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300180cc
编写于
8月 16, 2018
作者:
T
tensor-tang
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init fusion gru op
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paddle/fluid/operators/fusion_gru_op.cc
paddle/fluid/operators/fusion_gru_op.cc
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paddle/fluid/operators/fusion_gru_op.h
paddle/fluid/operators/fusion_gru_op.h
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paddle/fluid/operators/fusion_gru_op.cc
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/* 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. */
#include "paddle/fluid/operators/fusion_gru_op.h"
#include <string>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/gru_compute.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
namespace
paddle
{
namespace
operators
{
void
FusionGRUOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Input"
),
"Input(%s) of GRUOp should not be null."
,
"Input"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Weight"
),
"Input(%s) of GRUOp should not be null."
,
"Weight"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BatchGate"
),
"Output(%s) of GRUOp should not be null."
,
"BatchGate"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BatchResetHiddenPrev"
),
"Output(%s) of GRUOp should not be null."
,
"BatchResetHiddenPrev"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BatchHidden"
),
"Output(%s) of GRUOp should not be null."
,
"BatchHidden"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Hidden"
),
"Output(%s) of GRUOp should not be null."
,
"Hidden"
);
auto
input_dims
=
ctx
->
GetInputDim
(
"Input"
);
auto
weight_dims
=
ctx
->
GetInputDim
(
"Weight"
);
int
input_size
=
input_dims
[
1
];
int
frame_size
=
weight_dims
[
0
];
PADDLE_ENFORCE_EQ
(
input_size
,
frame_size
*
3
,
"The input_size must be 3 times of frame_size in GRUOp."
);
PADDLE_ENFORCE_EQ
(
weight_dims
[
1
],
frame_size
*
3
,
"The shape of Weight matrix must be [frame_size, frame_size * 3]."
);
if
(
ctx
->
HasInput
(
"H0"
))
{
auto
h0_dims
=
ctx
->
GetInputDim
(
"H0"
);
PADDLE_ENFORCE_EQ
(
h0_dims
[
1
],
frame_size
,
"The width of H0 must be equal to frame_size."
);
}
if
(
ctx
->
HasInput
(
"Bias"
))
{
auto
bias_dims
=
ctx
->
GetInputDim
(
"Bias"
);
int
bias_height
=
bias_dims
[
0
];
int
bias_width
=
bias_dims
[
1
];
PADDLE_ENFORCE_EQ
(
bias_height
,
1
,
"The shape of Bias must be [1, frame_size * 3]."
);
PADDLE_ENFORCE_EQ
(
bias_width
,
frame_size
*
3
,
"The shape of Bias must be [1, frame_size * 3]."
);
}
ctx
->
SetOutputDim
(
"BatchGate"
,
input_dims
);
ctx
->
SetOutputDim
(
"BatchResetHiddenPrev"
,
{
input_dims
[
0
],
frame_size
});
ctx
->
SetOutputDim
(
"BatchHidden"
,
{
input_dims
[
0
],
frame_size
});
ctx
->
SetOutputDim
(
"Hidden"
,
{
input_dims
[
0
],
frame_size
});
ctx
->
ShareLoD
(
"Input"
,
"Hidden"
);
}
framework
::
OpKernelType
FusionGRUOp
::
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
void
FusionGRUOpMaker
::
Make
()
{
AddInput
(
"Input"
,
"(LoDTensor) The first input is a LodTensor, which supports "
"variable-time length input sequence. The underlying tensor in "
"this LoDTenosr is a matrix with shape (T X 3D), where, T is the "
"total time steps in this mini-batch, D is the hidden size."
);
AddInput
(
"H0"
,
"(Tensor, optional) The initial hidden state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size, D is the hidden size."
)
.
AsDispensable
();
AddInput
(
"Weight"
,
"(Tensor) The learnable hidden-hidden weight matrix with shape "
"(D x 3D), where D is the hidden size. The elements continuous in "
"memory can be divided into two parts. The first part are weights of "
"the update gate and reset gate with shape (D x 2D), and the second "
"part are weights of output candidate with shape (D x D)."
);
AddInput
(
"Bias"
,
"(Tensor, optional) Bias vector with shape (1 x 3D) concating "
"bias of the update gate, reset gate and output candidate."
)
.
AsDispensable
();
AddOutput
(
"BatchGate"
,
"(LoDTensor) To compute with batches, sequence data will be "
"reorganized into several successive batches each containing "
"data from the same time step. The LoDTensor BatchGate contains "
"the update gate, reset gate and output candidate values "
"organized in batches. The LoD size is 2. The first LoD contains "
"the batch offsets and the second LoD contains the indexes in "
"the raw sequence data."
)
.
