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760d015c
编写于
12月 11, 2020
作者:
T
taixiurong
提交者:
GitHub
12月 11, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add xpu ops for training transformer in kunlun (#29539)
* 1.fix matmul bug 2. add one hot * add xpu error msg
上级
0fdd3656
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
811 addition
and
64 deletion
+811
-64
paddle/fluid/operators/controlflow/logical_op_xpu.h
paddle/fluid/operators/controlflow/logical_op_xpu.h
+170
-0
paddle/fluid/operators/controlflow/logicaland_op_xpu.cc
paddle/fluid/operators/controlflow/logicaland_op_xpu.cc
+21
-0
paddle/fluid/operators/controlflow/logicalnot_op_xpu.cc
paddle/fluid/operators/controlflow/logicalnot_op_xpu.cc
+19
-0
paddle/fluid/operators/controlflow/logicalor_op_xpu.cc
paddle/fluid/operators/controlflow/logicalor_op_xpu.cc
+22
-0
paddle/fluid/operators/matmul_op_xpu.cc
paddle/fluid/operators/matmul_op_xpu.cc
+68
-47
paddle/fluid/operators/one_hot_op_xpu.cc
paddle/fluid/operators/one_hot_op_xpu.cc
+71
-0
paddle/fluid/platform/xpu_header.h
paddle/fluid/platform/xpu_header.h
+1
-0
python/paddle/fluid/tests/unittests/xpu/test_logical_op_xpu.py
...n/paddle/fluid/tests/unittests/xpu/test_logical_op_xpu.py
+235
-0
python/paddle/fluid/tests/unittests/xpu/test_matmul_op_xpu.py
...on/paddle/fluid/tests/unittests/xpu/test_matmul_op_xpu.py
+20
-17
python/paddle/fluid/tests/unittests/xpu/test_one_hot_op_xpu.py
...n/paddle/fluid/tests/unittests/xpu/test_one_hot_op_xpu.py
+184
-0
未找到文件。
paddle/fluid/operators/controlflow/logical_op_xpu.h
0 → 100644
浏览文件 @
760d015c
/* 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
#ifdef PADDLE_WITH_XPU
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "xpu/refactor/math.h"
namespace
paddle
{
namespace
operators
{
typedef
enum
{
XPU_OR
,
XPU_AND
}
XpuLogicalType
;
std
::
string
XpuLogicalType2Str
(
XpuLogicalType
ty
)
{
switch
(
ty
)
{
case
XpuLogicalType
::
XPU_OR
:
return
std
::
string
(
"logical or"
);
case
XpuLogicalType
::
XPU_AND
:
return
std
::
string
(
"logical and"
);
default:
return
std
::
string
(
"unknown type"
);
}
return
std
::
string
(
"unknown"
);
}
template
<
XpuLogicalType
xpu_type
,
typename
T
>
class
BinaryLogicalOpXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
framework
::
Tensor
>
(
"Y"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
T
*
out_ptr
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
x_ptr
=
x
->
data
<
T
>
();
const
T
*
y_ptr
=
y
->
data
<
T
>
();
auto
&
dev_ctx
=
context
.
template
device_context
<
paddle
::
platform
::
XPUDeviceContext
>();
framework
::
Tensor
broadcast_x
;
framework
::
Tensor
broadcast_y
;
bool
need_broad_cast
=
false
;
if
(
x
->
numel
()
!=
out
->
numel
())
{
// x need broadcast
T
*
broadcast_x_ptr
=
broadcast_x
.
mutable_data
<
T
>
(
context
.
GetPlace
(),
out
->
numel
());
auto
&
out_dim
=
out
->
dims
();
auto
&
x_dim
=
x
->
dims
();
int
dims
=
out_dim
.
size
();
std
::
vector
<
int
>
bcast_xdims
;
std
::
vector
<
int
>
bcast_ydims
;
for
(
int
i
=
0
;
i
<
dims
;
++
i
)
{
if
(
out_dim
[
i
]
==
x_dim
[
i
])
{
bcast_xdims
.
push_back
(
x_dim
[
i
]);
bcast_ydims
.
push_back
(
x_dim
[
i
]);
continue
;
}
bcast_xdims
.
push_back
(
1
);
bcast_xdims
.
push_back
(
x_dim
[
i
]);
bcast_ydims
.
push_back
(
out_dim
[
i
]
/
x_dim
[
i
]);
bcast_ydims
.
push_back
(
x_dim
[
i
]);
}
int
ret
=
xpu
::
broadcast
<
int8_t
>
(
dev_ctx
.
x_context
(),
reinterpret_cast
<
const
int8_t
*>
x_ptr
,
reinterpret_cast
<
int8_t
*>
broadcast_x_ptr
,
bcast_xdims
,
bcast_ydims
);
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU broadcast kernel return wrong value[%d %s]"
,
ret
,
XPUAPIErrorMsg
[
ret
]));
x_ptr
=
(
const
T
*
)
broadcast_x_ptr
;
need_broad_cast
=
true
;
}
if
(
y
->
numel
()
!=
out
->
numel
())
{
// y need broadcast
T
*
broadcast_y_ptr
=
broadcast_y
.
mutable_data
<
T
>
(
context
.
