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f1be9cf1
编写于
7月 12, 2022
作者:
Q
qipengh
提交者:
GitHub
7月 12, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
[MLU]add sync_batch_norm op (#44176)
上级
75aaa08a
变更
6
展开全部
隐藏空白更改
内联
并排
Showing
6 changed file
with
974 addition
and
11 deletion
+974
-11
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+4
-0
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+218
-11
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+153
-0
paddle/fluid/operators/sync_batch_norm_op_mlu.cc
paddle/fluid/operators/sync_batch_norm_op_mlu.cc
+492
-0
python/paddle/fluid/tests/unittests/mlu/CMakeLists.txt
python/paddle/fluid/tests/unittests/mlu/CMakeLists.txt
+2
-0
python/paddle/fluid/tests/unittests/mlu/sync_batch_norm_op_mlu.py
...addle/fluid/tests/unittests/mlu/sync_batch_norm_op_mlu.py
+105
-0
未找到文件。
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
f1be9cf1
...
@@ -149,6 +149,10 @@ if (WITH_ASCEND_CL)
...
@@ -149,6 +149,10 @@ if (WITH_ASCEND_CL)
op_library
(
sync_batch_norm_op
)
op_library
(
sync_batch_norm_op
)
endif
()
endif
()
if
(
WITH_MLU
)
op_library
(
sync_batch_norm_op
)
endif
()
op_library
(
lstm_op DEPS
${
OP_HEADER_DEPS
}
lstm_compute
)
op_library
(
lstm_op DEPS
${
OP_HEADER_DEPS
}
lstm_compute
)
op_library
(
eye_op DEPS
${
OP_HEADER_DEPS
}
)
op_library
(
eye_op DEPS
${
OP_HEADER_DEPS
}
)
op_library
(
recurrent_op DEPS
${
OP_HEADER_DEPS
}
)
op_library
(
recurrent_op DEPS
${
OP_HEADER_DEPS
}
)
...
...
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
f1be9cf1
...
@@ -259,15 +259,16 @@ MLUCnnlTensorDesc::~MLUCnnlTensorDesc() {
...
@@ -259,15 +259,16 @@ MLUCnnlTensorDesc::~MLUCnnlTensorDesc() {
MLUCnnlActivationDesc
::
MLUCnnlActivationDesc
(
MLUCnnlActivationDesc
::
MLUCnnlActivationDesc
(
const
cnnlActivationMode_t
act_mode
,
const
float
ceof
)
{
const
cnnlActivationMode_t
act_mode
,
const
float
ceof
)
{
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlCreateActivationDescriptor
(
&
active_desc_
));
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlCreateActivationDescriptor
(
&
active_desc_
));
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSetActivationDescriptor_v4
(
PADDLE_ENFORCE_MLU_SUCCESS
(
active_desc_
,
cnnlSetActivationDescriptor_v5
(
active_desc_
,
act_mode
,
act_mode
,
CNNL_ACTIVATION_HIGH_PRECISION
,
CNNL_ACTIVATION_HIGH_PRECISION
,
CNNL_NOT_PROPAGATE_NAN
,
CNNL_NOT_PROPAGATE_NAN
,
ceof
,
ceof
,
1.0
f
/*sliced_dim*/
,
1.0
f
/*sliced_dim*/
,
1.67326319217681884765625
/*selu_alpha*/
,
1.67326319217681884765625
/*selu_alpha*/
,
1.05070102214813232421875
/*selu_lambda*/
));
1.05070102214813232421875
/*selu_lambda*/
,
false
/*is_elu_mode*/
));
}
}
MLUCnnlActivationDesc
::
MLUCnnlActivationDesc
(
MLUCnnlActivationDesc
::
MLUCnnlActivationDesc
(
...
@@ -278,14 +279,15 @@ MLUCnnlActivationDesc::MLUCnnlActivationDesc(
...
