forked from mindspore-Ecosystem/mindspore
Add SquareDifference kernel for GPU
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
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@ -105,6 +105,5 @@ bool RandomCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
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}
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return true;
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}
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} // namespace kernel
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} // namespace mindspore
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@ -48,6 +48,5 @@ bool SelectCPUKernel<T>::Launch(const std::vector<AddressPtr> &inputs, const std
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}
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return true;
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}
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} // namespace kernel
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} // namespace mindspore
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@ -192,6 +192,14 @@ struct AbsGradFunc<half2> {
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}
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};
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template <typename T>
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struct SquaredDifferenceFunc {
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__device__ __forceinline__ T operator()(const T &lhs, const T &rhs) {
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T diff = lhs - rhs;
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return diff * diff;
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}
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};
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// Element-wise Comparation
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template <typename T, typename Func>
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__global__ void ElewiseCmpKernel(const int nums, const T *x0, const T *x1, bool *y) {
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@ -260,6 +268,8 @@ void ElewiseArithKernel(const int &nums, enum BroadcastOpType op, const T *x0, c
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return ElewiseArithKernel<T, DivFunc<T>><<<(nums + 255) / 256, 256, 0, stream>>>(nums, x0, x1, y);
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case BROADCAST_TYPE_DIVNONAN:
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return ElewiseArithKernel<T, DivNoNanFunc<T>><<<(nums + 255) / 256, 256, 0, stream>>>(nums, x0, x1, y);
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case BROADCAST_TYPE_SQUARED_DIFFERENCE:
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return ElewiseArithKernel<T, SquaredDifferenceFunc<T>><<<(nums + 255) / 256, 256, 0, stream>>>(nums, x0, x1, y);
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default:
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break;
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}
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@ -481,6 +491,11 @@ void BroadcastArith(const std::vector<size_t> &x0_dims, const std::vector<size_t
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x0_dims[0], x0_dims[1], x0_dims[2], x0_dims[3], x0_dims[4], x0_dims[5], x0_dims[6], x1_dims[0], x1_dims[1],
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x1_dims[2], x1_dims[3], x1_dims[4], x1_dims[5], x1_dims[6], y_dims[0], y_dims[1], y_dims[2], y_dims[3],
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y_dims[4], y_dims[5], y_dims[6], x0, x1, y);
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case BROADCAST_TYPE_SQUARED_DIFFERENCE:
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return BroadcastArithKernel<T, SquaredDifferenceFunc<T>><<<(size + 255) / 256, 256, 0, stream>>>(
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x0_dims[0], x0_dims[1], x0_dims[2], x0_dims[3], x0_dims[4], x0_dims[5], x0_dims[6], x1_dims[0], x1_dims[1],
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x1_dims[2], x1_dims[3], x1_dims[4], x1_dims[5], x1_dims[6], y_dims[0], y_dims[1], y_dims[2], y_dims[3],
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y_dims[4], y_dims[5], y_dims[6], x0, x1, y);
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default:
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break;
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}
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@ -37,6 +37,7 @@ enum BroadcastOpType {
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BROADCAST_TYPE_DIV = 11,
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BROADCAST_TYPE_DIVNONAN = 12,
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BROADCAST_TYPE_EQUAL = 13,
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BROADCAST_TYPE_SQUARED_DIFFERENCE = 14,
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BROADCAST_TYPE_INVALID = 0xffffffff,
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};
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@ -0,0 +1,40 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "backend/kernel_compiler/gpu/math/squared_difference_kernel.h"
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namespace mindspore {
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namespace kernel {
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// fp32
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MS_REG_GPU_KERNEL_ONE(
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SquaredDifference,
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KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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SquaredDifferenceOpGpuKernel, float)
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// fp16
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MS_REG_GPU_KERNEL_ONE(
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SquaredDifference,
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KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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SquaredDifferenceOpGpuKernel, half)
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// int32
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MS_REG_GPU_KERNEL_ONE(
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SquaredDifference,
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KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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SquaredDifferenceOpGpuKernel, int)
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,144 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SQUARED_DIFFERENCE_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SQUARED_DIFFERENCE_GPU_KERNEL_H_
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#include <cuda_runtime_api.h>
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#include <vector>
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#include <string>
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#include <map>
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#include "backend/kernel_compiler/gpu/gpu_kernel.h"
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#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
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#include "backend/kernel_compiler/gpu/cuda_impl/broadcast_impl.cuh"
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#include "backend/kernel_compiler/gpu/kernel_constants.h"
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namespace mindspore {
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namespace kernel {
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constexpr int MAX_DIMS = 7;
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template <typename T>
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class SquaredDifferenceOpGpuKernel : public GpuKernel {
