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6e7b883b
编写于
4月 17, 2018
作者:
M
mozga-intel
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电子邮件补丁
差异文件
Initial implementation of multiplication operator for MKLDNN
上级
fee5b24c
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
349 addition
and
58 deletion
+349
-58
paddle/fluid/operators/mul_mkldnn_op.cc
paddle/fluid/operators/mul_mkldnn_op.cc
+197
-0
paddle/fluid/operators/mul_op.cc
paddle/fluid/operators/mul_op.cc
+40
-0
paddle/fluid/platform/mkldnn_helper.h
paddle/fluid/platform/mkldnn_helper.h
+27
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+27
-54
python/paddle/fluid/tests/unittests/test_mul_mkldnn_op.py
python/paddle/fluid/tests/unittests/test_mul_mkldnn_op.py
+44
-0
python/paddle/fluid/tests/unittests/test_mul_op.py
python/paddle/fluid/tests/unittests/test_mul_op.py
+12
-1
python/paddle/fluid/tests/unittests/test_operator_desc.py
python/paddle/fluid/tests/unittests/test_operator_desc.py
+2
-1
未找到文件。
paddle/fluid/operators/mul_mkldnn_op.cc
0 → 100644
浏览文件 @
6e7b883b
/* 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. */
#include "mkldnn.hpp"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/mul_op.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace
paddle
{
namespace
operators
{
using
paddle
::
framework
::
Tensor
;
using
paddle
::
platform
::
MKLDNNDeviceContext
;
template
<
typename
Format
=
mkldnn
::
memory
::
format
>
mkldnn
::
memory
::
desc
type
(
const
std
::
vector
<
int
>&
dims
,
Format
&&
f
)
{
return
platform
::
MKLDNNMemDesc
(
dims
,
mkldnn
::
memory
::
data_type
::
f32
,
f
);
}
template
<
typename
T
>
class
MulMKLDNNOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
"It must use CPUPlace."
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
MKLDNNDeviceContext
>();
auto
mkldnn_engine
=
dev_ctx
.
GetEngine
();
auto
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
weight
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
PADDLE_ENFORCE
(
input
->
dims
().
size
()
&
(
2
|
4
),
"Input must be with 2 or 4 dimensions, i.e. NC or NCHW"
);
PADDLE_ENFORCE
(
weight
->
dims
().
size
()
&
(
2
|
4
),
"Weights must be with 2 or 4 dimensions, i.e. OI or OIHW"
);
std
::
vector
<
int
>
w_tz
=
paddle
::
framework
::
vectorize2int
(
weight
->
dims
());
std
::
vector
<
int
>
src_tz
=
paddle
::
framework
::
vectorize2int
(
input
->
dims
());
auto
src_md
=
src_tz
.
size
()
!=
2
?
type
(
src_tz
,
mkldnn
::
memory
::
format
::
nchw
)
:
type
({
src_tz
[
0
],
src_tz
[
1
]},
mkldnn
::
memory
::
format
::
nc
);
auto
dst_md
=
type
({
src_tz
[
0
],
w_tz
[
1
]},
mkldnn
::
memory
::
format
::
nc
);
auto
weights_md
=
src_tz
.
size
()
!=
2
?
type
({
w_tz
[
1
],
src_tz
[
1
],
src_tz
[
2
],
src_tz
[
3
]},
mkldnn
::
memory
::
format
::
oihw
)
:
type
({
w_tz
[
1
],
src_tz
[
1
]},
mkldnn
::
memory
::
format
::
oi
);
auto
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
std
::
string
key
=
ctx
.
op
().
Output
(
"Out"
);
const
std
::
string
key_fc_pd
=
key
+
"@mul_pd"
;
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
w_data
=
weight
->
data
<
T
>
();
auto
dst_memory
=
mkldnn
::
memory
({
dst_md
,
mkldnn_engine
},
output_data
);
auto
src_memory
=
mkldnn
::
memory
({
src_md
,
mkldnn_engine
},
platform
::
to_void_cast
(
input_data
));
auto
weights_memory
=
mkldnn
::
memory
({
weights_md
,
mkldnn_engine
},
platform
::
to_void_cast
(
w_data
));
auto
pd
=
platform
::
MKLDNNFwdPrimitiveDesc
<
mkldnn
::
inner_product_forward
>
(
mkldnn_engine
,
src_md
,
weights_md
,
dst_md
);
dev_ctx
.
