forked from mindspore-Ecosystem/mindspore
!14732 Add L2NormalizeGrad for CPU
From: @he-botao Reviewed-by: @wuxuejian,@liangchenghui Signed-off-by:
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/**
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* Copyright 2021 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/cpu/l2normalize_grad_cpu_kernel.h"
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#include "runtime/device/cpu/cpu_device_address.h"
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namespace mindspore {
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namespace kernel {
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template <typename T>
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void L2NormalizeGradCPUKernel<T>::InitKernel(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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CheckIONumber(kernel_node);
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for (size_t i = 0; i < INPUT_SIZE; i++) {
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input_shape_list_.emplace_back(AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i));
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}
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auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
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CheckInputShape(output_shape);
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int output_dim_length = output_shape.size();
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dim_elem_num_list_.resize(output_dim_length, 1);
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for (int i = output_dim_length - 2; i >= 0; i--) {
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dim_elem_num_list_[i] = output_shape[i + 1] * dim_elem_num_list_[i + 1];
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}
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int axis = LongToInt(AnfAlgo::GetNodeAttr<int64_t>(kernel_node, "axis"));
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int input_dim_length = SizeToInt(input_shape_list_[0].size());
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axis_ = axis < 0 ? (axis + input_dim_length) : axis;
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epsilon_ = static_cast<T>(AnfAlgo::GetNodeAttr<float>(kernel_node, "epsilon"));
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}
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template <typename T>
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bool L2NormalizeGradCPUKernel<T>::Launch(const std::vector<AddressPtr> &inputs,
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const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) {
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auto input_x = reinterpret_cast<T *>(inputs[0]->addr);
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auto y = reinterpret_cast<T *>(inputs[1]->addr);
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auto dout = reinterpret_cast<T *>(inputs[2]->addr);
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auto output = reinterpret_cast<T *>(outputs[0]->addr);
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auto output_size = outputs[0]->size / sizeof(T);
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auto task = [&](size_t start, size_t end) {
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for (size_t i = start; i < end; i++) {
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std::vector<size_t> high_dim_index;
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OneDimIndexToHighDimIndex(i, &high_dim_index);
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std::vector<T> input_x_vector;
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GetVector(&input_x_vector, high_dim_index, input_x);
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std::vector<T> dout_vector;
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GetVector(&dout_vector, high_dim_index, dout);
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std::vector<T> y_vector;
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GetVector(&y_vector, high_dim_index, y);
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GetOutput(input_x_vector, y_vector, dout_vector, high_dim_index, &output[i]);
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}
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};
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CPUKernelUtils::ParallelFor(task, output_size);
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return true;
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}
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template <typename T>
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void L2NormalizeGradCPUKernel<T>::CheckInputShape(const std::vector<size_t> &output_shape) {
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for (const auto &shape : input_shape_list_) {
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if (output_shape != shape) {
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MS_LOG(EXCEPTION) << "Input shape and output shape should be same.";
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}
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}
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auto input_x_shape = input_shape_list_[0];
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if (input_x_shape.size() != 0) {
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if (std::any_of(input_x_shape.begin(), input_x_shape.end(), [](size_t i) { return i == 0; })) {
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MS_LOG(EXCEPTION) << "L2NormalizeCPUKernel input is null.";
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}
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}
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}
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template <typename T>
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void L2NormalizeGradCPUKernel<T>::CheckIONumber(const CNodePtr &kernel_node) {
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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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if (input_num != INPUT_SIZE) {
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MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but L2NormalizeGradCPUKernel needs 3 input.";
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}
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size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
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if (output_num != OUTPUT_SIZE) {
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MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but L2NormalizeGradCPUKernel needs 1 output.";
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}
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}
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template <typename T>
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void L2NormalizeGradCPUKernel<T>::OneDimIndexToHighDimIndex(size_t one_dim_index, std::vector<size_t> *high_dim_index) {
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for (const auto &item : dim_elem_num_list_) {
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high_dim_index->push_back(one_dim_index / item);
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one_dim_index %= item;
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}
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}
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template <typename T>
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void L2NormalizeGradCPUKernel<T>::HighDimIndexToOneDimIndex(size_t *one_dim_index,
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const std::vector<size_t> &high_dim_index) {
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*one_dim_index = 0;
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int len = high_dim_index.size();
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for (int i = 0; i < len; i++) {
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*one_dim_index += high_dim_index[i] * dim_elem_num_list_[i];
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}
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}
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template <typename T>
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void L2NormalizeGradCPUKernel<T>::GetVector(std::vector<T> *x_vector, const std::vector<size_t> &high_dim_index,
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const T *x) {
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auto x_shape = input_shape_list_[0];
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for (size_t i = 0; i < x_shape[axis_]; i++) {
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size_t oneDimIndex = 0;
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std::vector<size_t> tmp_high_dim_index = high_dim_index;
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tmp_high_dim_index[axis_] = i;
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HighDimIndexToOneDimIndex(&oneDimIndex, tmp_high_dim_index);
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x_vector->push_back(x[oneDimIndex]);