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public:
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SquaredDifferenceOpGpuKernel() { ResetResource(); }
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~SquaredDifferenceOpGpuKernel() override = default;
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const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
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const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
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const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
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const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
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T *lhs = GetDeviceAddress<T>(inputs, 0);
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T *rhs = GetDeviceAddress<T>(inputs, 1);
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T *output = GetDeviceAddress<T>(outputs, 0);
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if (need_broadcast_) {
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BroadcastArith(lhs_shape_, rhs_shape_, output_shape_, op_type_, lhs, rhs, output,
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reinterpret_cast<cudaStream_t>(stream_ptr));
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} else {
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ElewiseArith(output_num_, op_type_, lhs, rhs, output, reinterpret_cast<cudaStream_t>(stream_ptr));
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}
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return true;
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}
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bool Init(const CNodePtr &kernel_node) override {
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auto input_shape1 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
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auto input_shape2 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1);
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auto output_shape = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0);
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need_broadcast_ = IsBroadcast(input_shape1, input_shape2);
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if (need_broadcast_ && output_shape.size() > MAX_DIMS) {
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MS_LOG(EXCEPTION) << "Broadcast operation not support dim greater than 7";
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}
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lhs_shape_.resize(MAX_DIMS, 1);
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rhs_shape_.resize(MAX_DIMS, 1);
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output_shape_.resize(MAX_DIMS, 1);
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for (size_t i = 0; i < output_shape.size(); i++) {
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if (need_broadcast_) {
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output_shape_[i] = output_shape[i];
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}
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output_num_ *= output_shape[i];
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}
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int lhs_offset = output_shape.size() - input_shape1.size();
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for (size_t j = 0; j < input_shape1.size(); j++) {
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if (need_broadcast_) {
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lhs_shape_[j + lhs_offset] = input_shape1[j];
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}
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input1_num_ *= input_shape1[j];
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}
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int rhs_offset = output_shape.size() - input_shape2.size();
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for (size_t k = 0; k < input_shape2.size(); k++) {
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if (need_broadcast_) {
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rhs_shape_[k + rhs_offset] = input_shape2[k];
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}
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input2_num_ *= input_shape2[k];
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}
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InitSizeLists();
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return true;
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}
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void ResetResource() noexcept override {
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op_type_ = BROADCAST_TYPE_SQUARED_DIFFERENCE;
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need_broadcast_ = false;
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input1_num_ = 1;
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input2_num_ = 1;
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output_num_ = 1;
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lhs_shape_.clear();
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rhs_shape_.clear();
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output_shape_.clear();
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input_size_list_.clear();
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output_size_list_.clear();
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workspace_size_list_.clear();
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}
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protected:
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void InitResource() override { return; }
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void InitSizeLists() override {
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input_size_list_.push_back(input1_num_ * sizeof(T));
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input_size_list_.push_back(input2_num_ * sizeof(T));
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output_size_list_.push_back(output_num_ * sizeof(T));
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}
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private:
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bool IsBroadcast(const std::vector<size_t> &lhs, const std::vector<size_t> &rhs) {
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if (lhs.size() != rhs.size()) {
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return true;
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}
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for (size_t i = 0; i < lhs.size(); i++) {
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if (lhs[i] != rhs[i]) {
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return true;
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}
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}
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return false;
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}
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BroadcastOpType op_type_;
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bool need_broadcast_;
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bool is_comp_op_;
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size_t input1_num_;
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size_t input2_num_;
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size_t output_num_;
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std::vector<size_t> lhs_shape_;
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std::vector<size_t> rhs_shape_;
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std::vector<size_t> output_shape_;
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std::vector<size_t> input_size_list_;