SetBlob
(
key_fc_pd
,
pd
);
auto
forward
=
mkldnn
::
inner_product_forward
(
*
pd
,
src_memory
,
weights_memory
,
dst_memory
);
std
::
vector
<
mkldnn
::
primitive
>
pipeline
=
{
forward
};
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
}
};
template
<
typename
T
>
class
MulMKLDNNGradOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
"It must use CPUPlace."
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
MKLDNNDeviceContext
>();
auto
mkldnn_engine
=
dev_ctx
.
GetEngine
();
const
Tensor
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
w
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
const
Tensor
*
out_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
Tensor
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
Tensor
*
w_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
const
std
::
string
key
=
ctx
.
op
().
Input
(
"Out"
);
const
std
::
string
key_fc_pd
=
key
+
"@mul_pd"
;
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
w_data
=
w
->
data
<
T
>
();
const
T
*
out_grad_data
=
out_grad
->
data
<
T
>
();
T
*
input_grad_data
=
nullptr
;
T
*
w_grad_data
=
nullptr
;
if
(
input_grad
)
{
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
if
(
w_grad
)
{
w_grad_data
=
w_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
std
::
vector
<
int
>
src_tz
=
paddle
::
framework
::
vectorize2int
(
input
->
dims
());
std
::
vector
<
int
>
w_tz
=
paddle
::
framework
::
vectorize2int
(
w
->
dims
());
auto
src_md
=
src_tz
.
size
()
!=
2
?
type
(
src_tz
,
mkldnn
::
memory
::
format
::
nchw
)
:
type
({
src_tz
[
0
],
src_tz
[
1
]},
mkldnn
::
memory
::
format
::
nc
);
auto
dst_md
=
type
({
src_tz
[
0
],
w_tz
[
1
]},
mkldnn
::
memory
::
format
::
nc
);
auto
weights_md
=
src_tz
.
size
()
!=
2
?
type
({
w_tz
[
1
],
src_tz
[
1
],
src_tz
[
2
],
src_tz
[
3
]},
mkldnn
::
memory
::
format
::
oihw
)
:
type
({
w_tz
[
1
],
src_tz
[
1
]},
mkldnn
::
memory
::
format
::
oi
);
auto
src_memory
=
mkldnn
::
memory
({
src_md
,
mkldnn_engine
},
platform
::
to_void_cast
(
input_data
));
auto
dst_memory
=
mkldnn
::
memory
({
dst_md
,
mkldnn_engine
},
platform
::
to_void_cast
(
out_grad_data
));
auto
weight_memory
=
mkldnn
::
memory
({
weights_md
,
mkldnn_engine
},
platform
::
to_void_cast
(
w_data
));
auto
pd
=
std
::
static_pointer_cast
<
mkldnn
::
inner_product_forward
::
primitive_desc
>
(
dev_ctx
.
GetBlob
(
key_fc_pd
));
PADDLE_ENFORCE
(
pd
!=
nullptr
,
"Fail to find pd in device context"
);
if
(
w_grad
)
{
auto
weights_grad_memory
=
mkldnn
::
memory
(
{
weights_md
,
mkldnn_engine
},
platform
::
to_void_cast
(
w_grad_data
));
auto
bwd_weight_pd
=
platform
::
MKLDNNBwdPrimitiveDesc
<
mkldnn
::
inner_product_backward_weights
>
(
mkldnn_engine
,
*
pd
,
src_md
,
weights_md
,
dst_md
);
auto
bwd_weights_prim
=
mkldnn
::
inner_product_backward_weights
(
bwd_weight_pd
,
src_memory
,
dst_memory
,
weights_grad_memory
);
std
::
vector
<
mkldnn
::
primitive
>
pipeline
{
bwd_weights_prim
};
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
}
if
(
input_grad
)
{
auto
src_grad_memory
=
mkldnn
::
memory
(
{
src_md
,
mkldnn_engine
},
platform
::
to_void_cast
(
input_grad_data
));
auto
bwd_data_pd
=
platform
::
MKLDNNBwdPrimitiveDesc
<
mkldnn
::
inner_product_backward_data
>
(
mkldnn_engine
,
*
pd
,
src_md
,
weights_md
,
dst_md
);
auto
bwd_data_prim
=
mkldnn
::
inner_product_backward_data
(
bwd_data_pd
,
dst_memory
,
weight_memory
,
src_grad_memory
);
std
::
vector
<
mkldnn
::
primitive
>
pipeline
{
bwd_data_prim
};
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
}
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OP_KERNEL
(
mul
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
paddle
::
operators
::
MulMKLDNNOpKernel
<
float
>
);
REGISTER_OP_KERNEL
(
mul_grad
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
paddle
::
operators
::
MulMKLDNNGradOpKernel
<
float
>
);
paddle/fluid/operators/mul_op.cc
浏览文件 @
6e7b883b
...
