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Add dropout and log_loss for kunlun (#27790)

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* add dropout,log_loss, test=kunlun
* fix dropout, test=kunlun
* polish error message, test=kunlun
* change boost::get to BOOST_GET_CONST, test=kunlun
* fix copyright, test=kunlun
my_2.0rc
xiaoting 4 years ago committed by GitHub
parent 50619cd842
commit ae01801f0a
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4 changed files with 375 additions and 0 deletions
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130
paddle/fluid/operators/dropout_op_xpu.cc
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/* Copyright (c) 2020 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/dropout_op.h"
#include <memory>
#include <string>
#include "paddle/fluid/platform/xpu_header.h"
namespace paddle {
namespace operators {
#ifdef PADDLE_WITH_XPU
static std::map<int, float*> mask_data_tables;
static const int max_data_size = 32 * 1024 * 1024;
static std::mutex s_mask_data_table_lock;
template <typename DeviceContext, typename T>
class DropoutXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x = context.Input<Tensor>("X");
auto* y = context.Output<Tensor>("Out");
const auto* x_data = x->data<T>();
auto* y_data = y->mutable_data<T>(context.GetPlace());
float dropout_prob = context.Attr<float>("dropout_prob");
auto dropout_implementation =
context.Attr<std::string>("dropout_implementation");
float* mask_data_table = nullptr;
PADDLE_ENFORCE_EQ(!context.HasInput("Seed"), true,
platform::errors::InvalidArgument(
("Input(Seed) not supported on XPU")));
if (!context.Attr<bool>("is_test")) {
int dev_id =
BOOST_GET_CONST(platform::XPUPlace, context.GetPlace()).GetDeviceId();
int prop = static_cast<int>(dropout_prob * 100);
int is_upscale = (dropout_implementation == "upscale_in_train");
/* mask_data_tables key contains 3 part:
* | 31-16 | 15-8 | 7-0 |
* | dev_id | prob | is_upscale |
*/
int index = (dev_id << 16) + (prop << 8) + is_upscale;
std::lock_guard<std::mutex> lock(s_mask_data_table_lock);
if (mask_data_tables.find(index) == mask_data_tables.end()) {
float* mask_data_host = new float[max_data_size];
std::random_device rnd;
std::minstd_rand engine;
int seed =
context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
engine.seed(seed);
std::uniform_real_distribution<float> dist(0, 1);
for (size_t i = 0; i < max_data_size; ++i) {
if (dist(engine) < dropout_prob) {
mask_data_host[i] = 0.0f;
} else {
if (is_upscale) {
mask_data_host[i] = 1.0f / static_cast<T>(1.0f - dropout_prob);
} else {
mask_data_host[i] = 1.0;
}
}
}
PADDLE_ENFORCE(
xpu_malloc(reinterpret_cast<void**>(&mask_data_table),
max_data_size * sizeof(float)) == xpu::Error_t::SUCCESS,
"XPU no enough memory");
memory::Copy(BOOST_GET_CONST(platform::XPUPlace, context.GetPlace()),
mask_data_table, platform::CPUPlace(), mask_data_host,
max_data_size * sizeof(float));
mask_data_tables[index] = mask_data_table;
free(mask_data_host);
} else {
mask_data_table = mask_data_tables[index];
}
}
if (!context.Attr<bool>("is_test")) { // Train
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
size_t size = framework::product(mask->dims());
auto& dev_ctx = context.template device_context<DeviceContext>();
int r = xpu::dropout(dev_ctx.x_context(), mask_data_table, x_data,
mask_data, y_data, max_data_size, size);
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::InvalidArgument("XPU kernel error!"));
} else { // Infer
float scale = 0.0f;
if (dropout_implementation == "upscale_in_train") {
scale = 1.0f;
} else {
scale = static_cast<T>(1.0f - dropout_prob);
}
auto& dev_ctx = context.template device_context<DeviceContext>();
int r = xpu::scale(dev_ctx.x_context(), x->numel(), scale, 0.0f, 0,
x_data, y_data);
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::InvalidArgument("XPU kernel error!"));
}
}
};
template <typename DeviceContext, typename T>
class DropoutGradXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(!context.Attr<bool>("is_test"),
"GradOp is only callable when is_test is false");
auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* grad_y = context.Input<Tensor>(framework::GradVarName("Out"));
auto* mask = context.Input<Tensor>("Mask");
grad_x->mutable_data<T>(context.GetPlace());
auto& dev_ctx = context.template device_context<DeviceContext>();
int r = xpu::elementwise_mul(dev_ctx.x_context(), grad_y->data<T>(),
mask->data<T>(), grad_x->data<T>(),
grad_y->numel());
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::InvalidArgument("XPU kernel error!"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
dropout, ops::DropoutXPUKernel<paddle::platform::XPUDeviceContext, float>);
REGISTER_OP_XPU_KERNEL(
dropout_grad,
ops::DropoutGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
#endif

