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fee4316d
编写于
2月 07, 2022
作者:
T
tanzhipeng
提交者:
GitHub
2月 07, 2022
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电子邮件补丁
差异文件
add sequence_conv op in xpu place (#39025)
上级
24b2e8e6
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
569 addition
and
0 deletion
+569
-0
paddle/fluid/operators/sequence_ops/sequence_conv_op_xpu.cc
paddle/fluid/operators/sequence_ops/sequence_conv_op_xpu.cc
+288
-0
paddle/fluid/platform/device/xpu/xpu2_op_list.h
paddle/fluid/platform/device/xpu/xpu2_op_list.h
+4
-0
python/paddle/fluid/tests/unittests/xpu/test_sequence_conv_op_xpu.py
...le/fluid/tests/unittests/xpu/test_sequence_conv_op_xpu.py
+277
-0
未找到文件。
paddle/fluid/operators/sequence_ops/sequence_conv_op_xpu.cc
0 → 100644
浏览文件 @
fee4316d
/* Copyright (c) 2021 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/sequence_ops/sequence_conv_op.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceConvXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
auto
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
context_start
=
context
.
Attr
<
int
>
(
"contextStart"
);
int
context_length
=
context
.
Attr
<
int
>
(
"contextLength"
);
int
context_stride
=
context
.
Attr
<
int
>
(
"contextStride"
);
bool
padding_trainable
=
context
.
Attr
<
bool
>
(
"paddingTrainable"
);
PADDLE_ENFORCE_EQ
(
in
->
lod
().
empty
(),
false
,
platform
::
errors
::
InvalidArgument
(
"Input(X) Tensor of SequenceConvOp "
"does not contain LoD information."
));
PADDLE_ENFORCE_EQ
(
in
->
lod
().
size
(),
1UL
,
platform
::
errors
::
InvalidArgument
(
"Only support input sequence with lod level equal to 1 at "
"present. But received: lod level %u."
,
in
->
lod
().
size
()));
PADDLE_ENFORCE_EQ
(
padding_trainable
,
false
,
platform
::
errors
::
InvalidArgument
(
"Only support padding_trainable "
"equal false."
));
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
down_pad
=
std
::
max
(
0
,
context_start
+
context_length
-
1
);
PADDLE_ENFORCE_EQ
(
up_pad
,
2
,
platform
::
errors
::
InvalidArgument
(
"Only support up_pad equal 2."
));
PADDLE_ENFORCE_EQ
(
down_pad
,
2
,
platform
::
errors
::
InvalidArgument
(
"Only support down_pad equal 2."
));
auto
xpu_context
=
context
.
template
device_context
<
DeviceContext
>().
x_context
();
auto
sequence_width
=
static_cast
<
int64_t
>
(
in
->
dims
()[
1
]);
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
context_length
*
sequence_width
};
xpu
::
ctx_guard
RAII_GUARD
(
xpu_context
);
int
col_numel
=
col_shape
[
0
]
*
col_shape
[
1
];
T
*
col_data
=
RAII_GUARD
.
alloc_l3_or_gm
<
T
>
(
col_numel
);
PADDLE_ENFORCE_NOT_NULL
(
col_data
,
paddle
::
platform
::
errors
::
Fatal
(
"XPU memory is not enough"
));
auto
lod_level_0
=
in
->
lod
()[
0
];
int
lod_size
=
lod_level_0
.
size
();
// If batch size set to 256, the lod is {0, batch[0] - 0,
// batch[1] - batch [0], ..., batch[255] - batch[254]},
// so the lod_size will be 257.
PADDLE_ENFORCE_LE
(
lod_size
,
257
,
platform
::
errors
::
InvalidArgument
(
"Only support batch size <= 256."
));
std
::
vector
<
int
>
cpu_lodx
(
lod_size
);
for
(
int
i
=
0
;
i
<
lod_size
;
i
++
)
{
cpu_lodx
[
i
]
=
lod_level_0
[
i
];
}
xpu
::
VectorParam
<
int
>
lodx
=
{
cpu_lodx
.
data
(),
static_cast
<
int
>
(
cpu_lodx
.
size
()),
nullptr
};
int
r
=
xpu
::
sequence_context_projection
<
T
,
int
>
(
xpu_context
,
in
->
data
<
T
>
(),
col_data
,
nullptr
,
lodx
,
sequence_width
,
context_start
,
context_length
,
context_stride
,
{
2
,
2
});
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"sequence_context_projection"
);
bool
trans_a
=
false
;
bool
trans_b
=
false
;
int
m
=
col_shape
[
0
];
int
k
=
col_shape
[
1
];
int
k1
=
filter
.
dims
()[
0
];
int
n
=
filter
.
dims
()[
1
];
PADDLE_ENFORCE_EQ
(
k
,
k1
,
platform
::
errors
::
InvalidArgument
(
"The shape of FC in SequenceConvOp is invalid."
