lijiaxing1
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lijiaxing1999
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/**
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* Copyright 2022 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 <algorithm>
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#include <utility>
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#include <set>
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#include <map>
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#include <functional>
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#include <numeric>
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#include <iterator>
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#include <unordered_map>
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#include "plugin/device/cpu/kernel/sparse_add_grad_cpu_kernel.h"
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#include "mindspore/core/ops/grad/sparse_add_grad.h"
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namespace mindspore {
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namespace kernel {
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// Value check constant
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constexpr size_t kInputNum = 4;
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constexpr size_t kOutputNum = 2;
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// Input idx constant
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constexpr size_t kDoutIdx = 0;
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constexpr size_t kX1IndicesIdx = 1;
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constexpr size_t kX2IndicesIdx = 2;
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constexpr size_t kOutIndicesIdx = 3;
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// Output idx constant
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constexpr size_t kDx1Idx = 0;
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constexpr size_t kDx2Idx = 1;
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bool SparseAddGradCpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs) {
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auto kernel_ptr = std::dynamic_pointer_cast<ops::SparseAddGrad>(base_operator);
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kernel_name_ = kernel_ptr->name();
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size_t input_num = inputs.size();
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if (input_num != kInputNum) {
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MS_LOG(ERROR) << "For " << kernel_name_ << ", input should be dout, x1_indices, x2_indices and out_indices total "
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<< kInputNum << " tensors, but get " << input_num;
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return false;
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}
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if (!MatchKernelFunc(base_operator, inputs, outputs)) {
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return false;
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}
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return true;
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}
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void SparseAddGradCpuKernelMod::ResetResource() noexcept {
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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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int SparseAddGradCpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs,
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const std::map<uint32_t, tensor::TensorPtr> &inputsOnHost) {
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ResetResource();
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auto ret = KernelMod::Resize(base_operator, inputs, outputs, inputsOnHost);
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if (ret == KRET_UNKNOWN_OUT_SHAPE) {
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if (input_size_list_.size() != kInputNum) {
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MS_LOG(ERROR) << "Input size list should be " << kInputNum << ", but got " << input_size_list_.size();
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return KRET_RESIZE_FAILED;
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}
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auto dout_shape = inputs.at(kDoutIdx)->GetShapeVector();
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auto x1_indices_shape = inputs.at(kX1IndicesIdx)->GetShapeVector();
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auto x2_indices_shape = inputs.at(kX2IndicesIdx)->GetShapeVector();
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auto out_indices_shape = inputs.at(kOutIndicesIdx)->GetShapeVector();
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(void)std::transform(dout_shape.begin(), dout_shape.end(), std::back_inserter(dout_shape_), LongToSize);
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(void)std::transform(x1_indices_shape.begin(), x1_indices_shape.end(), std::back_inserter(x1_indices_shape_),
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LongToSize);
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(void)std::transform(x2_indices_shape.begin(), x2_indices_shape.end(), std::back_inserter(x2_indices_shape_),
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LongToSize);
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(void)std::transform(out_indices_shape.begin(), out_indices_shape.end(), std::back_inserter(out_indices_shape_),
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LongToSize);
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auto dout_size_ = std::accumulate(dout_shape_.begin(), dout_shape_.end(), 1, std::multiplies<size_t>());
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auto x1_indices_size_ =
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std::accumulate(x1_indices_shape_.begin(), x1_indices_shape_.end(), 1, std::multiplies<size_t>());
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auto x2_indices_size_ =
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std::accumulate(x2_indices_shape_.begin(), x2_indices_shape_.end(), 1, std::multiplies<size_t>());
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auto out_indices_size_ =
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std::accumulate(out_indices_shape_.begin(), out_indices_shape_.end(), 1, std::multiplies<size_t>());
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input_size_list_.push_back(dout_size_);
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input_size_list_.push_back(x1_indices_size_);
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input_size_list_.push_back(x2_indices_size_);
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input_size_list_.push_back(out_indices_size_);
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output_size_list_.push_back(x1_indices_size_);
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output_size_list_.push_back(x2_indices_size_);
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}
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return ret;
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}
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template <typename T, typename S>
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bool SparseAddGradCpuKernelMod::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
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const std::vector<AddressPtr> &workspace,
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const std::vector<kernel::AddressPtr> &outputs) {
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if (inputs.size() != kInputNum) {
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MS_LOG(EXCEPTION) << "For " << kernel_name_ << ", the number of inputs should be " << kInputNum << ", but got "
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<< inputs.size() << " input(s).";
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}
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if (outputs.size() != kOutputNum) {
