusionLSTMOp
::
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
void
FusionLSTMOpMaker
::
Make
()
{
AddInput
(
"X"
,
"(LoDTensor) the input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X M), where T is the "
"total time steps in this mini-batch, M is the dim size of x."
);
AddInput
(
"WeightX"
,
"(Tensor) the learnable weights of X."
" - The shape is (M x 4D), where M is the dim size of x, D is the "
"hidden size. "
" - Weight = {W_cx, W_ix, W_fx, W_ox}"
);
AddInput
(
"WeightH"
,
"(Tensor) same as LSTMOp, the learnable hidden-hidden weights."
" - The shape is (D x 4D), where D is the hidden size. "
" - Weight = {W_ch, W_ih, W_fh, W_oh}"
);
AddInput
(
"Bias"
,
"(Tensor) the learnable weights. Almost same as LSTMOp"
"Note: we should add the fc bias into this (1x4D) in bias."
"input-hidden bias weight and peephole connections weight if "
"setting `use_peepholes` True. "
"1. `use_peepholes = False` "
" - The shape is (1 x 4D). "
" - Bias = {b_c, b_i, b_f, b_o}."
"2. `use_peepholes = True` "
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}."
);
AddInput
(
"H0"
,
"(Tensor, optional) (same as LSTMOp) the initial hidden state is an "
"optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size and D is the hidden size."
)
.
AsDispensable
();
AddInput
(
"C0"
,
"(Tensor, optional) (same as LSTMOp) (the initial cell state is an "
"optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size. `H0` and `C0` can be NULL but only at the same time."
)
.
AsDispensable
();
AddOutput
(
"Hidden"
,
"(LoDTensor) (same as LSTMOp) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`."
);
AddOutput
(
"Cell"
,
"(LoDTensor) (same as LSTMOp) the cell state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`."
);
AddOutput
(
"XX"
,
"(LoDTensor) the result after X * WeightX (size is T x 4D)"
" or batched_X (size is T x M), this will be automatically chosen,"
" where T is the total time steps in this mini-batch,"
" D is the hidden size, M is the dim size of x input."
)
.
AsIntermediate
();
AddOutput
(
"BatchedGate"
,
"(LoDTensor) (same as LSTMOp)."
).
AsIntermediate
();
AddOutput
(
"BatchCellPreAct"
,
"(LoDTensor) (same as LSTMOp)."
)
.
AsIntermediate
();
AddAttr
<
bool
>
(
"use_peepholes"
,
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections."
)
.
SetDefault
(
true
);
AddAttr
<
bool
>
(
"is_reverse"
,
"(bool, defalut: False) "
"whether to compute reversed LSTM."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"gate_activation"
,
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default."
)
.
SetDefault
(
"sigmoid"
)
.
InEnum
({
"sigmoid"
,
"tanh"
,
"relu"
,
"identity"
});
AddAttr
<
std
::
string
>
(
"cell_activation"
,
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut."
)
.
SetDefault
(
"tanh"
)
.
InEnum
({
"sigmoid"
,
"tanh"
,
"relu"
,
"identity"
});
AddAttr
<
std
::
string
>
(
"candidate_activation"
,
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default."
)
.
SetDefault
(
"tanh"
)
.
InEnum
({
"sigmoid"
,
"tanh"
,
"relu"
,
"identity"
});
AddComment
(
R"DOC(
Fusion Long-Short Term Memory (LSTM) Operator.
This operator fuse the X into LSTM, more details can refer to LSTM op.
)DOC"
);
}
template
<
typename
DeviceContext
,
typename
T
>
inline
void
ReorderInitState
(
const
DeviceContext
&
ctx
,
const
framework
::
Tensor
&
src
,
framework
::
Vector
<
size_t
>
index_lod
,
framework
::
Tensor
*
dst
,
bool
indexed_src
)
{
math
::
CopyMatrixRowsFunctor
<
DeviceContext
,
T
>
row_shuffle
;
dst
->
mutable_data
<
T
>
(
src
.
dims
(),
ctx
.