AsIntermediate
();
AddOutput
(
"BatchResetHiddenPrev"
,
"(LoDTensor) The reseted hidden state LoDTensor organized in batches. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
"with `BatchGate`."
)
.
AsIntermediate
();
AddOutput
(
"BatchHidden"
,
"(LoDTensor) The hidden state LoDTensor organized in batches. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
"with `BatchGate`."
)
.
AsIntermediate
();
AddOutput
(
"Hidden"
,
"(LoDTensor) the hidden state LoDTensor organized in sequences. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
"with `BatchGate`."
);
AddAttr
<
std
::
string
>
(
"activation"
,
"(string, default tanh) "
"The activation type used for output candidate {h}_t."
)
.
SetDefault
(
"tanh"
);
AddAttr
<
std
::
string
>
(
"gate_activation"
,
"(string, default sigmoid) "
"The activation type used in update gate and reset gate."
)
.
SetDefault
(
"sigmoid"
);
AddAttr
<
bool
>
(
"is_reverse"
,
"(bool, defalut: False) "
"whether to compute reversed GRU."
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
The Fusion complete GRU Operator.
This operator fuse the fully-connected operator into GRU,
more details can refer to GRU 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
());
row_shuffle
(
ctx
,
src
,
index_lod
,
dst
,
indexed_src
);
}
template
<
typename
DeviceContext
,
typename
T
>
class
FusionGRUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
h
=
context
.
Input
<
LoDTensor
>
(
"H"
);
auto
*
h0
=
context
.
Input
<
Tensor
>
(
"H0"
);
auto
*
x_weight
=
context
.
Input
<
Tensor
>
(
"XWeight"
);
// x_dim*3D
auto
*
gate_weight
=
context
.
Input
<
Tensor
>
(
"HWeight"
);
// D*3D
auto
*
bias
=
context
.
Input
<
Tensor
>
(
"Bias"
);
// 1*3D
auto
hidden_dims
=
hidden
->
dims
();
bool
is_reverse
=
context
.
Attr
<
bool
>
(
"is_reverse"
);
math
::
LoDTensor2BatchFunctor
<
DeviceContext
,
T
>
to_batch
;
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
to_batch
(
dev_ctx
,
*
input
,
batch_gate
,
true
,
is_reverse
);
if
(
bias
)
{
math
::
RowwiseAdd
<
DeviceContext
,
T
>
add_bias
;
add_bias
(
dev_ctx
,
*
batch_gate
,
*
bias
,
batch_gate
);
}
int
frame_size
=
hidden_dims
[
1
];
math
::
GRUMetaValue
<
T
>
gru_value
;
gru_value
.
gate_weight
=
const_cast
<
T
*>
(
weight_data
);
gru_value
.
state_weight
=
const_cast
<
T
*>
(
weight_data
+
2
*
frame_size
*
frame_size
);
Tensor
ordered_h0
;
framework
::
Vector
<
size_t
>
order
(
batch_gate
->
lod
()[
2
]);
if
(
h0
)
{
// Since the batch computing for GRU reorders the input sequences
// according to their length. The initialized cell state also needs
// to reorder.
ReorderInitState
<
DeviceContext
,
T
>
(
context
.
template
device_context
<
DeviceContext
>(),
*
h0
,
order
,
&
ordered_h0
,
true
);
gru_value
.
prev_out_value
=
ordered_h0
.
data
<
T
>
();
}
else
{
gru_value
.
prev_out_value
=
nullptr
;
}
auto
batch_starts
=
batch_gate
->
lod
()[
0
];
size_t
seq_len
=
batch_starts
.
size
()
-
1
;
auto
active_node
=
math
::
detail
::
GetActivationType
(
context
.
Attr
<
std
::
string
>
(
"activation"
));
auto
active_gate
=
math
::
detail
::
GetActivationType
(
context
.
Attr
<
std
::
string
>
(
"gate_activation"
));
#ifdef PADDLE_WITH_MKLML
// use MKL packed to speedup GEMM
if
(
FLAGS_paddle_num_threads
>=
4
)
{
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
dev_ctx
);
T
*
packed_gate
=
blas
.
GEMM_ALLOC
(
CblasBMatrix
,
1
/*height of C*/
,
frame_size
*
2
/*width of weight*/
,
frame_size
/*height of height*/
);
PADDLE_ENFORCE
(
packed_gate
);
blas
.
GEMM_PACK
(
CblasBMatrix
,
CblasNoTrans
,
1
/*cur bs?*/
,
frame_size
*
2
,
frame_size
,
T
(
1.0
),
gru_value
.
gate_weight
,
frame_size
*
2
,
packed_gate
);
T
*
packed_state
=
blas
.
