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提交
e3e50ea8
编写于
1月 20, 2022
作者:
F
fwenguang
提交者:
GitHub
1月 20, 2022
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电子邮件补丁
差异文件
[MLU]add mlu kernel for cast and scale op (#38961)
上级
f1143f0c
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
633 addition
and
1 deletion
+633
-1
paddle/fluid/operators/cast_op.cc
paddle/fluid/operators/cast_op.cc
+16
-0
paddle/fluid/operators/cast_op_mlu.cc
paddle/fluid/operators/cast_op_mlu.cc
+94
-0
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+28
-1
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+49
-0
paddle/fluid/operators/scale_op_mlu.cc
paddle/fluid/operators/scale_op_mlu.cc
+106
-0
python/paddle/fluid/tests/unittests/mlu/test_cast_op_mlu.py
python/paddle/fluid/tests/unittests/mlu/test_cast_op_mlu.py
+135
-0
python/paddle/fluid/tests/unittests/mlu/test_scale_op_mlu.py
python/paddle/fluid/tests/unittests/mlu/test_scale_op_mlu.py
+205
-0
未找到文件。
paddle/fluid/operators/cast_op.cc
浏览文件 @
e3e50ea8
...
...
@@ -16,6 +16,9 @@ limitations under the License. */
#include <memory>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
#ifdef PADDLE_WITH_MLU
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
#endif
namespace
paddle
{
namespace
operators
{
...
...
@@ -102,6 +105,19 @@ class CastOp : public framework::OperatorWithKernel {
framework
::
DataLayout
::
kMKLDNN
,
framework
::
LibraryType
::
kMKLDNN
);
}
#endif
#ifdef PADDLE_WITH_MLU
auto
src_type
=
static_cast
<
VT
::
Type
>
(
ctx
.
Attr
<
int
>
(
"in_dtype"
));
auto
dst_type
=
static_cast
<
VT
::
Type
>
(
ctx
.
Attr
<
int
>
(
"out_dtype"
));
if
(
src_type
==
dst_type
||
MLUSupportsCast
(
src_type
,
dst_type
))
{
return
framework
::
OpKernelType
(
tensor
->
type
(),
tensor_place
);
}
else
{
VLOG
(
3
)
<<
"MLU not support cast type: "
<<
framework
::
DataTypeToString
(
src_type
)
<<
" to type: "
<<
framework
::
DataTypeToString
(
dst_type
)
<<
", fallbacking to CPU one!"
;
return
framework
::
OpKernelType
(
tensor
->
type
(),
platform
::
CPUPlace
());
}
#endif
return
framework
::
OpKernelType
(
tensor
->
type
(),
tensor_place
);
}
...
...
paddle/fluid/operators/cast_op_mlu.cc
0 → 100644
浏览文件 @
e3e50ea8
/* Copyright (c) 2022 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/cast_op.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
#include "paddle/fluid/platform/device/mlu/device_context.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
CastMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
src_type
=
static_cast
<
VT
::
Type
>
(
ctx
.
Attr
<
int
>
(
"in_dtype"
));
auto
dst_type
=
static_cast
<
VT
::
Type
>
(
ctx
.
Attr
<
int
>
(
"out_dtype"
));
auto
place
=
ctx
.