"The k of matrix A is %d, k1 of matrix B is %d."
"But expect k == k1"
,
k
,
k1
));
int
lda
=
(
!
trans_a
)
?
k
:
m
;
int
ldb
=
(
!
trans_b
)
?
n
:
k
;
int
ldc
=
n
;
T
alpha
=
static_cast
<
T
>
(
1.0
);
T
beta
=
static_cast
<
T
>
(
0.0
);
const
T
*
data_a
=
col_data
;
const
T
*
data_b
=
filter
.
data
<
T
>
();
T
*
data_c
=
out
->
data
<
T
>
();
r
=
xpu
::
fc_fusion
<
T
,
T
,
T
,
int32_t
>
(
xpu_context
,
data_a
,
data_b
,
data_c
,
m
,
n
,
k
,
trans_a
,
trans_b
,
nullptr
,
nullptr
,
nullptr
,
lda
,
ldb
,
ldc
,
alpha
,
beta
,
nullptr
,
xpu
::
Activation_t
::
LINEAR
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"fc_fusion"
);
if
(
xpu_context
->
xpu_stream
!=
nullptr
)
{
xpu_wait
(
xpu_context
->
xpu_stream
);
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceConvGradXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in_g
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
filter_g
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
filter
=
context
.
Input
<
Tensor
>
(
"Filter"
);
int
context_start
=
context
.
Attr
<
int
>
(
"contextStart"
);
int
context_length
=
context
.
Attr
<
int
>
(
"contextLength"
);
int
context_stride
=
context
.
Attr
<
int
>
(
"contextStride"
);
bool
padding_trainable
=
context
.
Attr
<
bool
>
(
"paddingTrainable"
);
PADDLE_ENFORCE_EQ
(
in
->
lod
().
empty
(),
false
,
platform
::
errors
::
InvalidArgument
(
"Input(X) Tensor of SequenceConvOp "
"does not contain LoD information."
));
PADDLE_ENFORCE_EQ
(
in
->
lod
().
size
(),
1UL
,
platform
::
errors
::
InvalidArgument
(
"Only support input sequence with lod level equal to 1 at "
"present. But received: lod level %u."
,
in
->
lod
().
size
()));
PADDLE_ENFORCE_EQ
(
padding_trainable
,
false
,
platform
::
errors
::
InvalidArgument
(
"Only support padding_trainable "
"equal false."
));
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
down_pad
=
std
::
max
(
0
,
context_start
+
context_length
-
1
);
PADDLE_ENFORCE_EQ
(
up_pad
,
2
,
platform
::
errors
::
InvalidArgument
(
"Only support up_pad equal 2."
));
PADDLE_ENFORCE_EQ
(
down_pad
,
2
,
platform
::
errors
::
InvalidArgument
(
"Only support down_pad equal 2."
));
auto
lod_level_0
=
in
->
lod
()[
0
];
int
lod_size
=
lod_level_0
.
size
();
PADDLE_ENFORCE_LE
(
lod_size
,
257
,
platform
::
errors
::
InvalidArgument
(
"Only support batch size <= 256."
));
std
::
vector
<
int
>
cpu_lodx
(
lod_size
);
for
(
int
i
=
0
;
i
<
lod_size
;
i
++
)
{
cpu_lodx
[
i
]
=
lod_level_0
[
i
];
}
xpu
::
VectorParam
<
int
>
lodx
=
{
cpu_lodx
.
data
(),
static_cast
<
int
>
(
cpu_lodx
.
size
()),
nullptr
};
auto
xpu_context
=
context
.
template
device_context
<
DeviceContext
>().
x_context
();
auto
sequence_width
=
static_cast
<
int64_t
>
(
in
->
dims
()[
1
]);
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
context_length
*
sequence_width
};
xpu
::
ctx_guard
RAII_GUARD
(
xpu_context
);
int
col_numel
=
col_shape
[
0
]
*
col_shape
[
1
];
T
*
col_data
=
RAII_GUARD
.