GEMM_ALLOC
(
CblasBMatrix
,
1
/*height of C*/
,
frame_size
/*width of weight*/
,
frame_size
/*height of height*/
);
PADDLE_ENFORCE
(
packed_state
);
blas
.
GEMM_PACK
(
CblasBMatrix
,
CblasNoTrans
,
1
/*cur bs?*/
,
frame_size
,
frame_size
,
T
(
1.0
),
gru_value
.
state_weight
,
frame_size
,
packed_state
);
for
(
size_t
n
=
0
;
n
<
seq_len
;
n
++
)
{
int
bstart
=
static_cast
<
int
>
(
batch_starts
[
n
]);
int
bend
=
static_cast
<
int
>
(
batch_starts
[
n
+
1
]);
int
cur_batch_size
=
bend
-
bstart
;
Tensor
gate_t
=
batch_gate
->
Slice
(
bstart
,
bend
);
Tensor
reset_hidden_prev_t
=
batch_reset_hidden_prev
->
Slice
(
bstart
,
bend
);
Tensor
hidden_t
=
batch_hidden
->
Slice
(
bstart
,
bend
);
gru_value
.
output_value
=
hidden_t
.
data
<
T
>
();
gru_value
.
gate_value
=
gate_t
.
data
<
T
>
();
gru_value
.
reset_output_value
=
reset_hidden_prev_t
.
data
<
T
>
();
if
(
gru_value
.
prev_out_value
)
{
blas
.
GEMM_COMPUTE
(
CblasNoTrans
,
CblasPacked
,
cur_batch_size
,
frame_size
*
2
,
frame_size
,
gru_value
.
prev_out_value
,
frame_size
,
packed_gate
,
frame_size
*
2
,
T
(
1
),
gru_value
.
gate_value
,
frame_size
*
3
);
}
math
::
detail
::
forward_reset_output
(
math
::
detail
::
forward
::
gru_resetOutput
<
T
>
(),
gru_value
,
frame_size
,
cur_batch_size
,
active_gate
);
if
(
gru_value
.
prev_out_value
)
{
blas
.
GEMM_COMPUTE
(
CblasNoTrans
,
CblasPacked
,
cur_batch_size
,
frame_size
,
frame_size
,
gru_value
.
reset_output_value
,
frame_size
,
packed_state
,
frame_size
,
T
(
1
),
gru_value
.
gate_value
+
frame_size
*
2
,
frame_size
*
3
);
}
math
::
detail
::
forward_final_output
(
math
::
detail
::
forward
::
gru_finalOutput
<
T
>
(),
gru_value
,
frame_size
,
cur_batch_size
,
active_node
);
gru_value
.
prev_out_value
=
gru_value
.
output_value
;
}
blas
.
GEMM_FREE
(
packed_gate
);
blas
.
GEMM_FREE
(
packed_state
);
}
else
{
#endif
for
(
size_t
n
=
0
;
n
<
seq_len
;
n
++
)
{
int
bstart
=
static_cast
<
int
>
(
batch_starts
[
n
]);
int
bend
=
static_cast
<
int
>
(
batch_starts
[
n
+
1
]);
int
cur_batch_size
=
bend
-
bstart
;
Tensor
gate_t
=
batch_gate
->
Slice
(
bstart
,
bend
);
Tensor
reset_hidden_prev_t
=
batch_reset_hidden_prev
->
Slice
(
bstart
,
bend
);
Tensor
hidden_t
=
batch_hidden
->
Slice
(
bstart
,
bend
);
gru_value
.
output_value
=
hidden_t
.
data
<
T
>
();
gru_value
.
gate_value
=
gate_t
.
data
<
T
>
();
gru_value
.
reset_output_value
=
reset_hidden_prev_t
.
data
<
T
>
();
math
::
GRUUnitFunctor
<
DeviceContext
,
T
>::
compute
(
dev_ctx
,
gru_value
,
frame_size
,
cur_batch_size
,
active_node
,
active_gate
);
gru_value
.
prev_out_value
=
gru_value
.
output_value
;
}
#ifdef PADDLE_WITH_MKLML
}
#endif
math
::
Batch2LoDTensorFunctor
<
DeviceContext
,
T
>
to_seq
;
batch_hidden
->
set_lod
(
batch_gate
->
lod
());
to_seq
(
dev_ctx
,
*
batch_hidden
,
hidden
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
fusion_gru
,
ops
::
FusionGRUOp
,
ops
::
FusionGRUOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OP_CPU_KERNEL
(
fusion_gru
,
ops
::
FusionGRUKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
GRUKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/fusion_gru_op.h
0 → 100644
浏览文件 @
300180cc
/* 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. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
using
Tensor
=
framework
::
Tensor
;
class
FusionGRUOp
:
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
FusionGRUOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
;
};
}
// namespace operators
}
// namespace paddle
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