@@ -278,14 +279,15 @@ MLUCnnlActivationDesc::MLUCnnlActivationDesc(
const
float
selu_lambda
)
{
const
float
selu_lambda
)
{
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlCreateActivationDescriptor
(
&
active_desc_
));
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlCreateActivationDescriptor
(
&
active_desc_
));
PADDLE_ENFORCE_MLU_SUCCESS
(
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSetActivationDescriptor_v
4
(
active_desc_
,
cnnlSetActivationDescriptor_v
5
(
active_desc_
,
act_mode
,
act_mode
,
CNNL_ACTIVATION_HIGH_PRECISION
,
CNNL_ACTIVATION_HIGH_PRECISION
,
CNNL_NOT_PROPAGATE_NAN
,
CNNL_NOT_PROPAGATE_NAN
,
ceof
,
ceof
,
sliced_dim
,
sliced_dim
,
selu_alpha
,
selu_alpha
,
selu_lambda
));
selu_lambda
,
false
/*is_elu_mode*/
));
}
}
const
cnnlActivationDescriptor_t
MLUCnnlActivationDesc
::
get
()
const
{
const
cnnlActivationDescriptor_t
MLUCnnlActivationDesc
::
get
()
const
{
...
@@ -2350,6 +2352,36 @@ MLURNNDesc::~MLURNNDesc() {
...
@@ -2350,6 +2352,36 @@ MLURNNDesc::~MLURNNDesc() {
workspace_size
));
workspace_size
));
}
}
/* static */
void
MLUCnnl
::
Pow
(
const
ExecutionContext
&
ctx
,
cnnlComputationPreference_t
prefer
,
const
cnnlTensorDescriptor_t
input1_desc
,
const
void
*
input1
,
const
cnnlTensorDescriptor_t
input2_desc
,
const
void
*
input2
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
size_t
workspace_size
;
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlGetPowWorkspaceSize
(
handle
,
input1_desc
,
input2_desc
,
output_desc
,
&
workspace_size
));
auto
&
dev_ctx
=
GetDevCtxFromCTX
(
ctx
);
Tensor
workspace
=
ctx
.
AllocateTmpTensor
<
int8_t
,
MLUDeviceContext
>
(
{
static_cast
<
int64_t
>
(
workspace_size
)},
dev_ctx
);
void
*
workspace_ptr
=
workspace
.
mutable_data
(
ctx
.
GetPlace
());
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlPow
(
handle
,
prefer
,
input1_desc
,
input1
,
input2_desc
,
input2
,
workspace_ptr
,
workspace_size
,
output_desc
,
output
));
}
/* static */
void
MLUCnnl
::
PowR
(
const
ExecutionContext
&
ctx
,
/* static */
void
MLUCnnl
::
PowR
(
const
ExecutionContext
&
ctx
,
cnnlComputationPreference_t
prefer
,
cnnlComputationPreference_t
prefer
,
const
cnnlTensorDescriptor_t
input1_desc
,
const
cnnlTensorDescriptor_t
input1_desc
,
...
@@ -4895,5 +4927,180 @@ MLURNNDesc::~MLURNNDesc() {
...