GetPlace
());
// TODO(TJ): check mem copy perf
row_shuffle
(
ctx
,
src
,
index_lod
,
dst
,
indexed_src
);
}
template
<
typename
DeviceContext
,
typename
T
>
class
FuisonLSTMKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
wx
=
ctx
.
Input
<
Tensor
>
(
"WeightX"
);
auto
*
wh
=
ctx
.
Input
<
Tensor
>
(
"WeightH"
);
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
*
hidden_t0
=
ctx
.
Input
<
Tensor
>
(
"H0"
);
auto
*
cell_t0
=
ctx
.
Input
<
Tensor
>
(
"C0"
);
auto
*
xx
=
ctx
.
Output
<
LoDTensor
>
(
"XX"
);
auto
*
batched_gate
=
ctx
.
Output
<
LoDTensor
>
(
"BatchedGate"
);
auto
*
hidden_out
=
ctx
.
Output
<
LoDTensor
>
(
"Hidden"
);
auto
*
cell_out
=
ctx
.
Output
<
LoDTensor
>
(
"Cell"
);
bool
is_reverse
=
ctx
.
Attr
<
bool
>
(
"is_reverse"
);
T
*
xx_data
=
xx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
batched_gate_data
=
batched_gate
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
hidden_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
cell_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
wx_data
=
wx
->
data
<
T
>
();
auto
x_dims
=
x
->
dims
();
auto
wx_dims
=
wx
->
dims
();
math
::
LoDTensor2BatchFunctor
<
DeviceContext
,
T
>
to_batch
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
dev_ctx
);
if
(
x_dims
[
1
]
>
wx_dims
[
1
])
{
math
::
FCCompute
<
DeviceContext
,
T
>
(
blas
,
x_dims
[
0
],
wx_dims
[
1
],
x_dims
[
1
],
x_data
,
wx_data
,
xx_data
,
bias
->
data
<
T
>
());
to_batch
(
dev_ctx
,
*
xx
,
batched_gate
,
true
,
is_reverse
);
}
else
{
to_batch
(
dev_ctx
,
*
x
,
xx
,
true
,
is_reverse
);
batched_gate
->
set_lod
(
xx
->
lod
());
math
::
FCCompute
<
DeviceContext
,
T
>
(
blas
,
x_dims
[
0
],
wx_dims
[
1
],
x_dims
[
1
],
xx_data
,
wx_data
,
batched_gate_data
,
bias
->
data
<
T
>
());
}
int
frame_size
=
static_cast
<
int
>
(
wx_dims
[
1
]
/
4
);
framework
::
DDim
out_dims
({
x_dims
[
0
],
frame_size
});
math
::
LstmMetaValue
<
T
>
lstm_value
;
// no peephole
lstm_value
.
check_ig
=
nullptr
;
lstm_value
.
check_fg
=
nullptr
;
lstm_value
.
check_og
=
nullptr
;
lstm_value
.
prev_state_value
=
nullptr
;
Tensor
ordered_c0
;
framework
::
Vector
<
size_t
>
order
(
batched_gate
->
lod
()[
2
]);
if
(
cell_t0
)
{
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized cell state also needs
// to reorder.
ReorderInitState
<
DeviceContext
,
T
>
(
dev_ctx
,
*
cell_t0
,
order
,
&
ordered_c0
,
true
);
lstm_value
.
prev_state_value
=
ordered_c0
.
data
<
T
>
();
}
// Use the local variable as here.
LoDTensor
batch_hidden
,
batch_cell
;
auto
*
batch_cell_pre_act
=
ctx
.
Output
<
LoDTensor
>
(
"BatchCellPreAct"
);
batch_hidden
.
mutable_data
<
T
>
(
out_dims
,
ctx
.
GetPlace
());
batch_cell
.
mutable_data
<
T
>
(
out_dims
,
ctx
.
GetPlace
());
batch_cell_pre_act
->
mutable_data
<
T
>
(
out_dims
,
ctx
.
