forked from mindspore-Ecosystem/mindspore
!22336 add rolling cpu kernel
Merge pull request !22336 from zhujingxuan/master
This commit is contained in:
Gitee
commit
c710b57efe
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@ -65,6 +65,11 @@ const char SORTED[] = "sorted";
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const char ADJ_ST[] = "adjoint_st";
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const char ADJ_dT[] = "adjoint_dt";
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const char PERIODS[] = "periods";
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const char WINDOW[] = "window";
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const char MIN_PERIODS[] = "min_periods";
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const char CENTER[] = "center";
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const char METHOD[] = "method";
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const char CLOSED[] = "closed";
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enum OperateType {
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ADD = 0,
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@ -0,0 +1,231 @@
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/**
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* Copyright 2020-2021 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/rolling_cpu_kernel.h"
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#include <cmath>
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#include <algorithm>
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#include <map>
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#include <limits>
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#include <functional>
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#include "common/thread_pool.h"
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namespace mindspore {
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namespace kernel {
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template <typename T, typename S>
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void RollingCpuKernel<T, S>::InitKernel(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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static const std::map<std::string, Method> kValidMethods = {
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{"max", Method::Max}, {"min", Method::Min}, {"mean", Method::Mean},
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{"sum", Method::Sum}, {"std", Method::Std}, {"var", Method::Var},
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};
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auto method = AnfAlgo::GetNodeAttr<std::string>(kernel_node, METHOD);
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if (kValidMethods.find(method) == kValidMethods.end()) {
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MS_LOG(EXCEPTION) << "[" << method << "] not supported";
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}
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method_ = kValidMethods.at(method);
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window_ = AnfAlgo::GetNodeAttr<int64_t>(kernel_node, WINDOW);
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if (window_ <= 0) {
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MS_LOG(EXCEPTION) << "window size should not less than 0, but got " << window_;
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}
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min_periods_ = AnfAlgo::GetNodeAttr<int64_t>(kernel_node, MIN_PERIODS);
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if (min_periods_ <= 0) {
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MS_LOG(EXCEPTION) << "min_periods should not less than 0, but got " << min_periods_;
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}
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center_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, CENTER);
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axis_ = AnfAlgo::GetNodeAttr<int64_t>(kernel_node, AXIS);
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closed_ = AnfAlgo::GetNodeAttr<std::string>(kernel_node, CLOSED);
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if (axis_ < 0) {
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axis_ += input_shape.size();
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}
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if ((axis_ < 0) || (axis_ >= static_cast<int64_t>(input_shape.size()))) {
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MS_LOG(EXCEPTION) << "axis should be smaller than the dimension of input tensor " << input_shape.size()
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<< "D, but got " << axis_;
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}
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AxisCalculate(input_shape);
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RollingBoundsCalculate();
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MethodSwitch();
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}
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template <typename T, typename S>
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void RollingCpuKernel<T, S>::AxisCalculate(const std::vector<size_t> &input_shape) {
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outer_size_ = 1;
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for (int i = 0; i < axis_; i++) {
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outer_size_ *= input_shape[i];
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}
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axis_size_ = input_shape[axis_];
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inner_size_ = 1;
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for (int i = axis_ + 1; i < static_cast<int>(input_shape.size()); ++i) {
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inner_size_ *= input_shape[i];
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}
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}
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template <typename T, typename S>
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void RollingCpuKernel<T, S>::RollingBoundsCalculate() {
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int offset = 0;
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if (center_) {
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offset = (window_ - 1) / 2;
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}
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starts_.resize(axis_size_);
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ends_.resize(axis_size_);
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int start_offset = 0;
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int end_offset = 0;
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if (closed_ == "left") {
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start_offset -= 1;
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end_offset -= 1;
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} else if (closed_ == "both") {
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start_offset -= 1;
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} else if (closed_ == "neither") {
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end_offset -= 1;
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}
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for (int i = 0; i < axis_size_; ++i) {
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int end = offset + i + 1;
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int start = end - window_;