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MS_LOG(EXCEPTION) << "For " << kernel_name_ << ", the number of inputs should be " << kOutputNum << ", but got "
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<< outputs.size() << " output(s).";
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}
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// Inputs
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const auto dout = reinterpret_cast<T *>(inputs[kDoutIdx]->addr);
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const auto x1_indices = reinterpret_cast<S *>(inputs[kX1IndicesIdx]->addr);
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const auto x2_indices = reinterpret_cast<S *>(inputs[kX2IndicesIdx]->addr);
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const auto out_indices = reinterpret_cast<S *>(inputs[kOutIndicesIdx]->addr);
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// Outputs
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auto dx1 = reinterpret_cast<T *>(outputs[kDx1Idx]->addr);
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auto dx2 = reinterpret_cast<T *>(outputs[kDx2Idx]->addr);
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const int64_t x1_indices_num = inputs[kX1IndicesIdx]->size / (sizeof(S) * 2);
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const int64_t x2_indices_num = inputs[kX2IndicesIdx]->size / (sizeof(S) * 2);
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const int64_t out_indices_num = inputs[kOutIndicesIdx]->size / (sizeof(S) * 2);
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auto arrayHash = [fn = std::hash<int>{}](const std::array<int, 2> &arr) -> size_t {
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return std::accumulate(arr.begin(), arr.end(), 0u, [&](size_t acc, int num) { return (acc << 1) ^ fn(num); });
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};
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constexpr int dimension_difference = 2;
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std::unordered_map<std::array<int, 2>, int, decltype(arrayHash)> out_map(0, arrayHash);
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for (int i = 0; i < out_indices_num * dimension_difference; i += dimension_difference) {
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std::array<int, 2> index{};
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index[0] = out_indices[i];
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index[1] = out_indices[i + 1];
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out_map[index] = static_cast<int>(i / dimension_difference);
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}
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for (int i = 0; i < x1_indices_num * dimension_difference; i += dimension_difference) {
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std::array<int, 2> index{};
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index[0] = x1_indices[i];
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index[1] = x1_indices[i + 1];
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if (out_map.find(index) != out_map.end()) {
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dx1[static_cast<int>(i / dimension_difference)] = dout[out_map[index]];
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}
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}
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for (int i = 0; i < x2_indices_num * dimension_difference; i += dimension_difference) {
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std::array<int, 2> index{};
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index[0] = x2_indices[i];
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index[1] = x2_indices[i + 1];
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if (out_map.find(index) != out_map.end()) {
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dx2[static_cast<int>(i / dimension_difference)] = dout[out_map[index]];
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}
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}
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return true;
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}
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const std::vector<std::pair<KernelAttr, SparseAddGradCpuKernelMod::KernelRunFunc>>
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&SparseAddGradCpuKernelMod::GetFuncList() const {
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static const std::vector<std::pair<KernelAttr, SparseAddGradCpuKernelMod::KernelRunFunc>> func_list = {
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{KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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&SparseAddGradCpuKernelMod::LaunchKernel<float, int32_t>},
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};
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return func_list;
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}
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MS_KERNEL_FACTORY_REG(NativeCpuKernelMod, SparseAddGrad, SparseAddGradCpuKernelMod);
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,63 @@
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/**
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* Copyright 2022 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_CPU_SPARSE_ADD_GRAD_CPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SPARSE_ADD_GRAD_CPU_KERNEL_H_
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#include <vector>
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#include <map>
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#include <utility>
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#include "plugin/device/cpu/kernel/cpu_kernel.h"
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#include "plugin/factory/ms_factory.h"
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namespace mindspore {
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namespace kernel {
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class SparseAddGradCpuKernelMod : public NativeCpuKernelMod, public MatchKernelHelper<SparseAddGradCpuKernelMod> {
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public:
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SparseAddGradCpuKernelMod() = default;
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~SparseAddGradCpuKernelMod() override = default;
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bool Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs) override;
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) override {
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return kernel_func_(this, inputs, workspace, outputs);
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}
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int Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs,
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const std::map<uint32_t, tensor::TensorPtr> &inputsOnHost) override;
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void ResetResource() noexcept;
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const std::vector<std::pair<KernelAttr, KernelRunFunc>> &GetFuncList() const override;
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protected:
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std::vector<KernelAttr> GetOpSupport() override { return OpSupport(); }
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private:
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template <typename T, typename S>
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bool LaunchKernel(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &workspace,
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const std::vector<kernel::AddressPtr> &outputs);
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std::vector<size_t> dout_shape_;
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std::vector<size_t> x1_indices_shape_;
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std::vector<size_t> x2_indices_shape_;
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std::vector<size_t> out_indices_shape_;
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};
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SPARSE_ADD_GRAD_CPU_KERNEL_H_
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