GetPlace
());
auto
batch_starts
=
batched_gate
->
lod
()[
0
];
size_t
max_seq_len
=
batch_starts
.
size
()
-
1
;
auto
gate_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"gate_activation"
));
auto
cell_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"cell_activation"
));
auto
cand_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"candidate_activation"
));
for
(
size_t
n
=
0
;
n
<
max_seq_len
;
n
++
)
{
int
bstart
=
static_cast
<
int
>
(
batch_starts
[
n
]);
int
bend
=
static_cast
<
int
>
(
batch_starts
[
n
+
1
]);
Tensor
gate_t
=
batched_gate
->
Slice
(
bstart
,
bend
);
Tensor
out_t
=
batch_hidden
.
Slice
(
bstart
,
bend
);
Tensor
cell_t
=
batch_cell
.
Slice
(
bstart
,
bend
);
Tensor
cell_pre_act_t
=
batch_cell_pre_act
->
Slice
(
bstart
,
bend
);
int
cur_batch_size
=
bend
-
bstart
;
if
(
n
>
0
)
{
int
pre_h_start
=
static_cast
<
int
>
(
batch_starts
[
n
-
1
]);
int
pre_h_end
=
pre_h_start
+
cur_batch_size
;
auto
pre_hidden_t
=
batch_hidden
.
Slice
(
pre_h_start
,
pre_h_end
);
// TODO(TJ): use gemm directly
blas
.
MatMul
(
pre_hidden_t
,
false
,
*
wh
,
false
,
static_cast
<
T
>
(
1.0
),
&
gate_t
,
static_cast
<
T
>
(
1.0
));
}
else
if
(
hidden_t0
)
{
// TODO(TJ): move h0 outside for
// If n == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros, the calculation W_h * H0 will be skiped.
// If n == 0 and there is initialized hidden state, calculate W_h * H0.
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized hidden state also needs
// to reorder.
Tensor
ordered_h0
;
ReorderInitState
<
DeviceContext
,
T
>
(
dev_ctx
,
*
hidden_t0
,
order
,
&
ordered_h0
,
true
);
// TODO(TJ): use gemm directly
blas
.
MatMul
(
ordered_h0
,
false
,
*
wh
,
false
,
static_cast
<
T
>
(
1.0
),
&
gate_t
,
static_cast
<
T
>
(
1.0
));
}
lstm_value
.
gate_value
=
gate_t
.
data
<
T
>
();
lstm_value
.
output_value
=
out_t
.
data
<
T
>
();
lstm_value
.
state_value
=
cell_t
.
data
<
T
>
();
lstm_value
.
state_active_value
=
cell_pre_act_t
.
data
<
T
>
();
math
::
LstmUnitFunctor
<
DeviceContext
,
T
>::
compute
(
dev_ctx
,
lstm_value
,
frame_size
,
cur_batch_size
,
gate_act
,
cell_act
,
cand_act
);
lstm_value
.
prev_state_value
=
lstm_value
.
state_value
;
}
math
::
Batch2LoDTensorFunctor
<
DeviceContext
,
T
>
to_seq
;
batch_hidden
.
set_lod
(
batched_gate
->
lod
());
// restore the output hidden in LoDTensor from the batch hidden
to_seq
(
dev_ctx
,
batch_hidden
,
hidden_out
);
batch_cell
.
set_lod
(
batched_gate
->
lod
());
// restore the output cell state in LoDTensor from the batch cell
to_seq
(
dev_ctx
,
batch_cell
,
cell_out
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
fusion_lstm
,
ops
::
FusionLSTMOp
,
ops
::
FusionLSTMOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OP_CPU_KERNEL
(
fusion_lstm
,
ops
::
FuisonLSTMKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
FuisonLSTMKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/fusion_lstm_op.h
0 → 100644
浏览文件 @
e9553612
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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. */
#pragma once
// #include <string>
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
using
Tensor
=
framework
::
Tensor
;
class
FusionLSTMOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
;
};
class
FusionLSTMOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
;
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/fc_compute.h
0 → 100644
浏览文件 @
e9553612
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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. */
#pragma once
#include "paddle/fluid/operators/math/blas.h"
DECLARE_int32
(
paddle_num_threads
);
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
DeviceContext
,
typename
T
>
inline
void
FCCompute
(
const
BlasT
<
DeviceContext
,
T
>&
blas
,
const
int
M
,
const
int
N
,
const
int
K
,
const
T
*
X
,
const
T
*
W
,
T
*
Y
,
const
T
*
B
=
NULL
)
{
blas
.