@@ -4895,5 +4927,180 @@ MLURNNDesc::~MLURNNDesc() {
grads_image
));
grads_image
));
}
}
/* static */
void
MLUCnnl
::
SyncBatchNormStats
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
float
eps
,
const
cnnlTensorDescriptor_t
mean_desc
,
void
*
mean
,
const
cnnlTensorDescriptor_t
invstd_desc
,
void
*
invstd
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSyncBatchNormStats
(
handle
,
x_desc
,
x
,
eps
,
mean_desc
,
mean
,
invstd_desc
,
invstd
));
}
/* static */
void
MLUCnnl
::
SyncBatchNormGatherStatsWithCounts
(
const
ExecutionContext
&
ctx
,
float
momentum
,
float
eps
,
const
cnnlTensorDescriptor_t
mean_all_desc
,
const
void
*
mean_all
,
const
cnnlTensorDescriptor_t
invstd_all_desc
,
const
void
*
invstd_all
,
const
cnnlTensorDescriptor_t
moving_mean_desc
,
void
*
moving_mean
,
const
cnnlTensorDescriptor_t
moving_var_desc
,
void
*
moving_var
,
const
cnnlTensorDescriptor_t
count_all_desc
,
const
void
*
count_all
,
const
cnnlTensorDescriptor_t
mean_desc
,
void
*
mean
,
const
cnnlTensorDescriptor_t
invstd_desc
,
void
*
invstd
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSyncBatchNormGatherStatsWithCounts
(
handle
,
mean_all_desc
,
mean_all
,
invstd_all_desc
,
invstd_all
,
moving_mean_desc
,
moving_mean
,
moving_var_desc
,
moving_var
,
momentum
,
eps
,
count_all_desc
,
count_all
,
mean_desc
,
mean
,
invstd_desc
,
invstd
));
}
/* static */
void
MLUCnnl
::
SyncBatchNormElemt
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
mean_desc
,
const
void
*
mean
,
const
cnnlTensorDescriptor_t
invstd_desc
,
const
void
*
invstd
,
const
cnnlTensorDescriptor_t
weight_desc
,
const
void
*
weight
,
const
cnnlTensorDescriptor_t
bias_desc
,
const
void
*
bias
,
const
cnnlTensorDescriptor_t
y_desc
,
void
*
y
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSyncBatchNormElemt
(
handle
,
x_desc
,
x
,
mean_desc
,
mean
,
invstd_desc
,
invstd
,
weight_desc
,
weight
,
bias_desc
,
bias
,
y_desc
,
y
));
}
/* static */
void
MLUCnnl
::
SyncBatchnormBackwardReduce
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
desc_dz
,
const
void
*
dz
,
const
cnnlTensorDescriptor_t
desc_x
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
desc_mean
,
const
void
*
mean
,
const
cnnlTensorDescriptor_t
desc_invstd
,
const
void
*
invstd
,
const
cnnlTensorDescriptor_t
desc_dweight
,
void
*
dweight
,
const
cnnlTensorDescriptor_t
desc_dbias
,
void
*
dbias
,
const
cnnlTensorDescriptor_t
desc_sum_dy
,
void
*
sum_dy
,
const
cnnlTensorDescriptor_t
desc_sum_dy_xmu
,
void
*
sum_dy_xmu
,
const
bool
needs_input_grad0
,
const
bool
needs_input_grad1
,
const
bool
needs_input_grad2
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSyncBatchnormBackwardReduce
(
handle
,
desc_dz
,
dz
,
desc_x
,
x
,
desc_mean
,
mean
,
desc_invstd
,
invstd
,
desc_dweight
,
dweight
,
desc_dbias
,
dbias
,
desc_sum_dy
,
sum_dy
,
desc_sum_dy_xmu
,
sum_dy_xmu
,
needs_input_grad0
,
needs_input_grad1
,
needs_input_grad2
));
}
/* static */
void
MLUCnnl
::
SyncBatchNormBackwardElemt
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
diff_y_desc
,
const
void
*
diff_y
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
mean_desc
,
const
void
*
mean
,
const
cnnlTensorDescriptor_t
invstd_desc
,
const
void
*
invstd
,
const
cnnlTensorDescriptor_t
weight_desc
,
const
void
*
weight
,
const
cnnlTensorDescriptor_t
sum_dy_desc
,
const
void
*
sum_dy
,
const
cnnlTensorDescriptor_t
sum_dy_xmu_desc
,
const
void
*
sum_dy_xmu
,
const
cnnlTensorDescriptor_t
count_desc
,
const
void
*
count
,
const
cnnlTensorDescriptor_t
diff_x_desc
,
void
*
diff_x
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSyncBatchNormBackwardElemtV2
(
handle
,
diff_y_desc
,
diff_y
,
x_desc
,
x
,
mean_desc
,
mean
,
invstd_desc
,
invstd
,
weight_desc
,
weight
,
sum_dy_desc
,
sum_dy
,
sum_dy_xmu_desc
,
sum_dy_xmu
,
count_desc
,
count
,
diff_x_desc
,
diff_x
));
}
}
// namespace operators
}
// namespace operators
}
// namespace paddle
}
// namespace paddle
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
f1be9cf1
...