GetPlace
();
if
(
src_type
==
dst_type
)
{
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
MLUDeviceContext
>();
output
->
mutable_data
<
T
>
(
place
);
framework
::
TensorCopy
(
*
input
,
place
,
dev_ctx
,
output
);
return
;
}
PADDLE_ENFORCE_EQ
(
MLUSupportsCast
(
src_type
,
dst_type
),
true
,
platform
::
errors
::
InvalidArgument
(
"MLU not support cast [%d] to [%d]"
,
framework
::
DataTypeToString
(
src_type
),
framework
::
DataTypeToString
(
dst_type
)));
switch
(
dst_type
)
{
case
VT
::
FP32
:
output
->
mutable_data
<
float
>
(
place
);
break
;
case
VT
::
FP16
:
output
->
mutable_data
<
paddle
::
platform
::
float16
>
(
place
);
break
;
case
VT
::
INT32
:
output
->
mutable_data
<
int32_t
>
(
place
);
break
;
case
VT
::
INT16
:
output
->
mutable_data
<
int16_t
>
(
place
);
break
;
case
VT
::
INT8
:
output
->
mutable_data
<
int8_t
>
(
place
);
break
;
case
VT
::
UINT8
:
output
->
mutable_data
<
uint8_t
>
(
place
);
break
;
case
VT
::
BOOL
:
output
->
mutable_data
<
bool
>
(
place
);
break
;
case
VT
::
INT64
:
output
->
mutable_data
<
int64_t
>
(
place
);
break
;
default:
PADDLE_THROW
(
platform
::
errors
::
Unavailable
(
"Not supported cast %d -> %d"
,
framework
::
DataTypeToString
(
src_type
),
framework
::
DataTypeToString
(
dst_type
)));
}
MLUCnnlTensorDesc
input_desc
(
*
input
);
MLUCnnlTensorDesc
output_desc
(
*
output
);
cnnlCastDataType_t
cast_type
=
GetCastDataType
(
src_type
,
dst_type
);
MLUCnnl
::
Cast
(
ctx
,
cast_type
,
input_desc
.
get
(),
GetBasePtr
(
input
),
output_desc
.
get
(),
GetBasePtr
(
output
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_MLU_KERNEL
(
cast
,
ops
::
CastMLUKernel
<
float
>
,
ops
::
CastMLUKernel
<
int
>
,
ops
::
CastMLUKernel
<
int16_t
>
,
ops
::
CastMLUKernel
<
uint8_t
>
,
ops
::
CastMLUKernel
<
bool
>
,
ops
::
CastMLUKernel
<
int64_t
>
,
ops
::
CastMLUKernel
<
paddle
::
platform
::
float16
>
);
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
e3e50ea8
...
...
@@ -20,6 +20,29 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
cnnlCastDataType_t
GetCastDataType
(
const
VT
::
Type
&
src_type
,
const
VT
::
Type
&
dst_type
)
{
cnnlCastDataType_t
cast_type
=
CNNL_CAST_FLOAT_TO_HALF
;
for
(
auto
it
=
MLU_SUPPORTED_CAST_TYPE
.
begin
();
it
!=
MLU_SUPPORTED_CAST_TYPE
.
end
();
++
it
)
{
if
(
it
->
first
.
first
==
src_type
&&
it
->
first
.
second
==
dst_type
)
{
cast_type
=
it
->
second
;
break
;
}
}
return
cast_type
;
}
bool
MLUSupportsCast
(
const
VT
::
Type
&
src_type
,
const
VT
::
Type
&
dst_type
)
{
for
(
auto
it
=
MLU_SUPPORTED_CAST_TYPE
.
begin
();
it
!=
MLU_SUPPORTED_CAST_TYPE
.
end
();
++
it
)
{
if
(
it
->
first
.
first
==
src_type
&&
it
->
first
.
second
==
dst_type
)
{
return
true
;
}
}
return
false
;
}
class
MLUCnnlTensorDescPool
{
public:
cnnlTensorDescriptor_t
Pop
()
{
...
...
@@ -153,6 +176,10 @@ MLUCnnlTensorDesc::MLUCnnlTensorDesc(const Tensor& tensor,
}
}
MLUCnnlTensorDesc
::
MLUCnnlTensorDesc
(
const
Tensor
&
tensor
)
:
MLUCnnlTensorDesc
(
tensor
,
CNNL_LAYOUT_ARRAY
,
ToCnnlDataType
(
tensor
.
type
()))
{}
MLUCnnlTensorDesc
::
MLUCnnlTensorDesc
(
const
Tensor
&
tensor
,
cnnlTensorLayout_t
layout
,
const
cnnlDataType_t
tensor_dtype
,
...
...
@@ -1848,7 +1875,7 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
if
(
is_training
)
{
/*
* I
f i
n Paddle, running_mean_output = momentum * runnning_mean_input +
* In Paddle, running_mean_output = momentum * runnning_mean_input +
* (1 - momentum) * batch_mean. However, In CNNL,
* running_mean_output = (1 - momentum) * running_mean_input +
* momentum * batch_mean. So we pass (1.0 - momentum) to momentum param.
