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体验新版 GitCode,发现更多精彩内容 >>
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bd06a828
编写于
7月 04, 2022
作者:
Z
zhaoying9105
提交者:
GitHub
7月 04, 2022
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电子邮件补丁
差异文件
[MLU]: add hard_sigmoid,hard_sigmoid_grad,hard_swish,hard_swish_grad kernel (#44044)
上级
8f8a6848
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
519 addition
and
0 deletion
+519
-0
paddle/fluid/operators/activation_op_mlu.cc
paddle/fluid/operators/activation_op_mlu.cc
+160
-0
python/paddle/fluid/tests/unittests/mlu/test_hard_sigmoid_op_mlu.py
...dle/fluid/tests/unittests/mlu/test_hard_sigmoid_op_mlu.py
+194
-0
python/paddle/fluid/tests/unittests/mlu/test_hard_swish_op_mlu.py
...addle/fluid/tests/unittests/mlu/test_hard_swish_op_mlu.py
+165
-0
未找到文件。
paddle/fluid/operators/activation_op_mlu.cc
浏览文件 @
bd06a828
...
...
@@ -256,6 +256,149 @@ class ExpGradMLUKernel : public framework::OpKernel<T> {
}
};
template
<
typename
T
>
class
HardSwishMLUKernel
:
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"
);
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
float
threshold
=
ctx
.
Attr
<
float
>
(
"threshold"
);
float
scale
=
ctx
.
Attr
<
float
>
(
"scale"
);
float
offset
=
ctx
.
Attr
<
float
>
(
"offset"
);
PADDLE_ENFORCE_EQ
(
threshold
,
6.0
f
,
platform
::
errors
::
External
(
"Not support threshold [%f] in MLU"
,
threshold
));
PADDLE_ENFORCE_EQ
(
scale
,
6.0
f
,
platform
::
errors
::
External
(
"Not support scale [%f] in MLU"
,
scale
));
PADDLE_ENFORCE_EQ
(
offset
,
3.0
f
,
platform
::
errors
::
External
(
"Not support offset [%f] in MLU"
,
offset
));
MLUCnnlActivationDesc
act_desc
(
CNNL_ACTIVATION_HARDSWISH
,
1.0
f
/*ceof useless*/
);
MLUCnnlTensorDesc
input_desc
(
*
input
);
MLUCnnlTensorDesc
output_desc
(
*
output
);
MLUCnnl
::
Active
(
ctx
,
act_desc
.
get
(),
input_desc
.
get
(),
GetBasePtr
(
input
),
output_desc
.
get
(),
GetBasePtr
(
output
));
}
};
template
<
typename
T
>
class
HardSwishGradMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
float
threshold
=
ctx
.
Attr
<
float
>
(
"threshold"
);
float
scale
=
ctx
.
Attr
<
float
>
(
"scale"
);
float
offset
=
ctx
.
Attr
<
float
>
(
"offset"
);
PADDLE_ENFORCE_EQ
(
threshold
,
6.0
f
,
platform
::
errors
::
External
(
"Not support threshold [%f] in MLU"
,
threshold
));
PADDLE_ENFORCE_EQ
(
scale
,
6.0
f
,
platform
::
errors
::
External
(
"Not support scale [%f] in MLU"
,
scale
));
PADDLE_ENFORCE_EQ
(
offset
,
3.0
f
,
platform
::
errors
::
External
(
"Not support offset [%f] in MLU"
,
offset
));
auto
*
out
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
dout
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
MLUCnnlTensorDesc
out_desc
(
*
out
);
MLUCnnlTensorDesc
dout_desc
(
*
dout
);
MLUCnnlTensorDesc
dx_desc
(
*
dx
);
MLUCnnlActivationDesc
act_desc
(
CNNL_ACTIVATION_HARDSWISH
,
1.0
f
/*ceof useless*/
);
MLUCnnl
::
ActiveGrad
(
ctx
,
act_desc
.
get
(),
nullptr
,
nullptr
,
nullptr
,
nullptr
,
dout_desc
.
get
(),
GetBasePtr
(
dout
),
out_desc
.
get
(),
GetBasePtr
(
out
),
dx_desc
.
get
(),
GetBasePtr
(
dx
));
}
};
template
<
typename
T
>
class
HardSigmoidMLUKernel
:
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"
);
float
slope
=
ctx
.
Attr
<
float
>
(
"slope"
);
float
offset
=
ctx
.
