* Copyright (c) 2026 Huawei Technologies Co., Ltd.
* This program is free software, you can redistribute it and/or modify it under the terms and conditions of
* CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
* @file test_geir_atan_grad.cpp
* @brief GE IR 图模式调用示例 - AtanGrad
*
* 数学公式: z = dy / (1 + y * y)
*
* 通过 GE Session 构建计算图，编译并执行 AtanGrad 算子。
*/
#include <iostream>
#include <fstream>
#include <string.h>
#include <stdint.h>
#include <vector>
#include <string>
#include <map>
#include <cmath>
#include "assert.h"
#include "graph.h"
#include "types.h"
#include "tensor.h"
#include "ge_error_codes.h"
#include "ge_api_types.h"
#include "ge_api.h"
#include "array_ops.h"
#include "ge_ir_build.h"
#include "../op_graph/atan_grad_proto.h"
#define FAILED -1
#define SUCCESS 0
using namespace ge;
using std::map;
using std::string;
using std::vector;
#define ADD_INPUT(inputIndex, inputName, inputDtype, inputShape) \
vector<int64_t> placeholder##inputIndex##_shape = inputShape; \
auto placeholder##inputIndex = op::Data("placeholder" #inputIndex).set_attr_index(inputIndex - 1); \
TensorDesc placeholder##inputIndex##_desc = \
TensorDesc(ge::Shape(placeholder##inputIndex##_shape), FORMAT_ND, inputDtype); \
placeholder##inputIndex##_desc.SetPlacement(ge::kPlacementHost); \
placeholder##inputIndex##_desc.SetFormat(FORMAT_ND); \
Tensor tensor_placeholder##inputIndex; \
ret = GenInputData( \
placeholder##inputIndex##_shape, tensor_placeholder##inputIndex, placeholder##inputIndex##_desc, \
inputDtype, inputIndex); \
if (ret != SUCCESS) { \
printf("%s - ERROR - [XIR]: Generate input data failed\n", GetTime().c_str()); \
return FAILED; \
} \
placeholder##inputIndex.update_input_desc_x(placeholder##inputIndex##_desc); \
input.push_back(tensor_placeholder##inputIndex); \
graph.AddOp(placeholder##inputIndex); \
atanGradOp.set_input_##inputName(placeholder##inputIndex); \
inputs.push_back(placeholder##inputIndex);
#define ADD_OUTPUT(outputIndex, outputName, outputDtype, outputShape) \
TensorDesc outputName##outputIndex##_desc = TensorDesc(ge::Shape(outputShape), FORMAT_ND, outputDtype); \
atanGradOp.update_output_desc_##outputName(outputName##outputIndex##_desc);
string GetTime()
{
time_t timep;
time(&timep);
char tmp[64];
strftime(tmp, sizeof(tmp), "%Y-%m-%d %H:%M:%S,000", localtime(&timep));
return tmp;
}
uint32_t GetDataTypeSize(DataType dt)
{
if (dt == ge::DT_FLOAT) {
return 4;
} else if (dt == ge::DT_FLOAT16 || dt == ge::DT_BF16) {
return 2;
}
return 4;
}
int32_t GenInputData(
vector<int64_t> shapes, Tensor& input_tensor, TensorDesc& input_tensor_desc, DataType data_type, int inputIndex)
{
input_tensor_desc.SetRealDimCnt(shapes.size());
size_t size = 1;
for (uint32_t i = 0; i < shapes.size(); i++) {
size *= shapes[i];
}
uint32_t data_len = size * GetDataTypeSize(data_type);
float* pData = new (std::nothrow) float[size];
if (pData == nullptr) {
return FAILED;
}
if (inputIndex == 1) {
for (size_t i = 0; i < size; ++i) {
pData[i] = static_cast<float>(i) * 0.1f - 3.0f;
}
} else {
for (size_t i = 0; i < size; ++i) {
pData[i] = 1.0f;
}
}
input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t*>(pData), data_len);
delete[] pData;
return SUCCESS;
}
int CreateOppInGraph(
DataType inDtype, std::vector<ge::Tensor>& input, std::vector<Operator>& inputs, std::vector<Operator>& outputs,
Graph& graph)
{
Status ret = SUCCESS;
auto atanGradOp = op::AtanGrad("atan_grad_1");