...
...
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
e3e50ea8
...
...
@@ -74,6 +74,9 @@ inline cnnlDataType_t ToCnnlDataType(const framework::proto::VarType::Type& t) {
case
framework
::
proto
::
VarType
::
INT8
:
type
=
CNNL_DTYPE_INT8
;
break
;
case
framework
::
proto
::
VarType
::
INT16
:
type
=
CNNL_DTYPE_INT16
;
break
;
case
framework
::
proto
::
VarType
::
INT32
:
type
=
CNNL_DTYPE_INT32
;
break
;
...
...
@@ -83,6 +86,9 @@ inline cnnlDataType_t ToCnnlDataType(const framework::proto::VarType::Type& t) {
case
framework
::
proto
::
VarType
::
BOOL
:
type
=
CNNL_DTYPE_BOOL
;
break
;
case
framework
::
proto
::
VarType
::
UINT8
:
type
=
CNNL_DTYPE_UINT8
;
break
;
default:
break
;
}
...
...
@@ -108,6 +114,47 @@ inline static const MLUDeviceContext& GetDevCtxFromCTX(
return
ctx
.
template
device_context
<
MLUDeviceContext
>();
}
using
VT
=
framework
::
proto
::
VarType
;
const
std
::
map
<
std
::
pair
<
VT
::
Type
,
VT
::
Type
>
,
cnnlCastDataType_t
>
MLU_SUPPORTED_CAST_TYPE
=
{
{{
VT
::
FP32
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_FLOAT_TO_HALF
},
{{
VT
::
FP32
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_FLOAT_TO_INT32
},
{{
VT
::
FP32
,
/*cast to*/
VT
::
INT16
},
CNNL_CAST_FLOAT_TO_INT16
},
{{
VT
::
FP32
,
/*cast to*/
VT
::
INT8
},
CNNL_CAST_FLOAT_TO_INT8
},
{{
VT
::
FP32
,
/*cast to*/
VT
::
UINT8
},
CNNL_CAST_FLOAT_TO_UINT8
},
{{
VT
::
FP32
,
/*cast to*/
VT
::
BOOL
},
CNNL_CAST_FLOAT_TO_BOOL
},
{{
VT
::
FP16
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_HALF_TO_FLOAT
},
{{
VT
::
FP16
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_HALF_TO_INT32
},
{{
VT
::
FP16
,
/*cast to*/
VT
::
INT16
},
CNNL_CAST_HALF_TO_INT16
},
{{
VT
::
FP16
,
/*cast to*/
VT
::
INT8
},
CNNL_CAST_HALF_TO_INT8
},
{{
VT
::
FP16
,
/*cast to*/
VT
::
UINT8
},
CNNL_CAST_HALF_TO_UINT8
},
{{
VT
::
FP16
,
/*cast to*/
VT
::
BOOL
},
CNNL_CAST_HALF_TO_BOOL
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_INT32_TO_FLOAT
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_INT32_TO_HALF
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
INT64
},
CNNL_CAST_INT32_TO_INT64
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
INT16
},
CNNL_CAST_INT32_TO_INT16
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
INT8
},
CNNL_CAST_INT32_TO_INT8
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
BOOL
},
CNNL_CAST_INT32_TO_BOOL
},
{{
VT
::
INT16
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_INT16_TO_FLOAT
},
{{
VT
::
INT16
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_INT16_TO_HALF
},
{{
VT
::
INT16
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_INT16_TO_INT32
},
{{
VT
::
INT8
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_INT8_TO_FLOAT
},
{{
VT
::
INT8
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_INT8_TO_HALF
},
{{
VT
::
INT8
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_INT8_TO_INT32
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_UINT8_TO_FLOAT
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_UINT8_TO_HALF
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
INT64
},
CNNL_CAST_UINT8_TO_INT64
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_UINT8_TO_INT32
},
{{
VT
::
BOOL
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_BOOL_TO_FLOAT
},
{{
VT
::
BOOL
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_BOOL_TO_HALF
},
{{
VT
::
BOOL
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_BOOL_TO_INT32
},
{{
VT
::
INT64
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_INT64_TO_INT32
},
};
cnnlCastDataType_t
GetCastDataType
(
const
VT
::
Type
&
src_type
,
const
VT
::
Type
&
dst_type
);
bool
MLUSupportsCast
(
const
VT
::
Type
&
src_type
,
const
VT
::
Type
&
dst_type
);
cnnlDeviceType_t
GetCnnlDev
(
int
dev_ordinal
);
using
CnnlTensorDesc
=
cnnlTensorDescriptor_t
;
...
