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!2962 gpu support SmoothL1Loss kernel

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Merge pull request !2962 from chenweifeng/smoothl1loss
pull/2962/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent 03ef509ee9 0fdc304a8e
commit cf5a27e97d
7 changed files with 376 additions and 0 deletions
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64
mindspore/ccsrc/kernel/gpu/cuda_impl/smooth_l1_loss_impl.cu
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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 "smooth_l1_loss_impl.cuh"
#include "device/gpu/cuda_common.h"
template <typename T>
__global__ void SmoothL1LossKernel(const int input_size, const float sigma, const T *prediction, const T *target,
T *loss) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < input_size; i += blockDim.x * gridDim.x) {
T value = (prediction[i] - target[i]) > 0 ? (prediction[i] - target[i]) : (target[i] - prediction[i]);
if (value < sigma) {
loss[i] = static_cast<T>(0.5) * value * value;
} else {
loss[i] = value - static_cast<T>(0.5);
}
}
}
template <typename T>
void SmoothL1Loss(const int &input_size, const float &sigma, const T *prediction, const T *target, T *loss,
cudaStream_t stream) {
SmoothL1LossKernel<<<GET_BLOCKS(input_size), GET_THREADS, 0, stream>>>(input_size, sigma, prediction, target, loss);
}
template <typename T>
__global__ void SmoothL1LossGradKernel(const int input_size, const float sigma, const T *prediction, const T *target,
const T *dloss, T *dx) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < input_size; i += blockDim.x * gridDim.x) {
T value = prediction[i] - target[i];
if (value > static_cast<T>(sigma)) {
dx[i] = dloss[i];
} else if (value < static_cast<T>(-sigma)) {
dx[i] = -dloss[i];
} else {
dx[i] = value * dloss[i];
}
}
}
template <typename T>
void SmoothL1LossGrad(const int &input_size, const float &sigma, const T *prediction, const T *target, const T *dloss,
T *dx, cudaStream_t stream) {
SmoothL1LossGradKernel<<<GET_BLOCKS(input_size), GET_THREADS, 0, stream>>>(input_size, sigma, prediction, target,
dloss, dx);
}
template void SmoothL1Loss(const int &input_size, const float &sigma, const float *prediction, const float *target,
float *loss, cudaStream_t stream);
template void SmoothL1LossGrad(const int &input_size, const float &sigma, const float *prediction, const float *target,
const float *dloss, float *dx, cudaStream_t stream);

25
mindspore/ccsrc/kernel/gpu/cuda_impl/smooth_l1_loss_impl.cuh
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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_KERNEL_GPU_CUDA_IMPL_SMOOTH_L1_LOSS_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SMOOTH_L1_LOSS_H_
template <typename T>
void SmoothL1Loss(const int &input_size, const float &sigma, const T *prediction, const T *target, T *loss,
cudaStream_t stream);
template <typename T>
void SmoothL1LossGrad(const int &input_size, const float &sigma, const T *prediction, const T *target, const T *dloss,
T *dx, cudaStream_t stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SMOOTH_L1_LOSS_H_

26
mindspore/ccsrc/kernel/gpu/nn/smooth_l1_loss_gpu_kernel.cc
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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 "kernel/gpu/nn/smooth_l1_loss_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(
SmoothL1Loss,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
SmoothL1LossGpuKernel, float)
} // namespace kernel
} // namespace mindspore

75
mindspore/ccsrc/kernel/gpu/nn/smooth_l1_loss_gpu_kernel.h
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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_KERNEL_GPU_NN_SMOOTH_L1_LOSS_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_NN_SMOOTH_L1_LOSS_GPU_KERNEL_H_
#include <vector>
#include "kernel/gpu/gpu_kernel.h"
#include "kernel/gpu/gpu_kernel_factory.h"
#include "kernel/gpu/cuda_impl/smooth_l1_loss_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class SmoothL1LossGpuKernel : public GpuKernel {
public:
SmoothL1LossGpuKernel() : input_size_(1), sigma_(1.0) {}
~SmoothL1LossGpuKernel() 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 *prediction = GetDeviceAddress<T>(inputs, 0);
T *target = GetDeviceAddress<T>(inputs, 1);
T *loss = GetDeviceAddress<T>(outputs, 0);
SmoothL1Loss(input_size_, sigma_, prediction, target, 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];
}
sigma_ = GetAttr<float>(kernel_node, "sigma");
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));
output_size_list_.push_back(input_size_ * sizeof(T));
}
private:
size_t input_size_;
float sigma_;
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_KERNEL_GPU_NN_SMOOTH_L1_LOSS_GPU_KERNEL_H_

