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243 lines
7.5 KiB
243 lines
7.5 KiB
/**
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* Copyright 2019-2020 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 GE_GRAPH_COMMON_BCAST_H_
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#define GE_GRAPH_COMMON_BCAST_H_
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#include <stdint.h>
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#include <functional>
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#include <vector>
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#include "common/debug/log.h"
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#include "common/types.h"
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#include "framework/common/debug/ge_log.h"
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#include "framework/common/ge_inner_error_codes.h"
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#include "graph/attr_value.h"
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#include "graph/ge_tensor.h"
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#include "graph/utils/tensor_adapter.h"
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namespace ge {
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static const size_t kMinDimNum = 2;
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class BCast {
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public:
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///
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/// @ingroup domi_calibration
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/// @brief define kVecInt
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///
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typedef std::vector<int64_t> kVecInt;
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///
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/// @ingroup domi_calibration
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/// @brief constructor
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///
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BCast() {}
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///
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/// @ingroup domi_calibration
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/// @brief destructor
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///
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~BCast() {}
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///
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/// @ingroup domi_calibration
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/// @brief Not optimize intermediate shapes
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/// @decrease dims, more efficient, set by user
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/// @param [in] x first Tensor dim
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/// @param [in] y second Tensor dim
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/// @return SUCCESS broadcast message successfully generated
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/// @return other broadcast message failed to generate
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///
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ge::Status GenerateBcastInfo(const kVecInt &x, const kVecInt &y);
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///
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/// @ingroup domi_calibration
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/// @brief get x_reshape
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///
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const kVecInt &GetXReshape() const { return x_reshape_; }
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///
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/// @ingroup domi_calibration
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/// @brief get x_bcast
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///
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const kVecInt &GetXBcast() const { return x_bcast_; }
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///
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/// @ingroup domi_calibration
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/// @brief get y_reshape
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///
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const kVecInt &GetYReshape() const { return y_reshape_; }
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///
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/// @ingroup domi_calibration
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/// @brief get y_bcast
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///
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const kVecInt &GetYBcast() const { return y_bcast_; }
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///
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/// @ingroup domi_calibration
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/// @brief get result_shape
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///
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const kVecInt &GetResultShape() const { return result_; }
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///
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/// @ingroup domi_calibration
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/// @brief get result_shape
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///
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const kVecInt &GetOutputShape() const { return output_; }
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const kVecInt &GetGradXReduceIdx() const { return grad_x_reduce_idx_; }
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const kVecInt &GetGradYReduceIdx() const { return grad_y_reduce_idx_; }
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///
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/// @ingroup domi_calibration
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/// @brief convert TensorDescriptor to kVecInt
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/// @param [in] shape Tensor descriptor
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/// @return kVecInt dim info
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///
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static kVecInt TransShapeToDimVec(const GeTensorDesc &shape);
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void BCastIndexes(kVecInt &x_indexes, kVecInt &y_indexes);
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template <typename InT, typename OutT>
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Status BCastCompute(const std::vector<ConstGeTensorPtr> &input, std::vector<OutT> &v_output,
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const std::function<OutT(InT const &, InT const &)> &func) {
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Status ret;
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if (func == nullptr) {
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REPORT_INNER_ERROR("E19999", "Check param func nullptr when %s", __FUNCTION__);
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GELOGE(domi::PARAM_INVALID, "Param func is null");
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return domi::PARAM_INVALID;
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}
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// Min input num is 2
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if (input.size() < kMinDimNum) {
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REPORT_INNER_ERROR("E19999", "Param input.size():%zu < %zu, check invalid when %s",
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input.size(), kMinDimNum, __FUNCTION__);
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GELOGE(domi::PARAM_INVALID, "Input size is smaller than two.");
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return domi::PARAM_INVALID;
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}
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// Only broadcast shape
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ret =
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GenerateBcastInfo(TransShapeToDimVec(input[0]->GetTensorDesc()), TransShapeToDimVec(input[1]->GetTensorDesc()));
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if (ret != domi::SUCCESS) {