...
@@ -150,6 +197,8 @@ class MLUCnnlTensorDesc {
MLUCnnlTensorDesc
(
const
Tensor
&
tensor
,
const
cnnlTensorLayout_t
layout
,
const
cnnlDataType_t
tensor_dtype
);
explicit
MLUCnnlTensorDesc
(
const
Tensor
&
tensor
);
MLUCnnlTensorDesc
(
const
Tensor
&
tensor
,
cnnlTensorLayout_t
layout
,
const
cnnlDataType_t
tensor_dtype
,
int
position
);
...
...
paddle/fluid/operators/scale_op_mlu.cc
0 → 100644
浏览文件 @
e3e50ea8
/* Copyright (c) 2022 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/scale_op.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
ScaleMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
virtual
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
&
dev_ctx
=
GetDevCtxFromCTX
(
ctx
);
auto
*
in_var
=
ctx
.
InputVar
(
"X"
);
auto
*
in
=
framework
::
GetLoDTensorOrSelectedRowsValueFromVar
(
*
in_var
);
// cnnl require input, scale, bias with same type. And all in device side.
auto
&
scale
=
ctx
.
Attr
<
float
>
(
"scale"
);
framework
::
Tensor
scale_tensor
;
if
(
ctx
.
HasInput
(
"ScaleTensor"
))
{
framework
::
Tensor
float_scale_tensor
=
*
ctx
.
Input
<
framework
::
Tensor
>
(
"ScaleTensor"
);
if
(
float_scale_tensor
.
type
()
!=
in
->
type
())
{
scale_tensor
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
({
1
},
dev_ctx
);
MLUCnnlTensorDesc
float_scale_desc
(
float_scale_tensor
);
MLUCnnlTensorDesc
final_scale_desc
(
scale_tensor
);
cnnlCastDataType_t
cast_type
=
GetCastDataType
(
float_scale_tensor
.
type
(),
scale_tensor
.
type
());
MLUCnnl
::
Cast
(
ctx
,
cast_type
,
float_scale_desc
.
get
(),
GetBasePtr
(
&
float_scale_tensor
),
final_scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
));
}
else
{
scale_tensor
=
float_scale_tensor
;
}
}
else
{
scale_tensor
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
({
1
},
dev_ctx
);
MLUCnnlTensorDesc
scale_desc
(
scale_tensor
);
MLUCnnl
::
Fill
(
ctx
,
scale
,
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
));
}
auto
&
bias
=
ctx
.
Attr
<
float
>
(
"bias"
);
framework
::
Tensor
bias_tensor
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
({
1
},
dev_ctx
);
MLUCnnlTensorDesc
bias_desc
(
bias_tensor
);
MLUCnnl
::
Fill
(
ctx
,
bias
,
bias_desc
.
get
(),
GetBasePtr
(
&
bias_tensor
));
auto
*
out_var
=
ctx
.
OutputVar
(
"Out"
);
if
(
in_var
->
IsType
<
framework
::
SelectedRows
>
()
&&
in_var
!=
out_var
)
{
auto
&
in_slr
=
in_var
->
Get
<
framework
::
SelectedRows
>
();
auto
*
out_slr
=
out_var
->
GetMutable
<
framework
::
SelectedRows
>
();
out_slr
->
set_rows
(
in_slr
.
rows
());
out_slr
->
set_height
(
in_slr
.
height
());
}
auto
*
out
=
framework
::
GetMutableLoDTensorOrSelectedRowsValueFromVar
(
out_var
);
out
->
mutable_data
<
T
>
(
in
->
place
());
MLUCnnlTensorDesc
input_desc
(
*
in
);
MLUCnnlTensorDesc
scale_desc
(
scale_tensor
);
MLUCnnlTensorDesc
output_desc
(
*
out
);
const
int
axis
=
std
::
max
(
in
->
dims
().
size
()
-
1
,
0
);
auto
bias_after_scale
=
ctx
.