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ends_[i] = std::max(0, std::min(end + end_offset, axis_size_));
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starts_[i] = std::max(0, std::min(start + start_offset, axis_size_));
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}
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}
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template <typename T, typename S>
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void RollingCpuKernel<T, S>::MethodSwitch() {
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switch (method_) {
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case Method::Max:
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reduceMethod_ = [this](const T *input_addr, int outer_offset, int w, int col) {
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T max_value = std::numeric_limits<T>::min();
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for (int x = starts_[w]; x < ends_[w]; ++x) {
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int index = outer_offset + x * inner_size_ + col;
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if (max_value < input_addr[index]) {
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max_value = input_addr[index];
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}
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}
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return max_value;
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};
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break;
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case Method::Min:
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reduceMethod_ = [this](const T *input_addr, int outer_offset, int w, int col) {
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T min_value = std::numeric_limits<T>::max();
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for (int x = starts_[w]; x < ends_[w]; ++x) {
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int index = outer_offset + x * inner_size_ + col;
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if (min_value > input_addr[index]) {
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min_value = input_addr[index];
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}
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}
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return min_value;
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};
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break;
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case Method::Sum:
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reduceMethod_ = [this](const T *input_addr, int outer_offset, int w, int col) {
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T sum = 0;
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for (int x = starts_[w]; x < ends_[w]; ++x) {
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int index = outer_offset + x * inner_size_ + col;
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sum += input_addr[index];
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}
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return sum;
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};
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break;
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case Method::Mean:
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reduceMethod_ = [this](const T *input_addr, int outer_offset, int w, int col) {
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T sum = 0;
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for (int x = starts_[w]; x < ends_[w]; ++x) {
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int index = outer_offset + x * inner_size_ + col;
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sum += input_addr[index];
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}
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return sum * 1.0 / (ends_[w] - starts_[w]);
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};
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break;
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case Method::Var:
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reduceMethod_ = [this](const T *input_addr, int outer_offset, int w, int col) {
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// float for division
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float n = ends_[w] - starts_[w];
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T sum1 = 0;
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for (int x = starts_[w]; x < ends_[w]; ++x) {
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int index = outer_offset + x * inner_size_ + col;
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sum1 += input_addr[index];
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}
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double mean = sum1 / n;
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double sum2 = 0;
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for (int x = starts_[w]; x < ends_[w]; ++x) {
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int index = outer_offset + x * inner_size_ + col;
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sum2 += (input_addr[index] - mean) * (input_addr[index] - mean);
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}
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// ddof = 1
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return sum2 / (n - 1);
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};
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break;
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case Method::Std:
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reduceMethod_ = [this](const T *input_addr, int outer_offset, int w, int col) {
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// float for division
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float n = ends_[w] - starts_[w];
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T sum1 = 0;
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for (int x = starts_[w]; x < ends_[w]; ++x) {
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int index = outer_offset + x * inner_size_ + col;
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sum1 += input_addr[index];
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}
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double mean = sum1 / n;
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double sum2 = 0;
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for (int x = starts_[w]; x < ends_[w]; ++x) {
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int index = outer_offset + x * inner_size_ + col;
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sum2 += (input_addr[index] - mean) * (input_addr[index] - mean);
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}
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// ddof = 1
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return std::sqrt(sum2 / (n - 1));
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};
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break;
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default:
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MS_LOG(EXCEPTION) << "reduce method is not yet supported: " << method_;
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}
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}
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template <typename T, typename S>
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bool RollingCpuKernel<T, S>::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) {
