forked from mindspore-Ecosystem/mindspore
!17663 Adding SearchSorted Operator in CPU
From: @huangbo77 Reviewed-by: @wuxuejian,@liangchenghui Signed-off-by: @wuxuejian
This commit is contained in:
Gitee
commit
a3d4dc5c2b
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@ -0,0 +1,109 @@
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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/searchsorted_cpu_kernel.h"
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#include <vector>
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#include <numeric>
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#include <functional>
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namespace mindspore {
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namespace kernel {
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namespace {
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constexpr size_t kInputSize = 2;
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constexpr size_t kOutputSize = 1;
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} // namespace
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template <typename S, typename T>
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void SearchSortedCPUKernel<S, T>::InitKernel(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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right_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, "right");
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sequence_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
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values_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
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search_len = sequence_shape_.back();
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}
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template <typename S, typename T>
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const S *SearchSortedCPUKernel<S, T>::CustomizedLowerBound(const S *seq_start, const S *seq_end, const S key) {
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while (seq_start < seq_end) {
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const S *mid = seq_start + ((seq_end - seq_start) >> 1);
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if (!(key <= *mid)) {
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seq_start = mid + 1;
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} else {
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seq_end = mid;
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}
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}
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return seq_start;
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}
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template <typename S, typename T>
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bool SearchSortedCPUKernel<S, T>::Launch(const std::vector<kernel::AddressPtr> &inputs,
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const std::vector<kernel::AddressPtr> &,
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const std::vector<kernel::AddressPtr> &outputs) {
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CheckParam(inputs, outputs);
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auto sequence = reinterpret_cast<S *>(inputs[0]->addr);
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auto values = reinterpret_cast<S *>(inputs[1]->addr);
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auto output = reinterpret_cast<T *>(outputs[0]->addr);
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size_t elem_num = inputs[1]->size / sizeof(S);
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size_t seq_dim = sequence_shape_.size();
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size_t search_repeat = values_shape_.back();
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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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auto seq_start = (seq_dim == 1) ? sequence : sequence + (i / search_repeat) * search_len;
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output[i] = right_ ? std::upper_bound(seq_start, seq_start + search_len, values[i]) - seq_start
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: CustomizedLowerBound(seq_start, seq_start + search_len, values[i]) - seq_start;
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}
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};
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CPUKernelUtils::ParallelFor(task, elem_num);
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return true;
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}
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template <typename S, typename T>
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void SearchSortedCPUKernel<S, T>::CheckParam(const std::vector<AddressPtr> &inputs,
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const std::vector<AddressPtr> &outputs) {
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// inputs: sequence, values
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if (inputs.size() != kInputSize) {
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MS_LOG(EXCEPTION) << "Input number is: " << inputs.size() << ", but SearchSorted needs" << kInputSize << " inputs.";
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}
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// outputs: positions
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if (outputs.size() != kOutputSize) {
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MS_LOG(EXCEPTION) << "Output number is " << outputs.size() << ", but SearchSorted needs " << kOutputSize
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<< " outputs";
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}
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if (outputs[0]->size / sizeof(T) != inputs[1]->size / sizeof(S)) {
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MS_LOG(EXCEPTION) << "The output dimensions " << outputs[0]->size << " must match the dimensions of input values "
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<< inputs[1]->size;
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}
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auto sequence = reinterpret_cast<S *>(inputs[0]->addr);
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size_t list_count = accumulate(sequence_shape_.begin(), sequence_shape_.end() - 1, 1, std::multiplies<int>());
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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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for (size_t j = 0; j < search_len - 1; j++) {
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if (sequence[i * search_len + j] > sequence[i * search_len + j + 1]) {
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MS_LOG(EXCEPTION) << "The input sequence must be sorted.";
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}
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}
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}
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};
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CPUKernelUtils::ParallelFor(task, list_count);
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}
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,111 @@
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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_SEARCHSORTED_CPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SEARCHSORTED_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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template <typename S, typename T>