alloc_l3_or_gm
<
T
>
(
col_numel
);
PADDLE_ENFORCE_NOT_NULL
(
col_data
,
paddle
::
platform
::
errors
::
Fatal
(
"XPU memory is not enough"
));
if
(
in_g
||
filter_g
)
{
int
r
=
xpu
::
constant
<
T
>
(
xpu_context
,
col_data
,
col_numel
,
T
(
0
));
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"constant"
);
bool
trans_a
=
false
;
bool
trans_b
=
true
;
int
m
=
out_g
->
dims
()[
0
];
int
k
=
out_g
->
dims
()[
1
];
int
n
=
filter
->
dims
()[
0
];
int
k1
=
filter
->
dims
()[
1
];
PADDLE_ENFORCE_EQ
(
k
,
k1
,
platform
::
errors
::
InvalidArgument
(
"The shape of FC in SequenceConvGradOp is invalid."
"The k of matrix A is %d, k1 of matrix B is %d."
"But expect k == k1"
,
k
,
k1
));
int
lda
=
(
!
trans_a
)
?
k
:
m
;
int
ldb
=
(
!
trans_b
)
?
n
:
k
;
int
ldc
=
n
;
T
alpha
=
static_cast
<
T
>
(
1.0
);
T
beta
=
static_cast
<
T
>
(
0.0
);
const
T
*
data_a
=
out_g
->
data
<
T
>
();
const
T
*
data_b
=
filter
->
data
<
T
>
();
T
*
data_c
=
col_data
;
r
=
xpu
::
fc_fusion
<
T
,
T
,
T
,
int32_t
>
(
xpu_context
,
data_a
,
data_b
,
data_c
,
m
,
n
,
k
,
trans_a
,
trans_b
,
nullptr
,
nullptr
,
nullptr
,
lda
,
ldb
,
ldc
,
alpha
,
beta
,
nullptr
,
xpu
::
Activation_t
::
LINEAR
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"fc_fusion"
);
}
if
(
in_g
)
{
PADDLE_ENFORCE_LT
(
sequence_width
,
512
,
platform
::
errors
::
InvalidArgument
(
"Only support sequence_width < 512."
));
in_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
in_g
->
set_lod
(
in
->
lod
());
xpu
::
constant
<
T
>
(
xpu_context
,
in_g
->
data
<
T
>
(),
in_g
->
numel
(),
T
(
0
));
int
r
=
xpu
::
sequence_context_projection_grad
<
T
,
int
>
(
xpu_context
,
in_g
->
data
<
T
>
(),
col_data
,
nullptr
,
lodx
,
sequence_width
,
context_start
,
context_length
,
context_stride
,
{
2
,
2
});
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"sequence_context_projection_grad"
);
}
if
(
filter_g
)
{
filter_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
xpu
::
constant
<
T
>
(
xpu_context
,
filter_g
->
data
<
T
>
(),
filter_g
->
numel
(),
T
(
0
));
int
r
=
xpu
::
sequence_context_projection
<
T
,
int
>
(
xpu_context
,
in
->
data
<
T
>
(),
col_data
,
nullptr
,
lodx
,
sequence_width
,
context_start
,
context_length
,
context_stride
,
{
2
,
2
});
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"sequence_context_projection"
);
bool
trans_a
=
true
;
bool
trans_b
=
false
;
int
k
=
col_shape
[
0
];
int
m
=
col_shape
[
1
];
int
k1
=
out_g
->
dims
()[
0
];
int
n
=
out_g
->
dims
()[
1
];
PADDLE_ENFORCE_EQ
(
k
,
k1
,
platform
::
errors
::
InvalidArgument
(
"The shape of FC in SequenceConvGradOp is invalid."
"The k of matrix A is %d, k1 of matrix B is %d."
"But expect k == k1"
,
k
,
k1
));
int
lda
=
(
!
trans_a
)
?
k
:
m
;
int
ldb
=
(
!
trans_b
)
?
