* 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_mul.cpp
* \brief
*/
#include <complex>
#include <ctime>
#include <cstdio>
#include <iostream>
#include <map>
#include <stdint.h>
#include <string>
#include <string.h>
#include <vector>
#include "assert.h"
#include "ge_api.h"
#include "ge_api_types.h"
#include "ge_error_codes.h"
#include "ge_ir_build.h"
#include "graph.h"
#include "graph/operator.h"
#include "graph/operator_reg.h"
#include "tensor.h"
#include "types.h"
#include "../op_graph/mul_proto.h"
#define FAILED -1
#define SUCCESS 0
namespace ge {
REG_OP(Data).INPUT(x, TensorType::ALL()).OUTPUT(y, TensorType::ALL()).ATTR(index, Int, 0).OP_END_FACTORY_REG(Data)
}
using namespace ge;
using std::map;
using std::string;
using std::vector;
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) {
return 2;
} else if (dt == ge::DT_DOUBLE) {
return 8;
} else if (dt == ge::DT_COMPLEX64) {
return 8;
} else if (dt == ge::DT_COMPLEX128) {
return 16;
} else if (dt == ge::DT_INT32) {
return 4;
} else if (dt == ge::DT_INT64) {
return 8;
}
return 0;
}
const char* DataTypeToString(DataType dt)
{
switch (dt) {
case DT_FLOAT:
return "DT_FLOAT";
case DT_FLOAT16:
return "DT_FLOAT16";
case DT_DOUBLE:
return "DT_DOUBLE";
case DT_COMPLEX64:
return "DT_COMPLEX64";
case DT_COMPLEX128:
return "DT_COMPLEX128";
case DT_INT32:
return "DT_INT32";
case DT_INT64:
return "DT_INT64";
default:
return "DTYPE(unknown)";
}
}
template <typename T>
int32_t GenTensorData(const vector<int64_t>& shapes, Tensor& input_tensor, TensorDesc& input_tensor_desc,
const vector<T>& values)
{
input_tensor_desc.SetRealDimCnt(shapes.size());
size_t size = 1;
for (uint32_t i = 0; i < shapes.size(); i++) {
size *= shapes[i];
}
if (size != values.size()) {
return FAILED;
}
size_t data_len = size * sizeof(T);
T* p_data = new (std::nothrow) T[size];
if (p_data == nullptr) {
return FAILED;
}
for (size_t i = 0; i < size; ++i) {
p_data[i] = values[i];
}
input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t*>(p_data), data_len);
delete[] p_data;
return SUCCESS;
}
int CreateOppInGraph(DataType input_dtype, std::vector<ge::Tensor>& input, std::vector<Operator>& inputs,
std::vector<Operator>& outputs, Graph& graph)
{
Status ret = SUCCESS;
auto mul_op = op::Mul("mul_op");
std::vector<int64_t> x1_shape = {2, 3};
std::vector<int64_t> x2_shape = {2, 3};
std::vector<std::complex<double>> x1_data = {{1.0, 0.0}, {2.0, 1.0}, {3.0, 0.0},
{4.0, -1.0}, {5.0, 0.0}, {6.0, 2.0}};
std::vector<std::complex<double>> x2_data = {{1.0, 1.0}, {1.0, 0.0}, {2.0, 0.0},
{1.0, -1.0}, {0.0, 1.0}, {1.0, 1.0}};
vector<int64_t> placeholder1_shape = x1_shape;
auto placeholder1 = op::Data("placeholder1").set_attr_index(0);
TensorDesc placeholder1_desc = TensorDesc(ge::Shape(placeholder1_shape), FORMAT_ND, input_dtype);
placeholder1_desc.SetPlacement(ge::kPlacementHost);
placeholder1_desc.SetFormat(FORMAT_ND);
Tensor tensor_placeholder1;
ret = GenTensorData(placeholder1_shape, tensor_placeholder1, placeholder1_desc, x1_data);
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Generate x1 data failed\n", GetTime().c_str());
return FAILED;
}
placeholder1.update_input_desc_x(placeholder1_desc);
placeholder1.update_output_desc_y(placeholder1_desc);
input.push_back(tensor_placeholder1);
graph.AddOp(placeholder1);
mul_op.set_input_x1(placeholder1);
inputs.push_back(placeholder1);
vector<int64_t> placeholder2_shape = x2_shape;
auto placeholder2 = op::Data("placeholder2").set_attr_index(1);
TensorDesc placeholder2_desc = TensorDesc(ge::Shape(placeholder2_shape), FORMAT_ND, input_dtype);
placeholder2_desc.SetPlacement(ge::kPlacementHost);
placeholder2_desc.SetFormat(FORMAT_ND);
Tensor tensor_placeholder2;
ret = GenTensorData(placeholder2_shape, tensor_placeholder2, placeholder2_desc, x2_data);
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Generate x2 data failed\n", GetTime().c_str());
return FAILED;
}
placeholder2.update_input_desc_x(placeholder2_desc);
placeholder2.update_output_desc_y(placeholder2_desc);
input.push_back(tensor_placeholder2);
graph.AddOp(placeholder2);
mul_op.set_input_x2(placeholder2);