...
@@ -13,8 +13,13 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/mul_op.h"
#include <string>
#include <vector>
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace
paddle
{
namespace
operators
{
...
...
@@ -71,6 +76,22 @@ class MulOp : public framework::OperatorWithKernel {
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_dims
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
private:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
framework
::
LibraryType
library
{
framework
::
LibraryType
::
kPlain
};
#ifdef PADDLE_WITH_MKLDNN
if
(
library
==
framework
::
LibraryType
::
kPlain
&&
platform
::
CanMKLDNNBeUsed
(
ctx
))
{
library
=
framework
::
LibraryType
::
kMKLDNN
;
}
#endif
framework
::
DataLayout
layout
{
framework
::
DataLayout
::
kAnyLayout
};
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
GetPlace
(),
layout
,
library
);
}
};
class
MulOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
...
...
@@ -100,6 +121,9 @@ class MulOpMaker : public framework::OpProtoAndCheckerMaker {
)DOC"
)
.
SetDefault
(
1
)
.
EqualGreaterThan
(
1
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddAttr
<
int
>
(
"y_num_col_dims"
,
R"DOC((int, default 1), The mul_op can take tensors with more than two,
...
...
@@ -154,6 +178,22 @@ class MulGradOp : public framework::OperatorWithKernel {
ctx
->
SetOutputDim
(
y_grad_name
,
y_dims
);
}
}
private:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
framework
::
LibraryType
library
{
framework
::
LibraryType
::
kPlain
};
#ifdef PADDLE_WITH_MKLDNN
if
(
library
==
framework
::
LibraryType
::
kPlain
&&
platform
::
CanMKLDNNBeUsed
(
ctx
))
{
library
=
framework
::
LibraryType
::
kMKLDNN
;
}
#endif
framework
::
DataLayout
layout
{
framework
::
DataLayout
::
kAnyLayout
};
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
GetPlace
(),
layout
,
library
);
}
};
}
// namespace operators
...
...
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @
6e7b883b
...
...
@@ -13,9 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <mkldnn.h>
#include <vector>
#include "mkldnn/include/mkldnn.hpp"
#include "paddle/fluid/framework/operator.h"
namespace
paddle
{
...
...
@@ -34,6 +33,32 @@ typedef std::unique_ptr<MKLDNNMemory> MKLDNNMemoryPtr;
typedef
std
::
unique_ptr
<
MKLDNNPrimitive
>
MKLDNNPrimitivePtr
;
typedef
std
::
unique_ptr
<
MKLDNNPrimitiveDesc
>
MKLDNNPrimitiveDescPtr
;
template
<
typename
Type
>
void
*
to_void_cast
(
const
Type
*
t
)
{
return
static_cast
<
void
*>
(
const_cast
<
Type
*>
(
t
));
}
template
<
class
Type
>
using
tf_desc
=
typename
Type
::
desc
;
template
<
class
Type
>
using
tf_pd
=
typename
Type
::
primitive_desc
;
template
<
typename
Type
,
typename
Engine
,
typename
...
Args
>
std
::
shared_ptr
<
tf_pd
<
Type
>>
MKLDNNFwdPrimitiveDesc
(
const
Engine
&
e
,
Args
&&
...
args
)
{
auto
desc
=
tf_desc
<
Type
>
(
mkldnn
::
prop_kind
::
forward
,
(
args
)...);
auto
pd
=
new
tf_pd
<
Type
>
(
desc
,
e
);
return
std
::
shared_ptr
<
tf_pd
<
Type
>>
(
pd
);
}
template
<
typename
Type
,
typename
Engine
,
typename
Primitive
,
typename
...
Args
>
tf_pd
<
Type
>
MKLDNNBwdPrimitiveDesc
(
const
Engine
&
e
,
const
Primitive
&
p
,
Args
&&
...
args
)
{
auto
desc
=
tf_desc
<
Type
>
(
args
...);
return
tf_pd
<
Type
>
(
desc
,
e
,
p
);
}
inline
mkldnn
::
memory
::
desc
MKLDNNMemDesc
(
const
std
::
vector
<
int
>&
dims
,
mkldnn
::
memory
::
data_type
data_type
,
mkldnn
::
memory
::
format
format
)
{
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
6e7b883b
...
...