@@ -1276,6 +1276,15 @@ class MLUCnnl {
...
@@ -1276,6 +1276,15 @@ class MLUCnnl {
const
cnnlTensorDescriptor_t
output_desc
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
);
void
*
output
);
static
void
Pow
(
const
ExecutionContext
&
ctx
,
cnnlComputationPreference_t
prefer
,
const
cnnlTensorDescriptor_t
input1_desc
,
const
void
*
input1
,
const
cnnlTensorDescriptor_t
input2_desc
,
const
void
*
input2
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
);
static
void
PowR
(
const
ExecutionContext
&
ctx
,
static
void
PowR
(
const
ExecutionContext
&
ctx
,
cnnlComputationPreference_t
prefer
,
cnnlComputationPreference_t
prefer
,
const
cnnlTensorDescriptor_t
input1_desc
,
const
cnnlTensorDescriptor_t
input1_desc
,
...
@@ -2030,8 +2039,152 @@ class MLUCnnl {
...
@@ -2030,8 +2039,152 @@ class MLUCnnl {
const
void
*
boxes
,
const
void
*
boxes
,
const
cnnlTensorDescriptor_t
grads_image_desc
,
const
cnnlTensorDescriptor_t
grads_image_desc
,
void
*
grads_image
);
void
*
grads_image
);
static
void
SyncBatchNormStats
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
float
eps
,
const
cnnlTensorDescriptor_t
mean_desc
,
void
*
mean
,
const
cnnlTensorDescriptor_t
invstd_desc
,
void
*
invstd
);
static
void
SyncBatchNormGatherStatsWithCounts
(
const
ExecutionContext
&
ctx
,
float
momentum
,
float
eps
,
const
cnnlTensorDescriptor_t
mean_all_desc
,
const
void
*
mean_all
,
const
cnnlTensorDescriptor_t
invstd_all_desc
,
const
void
*
invstd_all
,
const
cnnlTensorDescriptor_t
moving_mean_desc
,
void
*
moving_mean
,
const
cnnlTensorDescriptor_t
moving_var_desc
,
void
*
moving_var
,
const
cnnlTensorDescriptor_t
count_all_desc
,
const
void
*
count_all
,
const
cnnlTensorDescriptor_t
mean_desc
,
void
*
mean
,
const
cnnlTensorDescriptor_t
invstd_desc
,
void
*
invstd
);
static
void
SyncBatchNormElemt
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
mean_desc
,
const
void
*
mean
,
const
cnnlTensorDescriptor_t
invstd_desc
,
const
void
*
invstd
,
const
cnnlTensorDescriptor_t
weight_desc
,
const
void
*
weight
,
const
cnnlTensorDescriptor_t
bias_desc
,
const
void
*
bias
,
const
cnnlTensorDescriptor_t
y_desc
,
void
*
y
);
static
void
SyncBatchnormBackwardReduce
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
desc_dz
,
const
void
*
dz
,
const
cnnlTensorDescriptor_t
desc_x
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
desc_mean
,
const
void
*
mean
,
const
cnnlTensorDescriptor_t
desc_invstd
,
const
void
*
invstd
,
const
cnnlTensorDescriptor_t
desc_dweight
,
void
*
dweight
,
const
cnnlTensorDescriptor_t
desc_dbias
,
void
*
dbias
,
const
cnnlTensorDescriptor_t
desc_sum_dy
,
void
*
sum_dy
,
const
cnnlTensorDescriptor_t
desc_sum_dy_xmu
,
void
*
sum_dy_xmu
,
const
bool
needs_input_grad0
,
const
bool
needs_input_grad1
,
const
bool
needs_input_grad2
);
static
void
SyncBatchNormBackwardElemt
