162 lines
7.7 KiB
C++
162 lines
7.7 KiB
C++
/**
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* Copyright 2019-2021 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 "transform/graph_ir/op_declare/reduce_ops_declare.h"
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#include <vector>
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namespace mindspore::transform {
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// BNTrainingReduce
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INPUT_MAP(BNTrainingReduce) = {{1, INPUT_DESC(x)}};
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// 输入映射，x索引为1
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ATTR_MAP(BNTrainingReduce) = EMPTY_ATTR_MAP;
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// 属性映射，空
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OUTPUT_MAP(BNTrainingReduce) = {{0, OUTPUT_DESC(sum)}, {1, OUTPUT_DESC(square_sum)}};
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// 输出映射，sum索引为0，square_sum索引为1
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REG_ADPT_DESC(BNTrainingReduce, kNameBNTrainingReduce, ADPT_DESC(BNTrainingReduce))
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//注册BNTrainingReduce操作的适配器描述kNameBNTrainingReduce
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// BNTrainingReduceGrad
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INPUT_MAP(BNTrainingReduceGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(x)}, {3, INPUT_DESC(diff_scale)},
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{4, INPUT_DESC(diff_offset)}, {5, INPUT_DESC(scale)}, {6, INPUT_DESC(batch_mean)},
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{7, INPUT_DESC(batch_variance)}};
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//输入映射，共七个，grad索引为1，x索引为2，diff_scale索引为3，diff_offset索引为4，scale索引为5，batch_mean索引为6，batch_variance索引为7
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ATTR_MAP(BNTrainingReduceGrad) = {{"epsilon", ATTR_DESC(epsilon, AnyTraits<float>())}};
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// 属性映射，列出了"epsilon"类型为float
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OUTPUT_MAP(BNTrainingReduceGrad) = {{0, OUTPUT_DESC(y)}};
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//输出映射，y索引为0
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REG_ADPT_DESC(BNTrainingReduceGrad, kNameBNTrainingReduceGrad, ADPT_DESC(BNTrainingReduceGrad))
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// 注册BNTrainingReduceGrad操作的适配器描述kNameBNTrainingReduceGrad
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// BNTrainingUpdate
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INPUT_MAP(BNTrainingUpdate) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(sum)}, {3, INPUT_DESC(square_sum)},
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{4, INPUT_DESC(scale)}, {5, INPUT_DESC(offset)}, {6, INPUT_DESC(mean)},
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{7, INPUT_DESC(variance)}};
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// 输入映射，共七个，grad索引为1，x索引为2，diff_scale索引为3，diff_offset索引为4，scale索引为5，batch_mean索引为6，batch_variance索引为7
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ATTR_MAP(BNTrainingUpdate) = {{"factor", ATTR_DESC(factor, AnyTraits<float>())},
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{"epsilon", ATTR_DESC(epsilon, AnyTraits<float>())}};
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// 属性映射，列出了"factor""epsilon"类型为float
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OUTPUT_MAP(BNTrainingUpdate) = {{0, OUTPUT_DESC(y)},
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{1, OUTPUT_DESC(mean)},
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{2, OUTPUT_DESC(variance)},
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{3, OUTPUT_DESC(batch_mean)},
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{4, OUTPUT_DESC(batch_variance)}};
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// 输出映射，共五个，y索引为0，mean索引为1，variance索引为2，batch_mean索引为3，batch_variance索引为4
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REG_ADPT_DESC(BNTrainingUpdate, kNameBNTrainingUpdate, ADPT_DESC(BNTrainingUpdate))
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// 注册BNTrainingUpdate操作的适配器描述kNameBNTrainingUpdate
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// BNTrainingUpdateGrad
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INPUT_MAP(BNTrainingUpdateGrad) = {
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{1, INPUT_DESC(grads)}, {2, INPUT_DESC(x)}, {3, INPUT_DESC(batch_mean)}, {4, INPUT_DESC(batch_variance)}};
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// 输入映射，共五个，grads索引为1，x索引为2，batch_mean索引为3，batch_variance索引为4
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ATTR_MAP(BNTrainingUpdateGrad) = {{"epsilon", ATTR_DESC(epsilon, AnyTraits<float>())}};
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// 属性映射，"epsilon"类型为float
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OUTPUT_MAP(BNTrainingUpdateGrad) = {{0, OUTPUT_DESC(diff_scale)}, {1, OUTPUT_DESC(diff_offset)}};
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// 输出映射，共两个，diff_scale索引为0，diff_offset索引为1
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REG_ADPT_DESC(BNTrainingUpdateGrad, kNameBNTrainingUpdateGrad, ADPT_DESC(BNTrainingUpdateGrad))
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// 注册BNTrainingUpdateGrad操作的适配器描述kNameBNTrainingUpdateGrad
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// ReduceAnyD
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INPUT_MAP(ReduceAnyD) = {{1, INPUT_DESC(x)}};
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// 输入映射，x索引为1
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INPUT_ATTR_MAP(ReduceAnyD) = {
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{2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
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// 输入属性映射，axes索引为2，类型为int64_t
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ATTR_MAP(ReduceAnyD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
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// 属性映射，"keep_dims"类型为bool
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OUTPUT_MAP(ReduceAnyD) = {{0, OUTPUT_DESC(y)}};
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// 输出映射，y索引为0
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REG_ADPT_DESC(ReduceAnyD, kNameReduceAnyD, ADPT_DESC(ReduceAnyD))
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// 注册ReduceAnyD操作的适配器描述 kNameReduceAnyD
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// ReduceSumD
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INPUT_MAP(ReduceSumD) = {{1, INPUT_DESC(x)}};