@@ -156,64 +156,37 @@ def fc(input,
dtype
=
helper
.
input_dtype
()
mul_results
=
[]
if
use_mkldnn
:
tmp
=
helper
.
create_tmp_variable
(
dtype
)
input_shape
=
input
.
shape
for
input_var
,
param_attr
in
helper
.
iter_inputs_and_params
():
input_shape
=
input_var
.
shape
param_shape
=
[
reduce
(
lambda
a
,
b
:
a
*
b
,
input_shape
[
num_flatten_dims
:],
1
)
]
+
[
size
]
w
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
param_shape
,
dtype
=
dtype
,
is_bias
=
False
)
if
bias_attr
is
None
or
bias_attr
is
False
:
bias_attr
=
False
else
:
bias_attr
=
True
attr
=
param_attr
,
shape
=
param_shape
,
dtype
=
dtype
,
is_bias
=
False
)
tmp
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"
fc
"
,
inputs
=
{
"
Input"
:
input
,
"
W
"
:
w
},
type
=
"
mul
"
,
inputs
=
{
"
X"
:
input_var
,
"
Y
"
:
w
},
outputs
=
{
"Out"
:
tmp
},
attrs
=
{
"use_mkldnn"
:
use_mkldnn
,
"bias_attr"
:
bias_attr
})
return
helper
.
append_activation
(
tmp
)
attrs
=
{
"x_num_col_dims"
:
num_flatten_dims
,
"y_num_col_dims"
:
1
,
"use_mkldnn"
:
use_mkldnn
})
mul_results
.
append
(
tmp
)
if
len
(
mul_results
)
==
1
:
pre_bias
=
mul_results
[
0
]
else
:
for
input_var
,
param_attr
in
helper
.
iter_inputs_and_params
():
input_shape
=
input_var
.
shape
param_shape
=
[
reduce
(
lambda
a
,
b
:
a
*
b
,
input_shape
[
num_flatten_dims
:],
1
)
]
+
[
size
]
w
=
helper
.
create_parameter
(
attr
=
param_attr
,
shape
=
param_shape
,
dtype
=
dtype
,
is_bias
=
False
)
tmp
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
input_var
,
"Y"
:
w
},
outputs
=
{
"Out"
:
tmp
},
attrs
=
{
"x_num_col_dims"
:
num_flatten_dims
,
"y_num_col_dims"
:
1
,
})
mul_results
.
append
(
tmp
)
if
len
(
mul_results
)
==
1
:
pre_bias
=
mul_results
[
0
]
else
:
pre_bias
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
mul_results
},
outputs
=
{
"Out"
:
pre_bias
})
# add bias
pre_activation
=
helper
.
append_bias_op
(
pre_bias
,
dim_start
=
num_flatten_dims
)
# add activation
return
helper
.
append_activation
(
pre_activation
)
pre_bias
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
mul_results
},
outputs
=
{
"Out"
:
pre_bias
})
# add bias
pre_activation
=
helper
.
append_bias_op
(
pre_bias
,
dim_start
=
num_flatten_dims
)
# add activation
return
helper
.
append_activation
(
pre_activation
)
def
embedding
(
input
,
...
...
@@ -3688,8 +3661,8 @@ def label_smooth(label,
name
=
None
):
"""
Label smoothing is a mechanism to regularize the classifier layer and is
called label-smoothing regularization (LSR).
called label-smoothing regularization (LSR).
Label smoothing is proposed to encourage the model to be less confident,
since optimizing the log-likelihood of the correct label directly may
cause overfitting and reduce the ability of the model to adapt. Label
...
...
@@ -3713,10 +3686,10 @@ def label_smooth(label,
prior_dist(Variable): The prior distribution to be used to smooth
labels. If not provided, an uniform distribution
is used. The shape of :attr:`prior_dist` should
be :math:`(1, class\_num)`.
be :math:`(1, class\_num)`.
epsilon(float): The weight used to mix up the original ground-truth
distribution and the fixed distribution.
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32,
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32,
float_64, int etc.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
...
...
python/paddle/fluid/tests/unittests/test_mul_mkldnn_op.py
0 → 100644
浏览文件 @
6e7b883b
# 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.
import
unittest
from
test_mul_op
import
TestMulOp
,
TestMulOp2
,
TestFP16MulOp1
,
TestFP16MulOp2
class
TestMKLDNNMulOp
(
TestMulOp
):
def
init_op_test
(
self
):
super
(
TestMKLDNNMulOp
,
self
).
setUp
()
self
.
attrs
=
{
"use_mkldnn"
:
True
}
class
TestMKLDNNMulOp2
(
TestMulOp2
):
def
init_op_test
(
self
):
super
(
TestMKLDNNMulOp2
,
self
).
setUp
()
self
.
attrs
=
{
"use_mkldnn"
:
True
}
class
TestMKLDNNFP16MulOp1
(
TestFP16MulOp1
):
def
init_op_test
(
self
):
super
(
TestMKLDNNFP16MulOp1
,
self
).
setUp
()
self
.
attrs
=
{
"use_mkldnn"
:
True
}
class
TestMKLDNNFP16MulOp2
(
TestFP16MulOp2
):
def
init_op_test
(
self
):
super
(
TestMKLDNNFP16MulOp2
,
self
).
setUp
()
self
.
attrs
=
{
"use_mkldnn"
:
True
}
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_mul_op.py
浏览文件 @
6e7b883b
...
