!1949 in order to support dynamic broadcast reduce format with operator
Merge pull request !1949 from jjfeing/masterpull/1949/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
2005ecc284
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_KERNEL_TBE_KERNEL_BROADCAST_SELECTER_H_
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#define MINDSPORE_CCSRC_KERNEL_TBE_KERNEL_BROADCAST_SELECTER_H_
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#include <vector>
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#include <string>
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#include <utility>
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#include "ir/anf.h"
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#include "kernel/tbe/tbe_kernel_select/common_utils.h"
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namespace mindspore {
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namespace kernel {
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class TbeKernelBroadCastSelecter {
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public:
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explicit TbeKernelBroadCastSelecter(CNodePtr cnode_ptr) : cnode_ptr_(std::move(cnode_ptr)) {}
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~TbeKernelBroadCastSelecter() = default;
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bool GetShapeInfo(SupportFormat *support_format);
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bool IsBroadCastSupport5HD(SupportFormat *support_format) const;
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bool IsBroadCastSupportFracZ(SupportFormat *support_format) const;
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bool IsBroadCastSupportC1HWNCoC0(SupportFormat *support_format) const;
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bool IsBroadCastSupportFracNZ(SupportFormat *support_format) const;
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bool IsBroadCastSupportNDC1HWC0(SupportFormat *support_format) const;
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private:
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bool IsSameShape() const;
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void PadScalarShape(std::vector<size_t> *shape) const;
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bool Is4DShape(const std::vector<size_t> &shape) const;
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bool IsScalarShape(const std::vector<size_t> &shape) const;
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bool HasScalarInput() const;
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void GenOutputSupportFormat(const std::string &support_format, SupportFormatItem *output_support_item) const;
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void AssignSupportFormat(const std::string &support_format_str, SupportFormat *support_format) const;
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// broadcast
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CNodePtr cnode_ptr_;
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size_t input_num_{};
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size_t output_num_{};
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std::vector<std::vector<size_t>> input_shapes_;
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std::vector<std::vector<size_t>> output_shapes_;
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};
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_TBE_KERNEL_BROADCAST_SELECTER_HELPER_H
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "kernel/tbe/tbe_kernel_select/tbe_kernel_reduce_selecter.h"
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#include <string>
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#include <vector>
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#include "utils/utils.h"
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#include "session/anf_runtime_algorithm.h"
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#include "kernel/tbe/tbe_kernel_select/common_utils.h"
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namespace mindspore {
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namespace kernel {
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constexpr char kKeepDims[] = "keep_dims";
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constexpr char kAxis[] = "axis";
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constexpr char kTypeInt32[] = "Int32";
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constexpr size_t kInputIndex_0 = 0;
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constexpr size_t kOutputIndex_0 = 0;
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constexpr size_t kChannelN = 0;
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constexpr size_t kChannelC = 1;
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constexpr size_t kReduceNZMinDim = 3;
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bool TbeKernelReduceSelecter::GetShapeInfo(SupportFormat *support_format) {
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MS_EXCEPTION_IF_NULL(support_format);
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input_shape_.clear();
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output_shape_.clear();
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axis_.clear();
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auto input_num = AnfAlgo::GetInputTensorNum(cnode_ptr_);
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auto output_num = AnfAlgo::GetOutputTensorNum(cnode_ptr_);
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if (input_num != 1 || output_num != 1) {
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MS_LOG(EXCEPTION) << "Reduce operator only support one input/output, input num: " << input_num
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<< ", output num: " << output_num;
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}
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// get input/output shape
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input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, kInputIndex_0);
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PadScalarShape(&input_shape_);
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output_shape_ = AnfAlgo::GetOutputInferShape(cnode_ptr_, kOutputIndex_0);
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PadScalarShape(&output_shape_);
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// get keep dim attr
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GetReduceAttrKeepDim();
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// get axis attr
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GetReduceAttrAxis();
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AssignSupportFormat(kOpFormat_DEFAULT, support_format);
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return true;
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}
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bool TbeKernelReduceSelecter::IsReduceSupport5HD(SupportFormat *support_format) const {
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MS_EXCEPTION_IF_NULL(support_format);
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if (!Is4DShape(input_shape_)) {
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return false;
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}
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if (!keep_dims_ || axis_.empty()) {
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return false;
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}
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auto reduce_c_axis = std::any_of(axis_.begin(), axis_.end(), [](const size_t &elem) { return (elem == kChannelC); });
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if (reduce_c_axis) {
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return false;
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}
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AssignSupportFormat(kOpFormat_NC1HWC0, support_format);
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return true;
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}
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bool TbeKernelReduceSelecter::IsReduceSupportNDC1HWC0(SupportFormat *support_format) const {
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MS_EXCEPTION_IF_NULL(support_format);
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// like to 5HD
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return false;
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}
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bool TbeKernelReduceSelecter::IsReduceSupportFracZ(SupportFormat *support_format) const {
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return IsFracZAndC1HWNCoC0Common(kOpFormat_FRAC_Z, support_format);
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}
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bool TbeKernelReduceSelecter::IsReduceSupportC1HWNCoC0(SupportFormat *support_format) const {
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return IsFracZAndC1HWNCoC0Common(kOpFormat_C1HWNCoC0, support_format);
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}
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bool TbeKernelReduceSelecter::IsReduceSupportFracNZ(SupportFormat *support_format) const {
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MS_EXCEPTION_IF_NULL(support_format);
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if (input_shape_.size() < kReduceNZMinDim) {
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return false;
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}
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if (axis_.empty()) {
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return false;
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}