GetPlace
(),
out
->
numel
());
auto
&
out_dim
=
out
->
dims
();
auto
&
y_dim
=
y
->
dims
();
int
dims
=
out_dim
.
size
();
std
::
vector
<
int
>
bcast_xdims
;
std
::
vector
<
int
>
bcast_ydims
;
for
(
int
i
=
0
;
i
<
dims
;
++
i
)
{
if
(
out_dim
[
i
]
==
y_dim
[
i
])
{
bcast_xdims
.
push_back
(
y_dim
[
i
]);
bcast_ydims
.
push_back
(
y_dim
[
i
]);
continue
;
}
bcast_xdims
.
push_back
(
1
);
bcast_xdims
.
push_back
(
y_dim
[
i
]);
bcast_ydims
.
push_back
(
out_dim
[
i
]
/
y_dim
[
i
]);
bcast_ydims
.
push_back
(
y_dim
[
i
]);
}
int
ret
=
xpu
::
broadcast
<
int8_t
>
(
dev_ctx
.
x_context
(),
reinterpret_cast
<
const
int8_t
*>
y_ptr
,
reinterpret_cast
<
int8_t
*>
broadcast_y_ptr
,
bcast_xdims
,
bcast_ydims
);
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU broadcast kernel return wrong value[%d %s]"
,
ret
,
XPUAPIErrorMsg
[
ret
]));
y_ptr
=
(
const
T
*
)
broadcast_y_ptr
;
need_broad_cast
=
true
;
}
// logical kernel
int
ret
=
XPU_SUCCESS
;
switch
(
xpu_type
)
{
case
XpuLogicalType
::
XPU_OR
:
ret
=
xpu
::
logical_or
<
bool
>
(
dev_ctx
.
x_context
(),
x_ptr
,
y_ptr
,
out_ptr
,
out
->
numel
());
break
;
case
XpuLogicalType
::
XPU_AND
:
ret
=
xpu
::
logical_and
<
bool
>
(
dev_ctx
.
x_context
(),
x_ptr
,
y_ptr
,
out_ptr
,
out
->
numel
());
default:
LOG
(
ERROR
)
<<
"xpu not support logical xpu type = "
<<
XpuLogicalType2Str
(
xpu_type
);
break
;
}
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d %s] in "
"op_name[%s]."
,
ret
,
XPUAPIErrorMsg
[
ret
],
XpuLogicalType2Str
(
xpu_type
)));
if
(
need_broad_cast
&&
dev_ctx
.
x_context
()
->
xpu_stream
!=
nullptr
)
{
xpu_wait
();
}
}
};
template
<
typename
T
>
class
UnaryLogicalOpXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
if
(
x
->
numel
()
==
0
)
{
return
;
}
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
&
dev_ctx
=
context
.
template
device_context
<
paddle
::
platform
::
XPUDeviceContext
>();
int
ret
=
xpu
::
logical_not
<
bool
>
(
dev_ctx
.
x_context
(),
x
->
data
<
T
>
(),
out
->
data
<
T
>
(),
x
->
numel
());
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d %s]."
,
ret
,
XPUAPIErrorMsg
[
ret
]));
}
};
}
// namespace operators
}
// namespace paddle
#endif
paddle/fluid/operators/controlflow/logicaland_op_xpu.cc
0 → 100644
浏览文件 @
760d015c
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/controlflow/logical_op_xpu.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
logical_and
,
ops
::
BinaryLogicalOpXPUKernel
<
ops
::
XpuLogicalType
::
XPU_AND
,
bool
>
);
#endif
paddle/fluid/operators/controlflow/logicalnot_op_xpu.cc
0 → 100755
浏览文件 @
760d015c
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/controlflow/logical_op_xpu.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
logicalnot
,
ops
::
UnaryLogicalOpXPUKernel
<
bool
>
);
#endif
paddle/fluid/operators/controlflow/logicalor_op_xpu.cc
0 → 100644
浏览文件 @
760d015c
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/controlflow/logical_op_xpu.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
logical_or
,
ops
::
BinaryLogicalOpXPUKernel
<
ops
::
XpuLogicalType
::
XPU_OR
,
bool
>
);
#endif
paddle/fluid/operators/matmul_op_xpu.cc
浏览文件 @
760d015c
...
...
@@ -17,6 +17,7 @@ limitations under the License. */
#include <algorithm>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
...
...
@@ -120,30 +121,40 @@ class MatMulXPUKernel : public framework::OpKernel<T> {
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
float
*
data_c
=
out
->
data
<
T
>
();
if
(
mat_dim_a
.
batch_size_
==
0
||
mat_dim_a
.
batch_size_
==
1
)
{
int
r
=
xpu
::
fc_int16
(
dev_ctx
.
x_context
(),
mat_dim_a
.
trans_
,
mat_dim_b
.
trans_
,
mat_dim_a
.
height_
,
mat_dim_b
.
width_
,
mat_dim_a
.
width_
,
alpha
,
x
->
data
<
T
>
(),
y
->
data
<
T
>
(),
0.0
f
,
data_c
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed."