Attr
<
float
>
(
"offset"
);
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
MLUCnnlActivationDesc
act_desc
(
CNNL_ACTIVATION_HARDSIGMOID
,
1.0
f
/*ceof useless*/
,
1.0
f
/*sliced_dim useless*/
,
slope
,
offset
);
MLUCnnlTensorDesc
input_desc
(
*
input
);
MLUCnnlTensorDesc
output_desc
(
*
output
);
MLUCnnl
::
Active
(
ctx
,
act_desc
.
get
(),
input_desc
.
get
(),
GetBasePtr
(
input
),
output_desc
.
get
(),
GetBasePtr
(
output
));
}
};
template
<
typename
T
>
class
HardSigmoidGradMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
dout
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
out
=
ctx
.
Input
<
Tensor
>
(
"Out"
);
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
float
slope
=
ctx
.
Attr
<
float
>
(
"slope"
);
float
offset
=
ctx
.
Attr
<
float
>
(
"offset"
);
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
MLUCnnlActivationDesc
act_desc
(
CNNL_ACTIVATION_HARDSIGMOID
,
1.0
f
/*ceof useless*/
,
1.0
f
/*sliced_dim useless*/
,
slope
,
offset
);
MLUCnnlTensorDesc
out_desc
(
*
out
);
MLUCnnlTensorDesc
dout_desc
(
*
dout
);
MLUCnnlTensorDesc
dx_desc
(
*
dx
);
MLUCnnl
::
ActiveGrad
(
ctx
,
act_desc
.
get
(),
nullptr
,
nullptr
,
nullptr
,
nullptr
,
dout_desc
.
get
(),
GetBasePtr
(
dout
),
out_desc
.
get
(),
GetBasePtr
(
out
),
dx_desc
.
get
(),
GetBasePtr
(
dx
));
}
};
}
// namespace operators
}
// namespace paddle
...
...
@@ -359,3 +502,20 @@ REGISTER_OP_MLU_KERNEL(exp,
REGISTER_OP_MLU_KERNEL
(
exp_grad
,
ops
::
ExpGradMLUKernel
<
float
>
,
ops
::
ExpGradMLUKernel
<
paddle
::
platform
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
hard_swish
,
ops
::
HardSwishMLUKernel
<
float
>
,
ops
::
HardSwishMLUKernel
<
paddle
::
platform
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
hard_swish_grad
,
ops
::
HardSwishGradMLUKernel
<
float
>
,
ops
::
HardSwishGradMLUKernel
<
paddle
::
platform
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
hard_sigmoid
,
ops
::
HardSigmoidMLUKernel
<
float
>
,
ops
::
HardSigmoidMLUKernel
<
paddle
::
platform
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
hard_sigmoid_grad
,
ops
::
HardSigmoidGradMLUKernel
<
float
>
,
ops
::
HardSigmoidGradMLUKernel
<
paddle
::
platform
::
float16
>
);
python/paddle/fluid/tests/unittests/mlu/test_hard_sigmoid_op_mlu.py
0 → 100644
浏览文件 @
bd06a828
# 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
numpy
as
np
import
unittest
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.nn.functional
as
F
paddle
.
enable_static
()
SEED
=
2021
def
ref_hardsigmoid
(
x
,
slope
=
0.166666666666667
,
offset
=
0.5
):
return
np
.
maximum
(
np
.
minimum
(
x
*
slope
+
offset
,
1.
),
0.
).
astype
(
x
.
dtype
)
class
TestMLUHardSigmoid
(
OpTest
):
def
setUp
(
self
):
paddle
.
enable_static
()
self
.
op_type
=
"hard_sigmoid"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
set_attrs
()
x
=
np
.
random
.
uniform
(
-
5
,
5
,
[
10
,
12
]).
astype
(
self
.
dtype
)
lower_threshold
=
-
self
.
offset
/
self
.
slope
upper_threshold
=
(
1.
-
self
.
offset
)
/
self
.
slope
# Same reason as TestAbs
delta
=
0.005
x
[
np
.
abs
(
x
-
lower_threshold
)
<
delta
]
=
lower_threshold
-
0.02
x
[
np
.
abs
(
x
-
upper_threshold
)
<
delta
]
=
upper_threshold
-
0.02
out
=
ref_hardsigmoid
(
x
,
self
.
slope
,
self
.
offset
)
self
.
attrs
=
{
'slope'
:
self
.
slope
,
'offset'
:
self
.
offset
}
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
)
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
MLUPlace
(
0
)
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
set_attrs
(
self
):
self
.
slope
=
0.166666666666667
self
.
offset
=
0.5
class
TestMLUHardSigmoid2
(
TestMLUHardSigmoid
):
def
set_attrs
(
self
):
self
.
slope
=
0.2
self
.
offset
=
0.5
class
TestMLUHardSigmoid3
(
TestMLUHardSigmoid
):
def
set_attrs
(
self
):
self
.
slope
=
0.2
self
.
offset
=
0.4
class
TestMLUHardSigmoidFp16
(
unittest
.