68
paddle/fluid/operators/log_loss_op_xpu.cc
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/* Copyright (c) 2020 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/log_loss_op.h"
#include <memory>
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T, typename AttrType = T>
class LogLossXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* predict = ctx.Input<Tensor>("Predicted");
auto* labels = ctx.Input<Tensor>("Labels");
auto* loss = ctx.Output<Tensor>("Loss");
auto epsilon = static_cast<T>(ctx.Attr<AttrType>("epsilon"));
loss->mutable_data<T>(ctx.GetPlace());
int n = predict->numel();
auto& dev_ctx = ctx.template device_context<DeviceContext>();
int r =
xpu::log_loss_fwd(dev_ctx.x_context(), n, epsilon, predict->data<T>(),
labels->data<T>(), loss->data<T>());
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::InvalidArgument("XPU kernel error!"));
}
};
template <typename DeviceContext, typename T, typename AttrType = T>
class LogLossGradXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* predict = ctx.Input<Tensor>("Predicted");
auto* labels = ctx.Input<Tensor>("Labels");
auto* dloss = ctx.Input<Tensor>(framework::GradVarName("Loss"));
auto* dpred = ctx.Output<Tensor>(framework::GradVarName("Predicted"));
if (!dpred) {
return;
}
auto epsilon = static_cast<T>(ctx.Attr<AttrType>("epsilon"));
dpred->mutable_data<T>(ctx.GetPlace());
int n = predict->numel();
auto& dev_ctx = ctx.template device_context<DeviceContext>();
int r = xpu::log_loss_bwd(dev_ctx.x_context(), n, epsilon,
predict->data<T>(), labels->data<T>(),
dloss->data<T>(), dpred->data<T>());
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::InvalidArgument("XPU kernel error!"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
log_loss, ops::LogLossXPUKernel<paddle::platform::XPUDeviceContext, float>);
REGISTER_OP_XPU_KERNEL(
log_loss_grad,
ops::LogLossGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
#endif

112
python/paddle/fluid/tests/unittests/xpu/test_dropout_op_xpu.py
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# Copyright (c) 2020 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 sys
sys.path.append("..")
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest, skip_check_grad_ci
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
class TestDropoutOp(OpTest):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {'dropout_prob': 0.0, 'fix_seed': True, 'is_test': False}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64)).astype('uint8')
}
def test_check_output(self):
if paddle.is_compiled_with_xpu():
paddle.enable_static()
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad_normal(self):
if paddle.is_compiled_with_xpu():
paddle.enable_static()
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
class TestDropoutOpInput1d(OpTest):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((2000, )).astype("float32")}
self.attrs = {'dropout_prob': 0.0, 'fix_seed': True, 'is_test': False}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((2000)).astype('uint8')
}
def test_check_output(self):
if paddle.is_compiled_with_xpu():
paddle.enable_static()
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad_normal(self):
if paddle.is_compiled_with_xpu():
paddle.enable_static()
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
class TestDropoutOp2(TestDropoutOp):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {'dropout_prob': 1.0, 'fix_seed': True, 'is_test': False}
self.outputs = {
'Out': np.zeros((32, 64)).astype('float32'),
'Mask': np.zeros((32, 64)).astype('uint8')
}
class TestDropoutOp3(TestDropoutOp):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
self.attrs = {'dropout_prob': 0.0, 'fix_seed': True, 'is_test': False}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64, 2)).astype('uint8')
}
class TestDropoutOp6(TestDropoutOp):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
self.attrs = {
'dropout_prob': 0.0,
'fix_seed': True,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64, 2)).astype('uint8')
}
if __name__ == '__main__':
unittest.main()

65
python/paddle/fluid/tests/unittests/xpu/test_log_loss_op_xpu.py
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# Copyright (c) 2020 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 sys
sys.path.append("..")
import paddle.fluid.core as core
import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
def sigmoid_array(x):
return 1 / (1 + np.exp(-x))
class TestXPULogLossOp(OpTest):
def setUp(self):
self.op_type = 'log_loss'
samples_num = 100
x = np.random.random((samples_num, 1)).astype("float32")
predicted = sigmoid_array(x)
labels = np.random.randint(0, 2, (samples_num, 1)).astype("float32")
epsilon = 1e-7
self.inputs = {
'Predicted': predicted,
'Labels': labels,
}
self.attrs = {'epsilon': epsilon}
loss = -labels * np.log(predicted + epsilon) - (
1 - labels) * np.log(1 - predicted + epsilon)
self.outputs = {'Loss': loss}
def test_check_output(self):
if paddle.is_compiled_with_xpu():
paddle.enable_static()
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
paddle.enable_static()
place = paddle.XPUPlace(0)
self.check_grad(['Predicted'], 'Loss')
if __name__ == '__main__':
unittest.main()
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