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size_t input_size = inputs[0]->size / sizeof(T);
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if (input_size == 0) {
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MS_LOG(EXCEPTION) << "Input data size is 0.";
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}
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auto input_addr = reinterpret_cast<T *>(inputs[0]->addr);
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auto output_addr = reinterpret_cast<S *>(outputs[0]->addr);
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for (int i = 0; i < outer_size_; ++i) {
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int outer_offset = i * axis_size_ * inner_size_;
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for (int col = 0; col < inner_size_; ++col) {
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for (int w = 0; w < axis_size_; ++w) {
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int result_offset = outer_offset + w * inner_size_ + col;
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if (ends_[w] - starts_[w] < min_periods_) {
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if constexpr (std::is_same_v<T, float>) {
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output_addr[result_offset] = std::nanf("");
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} else if constexpr (std::is_same_v<T, double>) {
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output_addr[result_offset] = std::nan("");
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} else {
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// integer values not support nan
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output_addr[result_offset] = 0;
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}
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} else {
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output_addr[result_offset] = reduceMethod_(input_addr, outer_offset, w, col);
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}
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}
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}
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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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@ -0,0 +1,88 @@
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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_ROLLING_CPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_ROLLING_CPU_KERNEL_H_
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#include <vector>
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#include <string>
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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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#include "backend/kernel_compiler/cpu/nnacl/op_base.h"
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namespace mindspore {
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namespace kernel {
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enum Method : int {
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Max,
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Min,
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Mean,
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Sum,
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Std,
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Var,
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};
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template <typename T, typename S>
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class RollingCpuKernel : public CPUKernel {
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public:
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RollingCpuKernel() = default;
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~RollingCpuKernel() override = default;
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void InitKernel(const CNodePtr &kernel_node) 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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private:
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void AxisCalculate(const std::vector<size_t> &input_shape);
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void RollingBoundsCalculate();
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void MethodSwitch();
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int64_t window_{0};
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int64_t min_periods_{0};
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int axis_{0};
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bool center_{false};
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std::string closed_{};
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Method method_{};
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std::function<S(const T *input_addr, int outer_offset, int w, int col)> reduceMethod_{};
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// shape info
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int outer_size_{0};
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int axis_size_{0};
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int inner_size_{0};
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// rolling info
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std::vector<int> starts_{};
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std::vector<int> ends_{};
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};
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MS_REG_CPU_KERNEL_T_S(Rolling, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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RollingCpuKernel, float, float)
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MS_REG_CPU_KERNEL_T_S(Rolling, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
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RollingCpuKernel, double, double)
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MS_REG_CPU_KERNEL_T_S(Rolling, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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RollingCpuKernel, int32_t, int32_t)
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MS_REG_CPU_KERNEL_T_S(Rolling, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
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RollingCpuKernel, int64_t, int64_t)
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MS_REG_CPU_KERNEL_T_S(Rolling, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
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RollingCpuKernel, int32_t, float)
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MS_REG_CPU_KERNEL_T_S(Rolling, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat64),
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RollingCpuKernel, int64_t, double)
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SORT_CPU_KERNEL_H_
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@ -0,0 +1,144 @@
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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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from functools import partial
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from typing import Tuple, List
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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 PrimitiveWithInfer, prim_attr_register
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from mindspore._checkparam import Validator as validator
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from mindspore.common import dtype as mstype
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import numpy as np