n
:
k
;
int
ldc
=
n
;
T
alpha
=
static_cast
<
T
>
(
1.0
);
T
beta
=
static_cast
<
T
>
(
0.0
);
const
T
*
data_a
=
col_data
;
const
T
*
data_b
=
out_g
->
data
<
T
>
();
T
*
data_c
=
filter_g
->
data
<
T
>
();
r
=
xpu
::
fc_fusion
<
T
,
T
,
T
,
int32_t
>
(
xpu_context
,
data_a
,
data_b
,
data_c
,
m
,
n
,
k
,
trans_a
,
trans_b
,
nullptr
,
nullptr
,
nullptr
,
lda
,
ldb
,
ldc
,
alpha
,
beta
,
nullptr
,
xpu
::
Activation_t
::
LINEAR
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"fc_fusion"
);
if
(
xpu_context
->
xpu_stream
!=
nullptr
)
{
xpu_wait
(
xpu_context
->
xpu_stream
);
}
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
sequence_conv
,
ops
::
SequenceConvXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
REGISTER_OP_XPU_KERNEL
(
sequence_conv_grad
,
ops
::
SequenceConvGradXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
#endif
paddle/fluid/platform/device/xpu/xpu2_op_list.h
浏览文件 @
fee4316d
...
...
@@ -383,6 +383,10 @@ XPUOpMap& get_kl2_ops() {
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
// AddMore
{
"sequence_conv"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"sequence_conv_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
};
return
s_xpu2_kernels
;
...
...
python/paddle/fluid/tests/unittests/xpu/test_sequence_conv_op_xpu.py
0 → 100755
浏览文件 @
fee4316d
# 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
import
paddle
import
random
import
sys
sys
.
path
.
append
(
"../"
)
from
op_test_xpu
import
XPUOpTest
paddle
.
enable_static
()
np
.
set_printoptions
(
threshold
=
np
.
inf
)
def
seqconv
(
x
,
lod
,
filter
,
context_length
,
context_start
,
padding_trainable
=
False
,
padding_data
=
None
):
[
T
,
M
]
=
x
.
shape
col
=
np
.
zeros
((
T
,
context_length
*
M
)).
astype
(
'float32'
)
offset
=
[
0
]
for
seq_len
in
lod
[
0
]:
offset
.
append
(
offset
[
-
1
]
+
seq_len
)
begin_pad
=
np
.
max
([
0
,
-
context_start
])
for
i
in
range
(
len
(
offset
)
-
1
):
for
j
in
range
(
context_length
):
in_begin
=
offset
[
i
]
+
context_start
+
j
in_end
=
offset
[
i
+
1
]
+
context_start
+
j
out_begin
=
offset
[
i
]
out_end
=
offset
[
i
+
1
]
if
in_begin
<
offset
[
i
]:
pad_size
=
np
.
min
(
[
offset
[
i
]
-
in_begin
,
offset
[
i
+
1
]
-
offset
[
i
]])
if
padding_trainable
:
sub_w
=
padding_data
[
j
:
j
+
pad_size
,
:]
col
[
offset
[
i
]:
offset
[
i
]
+
pad_size
,
j
*
M
:(
j
+
1
)
*
M
]
=
sub_w
out_begin
=
offset
[
i
]
+
pad_size
in_begin
=
offset
[
i
]
if
in_end
>
offset
[
i
+
1
]:
pad_size
=
np
.
min
(
[
in_end
-
offset
[
i
+
1
],
offset
[
i
+
1
]
-
offset
[
i
]])
if
padding_trainable
:
sub_w
=
padding_data
[
begin_pad
+
context_start
+
j
-
pad_size
:
begin_pad
+
context_start
+
j
,
:]
col
[
offset
[
i
+
1
]
-
pad_size
:
offset
[
i
+
1
],
j
*
M
:(
j
+
1
)
*
M
]
=
sub_w
in_end
=
offset
[
i
+
1
]
out_end
=
offset
[
i
+
1
]
-
pad_size
if
in_end
<=
in_begin
:
continue
in_sub
=
x
[
in_begin
:
in_end
,
:]
col
[
out_begin
:
out_end
,
j
*
M
:(
j
+
1
)
*
M
]
+=
in_sub
return
np
.
dot
(
col
,
filter
)
class
TestSeqProject
(
XPUOpTest
):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
op_type
=
'sequence_conv'
self
.
use_xpu
=
True
if
self
.
context_length
==
1
\
and
self
.
context_start
==
0
\
and
self
.
padding_trainable
:
print
(
"If context_start is 0 "
\
"and context_length is 1,"
\
" padding_trainable should be false."