TestCase
):
def
setUp
(
self
):
paddle
.
disable_static
()
self
.
place
=
paddle
.
MLUPlace
(
0
)
self
.
dtype
=
np
.
float32
# float32
self
.
float32_x
=
np
.
random
.
uniform
(
-
5
,
5
,
[
10
,
12
]).
astype
(
np
.
float32
)
paddle
.
set_device
(
'cpu'
)
data
=
paddle
.
to_tensor
(
self
.
float32_x
,
stop_gradient
=
True
)
self
.
float32_y
=
F
.
hardsigmoid
(
data
)
# float16
self
.
float16_x
=
self
.
float32_x
.
astype
(
np
.
float16
)
self
.
float16_y
=
ref_hardsigmoid
(
self
.
float16_x
)
def
test_check_output_and_grad_mlu
(
self
):
# mlu float16
paddle
.
set_device
(
'mlu'
)
data
=
paddle
.
to_tensor
(
self
.
float16_x
,
stop_gradient
=
True
)
mlu_float16_y
=
F
.
hardsigmoid
(
data
)
cpu_diff_1
=
np
.
divide
(
np
.
sum
(
np
.
abs
(
self
.
float32_y
.
numpy
()
-
self
.
float16_y
)),
np
.
sum
(
np
.
abs
(
self
.
float32_y
.
numpy
())))
mlu_diff_1
=
np
.
divide
(
np
.
sum
(
np
.
abs
(
self
.
float32_y
.
numpy
()
-
mlu_float16_y
.
numpy
())),
np
.
sum
(
np
.
abs
(
self
.
float32_y
.
numpy
())))
cpu_diff_2
=
np
.
divide
(
np
.
sum
(
np
.
square
(
self
.
float32_y
.
numpy
()
-
self
.
float16_y
)),
np
.
sum
(
np
.
square
(
self
.
float32_y
.
numpy
())))
mlu_diff_2
=
np
.
divide
(
np
.
sum
(
np
.
square
(
self
.
float32_y
.
numpy
()
-
mlu_float16_y
.
numpy
())),
np
.
sum
(
np
.
square
(
self
.
float32_y
.
numpy
())))
assert
mlu_diff_1
<=
cpu_diff_1
assert
mlu_diff_2
<=
cpu_diff_2
class
TestHardsigmoidAPI
(
unittest
.
TestCase
):
# test paddle.nn.Hardsigmoid, paddle.nn.functional.hardsigmoid
def
setUp
(
self
):
self
.
x_np
=
np
.
random
.
uniform
(
-
1
,
1
,
[
10
,
12
]).
astype
(
np
.
float32
)
self
.
place
=
paddle
.
MLUPlace
(
0
)
def
test_static_api
(
self
):
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
x
=
paddle
.
static
.
data
(
'X'
,
self
.
x_np
.
shape
,
self
.
x_np
.
dtype
)
out1
=
F
.
hardsigmoid
(
x
)
m
=
paddle
.
nn
.
Hardsigmoid
()
out2
=
m
(
x
)
exe
=
paddle
.
static
.
Executor
(
self
.
place
)
res
=
exe
.
run
(
feed
=
{
'X'
:
self
.
x_np
},
fetch_list
=
[
out1
,
out2
])
out_ref
=
ref_hardsigmoid
(
self
.
x_np
)
for
r
in
res
:
self
.
assertTrue
(
np
.
allclose
(
out_ref
,
r
))
def
test_dygraph_api
(
self
):
paddle
.
disable_static
(
self
.
place
)
x
=
paddle
.
to_tensor
(
self
.
x_np
)
out1
=
F
.
hardsigmoid
(
x
)
m
=
paddle
.
nn
.