,
r
));
int
m
=
mat_dim_a
.
height_
;
int
n
=
mat_dim_b
.
width_
;
int
k
=
mat_dim_a
.
width_
;
int
ldx
=
mat_dim_a
.
trans_
?
m
:
k
;
int
ldy
=
mat_dim_b
.
trans_
?
k
:
n
;
int
ldout
=
n
;
int
batch_size
=
mat_dim_a
.
batch_size_
;
if
(
batch_size
==
0
||
batch_size
==
1
)
{
int
r
=
xpu
::
fc_fusion
<
float
,
float
,
float
,
int16_t
>
(
dev_ctx
.
x_context
(),
x
->
data
<
T
>
(),
y
->
data
<
T
>
(),
data_c
,
m
,
n
,
k
,
mat_dim_a
.
trans_
,
mat_dim_b
.
trans_
,
nullptr
,
nullptr
,
nullptr
,
ldx
,
ldy
,
ldout
,
alpha
,
0
,
nullptr
,
xpu
::
Activation_t
::
LINEAR
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU fc_fusion kernel return wrong value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
}
else
{
// batch matmul
int
r
=
xpu
::
batched_gemm_int16
(
dev_ctx
.
x_context
(),
mat_dim_a
.
trans_
,
mat_dim_b
.
trans_
,
mat_dim_a
.
batch_size_
,
mat_dim_a
.
height_
,
mat_dim_b
.
width_
,
mat_dim_a
.
width_
,
alpha
,
x
->
data
<
T
>
(),
y
->
data
<
T
>
(),
data_c
,
nullptr
,
nullptr
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed."
,
r
));
int
x_stride
=
mat_dim_a
.
stride_
;
int
y_stride
=
mat_dim_b
.
stride_
;
int
out_stride
=
m
*
n
;
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
const
float
*
x_data
=
x
->
data
<
T
>
()
+
x_stride
*
i
;
const
float
*
y_data
=
y
->
data
<
T
>
()
+
y_stride
*
i
;
float
*
out_data
=
data_c
+
out_stride
*
i
;
int
r
=
xpu
::
fc_fusion
<
float
,
float
,
float
,
int16_t
>
(
dev_ctx
.
x_context
(),
x_data
,
y_data
,
out_data
,
m
,
n
,
k
,
mat_dim_a
.
trans_
,
mat_dim_b
.
trans_
,
nullptr
,
nullptr
,
nullptr
,
ldx
,
ldy
,
ldout
,
alpha
,
0
,
nullptr
,
xpu
::
Activation_t
::
LINEAR
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU fc_fusion kernel return wrong value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
}
}
}
};
...
...
@@ -171,9 +182,8 @@ static framework::Tensor XPUFoldHeadAndLastDims(
in_shape_host
.
data
(),
axis_host
.
data
(),
/*ndims=*/
3
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed."
,
r
));
"XPU transpose kernel return wrong value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
output
.
Resize
({
in_dims
[
1
],
in_dims
[
0
]
*
in_dims
[
2
]});
return
output
;
...
...
@@ -224,30 +234,41 @@ class MatMulGradXPUKernel : public framework::OpKernel<T> {
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
float
*
data_c
=
out
->
data
<
T
>
();
if
(
mat_dim_a
.
batch_size_
==
0
||
mat_dim_a
.
batch_size_
==
1
)
{
int
r
=
xpu
::
fc_int16
(
dev_ctx
.
x_context
(),
mat_dim_a
.
trans_
,
mat_dim_b
.
trans_
,
mat_dim_a
.
height_
,
mat_dim_b
.
width_
,
mat_dim_a
.
width_
,
alpha
,
a
.
data
<
T
>
(),
b
.
data
<
T
>
(),
0.0
f
,
data_c
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed."
,
r
));
int
m
=
mat_dim_a
.
height_
;
int
n
=
mat_dim_b
.
width_
;
int
k
=
mat_dim_a
.
width_
;
int
ldx
=
mat_dim_a
.
trans_
?
m
:
k
;
int
ldy
=
mat_dim_b
.
trans_
?
k
:
n
;
int
ldout
=
n
;
int
batch_size
=
mat_dim_a
.
batch_size_
;
if
(
batch_size
==
0
||
batch_size
==
1
)
{
int
r
=
xpu
::
fc_fusion
<
float
,
float
,
float
,
int16_t
>
(
dev_ctx
.
x_context
(),
a
.
data
<
T
>
(),
b
.
data
<
T
>
(),
data_c
,
m
,
n
,
k
,
mat_dim_a
.
trans_
,
mat_dim_b
.
trans_
,
nullptr
,
nullptr
,
nullptr
,
ldx
,
ldy
,
ldout
,
alpha
,
0
,
nullptr
,
xpu
::
Activation_t
::
LINEAR
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU fc_fusion kernel return wrong value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
}
else
{
// batch matmul
int
r
=
xpu
::
batched_gemm_int16
(
dev_ctx
.
x_context
(),
mat_dim_a
.
trans_
,
mat_dim_b
.
trans_
,
mat_dim_a
.
batch_size_
,
mat_dim_a
.
height_
,
mat_dim_b
.
width_
,
mat_dim_a
.
width_
,
alpha
,
a
.
data
<
T
>
(),
b
.
data
<
T
>
(),
data_c
,
nullptr
,
nullptr
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed."