)
return
# one level, batch size
x
=
np
.
random
.
uniform
(
-
6.10907e-05
,
0.000104218
,
[
self
.
input_size
[
0
],
self
.
input_size
[
1
]]).
astype
(
'float32'
)
w
=
np
.
random
.
uniform
(
-
3.17068e-05
,
0.000159822
,
[
self
.
context_length
*
self
.
input_size
[
1
],
self
.
output_represention
]).
astype
(
'float32'
)
begin_pad
=
np
.
max
([
0
,
-
self
.
context_start
])
end_pad
=
np
.
max
([
0
,
self
.
context_start
+
self
.
context_length
-
1
])
total_pad
=
begin_pad
+
end_pad
padding_data
=
np
.
random
.
uniform
(
0
,
0
,
[
total_pad
,
self
.
input_size
[
1
]]).
astype
(
'float32'
)
self
.
pad_data
=
padding_data
self
.
inputs
=
{
'X'
:
(
x
,
self
.
lod
),
'Filter'
:
w
,
}
self
.
inputs_val
=
[
'X'
,
'Filter'
]
self
.
inputs_val_no_x
=
[
'Filter'
]
self
.
inputs_val_no_f
=
[
'X'
]
if
total_pad
!=
0
:
self
.
inputs
[
'PaddingData'
]
=
padding_data
self
.
inputs_val
=
[
'X'
,
'PaddingData'
,
'Filter'
]
self
.
inputs_val_no_x
=
[
'PaddingData'
,
'Filter'
]
self
.
inputs_val_no_f
=
[
'PaddingData'
,
'X'
]
self
.
attrs
=
{
'contextStart'
:
self
.
context_start
,
'contextLength'
:
self
.
context_length
,
'paddingTrainable'
:
self
.
padding_trainable
,
'contextStride'
:
self
.
context_stride
}
out
=
seqconv
(
x
,
self
.
lod
,
w
,
self
.
context_length
,
self
.
context_start
,
self
.
padding_trainable
,
self
.
pad_data
)
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
def
test_check_grad_input
(
self
):
self
.
check_grad
([
'X'
],
'Out'
,
no_grad_set
=
set
(
self
.
inputs_val_no_x
))
def
test_check_grad_padding_data
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
[
'PaddingData'
],
'Out'
,
no_grad_set
=
set
([
'X'
,
'Filter'
]))
def
test_check_grad_Filter
(
self
):
self
.
check_grad
(
[
'Filter'
],
'Out'
,
no_grad_set
=
set
(
self
.
inputs_val_no_f
))
def
test_check_grad_input_filter
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
[
'X'
,
'Filter'
],
'Out'
,
no_grad_set
=
set
([
'PaddingData'
]))
def
test_check_grad_padding_input
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
self
.
inputs_val_no_f
,
'Out'
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad_padding_filter
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
self
.
inputs_val_no_x
,
'Out'
,
no_grad_set
=
set
([
'X'
]))
def
init_test_case
(
self
):
self
.
input_row
=
7
self
.
input_col
=
25
self
.
context_start
=
-
2
self
.
context_length
=
5
self
.
padding_trainable
=
False
self
.
context_stride
=
1
self
.
input_size
=
[
self
.
input_row
,
self
.
input_col
]
offset_lod
=
[[
0
,
1
,
self
.
input_row
]]
self
.
lod
=
[[]]
# convert from offset-based lod to length-based lod
for
i
in
range
(
len
(
offset_lod
[
0
])
-
1
):
self
.
lod
[
0
].
append
(
offset_lod
[
0
][
i
+
1
]
-
offset_lod
[
0
][
i
])
self
.
output_represention
=
8
# output feature size
class
TestSeqProjectCase1
(
TestSeqProject
):
def
init_test_case
(
self
):
self
.
input_row
=
11
self
.
context_start
=
-
2
self
.
context_length
=
5
self
.
padding_trainable
=
False
self
.
context_stride
=
1
self
.
input_size
=
[
self
.
input_row
,
50
]
offset_lod
=
[[
0
,
4
,
5
,
8
,
self
.
input_row
]]
self
.
lod
=
[[]]
# convert from offset-based lod to length-based lod
for
i
in
range
(
len
(
offset_lod
[
0
])
-
1
):
self
.
lod
[
0
].
append
(
offset_lod
[
0
][
i
+
1
]
-
offset_lod
[
0
][
i
])
self
.
output_represention
=
8
# output feature size
class
TestSeqProjectCase2Len0
(
TestSeqProject
):
def
init_test_case
(
self
):
self
.
input_row
=
11
self
.
context_start
=
-
2
self
.
context_length
=
5
self
.
padding_trainable
=
False
self
.
context_stride
=
1
self
.
input_size
=
[
self
.
input_row
,
50
]
offset_lod
=
[[
0
,
0
,
4
,
5
,
5
,
8
,
self
.
input_row
,
self
.