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class SearchSortedCPUKernel : public CPUKernel {
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public:
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SearchSortedCPUKernel() = default;
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~SearchSortedCPUKernel() 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> &,
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const std::vector<AddressPtr> &outputs) override;
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private:
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const S *CustomizedLowerBound(const S *seq_start, const S *seq_end, const S key);
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void CheckParam(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
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bool right_{false};
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std::vector<size_t> sequence_shape_;
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std::vector<size_t> values_shape_;
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std::vector<size_t> output_shape_;
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size_t search_len;
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};
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeInt32),
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SearchSortedCPUKernel, double, int32_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeInt32),
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SearchSortedCPUKernel, float, int32_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt32),
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SearchSortedCPUKernel, int64_t, int32_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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SearchSortedCPUKernel, int32_t, int32_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt32),
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SearchSortedCPUKernel, int16_t, int32_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt32),
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SearchSortedCPUKernel, int8_t, int32_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeInt64),
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SearchSortedCPUKernel, double, int64_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeInt64),
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SearchSortedCPUKernel, float, int64_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
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SearchSortedCPUKernel, int64_t, int64_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt64),
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SearchSortedCPUKernel, int32_t, int64_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt64),
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SearchSortedCPUKernel, int16_t, int64_t);
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MS_REG_CPU_KERNEL_T_S(
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SearchSorted,
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KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt64),
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SearchSortedCPUKernel, int8_t, int64_t);
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SEARCHSORTED_CPU_KERNEL_H_
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@ -33,7 +33,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Stack, Unpack, Unsta
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Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin, UnsortedSegmentMax,
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UnsortedSegmentProd, UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch,
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BatchToSpace, SpaceToBatchND, BatchToSpaceND, BroadcastTo, InplaceUpdate, ReverseSequence,
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EmbeddingLookup, Unique, GatherD, Identity, Range, MaskedSelect)
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EmbeddingLookup, Unique, GatherD, Identity, Range, MaskedSelect, SearchSorted)
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from .comm_ops import (AllGather, AllReduce, _AlltoAll, AllSwap, ReduceScatter, Broadcast,
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_MirrorOperator, _MirrorMiniStepOperator, _MiniStepAllGather, ReduceOp, _VirtualDataset,
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_VirtualOutput, _VirtualDiv, _GetTensorSlice, _VirtualAdd,
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@ -438,6 +438,7 @@ __all__ = [
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"SparseTensorDenseMatmul",
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"MatrixInverse",
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"Range",
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"SearchSorted",
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"IndexAdd",
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"PQC",
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"Evolution",
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@ -5349,3 +5349,58 @@ class MaskedSelect(PrimitiveWithCheck):
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def check_dtype(self, x_dtype, mask_dtype):
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validator.check_tensor_dtype_valid('mask', mask_dtype, [mstype.bool_], self.name)
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class SearchSorted(PrimitiveWithInfer):
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"""
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Find the indices from the innermost dimension of `sequence` such that the order of the innermost dimension
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within `sequence` would be preserved when the corresponding values in `values` were inserted before the indices.
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Args:
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out_int32 (bool): Output datatype. Optional. If True, the output datatype will be int32;
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if False, the output datatype will be int64. Default is False.
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right (bool): Search Strategy. Optional. If True, return the last suitable index found.
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If False, return the first such index. Default is False.
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Inputs:
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- **sequence** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R-1, x_R)` or `(x_1)`.
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It must contain monitonically increasing sequence on the innermost dimension.
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- **values** (Tensor) - The shape of tensor is : math:`(x_1, x_2, ..., x_R-1, x_S)`.
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Outputs:
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Tensor containing the indices from the innermost dimension of the input sequence such that,
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if insert the corresponding value in the values tensor, the order of the tensor sequence would be preserved.
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The shape of tensor is :math:`(x_1, x_2, ..., x_R-1, x_S)`,
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whose datatype is int32 if out_int32 is True, otherwise int64, and shape is the same as the shape of values.
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Raises:
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ValueError: If `sequence` and `values` do not have proper shapes.