,
r
));
int
x_stride
=
mat_dim_a
.
stride_
;
int
y_stride
=
mat_dim_b
.
stride_
;
int
out_stride
=
m
*
n
;
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
const
float
*
x_data
=
a
.
data
<
T
>
()
+
x_stride
*
i
;
const
float
*
y_data
=
b
.
data
<
T
>
()
+
y_stride
*
i
;
float
*
out_data
=
data_c
+
out_stride
*
i
;
int
r
=
xpu
::
fc_fusion
<
float
,
float
,
float
,
int16_t
>
(
dev_ctx
.
x_context
(),
x_data
,
y_data
,
out_data
,
m
,
n
,
k
,
mat_dim_a
.
trans_
,
mat_dim_b
.
trans_
,
nullptr
,
nullptr
,
nullptr
,
ldx
,
ldy
,
ldout
,
alpha
,
0
,
nullptr
,
xpu
::
Activation_t
::
LINEAR
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU fc_fusion kernel return wrong value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
}
}
}
...
...
paddle/fluid/operators/one_hot_op_xpu.cc
0 → 100644
浏览文件 @
760d015c
// 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.
#ifdef PADDLE_WITH_XPU
#include <string>
#include <vector>
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/operators/one_hot_op.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
using
Tensor
=
framework
::
Tensor
;
template
<
typename
DeviceContext
,
typename
T
>
class
OneHotXPUKernel
:
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"
);
int
depth
=
context
.
Attr
<
int
>
(
"depth"
);
if
(
context
.
HasInput
(
"depth_tensor"
))
{
auto
*
depth_tensor
=
context
.
Input
<
Tensor
>
(
"depth_tensor"
);
auto
*
depth_data
=
depth_tensor
->
data
<
int32_t
>
();
if
(
depth_tensor
->
place
()
==
platform
::
XPUPlace
())
{
xpu_memcpy
(
static_cast
<
void
*>
(
&
depth
),
static_cast
<
const
void
*>
(
depth_data
),
sizeof
(
int32_t
),
XPU_DEVICE_TO_HOST
);
}
else
{
depth
=
depth_data
[
0
];
}
auto
in_dims
=
in
->
dims
();
framework
::
DDim
out_dims
(
in_dims
);
out_dims
[
out_dims
.
size
()
-
1
]
=
depth
;
out
->
Resize
(
out_dims
);
}
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
int
len
=
in
->
numel
();
int
ret
=
xpu
::
one_hot
<
T
>
(
dev_ctx
.
x_context
(),
in
->
data
<
T
>
(),
out
->
mutable_data
<
float
>
(
context
.
GetPlace
()),
len
,
depth
);
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU one_hot kernel return wrong value[%d %s]"
,
ret
,
XPUAPIErrorMsg
[
ret
]));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
one_hot
,
ops
::
OneHotXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
int
>
,
ops
::
OneHotXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
int64_t
>
);
#endif
paddle/fluid/platform/xpu_header.h
浏览文件 @
760d015c
...
...
@@ -21,6 +21,7 @@
#include "paddle/fluid/platform/errors.h"
#include "xpu/api.h"
#include "xpu/refactor/fusion.h"
#include "xpu/refactor/math.h"
#include "xpu/refactor/nn.h"
#include "xpu/runtime.h"
...
...