input_row
]]
self
.
lod
=
[[]]
# convert from offset-based lod to length-based lod
for
i
in
range
(
len
(
offset_lod
[
0
])
-
1
):
self
.
lod
[
0
].
append
(
offset_lod
[
0
][
i
+
1
]
-
offset_lod
[
0
][
i
])
self
.
output_represention
=
8
# output feature size
class
TestSeqProjectCase3
(
TestSeqProject
):
def
init_test_case
(
self
):
self
.
input_row
=
25
self
.
context_start
=
-
2
self
.
context_length
=
5
self
.
padding_trainable
=
False
self
.
context_stride
=
1
self
.
input_size
=
[
self
.
input_row
,
25
]
idx
=
list
(
range
(
self
.
input_size
[
0
]))
del
idx
[
0
]
offset_lod
=
[[
0
]
+
np
.
sort
(
random
.
sample
(
idx
,
8
)).
tolist
()
+
[
self
.
input_size
[
0
]]]
self
.
lod
=
[[]]
# convert from offset-based lod to length-based lod
for
i
in
range
(
len
(
offset_lod
[
0
])
-
1
):
self
.
lod
[
0
].
append
(
offset_lod
[
0
][
i
+
1
]
-
offset_lod
[
0
][
i
])
self
.
output_represention
=
8
# output feature size
class
TestSeqProjectCase4
(
TestSeqProject
):
def
init_test_case
(
self
):
self
.
input_row
=
7835
self
.
input_col
=
128
self
.
context_start
=
-
2
self
.
context_length
=
5
self
.
padding_trainable
=
False
self
.
context_stride
=
1
self
.
input_size
=
[
self
.
input_row
,
self
.
input_col
]
offset_lod
=
[[
0
,
1
,
2
,
3
,
131
,
241
,
242
,
263
,
264
,
265
,
266
,
267
,
268
,
387
,
515
,
516
,
644
,
645
,
772
,
794
,
922
,
923
,
924
,
944
,
945
,
1073
,
1074
,
1202
,
1330
,
1458
,
1556
,
1557
,
1558
,
1686
,
1748
,
1876
,
1912
,
1913
,
1914
,
2032
,
2066
,
2194
,
2308
,
2309
,
2347
,
2475
,
2476
,
2477
,
2478
,
2606
,
2607
,
2735
,
2736
,
2737
,
2738
,
2838
,
2966
,
2967
,
2968
,
2969
,
3097
,
3225
,
3353
,
3481
,
3482
,
3520
,
3642
,
3643
,
3754
,
3882
,
3883
,
4010
,
4011
,
4012
,
4140
,
4219
,
4228
,
4356
,
4357
,
4415
,
4475
,
4476
,
4604
,
4605
,
4606
,
4694
,
4695
,
4808
,
4936
,
4961
,
4962
,
5004
,
5132
,
5260
,
5312
,
5440
,
5441
,
5569
,
5570
,
5675
,
5676
,
5750
,
5810
,
5811
,
5939
,
6021
,
6149
,
6277
,
6278
,
6364
,
6425
,
6519
,
6647
,
6648
,
6739
,
6867
,
6995
,
6996
,
7120
,
7223
,
7244
,
7367
,
7407
,
7408
,
7467
,
7595
,
7699
,
7827
,
7835
]]
self
.
lod
=
[[]]
# convert from offset-based lod to length-based lod
for
i
in
range
(
len
(
offset_lod
[
0
])
-
1
):
self
.
lod
[
0
].
append
(
offset_lod
[
0
][
i
+
1
]
-
offset_lod
[
0
][
i
])
self
.
output_represention
=
8
# output feature size
class
TestSeqConvApi
(
unittest
.
TestCase
):
def
test_api
(
self
):
import
paddle.fluid
as
fluid
x
=
fluid
.
layers
.
data
(
'x'
,
shape
=
[
32
],
lod_level
=
1
)
y
=
fluid
.
layers
.
sequence_conv
(
input
=
x
,
num_filters
=
2
,
filter_size
=
3
,
padding_start
=
None
)
place
=
fluid
.
CPUPlace
()
x_tensor
=
fluid
.
create_lod_tensor
(
np
.
random
.
rand
(
10
,
32
).
astype
(
"float32"
),
[[
2
,
3
,
1
,
4
]],
place
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
ret
=
exe
.
run
(
feed
=
{
'x'
:
x_tensor
},
fetch_list
=
[
y
],
return_numpy
=
False
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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