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3ca713ee
编写于
7月 11, 2022
作者:
H
houj04
提交者:
GitHub
7月 11, 2022
浏览文件
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差异文件
rmsprop for xpu. test=kunlun (#44175)
* rmsprop for xpu. test=kunlun * minor fix (follow comments). test=kunlun
上级
9a3054c6
变更
5
显示空白变更内容
内联
并排
Showing
5 changed file
with
313 addition
and
443 deletion
+313
-443
cmake/external/xpu.cmake
cmake/external/xpu.cmake
+2
-2
paddle/fluid/operators/optimizers/rmsprop_op_xpu.cc
paddle/fluid/operators/optimizers/rmsprop_op_xpu.cc
+145
-141
paddle/fluid/platform/device/xpu/xpu2_op_list.h
paddle/fluid/platform/device/xpu/xpu2_op_list.h
+1
-0
python/paddle/fluid/tests/unittests/white_list/no_check_set_white_list.py
...uid/tests/unittests/white_list/no_check_set_white_list.py
+1
-0
python/paddle/fluid/tests/unittests/xpu/test_rmsprop_op_xpu.py
...n/paddle/fluid/tests/unittests/xpu/test_rmsprop_op_xpu.py
+164
-300
未找到文件。
cmake/external/xpu.cmake
浏览文件 @
3ca713ee
...
...
@@ -10,7 +10,7 @@ set(XPU_RT_LIB_NAME "libxpurt.so")
if
(
NOT DEFINED XPU_BASE_URL
)
set
(
XPU_BASE_URL_WITHOUT_DATE
"https://baidu-kunlun-product.cdn.bcebos.com/KL-SDK/klsdk-dev"
)
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/2022070
6
"
)
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/2022070
7
"
)
else
()
set
(
XPU_BASE_URL
"
${
XPU_BASE_URL
}
"
)
endif
()
...
...
@@ -19,7 +19,7 @@ endif()
if
(
NOT DEFINED XPU_XDNN_BASE_URL
)
set
(
XPU_XDNN_BASE_URL_WITHOUT_DATE
"https://klx-sdk-release-public.su.bcebos.com/xdnn/dev"
)
set
(
XPU_XDNN_BASE_URL
"
${
XPU_XDNN_BASE_URL_WITHOUT_DATE
}
/2022070
6
"
)
set
(
XPU_XDNN_BASE_URL
"
${
XPU_XDNN_BASE_URL_WITHOUT_DATE
}
/2022070
7
"
)
else
()
set
(
XPU_XDNN_BASE_URL
"
${
XPU_XDNN_BASE_URL
}
"
)
endif
()
...
...
paddle/fluid/operators/optimizers/rmsprop_op_xpu.cc
浏览文件 @
3ca713ee
/* Copyright (c) 202
0 PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 202
2 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.
...
...
@@ -90,6 +90,19 @@ class RmspropOpXPUKernel : public framework::OpKernel<T> {
T
decay
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"decay"
));
T
momentum
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"momentum"
));
bool
centered
=
ctx
.
Attr
<
bool
>
(
"centered"
);
PADDLE_ENFORCE_EQ
(
centered
,
false
,
platform
::
errors
::
Unimplemented
(
"centered=True is not supported in the xpu kernel of "
"rmsprop. use XPU_BLACK_LIST to disable this op."
));
/*
TODO(houj04): when XDNN api supports 'center', add input of
mean_grad_input and output of mean_grad_output. auto *mean_grad_input =
ctx.Input<Tensor>("MeanGrad"); auto *mean_grad_output =
ctx.Output<Tensor>("MeanGradOut");
*/
// outputs
auto
&
param_out
=
GET_DATA_SAFELY
(
ctx
.
Output
<
LoDTensor
>
(
"ParamOut"
),
"Output"
,
"ParamOut"
,
"Rmsprop"
);
...
...
@@ -101,18 +114,9 @@ class RmspropOpXPUKernel : public framework::OpKernel<T> {
"Rmsprop"
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
///// rmsprop优化算法
///
/// ms_out[i] = rho * ms[i] + (1 - rho) * (g[i] * g[i]);
///
/// mom_out[i] = momentum * mom[i] + lr *
/// (g[i] / ((float)sqrt(ms_out[i] + epsilon)));
///
/// p_out[i] = p[i] - mom_out[i];
/// DLL_EXPORT int rmsprop(Context* ctx, const float* p,
/// const float* ms, const float* g, const float* mom,
/// float epsilon, float rho, float momentum, float lr,
/// float *ms_out, float *mom_out, float *p_out, int n)
// int rmsprop(Context* ctx, const T* g, const T* p, const float* ms, const
// float* mom, T* p_out, float* ms_out, float* mom_out, float epsilon, float
// rho, float momentum, float lr, int n);
int
r
=
xpu
::
rmsprop
(
dev_ctx
.
x_context
(),
grad
.
template
data
<
T
>(),
param
.
template
data
<
T
>(),
...
