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448fee3d
编写于
1月 15, 2018
作者:
W
whs
提交者:
GitHub
1月 15, 2018
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差异文件
Merge pull request #7414 from wanghaoshuang/warpctc
Adapt warpctc grad op for gradient checking
上级
b9b75377
8f37c3c2
变更
7
显示空白变更内容
内联
并排
Showing
7 changed file
with
214 addition
and
25 deletion
+214
-25
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+1
-1
paddle/operators/math/CMakeLists.txt
paddle/operators/math/CMakeLists.txt
+2
-0
paddle/operators/math/sequence_scale.cc
paddle/operators/math/sequence_scale.cc
+46
-0
paddle/operators/math/sequence_scale.cu
paddle/operators/math/sequence_scale.cu
+57
-0
paddle/operators/math/sequence_scale.h
paddle/operators/math/sequence_scale.h
+55
-0
paddle/operators/warpctc_op.h
paddle/operators/warpctc_op.h
+12
-1
python/paddle/v2/fluid/tests/test_warpctc_op.py
python/paddle/v2/fluid/tests/test_warpctc_op.py
+41
-23
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
448fee3d
...
...
@@ -149,7 +149,7 @@ op_library(sequence_pool_op DEPS sequence_pooling)
op_library
(
lstm_op DEPS sequence2batch lstm_compute
)
op_library
(
gru_op DEPS sequence2batch gru_compute
)
op_library
(
recurrent_op DEPS executor
)
op_library
(
warpctc_op DEPS dynload_warpctc sequence_padding math_function
)
op_library
(
warpctc_op DEPS dynload_warpctc sequence_padding
sequence_scale
math_function
)
op_library
(
cos_sim_op DEPS cos_sim_functor
)
op_library
(
parallel_do_op DEPS executor
)
...
...
paddle/operators/math/CMakeLists.txt
浏览文件 @
448fee3d
...
...
@@ -13,6 +13,7 @@ if(WITH_GPU)
nv_library
(
context_project SRCS context_project.cc context_project.cu DEPS device_context math_function
)
nv_library
(
sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context tensor
)
nv_library
(
sequence_padding SRCS sequence_padding.cc sequence_padding.cu DEPS lod_tensor device_context
)
nv_library
(
sequence_scale SRCS sequence_scale.cc sequence_scale.cu DEPS lod_tensor device_context
)
nv_library
(
lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions
)
nv_library
(
maxouting SRCS maxouting.cc maxouting.cu DEPS device_context
)
nv_library
(
unpooling SRCS unpooling.cc unpooling.cu DEPS device_context
)
...
...
@@ -29,6 +30,7 @@ else()
cc_library
(
context_project SRCS context_project.cc DEPS device_context math_function
)
cc_library
(
sequence2batch SRCS sequence2batch.cc DEPS device_context tensor
)
cc_library
(
sequence_padding SRCS sequence_padding.cc DEPS lod_tensor device_context
)
cc_library
(
sequence_scale SRCS sequence_scale.cc DEPS lod_tensor device_context
)
cc_library
(
lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions
)
cc_library
(
maxouting SRCS maxouting.cc DEPS device_context
)
cc_library
(
unpooling SRCS unpooling.cc DEPS device_context
)
...
...
paddle/operators/math/sequence_scale.cc
0 → 100644
浏览文件 @
448fee3d
/* Copyright (c) 2016 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. */
#include "paddle/operators/math/sequence_scale.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
T
>
class
ScaleLoDTensorFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
public:
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
framework
::
LoDTensor
&
seq
,
const
T
*
scales
)
{
const
size_t
level
=
0
;
auto
lod
=
seq
.
lod
();
const
size_t
num_seq
=
lod
[
level
].
size
()
-
1
;
size_t
seq_width
=
seq
.
dims
()[
1
];
framework
::
LoD
abs_offset_lod
=
framework
::
ToAbsOffset
(
lod
);
T
*
seq_data
=
seq
.
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
size_t
i
=
0
;
i
<
num_seq
;
++
i
)
{
for
(
size_t
j
=
lod
[
level
][
i
]
*
seq_width
;
j
<
lod
[
level
][
i
+
1
]
*
seq_width
;
++
j
)
{
seq_data
[
j
]
*=
scales
[
i
];
}
}
}
};
template
class
ScaleLoDTensorFunctor
<
platform
::
CPUDeviceContext
,
float
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/sequence_scale.cu
0 → 100644
浏览文件 @
448fee3d
/* Copyright (c) 2016 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. */
#include "paddle/operators/math/sequence_scale.h"
#include "paddle/platform/cuda_helper.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
using
platform
::
PADDLE_CUDA_NUM_THREADS
;
template
<
typename
T
,
int
BlockSize
>
__global__
void
SequenceScaleKernel
(
T
*
seq
,
size_t
*
lod
,
const
T
*
scales
,
const
size_t
seq_width
)
{
for
(
int
i
=
threadIdx
.