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Supported Platforms:
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``CPU``
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Examples:
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>>> sequence = Tensor(np.array([[0, 1, 3, 5, 7], [2, 4, 6, 8, 10]]), mindspore.float32)
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>>> values = Tensor(np.array([[3, 6, 9], [3, 6, 9]]), mindspore.float32)
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>>> output = ops.SearchSorted()(sequence, values)
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>>> print(output)
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[[2, 4, 5]
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[1, 2, 4]]
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"""
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@prim_attr_register
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def __init__(self, out_int32=False, right=False):
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"""Initialize SearchSorted"""
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self.out_int32 = validator.check_value_type("out_int32", out_int32, [bool], self.name)
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self.right = validator.check_value_type("right", right, [bool], self.name)
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self.init_prim_io_names(inputs=['sequence', 'values'], outputs=['positions'])
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def infer_shape(self, sequence_shape, values_shape):
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if len(sequence_shape) != 1 and sequence_shape[:-1] != values_shape[:-1]:
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raise ValueError(f"Sequence should be 1 dimensional or has all but the last dimension matching "
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f" the dimensions of values, but got sequence's dimensions: {sequence_shape} "
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f"and values' dimensions: {values_shape}.")
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return values_shape
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def infer_dtype(self, sequence_dtype, values_dtype):
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args = {"sequence_dtype": sequence_dtype, "values_dtype": values_dtype}
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validator.check_tensors_dtypes_same_and_valid(args, mstype.number_type, self.name)
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return mstype.tensor_type(mstype.int32) if self.out_int32 else mstype.tensor_type(mstype.int64)
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@ -0,0 +1,115 @@
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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.nn as nn
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class SearchSortedNet(nn.Cell):
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def __init__(self, out_int32=False, right=False):
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super(SearchSortedNet, self).__init__()
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self.searchsorted = P.SearchSorted(out_int32=out_int32, right=right)
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def construct(self, sequence, values):
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return self.searchsorted(sequence, values)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_right_out32():
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np.random.seed(1)
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input1 = np.sort(np.array(np.random.randint(10, size=(2, 3, 9)), dtype=np.int32), axis=-1)
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sequence = Tensor(input1, mstype.int32)
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input2 = np.array(np.random.randint(10, size=(2, 3, 1)), dtype=np.int32)
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values = Tensor(input2, mstype.int32)
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net = SearchSortedNet(out_int32=True, right=True)
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output = net(sequence, values)
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expect = [[[9],
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[3],
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[6]],
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[[5],
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[9],
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[8]]]
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assert output.dtype == mstype.int32
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_out32():
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np.random.seed(1)
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input1 = np.sort(np.array(np.random.randint(10, size=(2, 3, 9)), dtype=np.int64), axis=-1)
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sequence = Tensor(input1, mstype.int64)
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input2 = np.array(np.random.randint(10, size=(2, 3, 1)), dtype=np.int64)
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values = Tensor(input2, mstype.int64)
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net = SearchSortedNet(out_int32=True, right=False)
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output = net(sequence, values)
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expect = [[[8],
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[0],
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[3]],
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[[5],
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[8],
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[7]]]
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assert output.dtype == mstype.int32
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_right_out64():
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np.random.seed(1)
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input1 = np.sort(np.array(np.random.random((2, 5)), dtype=np.float32), axis=-1)
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sequence = Tensor(input1, mstype.float32)
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input2 = np.array(np.random.random((2, 3)), dtype=np.float32)
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values = Tensor(input2, mstype.float32)
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net = SearchSortedNet(out_int32=False, right=True)
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output = net(sequence, values)
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expect = [[4, 4, 2],
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[5, 0, 5]]
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assert output.dtype == mstype.int64
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_out64():
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np.random.seed(1)
|
||||
input1 = np.sort(np.array(np.random.random((5)), dtype=np.float64), axis=-1)
|
||||
sequence = Tensor(input1, mstype.float64)
|
||||
input2 = np.array(np.random.random((2, 3)), dtype=np.float64)
|
||||
values = Tensor(input2, mstype.float64)
|
||||
|
||||
net = SearchSortedNet(out_int32=False, right=False)
|
||||
output = net(sequence, values)
|
||||
|
||||
expect = [[1, 2, 3],
|
||||
[3, 4, 4]]
|
||||
assert output.dtype == mstype.int64
|
||||
assert (output.asnumpy() == expect).all()
|
||||
Loading…
Reference in New Issue