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//输入映射，x索引为1
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INPUT_ATTR_MAP(ReduceSumD) = {
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{2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
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// 输入属性映射，axes索引为2，类型为int64_t
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ATTR_MAP(ReduceSumD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
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// 属性映射，"keep_dims"类型为bool
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OUTPUT_MAP(ReduceSumD) = {{0, OUTPUT_DESC(y)}};
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// 输出映射，y索引为0
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REG_ADPT_DESC(ReduceSumD, prim::kPrimReduceSum->name(), ADPT_DESC(ReduceSumD))
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// 注册ReduceSumD操作的适配器描述 prim::kPrimReduceSum->name()
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// ReduceProdD
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INPUT_MAP(ReduceProdD) = {{1, INPUT_DESC(x)}};
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// 输入映射，x索引为1
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INPUT_ATTR_MAP(ReduceProdD) = {
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{2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
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// 输入属性映射，axes索引为2，类型为int64_t
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ATTR_MAP(ReduceProdD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
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// 属性映射，"keep_dims"类型为bool
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OUTPUT_MAP(ReduceProdD) = {{0, OUTPUT_DESC(y)}};
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// 输出映射，y索引为0
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REG_ADPT_DESC(ReduceProdD, kNameReduceProd, ADPT_DESC(ReduceProdD))
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// 注册ReduceProdD操作的适配器描述kNameReduceProd
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// ReduceAllD
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INPUT_MAP(ReduceAllD) = {{1, INPUT_DESC(x)}};
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// 输入映射，x索引为1
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INPUT_ATTR_MAP(ReduceAllD) = {
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{2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
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// 属性映射，axes索引为2，类型为int64_t
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ATTR_MAP(ReduceAllD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
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// 属性映射，"keep_dims"类型为bool
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OUTPUT_MAP(ReduceAllD) = {{0, OUTPUT_DESC(y)}};
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// 输出映射，y索引为0
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REG_ADPT_DESC(ReduceAllD, prim::kPrimReduceAll->name(), ADPT_DESC(ReduceAllD))
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// 注册ReduceAllD操作的适配器描述prim::kPrimReduceAll->name()
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// ReduceMeanD
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INPUT_MAP(ReduceMeanD) = {{1, INPUT_DESC(x)}};
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// 输入映射，x索引为1
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INPUT_ATTR_MAP(ReduceMeanD) = {
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{2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
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// 输入属性映射，axes索引为2，类型为int64_t
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ATTR_MAP(ReduceMeanD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
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// 属性映射，"keep_dims"类型为bool
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OUTPUT_MAP(ReduceMeanD) = {{0, OUTPUT_DESC(y)}};
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REG_ADPT_DESC(ReduceMeanD, prim::kPrimReduceMean->name(), ADPT_DESC(ReduceMeanD))
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// 注册ReduceMeanD操作的适配器描述prim::kPrimReduceAll->name()
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//
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// ReduceMinD
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INPUT_MAP(ReduceMinD) = {{1, INPUT_DESC(x)}};
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// 输入映射，x索引为1
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INPUT_ATTR_MAP(ReduceMinD) = {
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{2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
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// 输入属性映射，axes索引为2，类型为int64_t
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ATTR_MAP(ReduceMinD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
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// 属性映射，"keep_dims"类型为bool
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OUTPUT_MAP(ReduceMinD) = {{0, OUTPUT_DESC(y)}};
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// 输出映射，y索引为0
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REG_ADPT_DESC(ReduceMinD, prim::kPrimReduceMin->name(), ADPT_DESC(ReduceMinD))
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// 注册ReduceMinD操作的适配器描述prim::kPrimReduceAll->name()
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// ReduceMaxD
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INPUT_MAP(ReduceMaxD) = {{1, INPUT_DESC(x)}};
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// 输入映射，x索引为1
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INPUT_ATTR_MAP(ReduceMaxD) = {
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{2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
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// 输入属性映射，axes索引为2，类型为int64_t
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ATTR_MAP(ReduceMaxD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
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// 属性映射，"keep_dims"类型为bool
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OUTPUT_MAP(ReduceMaxD) = {{0, OUTPUT_DESC(y)}};
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// 输出映射，y索引为0
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REG_ADPT_DESC(ReduceMaxD, prim::kPrimReduceMax->name(), ADPT_DESC(ReduceMaxD))
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// 注册ReduceMaxD操作的适配器描述prim::kPrimReduceAll->name()
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} // namespace mindspore::transform
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