添加测试用例 添加测试用例 添加测试用例 添加测试用例 清除告警 添加测试用例 添加测试用例 清除告警pull/9842/head
hebotao
4 years ago
parent
3ba3ffedd4
commit
f4ae9cdbc4
@ -0,0 +1,40 @@
|
||||
/**
|
||||
* 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/math/squared_difference_kernel.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace kernel {
|
||||
// fp32
|
||||
MS_REG_GPU_KERNEL_ONE(
|
||||
SquaredDifference,
|
||||
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
|
||||
SquaredDifferenceOpGpuKernel, float)
|
||||
|
||||
// fp16
|
||||
MS_REG_GPU_KERNEL_ONE(
|
||||
SquaredDifference,
|
||||
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
|
||||
SquaredDifferenceOpGpuKernel, half)
|
||||
|
||||
// int32
|
||||
MS_REG_GPU_KERNEL_ONE(
|
||||
SquaredDifference,
|
||||
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
|
||||
SquaredDifferenceOpGpuKernel, int)
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace mindspore
|
||||
@ -0,0 +1,144 @@
|
||||
/**
|
||||
* 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_SQUARED_DIFFERENCE_GPU_KERNEL_H_
|
||||
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SQUARED_DIFFERENCE_GPU_KERNEL_H_
|
||||
|
||||
#include <cuda_runtime_api.h>
|
||||
#include <vector>
|
||||
#include <string>
|
||||
#include <map>
|
||||
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
|
||||
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
|
||||
#include "backend/kernel_compiler/gpu/cuda_impl/broadcast_impl.cuh"
|
||||
#include "backend/kernel_compiler/gpu/kernel_constants.h"
|
||||
namespace mindspore {
|
||||
namespace kernel {
|
||||
constexpr int MAX_DIMS = 7;
|
||||
template <typename T>
|
||||
class SquaredDifferenceOpGpuKernel : public GpuKernel {
|
||||
public:
|
||||
SquaredDifferenceOpGpuKernel() { ResetResource(); }
|
||||
~SquaredDifferenceOpGpuKernel() 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 *lhs = GetDeviceAddress<T>(inputs, 0);
|
||||
T *rhs = GetDeviceAddress<T>(inputs, 1);
|
||||
T *output = GetDeviceAddress<T>(outputs, 0);
|
||||
if (need_broadcast_) {
|
||||
BroadcastArith(lhs_shape_, rhs_shape_, output_shape_, op_type_, lhs, rhs, output,
|
||||
reinterpret_cast<cudaStream_t>(stream_ptr));
|
||||
} else {
|
||||
ElewiseArith(output_num_, op_type_, lhs, rhs, output, reinterpret_cast<cudaStream_t>(stream_ptr));
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool Init(const CNodePtr &kernel_node) override {
|
||||
auto input_shape1 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
|
||||
auto input_shape2 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1);
|
||||
auto output_shape = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0);
|
||||
need_broadcast_ = IsBroadcast(input_shape1, input_shape2);
|
||||
if (need_broadcast_ && output_shape.size() > MAX_DIMS) {
|
||||
MS_LOG(EXCEPTION) << "Broadcast operation not support dim greater than 7";
|
||||
}
|
||||
|
||||
lhs_shape_.resize(MAX_DIMS, 1);
|
||||
rhs_shape_.resize(MAX_DIMS, 1);
|
||||
output_shape_.resize(MAX_DIMS, 1);
|
||||
for (size_t i = 0; i < output_shape.size(); i++) {
|
||||
if (need_broadcast_) {
|
||||
output_shape_[i] = output_shape[i];
|
||||
}
|
||||
output_num_ *= output_shape[i];
|
||||
}
|
||||
int lhs_offset = output_shape.size() - input_shape1.size();
|
||||
for (size_t j = 0; j < input_shape1.size(); j++) {
|
||||
if (need_broadcast_) {
|
||||
lhs_shape_[j + lhs_offset] = input_shape1[j];
|
||||
}
|
||||
input1_num_ *= input_shape1[j];
|
||||
}
|
||||
int rhs_offset = output_shape.size() - input_shape2.size();
|
||||
for (size_t k = 0; k < input_shape2.size(); k++) {
|
||||
if (need_broadcast_) {
|
||||
rhs_shape_[k + rhs_offset] = input_shape2[k];
|
||||
}
|
||||
input2_num_ *= input_shape2[k];
|
||||
}
|
||||
|
||||
InitSizeLists();
|
||||
return true;
|
||||
}
|
||||
|
||||
void ResetResource() noexcept override {
|
||||
op_type_ = BROADCAST_TYPE_SQUARED_DIFFERENCE;
|
||||
need_broadcast_ = false;
|
||||
input1_num_ = 1;
|
||||
input2_num_ = 1;
|
||||
output_num_ = 1;
|
||||
lhs_shape_.clear();
|
||||
rhs_shape_.clear();
|
||||
output_shape_.clear();
|
||||
input_size_list_.clear();
|
||||
output_size_list_.clear();
|
||||
workspace_size_list_.clear();
|
||||
}
|
||||
|
||||
protected:
|
||||
void InitResource() override { return; }
|
||||
void InitSizeLists() override {
|
||||
input_size_list_.push_back(input1_num_ * sizeof(T));
|
||||
input_size_list_.push_back(input2_num_ * sizeof(T));
|
||||
output_size_list_.push_back(output_num_ * sizeof(T));
|
||||
}
|
||||
|
||||
private:
|
||||
bool IsBroadcast(const std::vector<size_t> &lhs, const std::vector<size_t> &rhs) {
|
||||
if (lhs.size() != rhs.size()) {
|
||||
return true;
|
||||
}
|
||||
for (size_t i = 0; i < lhs.size(); i++) {
|
||||
if (lhs[i] != rhs[i]) {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
BroadcastOpType op_type_;
|
||||
bool need_broadcast_;
|
||||
bool is_comp_op_;
|
||||
size_t input1_num_;
|
||||
size_t input2_num_;
|
||||
size_t output_num_;
|
||||
std::vector<size_t> lhs_shape_;
|
||||
std::vector<size_t> rhs_shape_;
|
||||
std::vector<size_t> output_shape_;
|
||||
|
||||
std::vector<size_t> input_size_list_;
|
||||
std::vector<size_t> output_size_list_;
|
||||
std::vector<size_t> workspace_size_list_;
|
||||
}; // namespace kernel
|
||||
} // namespace kernel
|
||||
} // namespace mindspore
|
||||
|
||||
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SQUARED_DIFFERENCE_GPU_KERNEL_H_
|
||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in new issue