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auto reduce_last_axis = std::any_of(axis_.begin(), axis_.end(), [this](const size_t &elem) {
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return (elem == (this->input_shape_.size() - 1) || elem == (this->input_shape_.size() - 2));
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});
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if (reduce_last_axis) {
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return false;
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}
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AssignSupportFormat(kOpFormat_FRAC_NZ, support_format);
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return true;
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}
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bool TbeKernelReduceSelecter::IsFracZAndC1HWNCoC0Common(const std::string &format,
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mindspore::kernel::SupportFormat *support_format) const {
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MS_EXCEPTION_IF_NULL(support_format);
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if (!Is4DShape(input_shape_)) {
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return false;
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}
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if (!keep_dims_ || axis_.empty()) {
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return false;
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}
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auto reduce_n_c_axis = std::any_of(axis_.begin(), axis_.end(),
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[](const size_t &elem) { return (elem == kChannelC || elem == kChannelN); });
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if (reduce_n_c_axis) {
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return false;
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}
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AssignSupportFormat(format, support_format);
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return true;
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}
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void TbeKernelReduceSelecter::GetReduceAttrAxis() {
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auto primitive = AnfAlgo::GetCNodePrimitive(cnode_ptr_);
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MS_EXCEPTION_IF_NULL(primitive);
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auto axis = primitive->GetAttr(kAxis);
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if (axis == nullptr) {
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MS_LOG(INFO) << "This node does't have axie attr.";
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return;
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}
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auto type = axis->type();
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MS_EXCEPTION_IF_NULL(type);
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std::vector<int> axis_list;
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if (type->ToString() == kTypeInt32) {
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axis_list.emplace_back(GetValue<int>(axis));
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} else {
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axis_list = GetValue<std::vector<int>>(axis);
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}
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for (const auto &elem : axis_list) {
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if (elem < 0) {
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axis_.emplace_back(input_shape_.size() + elem);
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} else {
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axis_.emplace_back(IntToSize(elem));
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}
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}
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}
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void TbeKernelReduceSelecter::GetReduceAttrKeepDim() {
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if (!AnfAlgo::HasNodeAttr(kKeepDims, cnode_ptr_)) {
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MS_LOG(INFO) << "This node does't have keep_attr.";
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keep_dims_ = false;
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return;
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}
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keep_dims_ = AnfAlgo::GetNodeAttr<bool>(cnode_ptr_, kKeepDims);
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}
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void TbeKernelReduceSelecter::AssignSupportFormat(const std::string &support_format_str,
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mindspore::kernel::SupportFormat *support_format) const {
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MS_EXCEPTION_IF_NULL(support_format);
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SupportFormatItem input_support_format;
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SupportFormatItem output_support_format;
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input_support_format.emplace_back(support_format_str);
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output_support_format.emplace_back(support_format_str);
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support_format->input_format.emplace_back(input_support_format);
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support_format->output_format.emplace_back(output_support_format);
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}
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bool TbeKernelReduceSelecter::Is4DShape(const std::vector<size_t> &shape) const { return shape.size() == kShape4dDims; }
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void TbeKernelReduceSelecter::PadScalarShape(std::vector<size_t> *shape) const {
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MS_EXCEPTION_IF_NULL(shape);
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if (shape->empty()) {
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shape->emplace_back(1);
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}
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}
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} // namespace kernel
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} // namespace mindspore
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_KERNEL_TBE_KERNEL_REDUCE_SELECTER_H_
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#define MINDSPORE_CCSRC_KERNEL_TBE_KERNEL_REDUCE_SELECTER_H_
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#include <utility>
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#include <string>
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#include <vector>
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#include "ir/anf.h"
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#include "kernel/tbe/tbe_kernel_select/common_utils.h"
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namespace mindspore {
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namespace kernel {
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class TbeKernelReduceSelecter {
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public:
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explicit TbeKernelReduceSelecter(CNodePtr cnode_ptr) : cnode_ptr_(std::move(cnode_ptr)) {}
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~TbeKernelReduceSelecter() = default;
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bool GetShapeInfo(SupportFormat *support_format);
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bool IsReduceSupport5HD(SupportFormat *support_format) const;
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bool IsReduceSupportNDC1HWC0(SupportFormat *support_format) const;
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bool IsReduceSupportFracZ(SupportFormat *support_format) const;
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bool IsReduceSupportC1HWNCoC0(SupportFormat *support_format) const;
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bool IsReduceSupportFracNZ(SupportFormat *support_format) const;
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private:
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bool IsFracZAndC1HWNCoC0Common(const std::string &format, SupportFormat *support_format) const;
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void GetReduceAttrAxis();
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void GetReduceAttrKeepDim();
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void AssignSupportFormat(const std::string &support_format_str, SupportFormat *support_format) const;
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bool Is4DShape(const std::vector<size_t> &shape) const;
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void PadScalarShape(std::vector<size_t> *shape) const;
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CNodePtr cnode_ptr_;
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std::vector<size_t> input_shape_{};
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std::vector<size_t> output_shape_{};
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std::vector<size_t> axis_{};
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bool keep_dims_ = false;
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};
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_TBE_KERNEL_REDUCE_SELECTER_H
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,77 @@
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/**
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* Copyright 2019 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.