GEMM
(
CblasNoTrans
,
CblasNoTrans
,
M
,
N
,
K
,
static_cast
<
T
>
(
1
),
X
,
W
,
static_cast
<
T
>
(
0
),
Y
);
if
(
B
)
{
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for
(
int
i
=
0
;
i
<
M
;
i
++
)
{
blas
.
AXPY
(
N
,
static_cast
<
T
>
(
1
),
B
,
Y
+
i
*
N
);
}
}
}
}
// namespace math
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/tests/unittests/test_fc_op.py
浏览文件 @
e9553612
...
...
@@ -64,27 +64,47 @@ class TestFCOp(OpTest):
self
.
check_output
()
class
TestFCOp
BiasBoth
(
TestFCOp
):
class
TestFCOp
NoBias
(
TestFCOp
):
def
init_shapes
(
self
,
mb
,
ic
,
oc
,
h
,
w
):
for
with_bias
in
{
True
,
False
}:
self
.
with_bias
=
with_bias
self
.
matrix
=
MatrixGenerate
(
mb
,
ic
,
oc
,
h
,
w
)
self
.
with_bias
=
False
self
.
matrix
=
MatrixGenerate
(
mb
,
ic
,
oc
,
h
,
w
)
class
TestFCOp1
(
TestFCOpBiasBoth
):
class
TestFCOpWithBias
(
TestFCOp
):
def
init_shapes
(
self
,
mb
,
ic
,
oc
,
h
,
w
):
self
.
with_bias
=
True
self
.
matrix
=
MatrixGenerate
(
mb
,
ic
,
oc
,
h
,
w
)
class
TestFCOp1
(
TestFCOpNoBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
2
,
8
,
10
,
1
,
1
)
class
TestFCOp2
(
TestFCOp
BiasBoth
):
class
TestFCOp2
(
TestFCOp
NoBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
4
,
5
,
6
,
2
,
2
)
class
TestFCOp4
(
TestFCOp
BiasBoth
):
class
TestFCOp4
(
TestFCOp
NoBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
1
,
32
,
64
,
3
,
3
)
class
TestFCOpWithBias1
(
TestFCOpWithBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
3
,
8
,
10
,
2
,
1
)
class
TestFCOpWithBias2
(
TestFCOpWithBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
4
,
5
,
6
,
2
,
2
)
class
TestFCOpWithBias3
(
TestFCOpWithBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
1
,
64
,
32
,
3
,
3
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
0 → 100644
浏览文件 @
e9553612
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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
print_function
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
from
test_lstm_op
import
lstm
,
ACTIVATION
def
fc
(
x
,
w
,
b
):
return
np
.
dot
(
x
,
w
)
+
b
def
fusion_lstm
(
x
,
# T x M
lod
,
# 1 x N
wx
=
None
,
# M x 4D
bx
=
None
,
# 1 x 4D
h0
=
None
,
# N x D
c0
=
None
,
# N x D
w_h
=
None
,
# D x 4D
w_b
=
None
,
# 1 x 4D
w_c
=
None
,
# 1 x 3D
is_reverse
=
False
,
act_gate
=
None
,
act_cell
=
None
,
act_cand
=
None
):
return
lstm
(
fc
(
x
,
wx
,
bx
),
lod
,
h0
,
c0
,
w_h
,
w_b
,
w_c
,
is_reverse
,
act_gate
,
act_cell
,
act_cand
)
class
TestLstmOp
(
OpTest
):
def
set_argument
(
self
):
self
.
lod
=
[[
2
,
3
,
2
]]
def
setUp
(
self
):
self
.
op_type
=
'fusion_lstm'
self
.
lod
=
[[
2
,
3
,
2
]]
self
.