Attr
<
bool
>
(
"bias_after_scale"
);
if
(
bias_after_scale
)
{
MLUCnnl
::
Scale
(
ctx
,
axis
,
input_desc
.
get
(),
GetBasePtr
(
in
),
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
),
bias_desc
.
get
(),
GetBasePtr
(
&
bias_tensor
),
output_desc
.
get
(),
GetBasePtr
(
out
));
}
else
{
framework
::
Tensor
new_bias_tensor
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
({
1
},
dev_ctx
);
MLUCnnlTensorDesc
new_bias_desc
(
new_bias_tensor
);
MLUCnnlOpTensorDesc
mul_op_desc
(
CNNL_OP_TENSOR_MUL
,
ToCnnlDataType
(
in
->
type
()),
CNNL_NOT_PROPAGATE_NAN
);
MLUCnnl
::
OpTensor
(
ctx
,
mul_op_desc
.
get
(),
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
),
bias_desc
.
get
(),
GetBasePtr
(
&
bias_tensor
),
new_bias_desc
.
get
(),
GetBasePtr
(
&
new_bias_tensor
),
ToCnnlDataType
(
in
->
type
()));
MLUCnnl
::
Scale
(
ctx
,
axis
,
input_desc
.
get
(),
GetBasePtr
(
in
),
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
),
new_bias_desc
.
get
(),
GetBasePtr
(
&
new_bias_tensor
),
output_desc
.
get
(),
GetBasePtr
(
out
));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_MLU_KERNEL
(
scale
,
ops
::
ScaleMLUKernel
<
float
>
,
ops
::
ScaleMLUKernel
<
paddle
::
platform
::
float16
>
);
python/paddle/fluid/tests/unittests/mlu/test_cast_op_mlu.py
0 → 100644
浏览文件 @
e3e50ea8
# Copyright (c) 2022 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
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
from
paddle.fluid
import
compiler
,
Program
,
program_guard
class
TestCastOpFp32ToFp16
(
OpTest
):
def
setUp
(
self
):
ipt
=
np
.
random
.
random
(
size
=
[
10
,
10
])
self
.
inputs
=
{
'X'
:
ipt
.
astype
(
'float32'
)}
self
.
outputs
=
{
'Out'
:
ipt
.
astype
(
'float16'
)}
self
.
attrs
=
{
'in_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP32
),
'out_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP16
)
}
self
.
op_type
=
'cast'
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
__class__
.
no_need_check_grad
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
atol
=
1e-3
)
class
TestCastOpFp16ToFp32
(
OpTest
):
def
setUp
(
self
):
ipt
=
np
.
random
.
random
(
size
=
[
10
,
10
])
self
.
inputs
=
{
'X'
:
ipt
.
astype
(
'float16'
)}
self
.
outputs
=
{
'Out'
:
ipt
.
astype
(
'float32'
)}
self
.
attrs
=
{
'in_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP16
),
'out_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP32
)
}
self
.
op_type
=
'cast'
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
atol
=
1e-3
)
class
TestCastOpInt32ToInt32
(
OpTest
):
def
setUp
(
self
):
ipt
=
np
.
random
.
randint
(
1000
,
size
=
(
10
,
10
))
self
.
inputs
=
{
'X'
:
ipt
.
astype
(
'int32'
)}
self
.
outputs
=
{
'Out'
:
ipt
.
astype
(
'int32'
)}
self
.
attrs
=
{
'in_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
INT32
),
'out_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
INT32
)
}
self
.
op_type
=
'cast'
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
atol
=
1e-3
)
class
TestCastOpInt32ToFp32
(
OpTest
):
def
setUp
(
self
):
ipt
=
np
.
random
.
randint
(
1000
,
size
=
[
10
,
10
])
self
.
inputs
=
{
'X'
:
ipt
.
astype
(
'int32'
)}
self
.
outputs
=
{
'Out'
:
ipt
.
astype
(
'float32'
)}
self
.