...
paddle/fluid/platform/device/xpu/xpu2_op_list.h
浏览文件 @
3ca713ee
...
...
@@ -363,6 +363,7 @@ XPUOpMap& get_kl2_ops() {
pOpKernelType
(
vartype
::
INT32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
BOOL
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"rmsprop"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"rnn"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"rnn_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"roi_align"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
...
...
python/paddle/fluid/tests/unittests/white_list/no_check_set_white_list.py
浏览文件 @
3ca713ee
...
...
@@ -36,4 +36,5 @@ no_check_set_white_list = [
'eigvalsh'
,
'class_center_sample'
,
'einsum'
,
'rmsprop'
,
]
python/paddle/fluid/tests/unittests/xpu/test_rmsprop_op_xpu.py
浏览文件 @
3ca713ee
# Copyright (c) 20
18 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 20
22 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.
...
...
@@ -13,288 +13,152 @@
# limitations under the License.
from
__future__
import
print_function
import
sys
sys
.
path
.
append
(
".."
)
import
unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
op_test_xpu
import
XPUOpTest
import
paddle.fluid
as
fluid
import
paddle
'''
def create_selected_rows_and_tensor(scope, place, height, row_num,
embedding_size):
sr = scope.var("@selected_rows@").get_selected_rows()
tensor = scope.var("grad").get_tensor()
rows = np.random.random_integers(
low=0, high=height - 1, size=[row_num, ]).astype('int64')
sr_val = np.random.random(size=[row_num, embedding_size]).astype('float32')
sr.set_height(height)
sr.set_rows(rows)
sr.get_tensor().set(sr_val, place)
tensor_val = np.zeros(shape=[height, embedding_size], dtype='float32')
for i in range(row_num):
row = rows[i]
tensor_val[row, :] = tensor_val[row, :] + sr_val[i, :]
tensor.set(tensor_val, place)
return tensor_val, sr_val
'''
"""
class TestBase(XPUOpTest):
op_type = 'rmsprop'
def setup(self,
place,
is_sparse,
centered,
size,
row_num=None,
epsilon=1e-6):
np.random.seed(5) # fix seed
self.scope = fluid.global_scope()
self.place = place
self.param_name = 'param'
self.param = np.random.random(size).astype('float32')
self.mean_square_name = 'mean_square'
self.mean_square = np.random.uniform(
low=1, high=2, size=size).astype('float32')
self.mean_grad_name = 'mean_grad'
self.mean_grad = np.random.random(size).astype('float32')
self.lr_name = 'lr'
self.learning_rate = np.array([0.01]).astype('float32')
self.grad_name = 'grad'
self.is_sparse = is_sparse
self.grad = np.random.random(size).astype('float32')
grad_tensor = self.scope.var(self.grad_name).get_tensor()
grad_tensor.set(self.grad, place)
self.moment_name = 'moment'
self.moment = np.random.uniform(
low=0, high=1, size=size).astype('float32')
self.epsilon = epsilon
self.decay = 0.9
self.momentum = 0.1
self.centered = centered
self.ms_out = self.decay * self.mean_square + (1 - self.decay
) * self.grad * self.grad
if centered:
self.mg_out = self.decay * self.mean_grad + (1 - self.decay
) * self.grad
self.moment_out = self.momentum * self.moment + \
self.learning_rate * self.grad / np.sqrt(self.ms_out - np.square(self.mg_out) + self.epsilon)
else:
self.moment_out = self.momentum * self.moment + \
self.learning_rate * self.grad / np.sqrt(self.ms_out + self.epsilon)
self.param_out = self.param - self.moment_out
# create and initialize Param Variable
self.param_tensor = self.scope.var(self.param_name).get_tensor()
self.param_tensor.set(self.param, place)
self.mean_square_tensor = self.scope.var(
self.mean_square_name).get_tensor()
self.mean_square_tensor.set(self.mean_square, place)
lr = self.scope.var(self.lr_name).get_tensor()
lr.set(self.learning_rate, place)
self.moment_tensor = self.scope.var(self.moment_name).get_tensor()
self.moment_tensor.set(self.moment, place)
if self.centered:
self.mean_grad_tensor = self.scope.var(
self.mean_grad_name).get_tensor()
self.mean_grad_tensor.set(self.mean_grad, place)
def check(self, actual_t, expect_t, place, out_name, atol=1e-5):
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol),
'Output (' + out_name + ') has diff at ' + str(place) + '
\n
Expect '
+ str(expect_t) + '
\n
' + 'But Got' + str(actual_t))
import
sys
class TestRmspropOp(TestBase):
def check_with_place(self,
place,
is_sparse,
centered,
size,
row_num=None,
epsilon=1e-6):
self.setup(place, is_sparse, centered, size, row_num, epsilon)
self.run_and_check()
sys
.
path
.
append
(
".."