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GELOGE(ret, "Greater broadcasting failed.");
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return ret;
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}
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kVecInt x_indexes;
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kVecInt y_indexes;
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BCastIndexes(x_indexes, y_indexes);
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const void *x1_data = input[0]->GetData().data();
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const void *x2_data = input[1]->GetData().data();
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for (size_t i = 0; i < x_indexes.size(); i++) {
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int64_t x_index = x_indexes[i];
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int64_t y_index = y_indexes[i];
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auto value = func((*(reinterpret_cast<const InT *>(x1_data) + x_index)),
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(*(reinterpret_cast<const InT *>(x2_data) + y_index)));
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v_output.push_back(value);
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}
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return domi::SUCCESS;
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}
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template <typename InT, typename OutT>
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Status BCastComputeCheck(const std::vector<ConstGeTensorPtr> &input, std::vector<OutT> &v_output,
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const std::function<OutT(InT const &, InT const &, DataType &type, Status &)> &func) {
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if (func == nullptr) {
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REPORT_INNER_ERROR("E19999", "Check param func nullptr when %s", __FUNCTION__);
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GELOGE(PARAM_INVALID, "Param func is null");
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return PARAM_INVALID;
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}
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// Min input num is 2
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if (input.size() < kMinDimNum) {
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REPORT_INNER_ERROR("E19999", "Param input.size():%zu < %zu, check invalid when %s",
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input.size(), kMinDimNum, __FUNCTION__);
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GELOGE(PARAM_INVALID, "Input size is smaller than two.");
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return PARAM_INVALID;
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}
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// Only broadcast shape
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Status ret =
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GenerateBcastInfo(TransShapeToDimVec(input[0]->GetTensorDesc()), TransShapeToDimVec(input[1]->GetTensorDesc()));
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if (ret != SUCCESS) {
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GELOGE(ret, "Greater broadcasting failed.");
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return ret;
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}
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DataType data_type = input[0]->GetTensorDesc().GetDataType();
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kVecInt x_indexes;
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kVecInt y_indexes;
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BCastIndexes(x_indexes, y_indexes);
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const void *x1_data = input[0]->GetData().data();
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const void *x2_data = input[1]->GetData().data();
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for (size_t i = 0; i < x_indexes.size(); i++) {
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int64_t x_index = x_indexes[i];
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int64_t y_index = y_indexes[i];
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auto value = func((*(reinterpret_cast<const InT *>(x1_data) + x_index)),
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(*(reinterpret_cast<const InT *>(x2_data) + y_index)), data_type, ret);
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if (ret != SUCCESS) {
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REPORT_INNER_ERROR("E19999", "BCastComputeCheck func execute failed, datatype is %d.", data_type);
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GELOGE(ret, "BCastComputeCheck func execute failed, datatype is %d.", data_type);
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return ret;
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}
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v_output.push_back(value);
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}
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return SUCCESS;
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}
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private:
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///
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/// @ingroup domi_calibration
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/// @brief reverse elements in kVecInt
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/// @param [in] shape dim info
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/// @return null
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///
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static void Reverse(kVecInt &shape);
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///
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/// @ingroup domi_calibration
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/// @brief two Tensor with different shape, set broadcast info
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/// @param [in] x first input Tensor dim info
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/// @param [in] y second input Tensor dim info
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/// @return null
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///
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ge::Status SetShapeDifferentInfo(const kVecInt &x, const kVecInt &y);
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///
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/// @ingroup domi_calibration
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/// @brief extend Tensor dim
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/// @param [in] x first input Tensor dim info
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/// @param [in] y second input Tensor dim info
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/// @return null
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///
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void ExtendTensorDim(kVecInt &x, kVecInt &y);
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///
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/// @ingroup domi_calibration
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/// @brief reverse all intermediate shape params
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/// @param [in] void
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/// @return null
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///
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void ReverseAllIntermediateShapes();
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kVecInt x_reshape_;
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kVecInt x_bcast_;
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kVecInt y_reshape_;
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kVecInt y_bcast_;
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kVecInt result_;
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kVecInt output_;
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kVecInt grad_x_reduce_idx_;
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kVecInt grad_y_reduce_idx_;
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};
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} // namespace ge
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#endif // GE_GRAPH_COMMON_BCAST_H_
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