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
diff_y_desc
,
const
void
*
diff_y
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
mean_desc
,
const
void
*
mean
,
const
cnnlTensorDescriptor_t
invstd_desc
,
const
void
*
invstd
,
const
cnnlTensorDescriptor_t
weight_desc
,
const
void
*
weight
,
const
cnnlTensorDescriptor_t
sum_dy_desc
,
const
void
*
sum_dy
,
const
cnnlTensorDescriptor_t
sum_dy_xmu_desc
,
const
void
*
sum_dy_xmu
,
const
cnnlTensorDescriptor_t
count_desc
,
const
void
*
count
,
const
cnnlTensorDescriptor_t
diff_x_desc
,
void
*
diff_x
);
};
};
const
std
::
map
<
const
std
::
string
,
std
::
pair
<
std
::
vector
<
int
>
,
std
::
vector
<
int
>>>
TransPermMap
=
{
// trans_mode, (forward_perm, backward_perm)
{
"3D_NCHW2NHWC"
,
{{
0
,
2
,
1
},
{
0
,
2
,
1
}}},
{
"4D_NCHW2NHWC"
,
{{
0
,
2
,
3
,
1
},
{
0
,
3
,
1
,
2
}}},
{
"5D_NCHWD2NDHWC"
,
{{
0
,
4
,
2
,
3
,
1
},
{
0
,
4
,
2
,
3
,
1
}}},
{
"5D_NHWDC2NDHWC"
,
{{
0
,
3
,
1
,
2
,
4
},
{
0
,
2
,
3
,
4
,
1
}}}};
inline
void
SetMLUTransposePerm
(
const
framework
::
DDim
&
dims
,
const
DataLayout
&
data_layout
,
std
::
vector
<
int
>*
forward_perm
,
std
::
vector
<
int
>*
backward_perm
,
std
::
vector
<
int
>*
out_shape
)
{
const
int
dim_size
=
dims
.
size
();
PADDLE_ENFORCE_EQ
((
dim_size
>=
3
)
&&
(
dim_size
<=
5
),
true
,
platform
::
errors
::
InvalidArgument
(
"MLUTransposePerm func only support (dim_size >= 3) && "
"(dim_size <= 5), but now dim_size is %d."
,
dim_size
));
PADDLE_ENFORCE_EQ
(
(
data_layout
==
DataLayout
::
kNCHW
)
||
(
data_layout
==
DataLayout
::
kNHWC
),
true
,
platform
::
errors
::
InvalidArgument
(
"MLUTransposePerm func only support DataLayout: kNCHW or kNHWC, but "
"now data_layout is %s."
,
data_layout
));
// case 1: NCHW of Paddle != NHWC of MLU when dims==3,4
// case 2: NHWDC and NCHWD of Paddle != NDHWC of MLU when dims==5
std
::
string
map_key
=
""
;
if
(
data_layout
==
DataLayout
::
kNCHW
)
{
switch
(
dim_size
)
{
case
3
:
map_key
=
"3D_NCHW2NHWC"
;
break
;
case
4
:
map_key
=
"4D_NCHW2NHWC"
;
break
;
case
5
:
map_key
=
"5D_NCHWD2NDHWC"
;
break
;
}
}
else
if
(
data_layout
==
DataLayout
::
kNHWC
&&
dim_size
==
5
)
{
map_key
=
"5D_NHWDC2NDHWC"
;
}
assert
(
map_key
!=
""
);
forward_perm
->
assign
(
TransPermMap
.
at
(
map_key
).
first
.
begin
(),
TransPermMap
.
at
(
map_key
).
first
.
end
());
backward_perm
->
assign
(
TransPermMap
.
at
(
map_key
).
second
.
begin
(),
TransPermMap
.
at
(
map_key
).
second
.
end
());
auto
in_dims
=
phi
::
vectorize
(
dims
);
for
(
size_t
i
=
0
;
i
<
in_dims
.
size
();
i
++
)
{
out_shape
->
push_back
(
in_dims
[
forward_perm
->
at
(
i
)]);
}
}
template
<
typename
T
>
template
<
typename
T
>
inline
void
TransposeFromMLUTensor
(
const
ExecutionContext
&
ctx
,
inline
void
TransposeFromMLUTensor
(
const
ExecutionContext
&
ctx
,
const
std
::
vector
<
int
>
perm
,
const
std
::
vector
<
int
>
perm
,
...