python/paddle/fluid/tests/unittests/xpu/test_logical_op_xpu.py
0 → 100755
浏览文件 @
760d015c
# 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
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
from
paddle.fluid.op
import
Operator
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
import
paddle
from
op_test_xpu
import
XPUOpTest
from
paddle.static
import
Program
,
program_guard
TEST_META_OP_DATA
=
[{
'op_str'
:
'logical_and'
,
'binary_op'
:
True
},
{
'op_str'
:
'logical_or'
,
'binary_op'
:
True
},
{
'op_str'
:
'logical_not'
,
'binary_op'
:
False
}]
TEST_META_SHAPE_DATA
=
{
'XDimLargerThanYDim1'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
4
,
5
]
},
'XDimLargerThanYDim2'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
4
,
1
]
},
'XDimLargerThanYDim3'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
1
,
4
,
1
]
},
'XDimLargerThanYDim4'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
3
,
4
,
1
]
},
'XDimLargerThanYDim5'
:
{
'x_shape'
:
[
2
,
3
,
1
,
5
],
'y_shape'
:
[
3
,
1
,
1
]
},
'XDimLessThanYDim1'
:
{
'x_shape'
:
[
4
,
1
],
'y_shape'
:
[
2
,
3
,
4
,
5
]
},
'XDimLessThanYDim2'
:
{
'x_shape'
:
[
1
,
4
,
1
],
'y_shape'
:
[
2
,
3
,
4
,
5
]
},
'XDimLessThanYDim3'
:
{
'x_shape'
:
[
3
,
4
,
1
],
'y_shape'
:
[
2
,
3
,
4
,
5
]
},
'XDimLessThanYDim4'
:
{
'x_shape'
:
[
3
,
1
,
1
],
'y_shape'
:
[
2
,
3
,
1
,
5
]
},
'XDimLessThanYDim5'
:
{
'x_shape'
:
[
4
,
5
],
'y_shape'
:
[
2
,
3
,
4
,
5
]
},
'Axis1InLargerDim'
:
{
'x_shape'
:
[
1
,
4
,
5
],
'y_shape'
:
[
2
,
3
,
1
,
5
]
},
'EqualDim1'
:
{
'x_shape'
:
[
10
,
7
],
'y_shape'
:
[
10
,
7
]
},
'EqualDim2'
:
{
'x_shape'
:
[
1
,
1
,
4
,
5
],
'y_shape'
:
[
2
,
3
,
1
,
5
]
}
}
TEST_META_WRONG_SHAPE_DATA
=
{
'ErrorDim1'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
3
,
4
]
},
'ErrorDim2'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
4
,
3
]
}
}
def
run_static_xpu
(
x_np
,
y_np
,
op_str
,
binary_op
=
True
):
paddle
.
enable_static
()
startup_program
=
fluid
.
Program
()
main_program
=
fluid
.
Program
()
place
=
paddle
.
XPUPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
with
fluid
.
program_guard
(
main_program
,
startup_program
):
x
=
paddle
.
static
.
data
(
name
=
'x'
,
shape
=
x_np
.
shape
,
dtype
=
'bool'
)
op
=
getattr
(
paddle
,
op_str
)
feed_list
=
{
'x'
:
x_np
}
if
not
binary_op
:
res
=
op
(
x
)
else
:
y
=
paddle
.
static
.
data
(
name
=
'y'
,
shape
=
y_np
.
shape
,
dtype
=
'bool'
)
feed_list
[
'y'
]
=
y_np
res
=
op
(
x
,
y
)
exe
.
run
(
startup_program
)
static_result
=
exe
.
run
(
main_program
,
feed
=
feed_list
,
fetch_list
=
[
res
])
return
static_result
def
run_dygraph_xpu
(
x_np
,
y_np
,
op_str
,
binary_op
=
True
):
place
=
paddle
.
XPUPlace
(
0
)
paddle
.
disable_static
(
place
)
op
=
getattr
(
paddle
,
op_str
)
x
=
paddle
.
to_tensor
(
x_np
)
if
not
binary_op
:
dygraph_result
=
op
(
x
)
else
:
y
=
paddle
.
to_tensor
(
y_np
)
dygraph_result
=
op
(
x
,
y
)
return
dygraph_result
def
np_data_generator
(
np_shape
,
*
args
,
**
kwargs
):
return
np
.
random
.
choice
(
a
=
[
True
,
False
],
size
=
np_shape
).
astype
(
bool
)
def
test_xpu
(
unit_test
,
test_error
=
False
):
for
op_data
in
TEST_META_OP_DATA
:
meta_data
=
dict
(
op_data
)
np_op
=
getattr
(
np
,
meta_data
[
'op_str'
])
META_DATA
=
dict
(
TEST_META_SHAPE_DATA
)
if
test_error
:
META_DATA
=
dict
(
TEST_META_WRONG_SHAPE_DATA
)
for
shape_data
in
META_DATA
.
values
():
meta_data
[
'x_np'
]
=
np_data_generator
(
shape_data
[
'x_shape'
])
meta_data
[
'y_np'
]
=
np_data_generator
(
shape_data
[
'y_shape'
])
if
meta_data
[
'binary_op'
]
and
test_error
:
# catch C++ Exception
unit_test
.
assertRaises
(
BaseException
,
run_static_xpu
,
**
meta_data
)
continue
static_result
=
run_static_xpu
(
**
meta_data
)
dygraph_result
=
run_dygraph_xpu
(
**
meta_data
)
if
meta_data
[
'binary_op'
]:
np_result
=
np_op
(
meta_data
[
'x_np'
],
meta_data
[
'y_np'
])
else
:
np_result
=
np_op
(
meta_data
[
'x_np'
])
unit_test
.
assertTrue
((
static_result
==
np_result
).
all
())
unit_test
.
assertTrue
((
dygraph_result
.
numpy
()
==
np_result
).