...
@@ -21,10 +21,12 @@ from op_test import OpTest
class
TestMulOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"mul"
self
.
use_mkldnn
=
False
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
84
)).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
((
84
,
100
)).
astype
(
"float32"
)
}
self
.
attrs
=
{
'use_mkldnn'
:
self
.
use_mkldnn
}
self
.
outputs
=
{
'Out'
:
np
.
dot
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
...
...
@@ -45,11 +47,16 @@ class TestMulOp(OpTest):
class
TestMulOp2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"mul"
self
.
use_mkldnn
=
False
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
15
,
4
,
12
,
10
)).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
((
4
,
30
,
8
,
2
,
9
)).
astype
(
"float32"
)
}
self
.
attrs
=
{
'x_num_col_dims'
:
2
,
'y_num_col_dims'
:
2
}
self
.
attrs
=
{
'x_num_col_dims'
:
2
,
'y_num_col_dims'
:
2
,
'use_mkldnn'
:
self
.
use_mkldnn
}
result
=
np
.
dot
(
self
.
inputs
[
'X'
].
reshape
(
15
*
4
,
12
*
10
),
self
.
inputs
[
'Y'
].
reshape
(
4
*
30
,
8
*
2
*
9
))
result
=
result
.
reshape
(
15
,
4
,
8
,
2
,
9
)
...
...
@@ -73,9 +80,11 @@ class TestMulOp2(OpTest):
class
TestFP16MulOp1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"mul"
self
.
use_mkldnn
=
False
x
=
np
.
random
.
random
((
32
,
84
)).
astype
(
"float16"
)
y
=
np
.
random
.
random
((
84
,
100
)).
astype
(
"float16"
)
self
.
inputs
=
{
'X'
:
x
.
view
(
np
.
uint16
),
'Y'
:
y
.
view
(
np
.
uint16
)}
self
.
attrs
=
{
'use_mkldnn'
:
self
.
use_mkldnn
}
self
.
outputs
=
{
'Out'
:
np
.
dot
(
x
,
y
)}
def
test_check_output
(
self
):
...
...
@@ -88,12 +97,14 @@ class TestFP16MulOp1(OpTest):
class
TestFP16MulOp2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"mul"
self
.
use_mkldnn
=
False
x
=
np
.
random
.
random
((
15
,
4
,
12
,
10
)).
astype
(
"float16"
)
y
=
np
.
random
.
random
((
4
,
30
,
8
,
2
,
9
)).
astype
(
"float16"
)
self
.
inputs
=
{
'X'
:
x
.
view
(
np
.
uint16
),
'Y'
:
y
.
view
(
np
.
uint16
)}
self
.
attrs
=
{
'x_num_col_dims'
:
2
,
'y_num_col_dims'
:
2
,
'use_mkldnn'
:
self
.
use_mkldnn
}
result
=
np
.
dot
(
x
.
reshape
(
15
*
4
,
12
*
10
),
y
.
reshape
(
4
*
30
,
8
*
2
*
9
))
...
...
python/paddle/fluid/tests/unittests/test_operator_desc.py
浏览文件 @
6e7b883b
...
...
@@ -62,7 +62,8 @@ class TestOperator(unittest.TestCase):
self
.
assertEqual
(
mul_op
.
output_names
,
[
"Out"
])
self
.
assertEqual
(
mul_op
.
output
(
"Out"
),
[
"mul.out"
])
self
.
assertEqual
(
set
(
mul_op
.
attr_names
),
set
([
"x_num_col_dims"
,
"y_num_col_dims"
]))
set
(
mul_op
.
attr_names
),
set
([
"x_num_col_dims"
,
"y_num_col_dims"
,
"use_mkldnn"
]))
self
.
assertEqual
(
mul_op
.
has_attr
(
"x_num_col_dims"
),
True
)
self
.
assertEqual
(
mul_op
.
attr_type
(
"x_num_col_dims"
),
core
.
AttrType
.
INT
)
self
.
assertEqual
(
mul_op
.
attr
(
"x_num_col_dims"
),
1
)
...
...
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