attrs
=
{
'in_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
INT32
),
'out_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP32
)
}
self
.
op_type
=
'cast'
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
atol
=
1e-3
)
class
TestCastOpInt16ToFp64
(
OpTest
):
def
setUp
(
self
):
ipt
=
np
.
random
.
randint
(
1000
,
size
=
[
10
,
10
])
self
.
inputs
=
{
'X'
:
ipt
.
astype
(
'int16'
)}
self
.
outputs
=
{
'Out'
:
ipt
.
astype
(
'int64'
)}
self
.
attrs
=
{
'in_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
INT16
),
'out_dtype'
:
int
(
core
.
VarDesc
.
VarType
.
INT64
)
}
self
.
op_type
=
'cast'
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
atol
=
1e-3
)
class
TestCastOpError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
# The input type of cast_op must be Variable.
x1
=
fluid
.
create_lod_tensor
(
np
.
array
([[
-
1
]]),
[[
1
]],
fluid
.
MLUPlace
(
0
))
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
cast
,
x1
,
'int32'
)
# The input dtype of cast_op must be bool, float16, float32, float64, int32, int64, uint8.
x2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
4
],
dtype
=
'int16'
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
cast
,
x2
,
'int32'
)
def
test_dtype_type
():
x4
=
fluid
.
layers
.
data
(
name
=
'x4'
,
shape
=
[
4
],
dtype
=
'int32'
)
output
=
fluid
.
layers
.
cast
(
x
=
x4
,
dtype
=
'int16'
)
self
.
assertRaises
(
TypeError
,
test_dtype_type
)
if
__name__
==
'__main__'
:
paddle
.
enable_static
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/mlu/test_scale_op_mlu.py
0 → 100644
浏览文件 @
e3e50ea8
# Copyright (c) 2022 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
sys
sys
.
path
.
append
(
'..'
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
paddle.static
import
Program
,
program_guard
class
TestScaleOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"scale"
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
dtype
=
np
.
float32
self
.
init_dtype_type
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
10
,
10
)).
astype
(
self
.
dtype
)}
self
.
attrs
=
{
'scale'
:
-
2.3
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
dtype
(
self
.
attrs
[
'scale'
])
}
def
init_dtype_type
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
class
TestScaleOpScaleVariable
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"scale"
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
dtype
=
np
.
float32
self
.
init_dtype_type
()
self
.
scale
=
-
2.3
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
10
,
10
)).
astype
(
self
.
dtype
),
'ScaleTensor'
:
np
.
array
([
self
.
scale
]).
astype
(
'float32'
)
}
self
.
attrs
=
{}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
dtype
(
self
.
scale
)}
def
init_dtype_type
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
class
TestScaleOpSelectedRows
(
unittest
.
TestCase
):
def
init_dtype_type
(
self
):
pass
def
check_with_place
(
self
,
place
,
in_name
,
out_name
):
scope
=
core
.
Scope
()
self
.
dtype
=
np
.
float32
self
.
init_dtype_type
()
# create and initialize Grad Variable
in_height
=
10
in_rows
=
[
0
,
4
,
7
]
in_row_numel
=
12
scale
=
2.0
in_selected_rows
=
scope
.
var
(
in_name
).
get_selected_rows
()
in_selected_rows
.
set_height
(
in_height
)
in_selected_rows
.
set_rows
(
in_rows
)
in_array
=
np
.
random
.
random
(
(
len
(
in_rows
),
in_row_numel
)).
astype
(
self
.
dtype
)
in_tensor
=
in_selected_rows
.
get_tensor
()
in_tensor
.
set
(
in_array
,
place
)
# create and initialize Param Variable
out_selected_rows
=
scope
.
var
(
out_name
).
get_selected_rows
()
out_tensor
=
out_selected_rows
.
get_tensor
()
out_tensor
.
_set_dims
(
in_tensor
.