all
())
def
test_type_error
(
unit_test
,
type_str_map
):
def
check_type
(
op_str
,
x
,
y
,
binary_op
):
op
=
getattr
(
paddle
,
op_str
)
error_type
=
TypeError
if
isinstance
(
x
,
np
.
ndarray
):
x
=
paddle
.
to_tensor
(
x
)
y
=
paddle
.
to_tensor
(
y
)
error_type
=
BaseException
if
binary_op
:
if
type_str_map
[
'x'
]
!=
'bool'
or
type_str_map
[
'y'
]
!=
'bool'
:
unit_test
.
assertRaises
(
error_type
,
op
,
x
=
x
,
y
=
y
)
if
not
fluid
.
in_dygraph_mode
():
unit_test
.
assertRaises
(
error_type
,
op
,
x
=
x
,
y
=
y
,
out
=
1
)
else
:
if
type_str_map
[
'x'
]
!=
'bool'
:
unit_test
.
assertRaises
(
error_type
,
op
,
x
=
x
)
if
not
fluid
.
in_dygraph_mode
():
unit_test
.
assertRaises
(
error_type
,
op
,
x
=
x
,
out
=
1
)
place
=
paddle
.
XPUPlace
(
0
)
for
op_data
in
TEST_META_OP_DATA
:
meta_data
=
dict
(
op_data
)
binary_op
=
meta_data
[
'binary_op'
]
paddle
.
disable_static
(
place
)
x
=
np
.
random
.
choice
(
a
=
[
0
,
1
],
size
=
[
10
]).
astype
(
type_str_map
[
'x'
])
y
=
np
.
random
.
choice
(
a
=
[
0
,
1
],
size
=
[
10
]).
astype
(
type_str_map
[
'y'
])
check_type
(
meta_data
[
'op_str'
],
x
,
y
,
binary_op
)
paddle
.
enable_static
()
startup_program
=
paddle
.
static
.
Program
()
main_program
=
paddle
.
static
.
Program
()
with
paddle
.
static
.
program_guard
(
main_program
,
startup_program
):
x
=
paddle
.
static
.
data
(
name
=
'x'
,
shape
=
[
10
],
dtype
=
type_str_map
[
'x'
])
y
=
paddle
.
static
.
data
(
name
=
'y'
,
shape
=
[
10
],
dtype
=
type_str_map
[
'y'
])
check_type
(
meta_data
[
'op_str'
],
x
,
y
,
binary_op
)
def
type_map_factory
():
x_type_list
=
[
'float32'
,
'float64'
,
'int32'
,
'int64'
,
'bool'
]
y_type_list
=
[
'float32'
,
'float64'
,
'int32'
,
'int64'
,
'bool'
]
return
[{
'x'
:
x_type
,
'y'
:
y_type
}
for
x_type
in
x_type_list
for
y_type
in
y_type_list
]
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestXPU
(
unittest
.
TestCase
):
def
test
(
self
):
test_xpu
(
self
,
True
)
def
test_error
(
self
):
test_xpu
(
self
,
True
)
def
test_type_error
(
self
):
type_map_list
=
type_map_factory
()
for
type_map
in
type_map_list
:
test_type_error
(
self
,
type_map
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/xpu/test_matmul_op_xpu.py
浏览文件 @
760d015c
...
...
@@ -19,11 +19,13 @@ sys.path.append("..")
import
paddle.fluid.core
as
core
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
from
op_test
_xpu
import
XPU
OpTest
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
paddle
.
enable_static
()
def
generate_compatible_shapes
(
dim_X
,
dim_Y
,
transpose_X
,
transpose_Y
):
BATCH_SIZE
=
2
...
...
@@ -92,7 +94,9 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
class
Generator
(
object
):
def
setUp
(
self
):
self
.
use_xpu
=
True
self
.
op_type
=
"matmul"
# self.init_test_case()
X
=
np
.
random
.
random
(
self
.
shape_X
).
astype
(
"float32"
)
Y
=
np
.
random
.
random
(
self
.
shape_Y
).
astype
(
"float32"
)
Out
=
reference_matmul
(
X
,
Y
,
self
.
transpose_X
,
self
.
transpose_Y
)
...
...
@@ -104,7 +108,7 @@ class Generator(object):
self
.
outputs
=
{
'Out'
:
Out
}
def
test_check_output
(
self
):
self
.
check_output
()
if
paddle
.
is_compiled_with_xpu
()
and
len
(
self
.
inputs
[
'X'
].
shape
)
==
len
(
self
.
inputs
[
'Y'
].
shape
)
and
self
.
inputs
[
'X'
].
shape
[
0
]
==
self
.
inputs
[
'Y'
].
shape
[
0
]:
...
...
@@ -112,7 +116,7 @@ class Generator(object):
self
.
check_output_with_place
(
place
,
atol
=
1e-3
)
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
1e-3
)
if
paddle
.
is_compiled_with_xpu
()
and
len
(
self
.
inputs
[
'X'
].
shape
)
==
len
(
self
.
inputs
[
'Y'
].
shape
)
and
self
.
inputs
[
'X'
].
shape
[
0
]
==
self
.
inputs
[
'Y'
].
shape
[
0
]:
...