std::vector<int64_t> xShape = {4, 4, 4};
ADD_INPUT(1, y, inDtype, xShape);
ADD_INPUT(2, dy, inDtype, xShape);
ADD_OUTPUT(1, z, inDtype, xShape);
outputs.push_back(atanGradOp);
return SUCCESS;
}
int main(int argc, char* argv[])
{
const char* graph_name = "atan_grad_ge_ir_test";
Graph graph(graph_name);
std::vector<ge::Tensor> input;
printf("%s - INFO - [XIR]: Start to initialize ge using ge global options\n", GetTime().c_str());
std::map<AscendString, AscendString> global_options = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "1"}};
Status ret = ge::GEInitialize(global_options);
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Initialize ge using ge global options failed\n", GetTime().c_str());
return FAILED;
}
printf("%s - INFO - [XIR]: Initialize ge using ge global options success\n", GetTime().c_str());
std::vector<Operator> inputs{};
std::vector<Operator> outputs{};
DataType inDtype = DT_FLOAT;
if (argc > 1) {
std::string dtypeArg(argv[1]);
if (dtypeArg == "fp16") {
inDtype = DT_FLOAT16;
} else if (dtypeArg == "bf16") {
inDtype = DT_BF16;
}
}
printf("%s - INFO - [XIR]: Using dtype = %d\n", GetTime().c_str(), static_cast<int>(inDtype));
ret = CreateOppInGraph(inDtype, input, inputs, outputs, graph);
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Create op in graph failed\n", GetTime().c_str());
return FAILED;
}
if (!inputs.empty() && !outputs.empty()) {
graph.SetInputs(inputs).SetOutputs(outputs);
}
std::map<AscendString, AscendString> build_options = {};
ge::Session* session = new Session(build_options);
if (session == nullptr) {
printf("%s - ERROR - [XIR]: Create ir session failed\n", GetTime().c_str());
return FAILED;
}
std::map<AscendString, AscendString> graph_options = {};
uint32_t graph_id = 0;
ret = session->AddGraph(graph_id, graph, graph_options);
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Add graph to session failed\n", GetTime().c_str());
delete session;
GEFinalize();
return FAILED;
}
std::vector<ge::Tensor> output;
ret = session->RunGraph(graph_id, input, output);
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Run graph failed\n", GetTime().c_str());
delete session;
GEFinalize();
return FAILED;
}
printf("%s - INFO - [XIR]: Session run ir compute graph success\n", GetTime().c_str());
int output_num = output.size();
for (int i = 0; i < output_num; i++) {
int64_t output_shape = output[i].GetTensorDesc().GetShape().GetShapeSize();
printf("%s - INFO - [XIR]: output %d shape size = %ld, dtype = %d\n",
GetTime().c_str(), i, output_shape,
static_cast<int>(output[i].GetTensorDesc().GetDataType()));
if (output[i].GetTensorDesc().GetDataType() == DT_FLOAT && output_shape > 0) {
float* outData = reinterpret_cast<float*>(output[i].GetData());
int64_t printNum = (output_shape < 10) ? output_shape : 10;
for (int64_t j = 0; j < printNum; j++) {
float y = static_cast<float>(j) * 0.1f - 3.0f;
float expected = 1.0f / (1.0f + y * y);
printf(" output[%ld] = %.6f (expected = %.6f)\n", j, outData[j], expected);
}
}
}
ge::AscendString error_msg = ge::GEGetErrorMsgV2();
std::string error_str(error_msg.GetString());
if (!error_str.empty()) {
std::cout << "Error message: " << error_str << std::endl;
}
delete session;
ret = ge::GEFinalize();
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Finalize ir graph session failed\n", GetTime().c_str());
return FAILED;
}
return SUCCESS;
}
