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9affc36c
编写于
8月 20, 2018
作者:
T
tensor-tang
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init attention lstm
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paddle/fluid/operators/attention_lstm_op.cc
paddle/fluid/operators/attention_lstm_op.cc
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paddle/fluid/operators/attention_lstm_op.h
paddle/fluid/operators/attention_lstm_op.h
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paddle/fluid/operators/attention_lstm_op.cc
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/* 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/attention_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/attention_lstm_op.h
0 → 100644
浏览文件 @
9affc36c
/* 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
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