M
=
8
self
.
D
=
16
self
.
has_initial_state
=
False
self
.
is_reverse
=
False
self
.
act_gate
=
'sigmoid'
self
.
act_cell
=
'tanh'
self
.
act_cand
=
'tanh'
self
.
use_peepholes
=
False
self
.
set_argument
()
T
=
sum
(
self
.
lod
[
0
])
bs
=
len
(
self
.
lod
[
0
])
x
=
np
.
random
.
normal
(
size
=
(
T
,
self
.
M
)).
astype
(
'float64'
)
if
self
.
has_initial_state
:
h0
=
np
.
random
.
normal
(
size
=
(
bs
,
self
.
D
)).
astype
(
'float64'
)
c0
=
np
.
random
.
normal
(
size
=
(
bs
,
self
.
D
)).
astype
(
'float64'
)
else
:
h0
=
np
.
zeros
((
bs
,
self
.
D
)).
astype
(
'float64'
)
c0
=
np
.
zeros
((
bs
,
self
.
D
)).
astype
(
'float64'
)
wh
=
np
.
random
.
normal
(
size
=
(
self
.
D
,
4
*
self
.
D
)).
astype
(
'float64'
)
if
self
.
use_peepholes
:
b
=
np
.
random
.
normal
(
size
=
(
1
,
7
*
self
.
D
)).
astype
(
'float64'
)
else
:
b
=
np
.
random
.
normal
(
size
=
(
1
,
4
*
self
.
D
)).
astype
(
'float64'
)
w_b
=
np
.
copy
(
b
[:,
0
:
4
*
self
.
D
])
w_c
=
b
[:,
4
*
self
.
D
:]
if
self
.
use_peepholes
else
None
# this is the weight of fc
wx
=
np
.
random
.
normal
(
size
=
(
self
.
M
,
4
*
self
.
D
)).
astype
(
'float64'
)
# this is the bias of fc
# and it should be manually added into the bias of this fusion LSTM
bx
=
np
.
random
.
normal
(
size
=
(
1
,
4
*
self
.
D
)).
astype
(
'float64'
)
b
[
0
,
0
:
4
*
self
.
D
]
+=
bx
[
0
,
:]
h
,
c
=
fusion_lstm
(
x
,
self
.
lod
,
wx
,
bx
,
h0
,
c0
,
wh
,
w_b
,
w_c
,
self
.
is_reverse
,
ACTIVATION
[
self
.
act_gate
],
ACTIVATION
[
self
.
act_cell
],
ACTIVATION
[
self
.
act_cand
])
self
.
inputs
=
{
'X'
:
(
x
,
self
.
lod
),
'WeightX'
:
wx
,
'WeightH'
:
wh
,
'Bias'
:
b
}
if
self
.
has_initial_state
:
self
.
inputs
[
'H0'
]
=
h0
self
.
inputs
[
'C0'
]
=
c0
self
.
outputs
=
{
'Hidden'
:
(
h
,
self
.
lod
),
'Cell'
:
(
c
,
self
.
lod
),
}
self
.
attrs
=
{
'use_peepholes'
:
self
.
use_peepholes
,
'is_reverse'
:
self
.
is_reverse
,
'gate_activation'
:
self
.
act_gate
,
'cell_activation'
:
self
.
act_cell
,
'candidate_activation'
:
self
.
act_cand
}
def
test_check_output
(
self
):
self
.
check_output
(
atol
=
1e-8
)
class
TestLstmOpInitReverse
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
has_initial_state
=
True
self
.
is_reverse
=
True
class
TestLstmOpMD1
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
M
=
36
self
.
D
=
8
class
TestLstmOpMD2
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
M
=
8
self
.
D
=
8
class
TestLstmOpMD3
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
M
=
15
self
.
D
=
3
class
TestLstmOpBS1
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
lod
=
[[
3
]]
self
.
D
=
16
if
__name__
==
'__main__'
:
unittest
.
main
()
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录