x
;
i
<
(
lod
[
blockIdx
.
x
+
1
]
-
lod
[
blockIdx
.
x
])
*
seq_width
;
i
+=
BlockSize
)
{
int
idx
=
lod
[
blockIdx
.
x
]
*
seq_width
+
i
;
seq
[
idx
]
*=
scales
[
blockIdx
.
x
];
}
}
template
<
typename
T
>
class
ScaleLoDTensorFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
public:
void
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
framework
::
LoDTensor
&
seq
,
const
T
*
scales
)
{
const
size_t
level
=
0
;
auto
lod
=
seq
.
lod
();
const
size_t
num_seq
=
lod
[
level
].
size
()
-
1
;
const
size_t
seq_width
=
seq
.
numel
()
/
seq
.
dims
()[
0
];
framework
::
LoD
abs_offset_lod
=
framework
::
ToAbsOffset
(
lod
);
T
*
seq_data
=
seq
.
mutable_data
<
T
>
(
context
.
GetPlace
());
SequenceScaleKernel
<
T
,
PADDLE_CUDA_NUM_THREADS
><<<
num_seq
,
PADDLE_CUDA_NUM_THREADS
,
0
,
context
.
stream
()
>>>
(
seq_data
,
abs_offset_lod
[
level
].
data
(),
scales
,
seq_width
);
}
};
template
class
ScaleLoDTensorFunctor
<
platform
::
CUDADeviceContext
,
float
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/sequence_scale.h
0 → 100644
浏览文件 @
448fee3d
/* Copyright (c) 2016 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. */
#pragma once
#include "paddle/framework/lod_tensor.h"
#include "paddle/platform/device_context.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
/*
* \brief Scale a sequence.
*
* All sequences will be padded to the same length and stored in a transposed
* shape.
* Example:
* Given:
* seq = (s0, s0, s0, s0; s1, s1; s2, s2, s2; s3)
* scales = (2, 3, 4, 5)
* then:
* result = (2*s0, 2*s0, 2*s0, 2*s0; 3*s1, 3*s1; 4*s2, 4*s2, 4*s2; 5*s3)
*
* \param context Device context of this functor.
* \param seq LoDTensor which is stored in sequence format, the shape
* is [total_sequence_length, sequence_width] where
* total_sequence_length is the sum of all sequences'
* length.
* \param scales Array<T>. The i-th sequence will be scaled by scales[i].
* \param num_seq Number of sequence
*
*/
template
<
typename
DeviceContext
,
typename
T
>
class
ScaleLoDTensorFunctor
{
public:
void
operator
()(
const
DeviceContext
&
context
,
framework
::
LoDTensor
&
seq
,
const
T
*
scales
);
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/warpctc_op.h
浏览文件 @
448fee3d
...
...
@@ -17,6 +17,7 @@ limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/sequence_padding.h"
#include "paddle/operators/math/sequence_scale.h"
#include "paddle/platform/dynload/warpctc.h"
namespace
paddle
{
...
...
@@ -178,11 +179,14 @@ class WarpCTCKernel : public framework::OpKernel<T> {
T
*
warpctc_grad_data
=
warpctc_grad
->
mutable_data
<
T
>
(
warpctc_logits
.
dims
(),
ctx
.
GetPlace
());
math
::
SetConstant
<
DeviceContext
,
T
>
()(
ctx
.
template
device_context
<
DeviceContext
>(),
warpctc_grad
,
static_cast
<
T
>
(
0
));
// warpctc accesses labels in CPU memory
Tensor
warpctc_label
;
Copy
(
*
label
,
platform
::
CPUPlace
(),
ctx
.
device_context
(),
&
warpctc_label
);
const
int
*
warpctc_label_data
=
warpctc_label
.
data
<
int
>
();
// warpctc stores loss in CPU memory
Tensor
warpctc_loss
;
T
*
warpctc_loss_data
=
...
...
@@ -206,11 +210,18 @@ class WarpCTCGradKernel : public framework::OpKernel<T> {
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
warpctc_grad
=
ctx
.
Input
<
Tensor
>
(
"WarpCTCGrad"
);
auto
*
logits_grad
=
ctx
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"Logits"
));
const
Tensor
*
loss_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Loss"
));
logits_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
bool
norm_by_times
=
ctx
.
Attr
<
bool
>
(
"norm_by_times"
);
math
::
UnpaddingLoDTensorFunctor
<
DeviceContext
,
T
>
()(
ctx
.
template
device_context
<
DeviceContext
>(),
*
logits_grad
,
*
warpctc_grad
,
norm_by_times
);
const
T
*
loss_grad_data
=
loss_grad
->
data
<
T
>
();
math
::
ScaleLoDTensorFunctor
<
DeviceContext
,
T
>
()(
ctx
.
template
device_context
<
DeviceContext
>(),
*
logits_grad
,
loss_grad_data
);
}
};
...