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std::vector<size_t> output_size_list_;
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std::vector<size_t> workspace_size_list_;
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}; // namespace kernel
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SQUARED_DIFFERENCE_GPU_KERNEL_H_
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@ -0,0 +1,314 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class SquaredDifference(nn.Cell):
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def __init__(self):
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super(SquaredDifference, self).__init__()
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self.squaredDiff = P.SquaredDifference()
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def construct(self, x, y):
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return self.squaredDiff(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nobroadcast_f16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.uniform(10, 20, (3, 4, 5, 2)).astype(np.float16)
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input_y = np.random.uniform(40, 50, (3, 4, 5, 2)).astype(np.float16)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nobroadcast_f32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(3, 4, 5, 2).astype(np.float32)
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input_y = np.random.rand(3, 4, 5, 2).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nobroadcast_int32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(3, 4, 5, 2).astype(np.int32)
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input_y = np.random.rand(3, 4, 5, 2).astype(np.int32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_broadcast_int32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.int32)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.int32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_broadcast_f32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.float32)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_broadcast_f16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.float16)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.float16)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_broadcast_bool():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.bool)
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input_y = np.random.uniform(10, 20, (3, 1, 5, 1)).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-6
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double_check = np.abs(output-expect)/expect
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assert np.all(double_check < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nobroadcast_bool():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(3, 4, 5, 2).astype(np.bool)
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input_y = np.random.rand(3, 4, 5, 2).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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||||
diff = input_x-input_y
|
||||
expect = diff*diff
|
||||
error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-6
|
||||
double_check = np.abs(output-expect)/expect
|
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assert np.all(double_check < error)
|
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|
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|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_broadcast_int32_f16():
|
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.rand(1, 4, 1, 2).astype(np.int32)
|
||||
input_y = np.random.uniform(10, 20, (3, 1, 5, 1)).astype(np.float16)
|
||||
output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
|
||||
diff = input_x-input_y
|
||||
expect = diff*diff
|
||||
error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-3
|
||||
double_check = np.abs(output-expect)/expect
|
||||
assert np.all(double_check < error)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_broadcast_int32_f32():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.rand(1, 4, 1, 2).astype(np.int32)
|
||||
input_y = np.random.uniform(10, 20, (3, 1, 5, 1)).astype(np.float32)
|
||||
output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
|
||||
diff = input_x-input_y
|
||||
expect = diff*diff
|
||||
error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-6
|
||||
double_check = np.abs(output-expect)/expect
|
||||
assert np.all(double_check < error)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_nobroadcast_int32_f16():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.rand(2, 4, 3, 2).astype(np.int32)
|
||||
input_y = np.random.uniform(10, 20, (2, 4, 3, 2)).astype(np.float16)
|
||||
output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
|
||||
diff = input_x-input_y
|
||||
expect = diff*diff
|
||||
error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-3
|
||||
double_check = np.abs(output-expect)/expect
|
||||
assert np.all(double_check < error)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_nobroadcast_int32_f32():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.rand(2, 4, 3, 2).astype(np.int32)
|
||||
input_y = np.random.uniform(10, 20, (2, 4, 3, 2)).astype(np.float32)
|
||||
output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
|
||||
diff = input_x-input_y
|
||||
expect = diff*diff
|
||||
error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-6
|
||||
double_check = np.abs(output-expect)/expect
|
||||
assert np.all(double_check < error)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_broadcast_f32_scalar_tensor():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.rand(2).astype(np.float32)
|
||||
input_y = np.random.rand(3, 1, 5, 1).astype(np.float32)
|
||||
output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
|
||||
diff = input_x-input_y
|
||||
expect = diff*diff
|
||||
assert np.all(output == expect)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_broadcast_f32_tensor_tensor():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.rand(1, 2).astype(np.float32)
|
||||
input_y = np.random.rand(3, 1, 5, 1).astype(np.float32)
|
||||
output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
|
||||
diff = input_x-input_y
|
||||
expect = diff*diff
|
||||
assert np.all(output == expect)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_broadcast_f32_tensor_tensor_dim_over_7():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.rand(1, 2).astype(np.float32)
|
||||
input_y = np.random.rand(3, 1, 5, 1, 3, 4, 2, 1).astype(np.float32)
|
||||
try:
|
||||
net(Tensor(input_x), Tensor(input_y))
|
||||
except RuntimeError:
|
||||
assert True
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_broadcast_f32_tensor_tensor_cannot_brocast():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.rand(5, 3).astype(np.float32)
|
||||
input_y = np.random.rand(3, 1, 5, 1, 3, 4, 2).astype(np.float32)
|
||||
try:
|
||||
net(Tensor(input_x), Tensor(input_y))
|
||||
except ValueError:
|
||||
assert True
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_broadcast_int_f32_precision():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.randint(20, 30, (1, 2)).astype(np.int32)
|
||||
input_y = np.random.rand(3, 1, 5, 1).astype(np.float32)
|
||||
output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
|
||||
diff = input_x-input_y
|
||||
expect = diff*diff
|
||||
error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-3
|
||||
double_thousand = np.abs(output-expect)/expect
|
||||
assert np.all(double_thousand < error)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_broadcast_type_error():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
np.random.seed(42)
|
||||
net = SquaredDifference()
|
||||
input_x = np.random.randint(20, 30, (1, 2)).astype(np.bool)
|
||||
input_y = np.random.rand(3, 1, 5, 1).astype(np.bool)
|
||||
try:
|
||||
net(Tensor(input_x), Tensor(input_y))
|
||||
except TypeError:
|
||||
assert True
|
||||
Loading…
Reference in New Issue