Hardsigmoid
()
out2
=
m
(
x
)
out_ref
=
ref_hardsigmoid
(
self
.
x_np
)
for
r
in
[
out1
,
out2
]:
self
.
assertTrue
(
np
.
allclose
(
out_ref
,
r
.
numpy
()))
paddle
.
enable_static
()
def
test_fluid_api
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
x
=
fluid
.
data
(
'X'
,
self
.
x_np
.
shape
,
self
.
x_np
.
dtype
)
out
=
fluid
.
layers
.
hard_sigmoid
(
x
)
exe
=
fluid
.
Executor
(
self
.
place
)
res
=
exe
.
run
(
feed
=
{
'X'
:
self
.
x_np
},
fetch_list
=
[
out
])
out_ref
=
ref_hardsigmoid
(
self
.
x_np
,
0.2
,
0.5
)
self
.
assertTrue
(
np
.
allclose
(
out_ref
,
res
[
0
]))
paddle
.
disable_static
(
self
.
place
)
x
=
paddle
.
to_tensor
(
self
.
x_np
)
out
=
paddle
.
fluid
.
layers
.
hard_sigmoid
(
x
)
self
.
assertTrue
(
np
.
allclose
(
out_ref
,
out
.
numpy
()))
paddle
.
enable_static
()
def
test_errors
(
self
):
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
# The input type must be Variable.
self
.
assertRaises
(
TypeError
,
F
.
hardsigmoid
,
1
)
# The input dtype must be float16, float32, float64.
x_int32
=
paddle
.
fluid
.
data
(
name
=
'x_int32'
,
shape
=
[
12
,
10
],
dtype
=
'int32'
)
self
.
assertRaises
(
TypeError
,
F
.
hardsigmoid
,
x_int32
)
# support the input dtype is float16
x_fp16
=
paddle
.
fluid
.
data
(
name
=
'x_fp16'
,
shape
=
[
12
,
10
],
dtype
=
'float16'
)
F
.
hardsigmoid
(
x_fp16
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/mlu/test_hard_swish_op_mlu.py
0 → 100644
浏览文件 @
bd06a828
# 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
paddle.nn.functional
as
F
import
paddle.fluid
as
fluid
import
paddle
from
op_test
import
OpTest
import
numpy
as
np
import
unittest
import
sys
sys
.
path
.
append
(
".."
)
paddle
.
enable_static
()
SEED
=
2020
def
scalarToType
(
val
,
data_type
):
converted_val
=
np
.
array
([
val
]).
astype
(
data_type
)[
0
]
print
(
"converted_val type: "
,
type
(
converted_val
))
return
converted_val
def
ref_hard_swish_grad
(
x
,
threshold
,
scale
,
offset
,
data_type
):
threshold
=
scalarToType
(
threshold
,
data_type
)
scale
=
scalarToType
(
scale
,
data_type
)
offset
=
scalarToType
(
offset
,
data_type
)
dout
=
np
.
full_like
(
x
,
fill_value
=
1.
/
x
.
size
)
tmp
=
((
x
+
offset
)
<
threshold
).
astype
(
x
.
dtype
)
dx
=
dout
*
(((
x
+
offset
)
>
0
).
astype
(
x
.
dtype
)
*
(
2
*
x
+
offset
)
*
tmp
/
scale
+
1.0
-
tmp
)
return
dx
class
TestHardSwishMLU
(
OpTest
):
def
setUp
(
self
):
paddle
.
enable_static
()
self
.
op_type
=
"hard_swish"
self
.
place
=
paddle
.
MLUPlace
(
0
)
self
.
init_dtype
()
x
=
np
.
random
.
uniform
(
-
2
,
2
,
[
10
,
12
]).
astype
(
self
.
dtype
)
threshold
=
6.0
scale
=
6.0
offset
=
3.0
x
[
np
.
abs
(
x
+
offset
)
<
0.005
]
=
0.02
x
[
np
.
abs
(
x
-
threshold
+
offset
)
<
0.005
]
=
threshold
-
offset
+
0.02
out
=
(
x
*
(
np
.
minimum
(
np
.
maximum
(
x
+
offset
,
0.
),
threshold
)
/
scale
)).
astype
(
self
.
dtype
)
self
.
x_grad
=
ref_hard_swish_grad
(
x
,
threshold
,
scale
,
offset
,
self
.
dtype
)
self
.
set_mlu
()
self
.
inputs
=
{
'X'
:
x
}
self
.
attrs
=
{
'threshold'
:
threshold
,
'scale'
:
scale
,
'offset'
:
offset
}
self
.
outputs
=
{
'Out'
:
out
}
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
)
class
TestHardSwishMLUWithCPUFloat16
(
unittest
.