...
paddle/fluid/operators/sync_batch_norm_op_mlu.cc
0 → 100644
浏览文件 @
f1be9cf1
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/mlu/CMakeLists.txt
浏览文件 @
f1be9cf1
...
@@ -50,5 +50,7 @@ if(WITH_MLU)
...
@@ -50,5 +50,7 @@ if(WITH_MLU)
set_tests_properties
(
test_collective_allgather_api_mlu PROPERTIES TIMEOUT
set_tests_properties
(
test_collective_allgather_api_mlu PROPERTIES TIMEOUT
120
)
120
)
set_tests_properties
(
test_c_comm_init_op_mlu PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_c_comm_init_op_mlu PROPERTIES TIMEOUT 120
)
set_tests_properties
(
test_sync_batch_norm_op_mlu_baseline PROPERTIES TIMEOUT
120
)
endif
()
endif
()
endif
()
endif
()
python/paddle/fluid/tests/unittests/mlu/sync_batch_norm_op_mlu.py
0 → 100644
浏览文件 @
f1be9cf1
# 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
argparse
import
os
import
sys
sys
.
path
.
append
(
".."
)
import
signal
import
time
from
contextlib
import
closing
from
six
import
string_types
import
math
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.profiler
as
profiler
import
paddle.fluid.unique_name
as
nameGen
from
paddle.fluid
import
core
import
unittest
from
multiprocessing
import
Process
import
paddle.fluid.layers
as
layers
from
functools
import
reduce
from
test_sync_batch_norm_base_mlu
import
TestSyncBatchNormRunnerBase
,
runtime_main
from
paddle.fluid.tests.unittests.op_test
import
OpTest
,
_set_use_system_allocator
from
paddle.fluid.tests.unittests.test_sync_batch_norm_op
import
create_or_get_tensor
_set_use_system_allocator
(
False
)
paddle
.
enable_static
()
class
TestSyncBatchNormOpTraining
(
TestSyncBatchNormRunnerBase
):
def
__init__
(
self
):
self
.
global_ring_id
=
0
self
.
dtype
=
np
.
float32
self
.
N
=
8
self
.
C
=
16
self
.
H
=
32
self
.
W
=
32
self
.
dshape
=
[
self
.
N
,
self
.
C
,
self
.
H
,
self
.
W
]
self
.
atol
=
1e-3
def
get_model
(
self
,
main
,
startup
,
place
,
layout
,
seed
,
sync_bn
=
False
,
only_forward
=
False
):
"""Build program."""
use_cudnn
=
False
with
fluid
.
unique_name
.
guard
():
with
fluid
.
program_guard
(
main
,
startup
):
data
=
fluid
.
layers
.
data
(
name
=
'input'
,
shape
=
self
.
dshape
,
dtype
=
self
.
dtype
,
append_batch_size
=
False
)
conv
=
fluid
.
layers
.
conv2d
(
input
=
data
,
num_filters
=
32
,
filter_size
=
1
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'conv2d_weight'
),
bias_attr
=
False
,
use_cudnn
=
use_cudnn
)
bn
=
fluid
.
layers
.
batch_norm
(
conv
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'bn_scale'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
'bn_bias'
),
moving_mean_name
=
'bn_moving_mean'
,
moving_variance_name
=
'bn_moving_variance'
,
data_layout
=
layout
,
is_test
=
only_forward
)
# if self.dtype == np.float16:
# bn = fluid.layers.cast(bn, 'float32')
sigmoid
=
fluid
.
layers
.
sigmoid
(
bn
)
out
=
fluid
.
layers
.
reduce_sum
(
sigmoid
)
# if not sync_bn:
# out = out / core.get_mlu_device_count()
if
not
only_forward
:
sgd_opt
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.0
)
sgd_opt
.
backward
(
out
)
return
[
out
,
conv
,
bn
]
if
__name__
==
"__main__"
:
# print('sync_batch_norm_op_mlu.py __main__')
runtime_main
(
TestSyncBatchNormOpTraining
,
"identity"
,
0
)
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