...
python/paddle/v2/fluid/tests/test_warpctc_op.py
浏览文件 @
448fee3d
...
...
@@ -17,6 +17,8 @@ import numpy as np
from
op_test
import
OpTest
from
test_softmax_op
import
stable_softmax
CUDA_BLOCK_SIZE
=
512
class
CTCForward
(
object
):
def
__init__
(
self
,
softmax
,
softmax_lod
,
labels
,
labels_lod
,
blank
,
...
...
@@ -167,47 +169,63 @@ class CTCForward(object):
class
TestWarpCTCOp
(
OpTest
):
def
config
(
self
):
self
.
batch_size
=
4
self
.
num_classes
=
8
self
.
logits_lod
=
[[
0
,
4
,
5
,
8
,
11
]]
self
.
labels_lod
=
[[
0
,
3
,
4
,
8
,
12
]]
self
.
blank
=
self
.
num_classes
-
1
self
.
norm_by_times
=
False
def
setUp
(
self
):
self
.
op_type
=
"warpctc"
self
.
config
()
batch_size
=
4
num_classes
=
8
logits_lod
=
[[
0
,
4
,
5
,
8
,
11
]]
logits
=
np
.
random
.
uniform
(
0.1
,
1.0
,
[
11
,
num_classes
]).
astype
(
"float32"
)
logits
=
np
.
random
.
uniform
(
0.1
,
1.0
,
[
self
.
logits_lod
[
0
][
-
1
],
self
.
num_classes
]).
astype
(
"float32"
)
softmax
=
np
.
apply_along_axis
(
stable_softmax
,
1
,
logits
)
labels_lod
=
[[
0
,
3
,
4
,
8
,
12
]]
# labels should not be blank
labels
=
np
.
random
.
randint
(
0
,
num_classes
-
1
,
[
12
,
1
],
dtype
=
"int32"
)
blank
=
num_classes
-
1
norm_by_times
=
False
labels
=
np
.
random
.
randint
(
0
,
self
.
num_classes
-
1
,
[
self
.
labels_lod
[
0
][
-
1
],
1
],
dtype
=
"int32"
)
ctc
=
CTCForward
(
softmax
,
logits_lod
,
labels
,
labels_lod
,
blank
,
norm_by_times
)
ctc
=
CTCForward
(
softmax
,
self
.
logits_lod
,
labels
,
self
.
labels_lod
,
self
.
blank
,
self
.
norm_by_times
)
loss
=
ctc
.
forward
()
max_sequence_length
=
0
for
i
in
range
(
batch_size
):
max_sequence_length
=
max
(
max_sequence_length
,
logits_lod
[
0
][
i
+
1
]
-
logits_lod
[
0
][
i
])
gradient
=
np
.
zeros
(
[
max_sequence_length
,
batch_size
,
num_classes
],
dtype
=
"float32"
)
for
i
in
range
(
self
.
batch_size
):
max_sequence_length
=
max
(
max_sequence_length
,
self
.
logits_lod
[
0
][
i
+
1
]
-
self
.
logits_lod
[
0
][
i
])
self
.
gradient
=
np
.
zeros
(
[
max_sequence_length
,
self
.
batch_size
,
self
.
num_classes
],
dtype
=
"float32"
)
self
.
inputs
=
{
"Logits"
:
(
logits
,
logits_lod
),
"Label"
:
(
labels
,
labels_lod
)
"Logits"
:
(
logits
,
self
.
logits_lod
),
"Label"
:
(
labels
,
self
.
labels_lod
)
}
self
.
outputs
=
{
"Loss"
:
loss
}
self
.
attrs
=
{
"blank"
:
blank
,
"norm_by_times"
:
norm_by_times
}
self
.
attrs
=
{
"blank"
:
self
.
blank
,
"norm_by_times"
:
self
.
norm_by_times
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
outputs
[
'WarpCTCGrad'
]
=
self
.
gradient
self
.
check_grad
([
"Logits"
],
"Loss"
,
max_relative_error
=
0.007
)
class
TestWarpCTCOpCase1
(
TestWarpCTCOp
):
def
config
(
self
):
self
.
batch_size
=
4
self
.
num_classes
=
CUDA_BLOCK_SIZE
+
2
self
.
logits_lod
=
[[
0
,
4
,
5
,
8
,
11
]]
self
.
labels_lod
=
[[
0
,
3
,
4
,
8
,
12
]]
self
.
blank
=
0
self
.
norm_by_times
=
False
# def test_check_grad(self):
# self.outputs["WarpCTCGrad"] = None
# self.check_grad(["Logits"], "Loss", max_relative_error=0.01)
if
__name__
==
"__main__"
:
unittest
.
main
()
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