TestCase
):
def
setUp
(
self
):
paddle
.
disable_static
()
self
.
place
=
paddle
.
MLUPlace
(
0
)
self
.
dtype
=
np
.
float32
# float32
self
.
float32_x
=
np
.
random
.
uniform
(
-
6
,
10
,
[
8
,
15
]).
astype
(
np
.
float32
)
paddle
.
set_device
(
'cpu'
)
data
=
paddle
.
to_tensor
(
self
.
float32_x
,
stop_gradient
=
False
)
self
.
float32_y
=
F
.
hardswish
(
data
)
self
.
float32_y
.
sum
().
backward
()
self
.
float32_grad
=
data
.
grad
# float16
self
.
float16_x
=
self
.
float32_x
.
astype
(
np
.
float16
)
threshold
=
6.0
scale
=
6.0
offset
=
3.0
threshold
=
scalarToType
(
threshold
,
np
.
float16
)
scale
=
scalarToType
(
scale
,
np
.
float16
)
offset
=
scalarToType
(
offset
,
np
.
float16
)
self
.
float16_y
=
(
self
.
float16_x
*
(
np
.
minimum
(
np
.
maximum
(
self
.
float16_x
+
offset
,
scalarToType
(
0.
,
np
.
float16
)),
threshold
)
/
scale
)).
astype
(
np
.
float16
)
self
.
float16_grad
=
ref_hard_swish_grad
(
self
.
float16_x
,
threshold
,
scale
,
offset
,
np
.
float16
)
def
test_check_output_and_grad_mlu
(
self
):
# mlu float16
paddle
.
set_device
(
'mlu'
)
data
=
paddle
.
to_tensor
(
self
.
float16_x
,
stop_gradient
=
False
)
mlu_float16_y
=
F
.
hardswish
(
data
)
mlu_float16_y
.
sum
().
backward
()
mlu_float16_grad
=
data
.
grad
cpu_diff_1
=
np
.
divide
(
np
.
sum
(
np
.
abs
(
self
.
float32_y
.
numpy
()
-
self
.
float16_y
)),
np
.
sum
(
np
.
abs
(
self
.
float32_y
.
numpy
())))
mlu_diff_1
=
np
.
divide
(
np
.
sum
(
np
.
abs
(
self
.
float32_y
.
numpy
()
-
mlu_float16_y
.
numpy
())),
np
.
sum
(
np
.
abs
(
self
.
float32_y
.
numpy
())))
cpu_diff_2
=
np
.
divide
(
np
.
sum
(
np
.
square
(
self
.
float32_y
.
numpy
()
-
self
.
float16_y
)),
np
.
sum
(
np
.
square
(
self
.
float32_y
.
numpy
())))
mlu_diff_2
=
np
.
divide
(
np
.
sum
(
np
.
square
(
self
.
float32_y
.
numpy
()
-
mlu_float16_y
.
numpy
())),
np
.
sum
(
np
.
square
(
self
.
float32_y
.
numpy
())))
assert
mlu_diff_1
<=
cpu_diff_1
assert
mlu_diff_2
<=
cpu_diff_2
cpu_diff_1
=
np
.
divide
(
np
.
sum
(
np
.
abs
(
self
.
float32_grad
.
numpy
()
-
self
.
float16_grad
)),
np
.
sum
(
np
.
abs
(
self
.
float32_grad
.
numpy
())))
mlu_diff_1
=
np
.
divide
(
np
.
sum
(
np
.
abs
(
self
.
float32_grad
.
numpy
()
-
mlu_float16_grad
.
numpy
())),
np
.
sum
(
np
.
abs
(
self
.
float32_grad
.
numpy
())))
cpu_diff_2
=
np
.
divide
(
np
.
sum
(
np
.
square
(
self
.
float32_grad
.
numpy
()
-
self
.
float16_grad
)),
np
.
sum
(
np
.
square
(
self
.
float32_grad
.
numpy
())))
mlu_diff_2
=
np
.
divide
(
np
.
sum
(
np
.
square
(
self
.
float32_grad
.
numpy
()
-
mlu_float16_grad
.
numpy
())),
np
.
sum
(
np
.
square
(
self
.
float32_grad
.
numpy
())))
assert
mlu_diff_1
<=
cpu_diff_1
assert
mlu_diff_2
<=
cpu_diff_2
if
__name__
==
'__main__'
:
unittest
.
main
()
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