inputs.push_back(placeholder2);
TensorDesc output_desc = TensorDesc(ge::Shape(x1_shape), FORMAT_ND, input_dtype);
output_desc.SetPlacement(ge::kPlacementHost);
output_desc.SetFormat(FORMAT_ND);
mul_op.update_output_desc_y(output_desc);
outputs.push_back(mul_op);
return SUCCESS;
}
int main(int argc, char* argv[])
{
const char* graph_name = "tc_ge_irrun_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 - INFO - [XIR]: Initialize ge using ge global options failed.ret = %d\n", GetTime().c_str(), ret);
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{};
if (argc > 1) {
std::cout << argv[1] << std::endl;
}
DataType input_dtype = DT_COMPLEX128;
ret = CreateOppInGraph(input_dtype, input, inputs, outputs, graph);
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Create ir session using build options failed\n", GetTime().c_str());
GEFinalize();
return FAILED;
}
if (!inputs.empty() && !outputs.empty()) {
graph.SetInputs(inputs).SetOutputs(outputs);
}
std::map<AscendString, AscendString> build_options = {};
printf("%s - INFO - [XIR]: Start to create ir session using build options\n", GetTime().c_str());
ge::Session* session = new Session(build_options);
if (session == nullptr) {
printf("%s - ERROR - [XIR]: Create ir session using build options failed\n", GetTime().c_str());
GEFinalize();
return FAILED;
}
printf("%s - INFO - [XIR]: Create ir session using build options success\n", GetTime().c_str());
printf("%s - INFO - [XIR]: Start to add compute graph to ir session\n", GetTime().c_str());
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]: Session add ir compute graph failed\n", GetTime().c_str());
delete session;
GEFinalize();
return FAILED;
}
printf("%s - INFO - [XIR]: Session add ir compute graph to ir session success\n", GetTime().c_str());
printf("%s - INFO - [XIR]: dump graph to txt\n", GetTime().c_str());
std::string file_path = "./dump";
aclgrphDumpGraph(graph, file_path.c_str(), file_path.length());
printf("%s - INFO - [XIR]: Start to run ir compute graph\n", GetTime().c_str());
std::vector<ge::Tensor> output;
ret = session->RunGraph(graph_id, input, output);
if (ret != SUCCESS) {
printf("%s - INFO - [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());
if (!output.empty()) {
uint8_t* output_data = output[0].GetData();
int64_t output_shape_size = output[0].GetTensorDesc().GetShape().GetShapeSize();
DataType output_dtype = output[0].GetTensorDesc().GetDataType();
printf("%s - INFO - [XIR]: Output dtype: %s, shape size: %ld\n", GetTime().c_str(),
DataTypeToString(output_dtype), output_shape_size);
std::complex<double>* result = reinterpret_cast<std::complex<double>*>(output_data);
std::complex<double> x1_vals[6] = {{1.0, 0.0}, {2.0, 1.0}, {3.0, 0.0}, {4.0, -1.0}, {5.0, 0.0}, {6.0, 2.0}};
std::complex<double> x2_vals[6] = {{1.0, 1.0}, {1.0, 0.0}, {2.0, 0.0}, {1.0, -1.0}, {0.0, 1.0}, {1.0, 1.0}};
bool match = true;
for (int64_t i = 0; i < output_shape_size; ++i) {
std::complex<double> expected = x1_vals[i] * x2_vals[i];
printf("result[%ld] = (%.6f, %.6f), expected = (%.6f, %.6f)\n", i, result[i].real(), result[i].imag(),
expected.real(), expected.imag());
if (std::abs(result[i] - expected) > 1e-6) {
printf("MISMATCH at[%ld]\n", i);
match = false;
}
}
if (match) {
printf("%s - INFO - [XIR]: Output verification PASSED\n", GetTime().c_str());
} else {
printf("%s - ERROR - [XIR]: Output verification FAILED\n", GetTime().c_str());
}
}
delete session;
printf("%s - INFO - [XIR]: Start to finalize ir graph session\n", GetTime().c_str());
ret = ge::GEFinalize();
if (ret != SUCCESS) {
printf("%s - INFO - [XIR]: Finalize ir graph session failed\n", GetTime().c_str());
return FAILED;
}
printf("%s - INFO - [XIR]: Finalize ir graph session success\n", GetTime().c_str());
ge::AscendString err_msg_asc = ge::GEGetErrorMsgV2();
std::string err_msg = err_msg_asc.GetString();
if (!err_msg.empty()) {
printf("Error message: %s\n", err_msg.c_str());
}
ge::AscendString warn_msg_asc = ge::GEGetWarningMsgV2();
std::string warn_msg = warn_msg_asc.GetString();
if (!warn_msg.empty()) {
printf("Warning message: %s\n", warn_msg.c_str());
}
return SUCCESS;
}