)
def run_and_check(self):
#grad_name = self.grad_sr_name if self.is_sparse else self.grad_name
grad_name = self.grad_name
import
paddle
import
paddle.fluid.core
as
core
kwargs = {
'Param': self.param_name,
'Grad': grad_name,
'MeanSquare': self.mean_square_name,
'Moment': self.moment_name,
'LearningRate': self.lr_name,
'ParamOut': self.param_name,
'MeanSquareOut': self.mean_square_name,
'MomentOut': self.moment_name,
from
op_test
import
OpTest
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
paddle
.
enable_static
()
def
calculate_rmsprop_by_numpy
(
param
,
grad
,
mean_square
,
moment
,
learning_rate
,
epsilon
,
decay
,
momentum
):
mean_square_out
=
decay
*
mean_square
+
(
1
-
decay
)
*
grad
*
grad
moment_out
=
momentum
*
moment
+
learning_rate
*
grad
/
np
.
sqrt
(
mean_square_out
+
epsilon
)
param_out
=
param
-
moment_out
return
param_out
,
mean_square_out
,
moment_out
class
XPUTestRMSPropOP
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'rmsprop'
self
.
use_dynamic_create_class
=
False
class
TestRMSPropOPBase
(
XPUOpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
xpu_version
=
core
.
get_xpu_device_version
(
0
)
self
.
init_dtype
()
self
.
set_case
()
def
set_case
(
self
):
self
.
op_type
=
'rmsprop'
self
.
dtype
=
self
.
in_type
self
.
init_config
()
self
.
param
=
np
.
random
.
uniform
(
-
1
,
1
,
self
.
input_shape
).
astype
(
self
.
dtype
)
self
.
grad
=
np
.
random
.
uniform
(
-
1
,
1
,
self
.
input_shape
).
astype
(
self
.
dtype
)
self
.
mean_square
=
np
.
random
.
uniform
(
0
,
1
,
self
.
input_shape
).
astype
(
self
.
dtype
)
self
.
moment
=
np
.
random
.
uniform
(
-
1
,
1
,
self
.
input_shape
).
astype
(
self
.
dtype
)
self
.
mean_grad
=
np
.
random
.
uniform
(
-
1
,
1
,
self
.
input_shape
).
astype
(
self
.
dtype
)
self
.
mean_grad_out
=
np
.
random
.
uniform
(
-
1
,
1
,
self
.
input_shape
).
astype
(
self
.
dtype
)
param_out
,
mean_square_out
,
moment_out
=
calculate_rmsprop_by_numpy
(
param
=
self
.
param
,
grad
=
self
.
grad
,
mean_square
=
self
.
mean_square
,
moment
=
self
.
moment
,
learning_rate
=
self
.
learning_rate
,
epsilon
=
self
.
epsilon
,
decay
=
self
.
decay
,
momentum
=
self
.
momentum
)
self
.
inputs
=
{
'Param'
:
self
.
param
,
'Grad'
:
self
.
grad
,
'MeanSquare'
:
self
.
mean_square
,
'Moment'
:
self
.
moment
,
'LearningRate'
:
self
.
learning_rate
,
'MeanGrad'
:
self
.
mean_grad
,
'MeanGradOut'
:
self
.
mean_grad_out
,
}
self
.
attrs
=
{
'use_xpu'
:
True
,
'epsilon'
:
self
.
epsilon
,
'decay'
:
self
.
decay
,
'momentum'
:
self
.
momentum
,
'centered': self.centered
'centered'
:
False
,
# TODO(houj04): when XDNN api supports 'center = True', add more test cases
}
self
.
outputs
=
{
'ParamOut'
:
param_out
,
'MomentOut'
:
moment_out
,
'MeanSquareOut'
:
mean_square_out
,
'MeanGradOut'
:
self
.
mean_grad_out
}
if self.centered:
kwargs['MeanGrad'] = self.mean_grad_name
kwargs['MeanGradOut'] = self.mean_grad_name
rmsprop_op = Operator('rmsprop', **kwargs)
atol = 1e-6
rmsprop_op.run(self.scope, self.place)
self.check(
np.array(self.mean_square_tensor),
self.ms_out,
self.place,
self.mean_square_name,
atol=atol)
self.check(
np.array(self.moment_tensor),
self.moment_out,
self.place,
self.moment_name,
atol=atol)
self.check(
np.array(self.param_tensor),
self.param_out,
self.place,
self.param_name,
atol=atol)
if self.centered:
self.check(
np.array(self.mean_grad_tensor), self.mg_out, self.place,
self.mean_grad_name)
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def test_rmsprop(self):
places = [paddle.XPUPlace(0)]
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
no_check_set
=
[
'MeanGradOut'
])
size = (128, 320)
for place in places:
for centered in [False]:
with fluid.scope_guard(core.Scope()):
self.check_with_place(
place, is_sparse=False, centered=centered, size=size)
def
init_config
(
self
):
self
.