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import pytest
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Rolling(PrimitiveWithInfer):
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"""
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Shift op frontend implementation
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"""
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@prim_attr_register
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def __init__(self, window: int, min_periods: int, center: bool, axis: int, closed: str,
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method: str):
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"""Initialize Sort"""
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self.window = validator.check_value_type("window", window, [int], self.name)
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self.min_periods = validator.check_value_type("min_periods", min_periods, [int], self.name)
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self.center = validator.check_value_type("center", center, [bool], self.name)
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self.axis = validator.check_value_type("axis", axis, [int], self.name)
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self.closed = validator.check_value_type("closed", closed, [str], self.name)
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self.method = validator.check_value_type("method", method, [str], self.name)
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self.init_prim_io_names(inputs=['x'], outputs=['output'])
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def __infer__(self, x):
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out_shapes = x['shape']
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return {
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'shape': tuple(out_shapes),
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'dtype': x['dtype'],
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'value': None
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}
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def infer_dtype(self, x_dtype):
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validator.check_tensor_dtype_valid(x_dtype, [mstype.float32, mstype.float64, mstype.int32, mstype.int64],
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self.name, True)
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return x_dtype
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class RollingNet(nn.Cell):
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def __init__(self, window: int, min_periods: int, center: bool, axis: int, closed: str,
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method: str):
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super(RollingNet, self).__init__()
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self.rolling = Rolling(window, min_periods, center, axis, closed, method)
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def construct(self, x):
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return self.rolling(x)
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def get_window_bounds(num_values: int, window_size: int, center: bool, closed: str = 'right') -> Tuple[List, List]:
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assert closed in {'left', 'both', 'right', 'neither'}
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offset = (window_size - 1) // 2 if center else 0
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end = np.arange(offset + 1, num_values + 1 + offset, dtype=np.int64)
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start = end - window_size
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if closed in {'left', 'both'}:
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start -= 1
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if closed in {'left', 'neither'}:
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end -= 1
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end = np.clip(end, 0, num_values)
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start = np.clip(start, 0, num_values)
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return list(start), list(end)
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def numpy_rolling(array: np.ndarray, window: int, min_periods: int, center: bool, axis: int, closed: str,
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method: str) -> np.ndarray:
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assert window > 0
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assert 0 < min_periods <= window
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assert axis in range(-array.ndim, array.ndim)
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reduce_map = {'max': np.max, 'min': np.min, 'mean': np.mean, 'sum': np.sum, 'std': partial(np.std, ddof=1),
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'var': partial(np.var, ddof=1)}
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assert method in reduce_map
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size = array.shape[axis]
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start, end = get_window_bounds(size, window, center, closed)
|
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|
||||
rolling_indices = [[slice(None)] * array.ndim for _ in range(len(start))]
|
||||
for i, j, indice in zip(start, end, rolling_indices):
|
||||
indice[axis] = None if j - i < min_periods else slice(i, j)
|
||||
# print(f'i={i}, j={j}, index={index}, indice={rolling_indices[index][axis]}')
|
||||
|
||||
shape = list(array.shape)
|
||||
shape[axis] = 1
|
||||
nan_array = np.empty(shape)
|
||||
if array.dtype == np.float32 or array.dtype == np.float64:
|
||||
nan_array[:] = np.nan
|
||||
elif array.dtype == np.int32 or array.dtype == np.int64:
|
||||
nan_array[:] = 0
|
||||
|
||||
arrays = [
|
||||
nan_array.copy() if not indice[axis]
|
||||
else reduce_map[method](array[tuple(indice)], axis=axis, keepdims=True).reshape(shape)
|
||||
for indice in rolling_indices]
|
||||
|
||||
return np.stack(arrays, axis=axis).reshape(array.shape).astype(array.dtype)
|
||||
|
||||
|
||||
@pytest.mark.parametrize('shape', [(10, 8, 15, 7), (5, 3, 8, 10)])
|
||||
@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.int32, np.int64])
|
||||
@pytest.mark.parametrize('window, min_periods', [(3, 3), (5, 3)])
|
||||
@pytest.mark.parametrize('center', [True, False])
|
||||
@pytest.mark.parametrize('axis', [2, 3, -1])
|
||||
@pytest.mark.parametrize('closed', ['left', 'both', 'right', 'neither'])
|
||||
@pytest.mark.parametrize('method', ['max', 'min', 'mean', 'sum', 'std', 'var'])
|
||||
def test_two_way(shape: List[int], dtype, window: int, min_periods: int, center: bool, axis: int, closed: str,
|
||||
method: str) -> np.ndarray:
|
||||
if dtype in (np.int32, np.int64):
|
||||
arr = np.random.randint(0, 100, size=shape)
|
||||
else:
|
||||
arr = np.random.random(shape).astype(dtype)
|
||||
expect_result = numpy_rolling(arr, window=window, min_periods=min_periods, center=center, axis=axis, closed=closed,
|
||||
method=method)
|
||||
rolling = RollingNet(window=window, min_periods=min_periods, center=center, axis=axis, closed=closed,
|
||||
method=method)
|
||||
actual_result = rolling(Tensor(arr)).asnumpy()
|
||||
print('arr: \n', arr, arr.dtype, arr.shape)
|
||||
print('np: \n', expect_result, expect_result.dtype, expect_result.shape)
|
||||
print('mine: \n', actual_result, actual_result.dtype, actual_result.shape)
|
||||
print(f'center: {center}, axis: {axis}, method: {method}')
|
||||
assert np.allclose(expect_result, actual_result, equal_nan=True)
|
||||
Loading…
Reference in New Issue