...
@@ -121,8 +125,7 @@ class Generator(object):
place
,
[
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
5e-2
)
def
test_check_grad_ignore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
1e-3
,
no_grad_set
=
set
(
"X"
))
if
paddle
.
is_compiled_with_xpu
()
and
len
(
self
.
inputs
[
'X'
].
shape
)
==
len
(
self
.
inputs
[
'Y'
].
shape
)
and
self
.
inputs
[
'X'
].
shape
[
0
]
==
self
.
inputs
[
'Y'
].
shape
[
0
]:
...
...
@@ -134,8 +137,7 @@ class Generator(object):
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ignore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
1e-3
,
no_grad_set
=
set
(
'Y'
))
if
paddle
.
is_compiled_with_xpu
()
and
len
(
self
.
inputs
[
'X'
].
shape
)
==
len
(
self
.
inputs
[
'Y'
].
shape
)
and
self
.
inputs
[
'X'
].
shape
[
0
]
==
self
.
inputs
[
'Y'
].
shape
[
0
]:
...
...
@@ -192,7 +194,7 @@ def test_negative_dims_program(obj):
for
idx
in
range
(
len
(
Ref
.
shape
)):
if
output
.
shape
[
idx
]
!=
-
1
:
obj
.
assertEqual
(
Ref
.
shape
[
idx
],
output
.
shape
[
idx
])
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
(
))
exe
=
fluid
.
Executor
(
fluid
.
XPUPlace
(
0
))
res
,
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
'x'
:
X
,
'y'
:
Y
},
...
...
@@ -221,7 +223,7 @@ def inject_test(dim_x, dim_y, trans_x, trans_y):
dim_x
,
dim_y
,
trans_x
,
trans_y
))
shape_x
,
shape_y
=
generate_compatible_shapes
(
dim_x
,
dim_y
,
trans_x
,
trans_y
)
globals
()[
test_name
]
=
type
(
test_name
,
(
Generator
,
OpTest
),
{
globals
()[
test_name
]
=
type
(
test_name
,
(
Generator
,
XPU
OpTest
),
{
'shape_X'
:
shape_x
,
'shape_Y'
:
shape_y
,
'transpose_X'
:
trans_x
,
...
...
@@ -231,10 +233,11 @@ def inject_test(dim_x, dim_y, trans_x, trans_y):
for
dim_X
in
(
1
,
2
,
3
):
for
dim_Y
in
(
1
,
2
,
3
):
for
transose_x
in
(
False
,
True
):
for
transose_y
in
(
False
,
True
):
inject_test
(
dim_X
,
dim_Y
,
transose_x
,
transose_y
)
api_test
(
dim_X
,
dim_Y
,
transose_x
,
transose_y
)
transose_x
=
False
transose_y
=
False
if
dim_X
==
3
and
dim_Y
==
3
:
inject_test
(
dim_X
,
dim_Y
,
transose_x
,
transose_y
)
api_test
(
dim_X
,
dim_Y
,
transose_x
,
transose_y
)
# Test case n-dim
...
...
@@ -267,7 +270,7 @@ for dim in [4]:
dim
,
dim
,
transpose_X
,
transpose_Y
))
shape_X
,
shape_Y
=
generate_compatible_shapes
(
dim
,
transpose_X
,
transpose_Y
)
globals
()[
test_name
]
=
type
(
test_name
,
(
Generator
,
OpTest
),
{
globals
()[
test_name
]
=
type
(
test_name
,
(
Generator
,
XPU
OpTest
),
{
'shape_X'
:
shape_X
,
'shape_Y'
:
shape_Y
,
'transpose_X'
:
transpose_X
,
...
...
@@ -282,7 +285,7 @@ class API_TestMm(unittest.TestCase):
y
=
fluid
.
data
(
name
=
'y'
,
shape
=
[
2
],
dtype
=
'float64'
)
res
=
fluid
.
data
(
name
=
"output"
,
shape
=
[
1
],
dtype
=
"float64"
)
result
=
paddle
.
mm
(
x
,
y
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
(
))
exe
=
fluid
.
Executor
(
fluid
.
XPUPlace
(
0
))
data1
=
np
.
random
.
rand
(
2
)
data2
=
np
.
random
.
rand
(
2
)
np_res
=
exe
.
run
(
feed
=
{
'x'
:
data1
,
'y'
:
data2
},
fetch_list
=
[
result
])
...
...
@@ -296,7 +299,7 @@ class API_TestMm(unittest.TestCase):
{}
\n
{}, check diff!"
.
format
(
np_res
,
expected_result
))
def
test_dygraph_without_out
(
self
):
device
=
fluid
.
CPUPlace
(
)
device
=
fluid
.