_get_dims
())
# create and run sgd operator
scale_op
=
Operator
(
"scale"
,
X
=
in_name
,
Out
=
out_name
,
scale
=
scale
)
scale_op
.
run
(
scope
,
place
)
# get and compare result
out_height
=
out_selected_rows
.
height
()
out_rows
=
out_selected_rows
.
rows
()
result_array
=
np
.
array
(
out_tensor
)
assert
(
in_array
*
scale
==
result_array
).
all
()
assert
in_height
==
out_height
assert
in_rows
==
out_rows
def
test_scale_selected_rows
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_mlu
():
places
.
append
(
core
.
MLUPlace
(
0
))
for
place
in
places
:
self
.
check_with_place
(
place
,
'in'
,
'out'
)
def
test_scale_selected_rows_inplace
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_mlu
():
places
.
append
(
core
.
MLUPlace
(
0
))
for
place
in
places
:
self
.
check_with_place
(
place
,
'in'
,
'in'
)
class
TestScaleRaiseError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
def
test_type
():
fluid
.
layers
.
scale
([
10
])
self
.
assertRaises
(
TypeError
,
test_type
)
# Add FP16 test
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_mlu
(),
"core is not compiled with MLU"
)
class
TestScaleFp16Op
(
TestScaleOp
):
def
init_dtype_type
(
self
):
self
.
dtype
=
np
.
float16
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
atol
=
0.002
)
@
unittest
.
skipIf
(
not
core
.
is_compiled_with_mlu
(),
"core is not compiled with MLU"
)
class
TestScaleFp16OpSelectedRows
(
TestScaleOpSelectedRows
):
def
init_dtype_type
(
self
):
self
.
dtype
=
np
.
float16
def
test_scale_selected_rows
(
self
):
place
=
core
.
MLUPlace
(
0
)
self
.
check_with_place
(
place
,
'in'
,
'out'
)
def
test_scale_selected_rows_inplace
(
self
):
place
=
core
.
MLUPlace
(
0
)
self
.
check_with_place
(
place
,
'in'
,
'in'
)
class
TestScaleApiStatic
(
unittest
.
TestCase
):
def
_executed_api
(
self
,
x
,
scale
=
1.0
,
bias
=
0.0
):
return
paddle
.
scale
(
x
,
scale
,
bias
)
def
test_api
(
self
):
paddle
.
enable_static
()
input
=
np
.
random
.
random
([
2
,
25
]).
astype
(
"float32"
)
main_prog
=
Program
()
with
program_guard
(
main_prog
,
Program
()):
x
=
paddle
.
static
.
data
(
name
=
"x"
,
shape
=
[
2
,
25
],
dtype
=
"float32"
)
out
=
self
.
_executed_api
(
x
,
scale
=
2.0
,
bias
=
3.0
)
exe
=
paddle
.
static
.
Executor
(
place
=
paddle
.
CPUPlace
())
out
=
exe
.
run
(
main_prog
,
feed
=
{
"x"
:
input
},
fetch_list
=
[
out
])
self
.
assertEqual
(
np
.
array_equal
(
out
[
0
],
input
*
2.0
+
3.0
),
True
)
class
TestScaleInplaceApiStatic
(
TestScaleApiStatic
):
def
_executed_api
(
self
,
x
,
scale
=
1.0
,
bias
=
0.0
):
return
x
.
scale_
(
scale
,
bias
)
class
TestScaleApiDygraph
(
unittest
.
TestCase
):
def
_executed_api
(
self
,
x
,
scale
=
1.0
,
bias
=
0.0
):
return
paddle
.
scale
(
x
,
scale
,
bias
)
def
test_api
(
self
):
paddle
.
disable_static
()
input
=
np
.
random
.
random
([
2
,
25
]).
astype
(
"float32"
)
x
=
paddle
.
to_tensor
(
input
)
out
=
self
.
_executed_api
(
x
,
scale
=
2.0
,
bias
=
3.0
)
self
.
assertEqual
(
np
.
array_equal
(
out
.
numpy
(),
input
*
2.0
+
3.0
),
True
)
paddle
.
enable_static
()
class
TestScaleInplaceApiDygraph
(
TestScaleApiDygraph
):
def
_executed_api
(
self
,
x
,
scale
=
1.0
,
bias
=
0.0
):
return
x
.
scale_
(
scale
,
bias
)
if
__name__
==
"__main__"
:
paddle
.
enable_static
()
unittest
.
main
()
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