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}
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}
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template <typename T>
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void L2NormalizeGradCPUKernel<T>::GetSumOfProduct(const std::vector<T> &x_vector, const std::vector<T> &y_vector,
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T *ss) {
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size_t len = x_vector.size();
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std::vector<T> tmp_vector(len);
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for (size_t i = 0; i < len; i++) {
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tmp_vector[i] = x_vector[i] * y_vector[i];
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}
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if (len % 2 == 1) {
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tmp_vector[0] += tmp_vector[len - 1];
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}
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for (size_t stride = len / 2; stride > 0; stride >>= 1) {
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for (size_t i = 0; i < stride; i++) {
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tmp_vector[i] += tmp_vector[i + stride];
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}
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if (stride > 2 && stride % 2 == 1) {
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tmp_vector[0] += tmp_vector[stride - 1];
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}
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}
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*ss = tmp_vector[0];
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}
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template <typename T>
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void L2NormalizeGradCPUKernel<T>::GetOutput(const std::vector<T> &input_x_vector, const std::vector<T> &y_vector,
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const std::vector<T> &dout_vector,
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const std::vector<size_t> &high_dim_index, T *output) {
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size_t axis_index = high_dim_index[axis_];
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T dout = dout_vector[axis_index];
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T y = y_vector[axis_index];
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T tmp_sum1;
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GetSumOfProduct(y_vector, dout_vector, &tmp_sum1);
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T tmp_sum2;
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GetSumOfProduct(input_x_vector, input_x_vector, &tmp_sum2);
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tmp_sum2 = sqrt(tmp_sum2);
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if (tmp_sum2 >= epsilon_) {
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*output = (dout - y * tmp_sum1) / tmp_sum2;
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} else {
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*output = (dout - y * tmp_sum1) / epsilon_;
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}
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}
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,70 @@
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/**
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* Copyright 2021 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_L2NORMALIZE_GRAD_CPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_L2NORMALIZE_GRAD_CPU_KERNEL_H_
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#include <vector>
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#include "backend/kernel_compiler/cpu/cpu_kernel.h"
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#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
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namespace mindspore {
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namespace kernel {
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constexpr size_t INPUT_SIZE = 3;
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constexpr size_t OUTPUT_SIZE = 1;
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template <typename T>
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class L2NormalizeGradCPUKernel : public CPUKernel {
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public:
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L2NormalizeGradCPUKernel() = default;
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~L2NormalizeGradCPUKernel() override = default;
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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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void InitKernel(const CNodePtr &kernel_node) override;
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private:
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void CheckInputShape(const std::vector<size_t> &output_shape);
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void CheckIONumber(const CNodePtr &kernel_node);
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void OneDimIndexToHighDimIndex(size_t one_dim_index, std::vector<size_t> *high_dim_index);
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void HighDimIndexToOneDimIndex(size_t *one_dim_index, const std::vector<size_t> &high_dim_index);
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void GetVector(std::vector<T> *x_vector, const std::vector<size_t> &high_dim_index, const T *x);
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void GetSumOfProduct(const std::vector<T> &x_vector, const std::vector<T> &y_vector, T *ss);
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void GetOutput(const std::vector<T> &input_x_vector, const std::vector<T> &y_vector,
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const std::vector<T> &dout_vector, const std::vector<size_t> &high_dim_index, T *output);
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std::vector<std::vector<size_t>> input_shape_list_;
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std::vector<size_t> dim_elem_num_list_;
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int axis_{0};
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T epsilon_{0};
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};
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MS_REG_CPU_KERNEL_T(L2NormalizeGrad,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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L2NormalizeGradCPUKernel, float);
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MS_REG_CPU_KERNEL_T(L2NormalizeGrad,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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L2NormalizeGradCPUKernel, float16);
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_L2NORMALIZE_GRAD_CPU_KERNEL_H_
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# Copyright 2021 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.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.operations import _grad_ops as G
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class Net(nn.Cell):
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def __init__(self, axis=0, epsilon=1e-4):
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super(Net, self).__init__()
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self.ops = G.L2NormalizeGrad(axis, epsilon)
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def construct(self, input_x, output, dout):
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return self.ops(input_x, output, dout)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_net01():
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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axis = 1
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net = Net(axis)
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input_x = np.arange(24).astype(np.float32).reshape((2, 3, 4))
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dout = np.arange(24, 48).astype(np.float32).reshape((2, 3, 4))
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output = input_x / np.sqrt(np.sum(input_x**2, axis=axis, keepdims=True))
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except_asn = (dout - output * np.sum(output * dout, axis=axis, keepdims=True)
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) / np.sqrt(np.sum(input_x**2, axis=axis, keepdims=True))
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input_x = Tensor(input_x, mstype.float32)
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output = Tensor(output, mstype.float32)
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dout = Tensor(dout, mstype.float32)
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net_output = net(input_x, output, dout).asnumpy()
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precision = np.ones(shape=(2, 3, 4), dtype=np.float32) * 1.0e-5
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absolute_error = np.abs(except_asn-net_output)
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assert np.all(absolute_error < precision)
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