XPUPlace
(
0
)
with
fluid
.
dygraph
.
guard
(
device
):
input_array1
=
np
.
random
.
rand
(
3
,
4
).
astype
(
"float64"
)
input_array2
=
np
.
random
.
rand
(
4
,
3
).
astype
(
"float64"
)
...
...
@@ -309,7 +312,7 @@ class API_TestMm(unittest.TestCase):
class
Test_API_Matmul
(
unittest
.
TestCase
):
def
test_dygraph_without_out
(
self
):
device
=
fluid
.
CPUPlace
(
)
device
=
fluid
.
XPUPlace
(
0
)
with
fluid
.
dygraph
.
guard
(
device
):
input_array1
=
np
.
random
.
rand
(
3
,
4
).
astype
(
"float64"
)
input_array2
=
np
.
random
.
rand
(
4
,
3
).
astype
(
"float64"
)
...
...
python/paddle/fluid/tests/unittests/xpu/test_one_hot_op_xpu.py
0 → 100644
浏览文件 @
760d015c
# 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
paddle.fluid.core
as
core
import
sys
sys
.
path
.
append
(
".."
)
from
op_test_xpu
import
XPUOpTest
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
import
time
paddle
.
enable_static
()
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestOneHotOp
(
XPUOpTest
):
def
setUp
(
self
):
self
.
use_xpu
=
True
self
.
op_type
=
'one_hot'
depth
=
10
depth_np
=
np
.
array
(
10
).
astype
(
'int32'
)
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
]),
1
])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
[:
-
1
]),
depth
)).
astype
(
'float32'
)
for
i
in
range
(
np
.
product
(
x
.
shape
)):
out
[
i
,
x
[
i
]]
=
1.0
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
),
'depth_tensor'
:
depth_np
}
self
.
attrs
=
{
'dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP32
)}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
def
test_check_output
(
self
):
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
check_dygraph
=
False
)
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestOneHotOp_attr
(
XPUOpTest
):
def
setUp
(
self
):
self
.
op_type
=
'one_hot'
depth
=
10
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
]),
1
])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
[:
-
1
]),
depth
)).
astype
(
'float32'
)
for
i
in
range
(
np
.
product
(
x
.
shape
)):
out
[
i
,
x
[
i
]]
=
1.0
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
)}
self
.
attrs
=
{
'dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP32
),
'depth'
:
depth
}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
def
test_check_output
(
self
):
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
check_dygraph
=
False
)
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestOneHotOp_default_dtype
(
XPUOpTest
):
def
setUp
(
self
):
self
.
op_type
=
'one_hot'
depth
=
10
depth_np
=
np
.
array
(
10
).
astype
(
'int32'
)
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
]),
1
])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
[:
-
1
]),
depth
)).
astype
(
'float32'
)
for
i
in
range
(
np
.
product
(
x
.
shape
)):
out
[
i
,
x
[
i
]]
=
1.0
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
),
'depth_tensor'
:
depth_np
}
self
.
attrs
=
{}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
def
test_check_output
(
self
):
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
check_dygraph
=
False
)
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestOneHotOp_default_dtype_attr
(
XPUOpTest
):
def
setUp
(
self
):
self
.
op_type
=
'one_hot'
depth
=
10
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
]),
1
])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
[:
-
1
]),
depth
)).
astype
(
'float32'
)
for
i
in
range
(
np
.
product
(
x
.
shape
)):
out
[
i
,
x
[
i
]]
=
1.0
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
)}
self
.
attrs
=
{
'depth'
:
depth
}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
def
test_check_output
(
self
):
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
check_dygraph
=
False
)
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestOneHotOp_out_of_range
(
XPUOpTest
):
def
setUp
(
self
):
self
.
op_type
=
'one_hot'
depth
=
10
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
choice
([
-
1
,
depth
])
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
]),
1
])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
[:
-
1
]),
depth
)).
astype
(
'float32'
)
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
)}
self
.
attrs
=
{
'depth'
:
depth
,
'allow_out_of_range'
:
True
}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
def
test_check_output
(
self
):
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
check_dygraph
=
False
)
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_xpu
(),
"core is not compiled with XPU"
)
class
TestOneHotOpError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
# the input must be Variable
in_w
=
np
.
random
.
random
((
4
,
1
)).
astype
(
"int32"
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
one_hot
,
in_w
)
# the input must be int32 or int 64
in_w2
=
fluid
.
layers
.
data
(
name
=
"in_w2"
,
shape
=
[
4
,
1
],
append_batch_size
=
False
,
dtype
=
"float32"
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
one_hot
,
in_w2
)
# the depth must be int, long or Variable
in_r
=
fluid
.
layers
.
data
(
name
=
"in_r"
,
shape
=
[
4
,
1
],
append_batch_size
=
False
,
dtype
=
"int32"
)
depth_w
=
np
.
array
([
4
])
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
one_hot
,
in_r
,
4.1
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
one_hot
,
in_r
,
depth_w
)
if
__name__
==
'__main__'
:
paddle
.
enable_static
()
unittest
.
main
()
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