|
||||||
|
* You may obtain a copy of the License at
|
||||||
|
*
|
||||||
|
* http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
*
|
||||||
|
* Unless required by applicable law or agreed to in writing, software
|
||||||
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
* See the License for the specific language governing permissions and
|
||||||
|
* limitations under the License.
|
||||||
|
*/
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|
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#ifndef MINDSPORE_TBE_KERNEL_SELECT_H
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#define MINDSPORE_TBE_KERNEL_SELECT_H
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#include <string>
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#include <vector>
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#include <memory>
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#include "kernel/oplib/opinfo.h"
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#include "kernel/kernel_build_info.h"
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#include "kernel/tbe/tbe_kernel_select/common_utils.h"
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namespace mindspore {
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namespace kernel {
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void TbeMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<KernelBuildInfo>> *kernel_info_list);
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class TbeKernelSelect {
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using OpInfoPtr = std::shared_ptr<OpInfo>;
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using KernelBuildInfoIter = std::vector<std::shared_ptr<KernelBuildInfo>>::iterator;
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public:
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TbeKernelSelect(CNodePtr kernel_node, std::vector<std::shared_ptr<KernelBuildInfo>> *kernel_info_list);
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~TbeKernelSelect() = default;
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void TbeMetadataInfoEx();
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private:
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void GetCommonPatternKernelInfo(const OpInfo &op_info);
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void GetDynamicFormatPatternKernelInfo(const OpInfo &op_info);
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void GetAgnosticPatternKernelInfo(const OpInfo &op_info);
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void GetBroadcastPatternKernelInfo(const OpInfo &op_info);
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void GetReducePatternKernelInfo(const OpInfo &op_info);
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void FilterInVaildKernelInfo();
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bool FilterInVaildShape(const KernelBuildInfoIter &kernel_build_info_iter);
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static bool IsShapeMatchFormat(const std::vector<size_t> &shape, const std::string &format);
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bool TbeCheckSupported(const KernelBuildInfoIter &kernel_build_info_iter);
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static void SetTbeBuildCommonInfo(const OpInfo &op_info, KernelBuildInfo::KernelBuildInfoBuilder *builder);
|
||||||
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bool GenBuilderItem(bool is_input, size_t kernel_build_info_index, size_t real_io_tensor_num,
|
||||||
|
const std::vector<std::shared_ptr<OpIOInfo>> &ios_info, const std::vector<int> &dyn_input_sizes,
|
||||||
|
std::vector<std::string> *formats, std::vector<TypeId> *device_types,
|
||||||
|
std::vector<std::vector<Axis>> *reshape_types);
|
||||||
|
static void StringToAxisVector(const std::string &reshape_type_str, std::vector<Axis> *reshape_type_vec);
|
||||||
|
static void CreateNewOpInfo(const OpInfo &op_info, const SupportFormat &support_format, OpInfo *op_info_new);
|
||||||
|
static void CreateNewOpIOInfo(const OpIOInfo &op_io_info,
|
||||||
|
const std::vector<std::vector<std::string>> &support_format_item, size_t index,
|
||||||
|
OpIOInfo *op_io_info_new);
|
||||||
|
// op select(dynamic)
|
||||||
|
void CreateNewOpInfo(const mindspore::kernel::OpInfo &op_info, mindspore::kernel::OpInfo *op_info_new);
|
||||||
|
static void CreateNewOpIOInfo(const OpIOInfo &op_io_info, const std::vector<std::string> &support_dtype,
|
||||||
|
const std::vector<std::string> &support_format, OpIOInfo *op_io_info_new);
|
||||||
|
static std::vector<std::string> SplitStrToVec(const std::string &op_select_json_item);
|
||||||
|
std::string OpSelectFormat();
|
||||||
|
|
||||||
|
static void PrintSupportedFormat(const SupportFormat &support_format);
|
||||||
|
|
||||||
|
private:
|
||||||
|
CNodePtr cnode_ptr_;
|
||||||
|
std::vector<std::shared_ptr<KernelBuildInfo>> *kernel_info_list_;
|
||||||
|
std::string node_name_;
|
||||||
|
};
|
||||||
|
} // namespace kernel
|
||||||
|
} // namespace mindspore
|
||||||
|
|
||||||
|
#endif // MINDSPORE_TBE_KERNEL_SELECT_H
|
||||||
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