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!3780 add gpu BinaryCrossEntropy 

Merge pull request !3780 from baihuawei/losscuda
This commit is contained in:
mindspore-ci-bot 2020-07-31 18:34:01 +08:00 committed by Gitee
parent 741c2898dd aa9ea1707c
commit 82b103a740
5 changed files with 320 additions and 0 deletions
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28
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/binary_cross_entropy_gpu_kernel.cc Normal file
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/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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 "backend/kernel_compiler/gpu/nn/binary_cross_entropy_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(BinaryCrossEntropy,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
BinaryCrossEntropyGpuKernel, float)
} // namespace kernel
} // namespace mindspore

89
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/binary_cross_entropy_gpu_kernel.h Normal file
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/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BINARY_CROSS_ENTROPY_KERNEL_H
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BINARY_CROSS_ENTROPY_KERNEL_H
#include <vector>
#include <string>
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
#include "backend/kernel_compiler/gpu/cuda_impl/loss_with_reduction_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class BinaryCrossEntropyGpuKernel : public GpuKernel {
public:
BinaryCrossEntropyGpuKernel() : input_size_(1), reduction_(1) {}
~BinaryCrossEntropyGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
T *input_x = GetDeviceAddress<T>(inputs, 0);
T *input_y = GetDeviceAddress<T>(inputs, 1);
T *weight = GetDeviceAddress<T>(inputs, 2);
T *loss = GetDeviceAddress<T>(outputs, 0);
BinaryCrossEntropyLoss(input_size_, reduction_, input_x, input_y, weight, loss,
reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
for (size_t i = 0; i < input_shape.size(); i++) {
input_size_ *= input_shape[i];
}
string reduction = GetAttr<string>(kernel_node, "reduction");
if (reduction == "none") {
reduction_ = 0;
} else if (reduction == "sum") {
reduction_ = 2;
}
InitSizeLists();
return true;
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(input_size_ * sizeof(T));
input_size_list_.push_back(input_size_ * sizeof(T));
input_size_list_.push_back(input_size_ * sizeof(T));
if (reduction_ == 0) {
output_size_list_.push_back(input_size_ * sizeof(T));
} else {
output_size_list_.push_back(sizeof(T));
}
}
private:
size_t input_size_;
int reduction_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;
std::vector<size_t> workspace_size_list_;
};
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BINARY_CROSS_ENTROPY_H

30
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/binary_cross_entropy_grad_kernel.cc Normal file
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/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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 "backend/kernel_compiler/gpu/nn/binary_cross_entropy_grad_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(BinaryCrossEntropyGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
BinaryCrossEntropyGradGpuKernel, float)
} // namespace kernel
} // namespace mindspore

90
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/binary_cross_entropy_grad_kernel.h Normal file
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/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BINARY_CROSS_ENTROPY_GRAD_KERNEL_H
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BINARY_CROSS_ENTROPY_GRAD_KERNEL_H
#include <string>
#include <vector>
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
#include "backend/kernel_compiler/gpu/cuda_impl/loss_with_reduction_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class BinaryCrossEntropyGradGpuKernel : public GpuKernel {
public:
BinaryCrossEntropyGradGpuKernel() : input_size_(1), reduction_(1) {}
~BinaryCrossEntropyGradGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
T *input_x = GetDeviceAddress<T>(inputs, 0);
T *input_y = GetDeviceAddress<T>(inputs, 1);
T *dloss = GetDeviceAddress<T>(inputs, 2);
T *weight = GetDeviceAddress<T>(inputs, 3);
T *dx = GetDeviceAddress<T>(outputs, 0);
BinaryCrossEntropyLossGrad(input_size_, reduction_, input_x, input_y, weight, dloss, dx,
reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
for (size_t i = 0; i < input_shape.size(); i++) {
input_size_ *= input_shape[i];
}
string reduction = GetAttr<string>(kernel_node, "reduction");
if (reduction == "none") {
reduction_ = 0;
} else if (reduction == "sum") {
reduction_ = 2;
}
InitSizeLists();
return true;
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(input_size_ * sizeof(T));
input_size_list_.push_back(input_size_ * sizeof(T));
input_size_list_.push_back(input_size_ * sizeof(T));
if (reduction_ == 0) {
input_size_list_.push_back(input_size_ * sizeof(T));
output_size_list_.push_back(input_size_ * sizeof(T));
} else {
input_size_list_.push_back(sizeof(T));
output_size_list_.push_back(sizeof(T));
}
}
private:
size_t input_size_;
int reduction_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;
std::vector<size_t> workspace_size_list_;
};
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_BINARY_CROSS_ENTROPY_GRAD_KERNEL_H

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tests/st/ops/gpu/test_binary_cross_entropy_op.py Normal file
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 numpy as np
import pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
class Net(nn.Cell):
def __init__(self, reduction="none"):
super(Net, self).__init__()
self.BinaryCrossEntropy = P.BinaryCrossEntropy("none")
def construct(self, x, y, weight):
return self.BinaryCrossEntropy(x, y, weight)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_binary_cross_entropy_loss():
np.random.seed(42)
prediction = np.random.rand(20).astype(np.float32)
target = np.random.rand(20).astype(np.float32)
weight = np.random.rand(20).astype(np.float32)
net = Net()
loss = net(Tensor(prediction), Tensor(target), Tensor(weight))
expect = [0.09555826, 1.2861121, 0.03518666, 0.6969416, 0.24313456, 0.99062896,
0.19205657, 0.5465214, 0.36964455, 0.21999404, 2.2953863, 2.2566645,
1.5803775, 1.3266402, 0.9883408, 1.2997618, 0.05439841, 0.14389999,
0.03405444, 0.23934692]
assert np.allclose(loss.asnumpy(), expect)
class Grad(nn.Cell):
def __init__(self, network):
super(Grad, self).__init__()
self.grad = C.GradOperation(name="get_all", get_all=True, sens_param=True)
self.network = network
def construct(self, x1, x2, sens, weight):
gout = self.grad(self.network)(x1, x2, sens, weight)
return gout
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_binary_cross_entropy_loss_grad():
np.random.seed(42)
prediction = np.random.rand(20).astype(np.float32)
target = np.random.rand(20).astype(np.float32)
sens = np.random.rand(20).astype(np.float32)
weight = np.random.rand(20).astype(np.float32)
grad = Grad(Net())
dx = grad(Tensor(prediction), Tensor(target), Tensor(sens), Tensor(weight))
dx1_expect = [-4.80516590e-02, 2.32625079e+00, 6.38972521e-02, 3.13642323e-01,
-1.65661633e-01, -1.71821892e+00, -1.13685496e-01, 1.26669514e+00,
1.47891801e-03, 5.83921909e-01, -2.17992840e+01, 4.21899414e+00,
2.85430793e-02, -3.21346498e+00, -2.22674108e+00, -2.80453944e+00,
-1.19787852e-04, 2.48514321e-02, -1.66696273e-02, -2.71965731e-02]
assert np.allclose(dx[0].asnumpy(), dx1_expect)