29
mindspore/ccsrc/kernel/gpu/nn/smooth_l1_loss_grad_gpu_kernel.cc
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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 "kernel/gpu/nn/smooth_l1_loss_grad_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(SmoothL1LossGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
SmoothL1LossGradGpuKernel, float)
} // namespace kernel
} // namespace mindspore

76
mindspore/ccsrc/kernel/gpu/nn/smooth_l1_loss_grad_gpu_kernel.h
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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_KERNEL_GPU_NN_SMOOTH_L1_LOSS_GRAD_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_NN_SMOOTH_L1_LOSS_GRAD_GPU_KERNEL_H_
#include <vector>
#include "kernel/gpu/gpu_kernel.h"
#include "kernel/gpu/gpu_kernel_factory.h"
#include "kernel/gpu/cuda_impl/smooth_l1_loss_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class SmoothL1LossGradGpuKernel : public GpuKernel {
public:
SmoothL1LossGradGpuKernel() : input_size_(1), sigma_(1.0) {}
~SmoothL1LossGradGpuKernel() 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 *prediction = GetDeviceAddress<T>(inputs, 0);
T *target = GetDeviceAddress<T>(inputs, 1);
T *dloss = GetDeviceAddress<T>(inputs, 2);
T *dx = GetDeviceAddress<T>(outputs, 0);
SmoothL1LossGrad(input_size_, sigma_, prediction, target, 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];
}
sigma_ = GetAttr<float>(kernel_node, "sigma");
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));
output_size_list_.push_back(input_size_ * sizeof(T));
}
private:
size_t input_size_;
float sigma_;
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_KERNEL_GPU_NN_SMOOTH_L1_LOSS_GRAD_GPU_KERNEL_H_

81
tests/st/ops/gpu/test_smoothl1loss_op.py
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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
context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=True)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_smoothl1loss():
np.random.seed(42)
prediction = np.random.randn(20).astype(np.float32)
target = np.random.randn(20).astype(np.float32)
sigma = 1.0
net = nn.SmoothL1Loss(sigma)
loss = net(Tensor(prediction), Tensor(target))
expect = [0.46941718, 0.00382918, 0.16829303, 2.447778, 0.04812113, 0.05953304,
2.2302065, 0.07672881, 0.00860204, 0.34798968, 0.00956192, 1.818008,
0.03262977, 0.36599946, 2.047463, 0.2168481, 0.7216947, 1.7739174,
0.08826803, 1.109165]
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):
gout = self.grad(self.network)(x1, x2, sens)
return gout
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_smoothl1loss_grad():
np.random.seed(42)
prediction = np.random.randn(20).astype(np.float32)
target = np.random.randn(20).astype(np.float32)
sens = np.random.randn(20).astype(np.float32)
sigma = 1.0
net = nn.SmoothL1Loss(sigma)
grad = Grad(net)
dx = grad(Tensor(prediction), Tensor(target), Tensor(sens))
dx1_expect = [-0.71552587, 0.01499678, -0.06709455, -0.30110368, -0.45868093,
0.24838912, -0.46063876, 0.41411355, 0.04507046, -1.4708229,
0.04481723, 0.38508227, -0.17292616, -0.52333146, -1.0309995,
0.61330026, 0.83921754, -0.3092124, 0.1391843, -0.9755451]
dx2_expect = [0.71552587, -0.01499678, 0.06709455, 0.30110368, 0.45868093,
-0.24838912, 0.46063876, -0.41411355, -0.04507046, 1.4708229,
-0.04481723, -0.38508227, 0.17292616, 0.52333146, 1.0309995,
-0.61330026, -0.83921754, 0.3092124, -0.1391843, 0.9755451]
assert np.allclose(dx[0].asnumpy(), dx1_expect)
assert np.allclose(dx[1].asnumpy(), dx2_expect)
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