input_shape
=
[
864
]
self
.
learning_rate
=
np
.
array
([
0.001
]).
astype
(
self
.
dtype
)
self
.
epsilon
=
1e-4
self
.
decay
=
0.9
self
.
momentum
=
0.1
with fluid.scope_guard(core.Scope()):
self.check_with_place(
place,
is_sparse=True,
centered=centered,
row_num=512,
size=size)
class
XPUTestRMSProp1
(
TestRMSPropOPBase
):
with fluid.scope_guard(core.Scope()):
self.check_with_place(
place,
is_sparse=True,
centered=centered,
row_num=60,
size=size, )
def
init_config
(
self
):
self
.
input_shape
=
[
2
,
768
]
self
.
learning_rate
=
np
.
array
([
0.002
]).
astype
(
self
.
dtype
)
self
.
epsilon
=
1e-4
self
.
decay
=
0.9
self
.
momentum
=
0.1
class
XPUTestRMSProp2
(
TestRMSPropOPBase
):
class TestRMSPropV2(XPUOpTest):
op_type = 'rmsprop'
def
init_config
(
self
):
self
.
input_shape
=
[
3
,
8
,
4096
]
self
.
learning_rate
=
np
.
array
([
0.005
]).
astype
(
self
.
dtype
)
self
.
epsilon
=
1e-6
self
.
decay
=
0.95
self
.
momentum
=
0
def test_rmsprop_dygraph(self):
paddle.disable_static()
value = np.arange(26).reshape(2, 13).astype('float32')
a = paddle.to_tensor(value)
linear = paddle.nn.Linear(13, 5)
# This can be any optimizer supported by dygraph.
adam = paddle.optimizer.RMSProp(
learning_rate=0.01,
parameters=linear.parameters(),
weight_decay=0.01)
out = linear(a)
out.backward()
adam.step()
adam.clear_gradients()
class
XPUTestRMSProp3
(
TestRMSPropOPBase
):
def test_rmsprop(self):
place = paddle.XPUPlace(0)
paddle.enable_static()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = paddle.mean(cost)
def
init_config
(
self
):
self
.
input_shape
=
[
1024
]
self
.
learning_rate
=
np
.
array
([
0.01
]).
astype
(
self
.
dtype
)
self
.
epsilon
=
1e-5
self
.
decay
=
0.99
self
.
momentum
=
0.02
print(avg_cost.shape)
linear = paddle.nn.Linear(13, 5)
rms_optimizer = paddle.optimizer.RMSProp(
learning_rate=0.1, parameters=linear.parameters())
rms_optimizer.minimize(avg_cost)
class
XPUTestRMSProp4
(
TestRMSPropOPBase
):
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
def
init_config
(
self
):
self
.
input_shape
=
[
2
,
2
,
255
]
self
.
learning_rate
=
np
.
array
([
0.0005
]).
astype
(
self
.
dtype
)
self
.
epsilon
=
1e-3
self
.
decay
=
0.8
self
.
momentum
=
0.002
def test_raise_error(self):
self.assertRaises(ValueError, paddle.optimizer.RMSProp, None)
self.assertRaises(
ValueError, paddle.optimizer.RMSProp, learning_rate=0.1, rho=None)
self.assertRaises(
ValueError,
paddle.optimizer.RMSProp,
learning_rate=0.1,
epsilon=None)
self.assertRaises(
ValueError,
paddle.optimizer.RMSProp,
learning_rate=0.1,
momentum=None)
def test_rmsprop_op_invalid_input(self):
paddle.disable_static()
linear = paddle.nn.Linear(10, 10)
with self.assertRaises(ValueError):
adam = paddle.optimizer.RMSProp(
0.1, epsilon=-1, parameters=linear.parameters())
with self.assertRaises(ValueError):
adam = paddle.optimizer.RMSProp(
0.1, momentum=-1, parameters=linear.parameters())
with self.assertRaises(ValueError):
adam = paddle.optimizer.RMSProp(
0.1, rho=-1, parameters=linear.parameters())
"""
support_types
=
get_xpu_op_support_types
(
'rmsprop'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestRMSPropOP
,
stype
)
if
__name__
==
"__main__"
:
paddle
.
enable_static
()
unittest
.
main
()
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