* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2026 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under 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.
*/
#include <iostream>
#include <fstream>
#include <string.h>
#include <stdint.h>
#include <ctime>
#include <vector>
#include <string>
#include <map>
#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 "nn_other.h"
#include "../op_graph/acosh_proto.h"
#define FAILED -1
#define SUCCESS 0
using namespace ge;
using std::map;
using std::string;
using std::vector;
constexpr size_t kComplexValueParts = 2U;
#define ADD_INPUT(inputIndex, inputName, inputDtype, inputShape, inputValues) \
do { \
vector<int64_t> placeholder##inputIndex##_shape = inputShape; \
auto placeholder##inputIndex = op::Data("placeholder" #inputIndex).set_attr_index(0); \
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 = GenSampleData(placeholder##inputIndex##_shape, tensor_placeholder##inputIndex, \
placeholder##inputIndex##_desc, inputDtype, inputValues); \
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); \
placeholder##inputIndex.update_output_desc_y(placeholder##inputIndex##_desc); \
input.push_back(tensor_placeholder##inputIndex); \
graph.AddOp(placeholder##inputIndex); \
acosh1.set_input_##inputName(placeholder##inputIndex); \
inputs.push_back(placeholder##inputIndex); \
} while (0)
#define LOG_PRINT(message, ...) \
do { \
printf(message, ##__VA_ARGS__); \
} while (0)
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)
{
int32_t dtype_size = ge::GetSizeByDataType(dt);
return dtype_size > 0 ? static_cast<uint32_t>(dtype_size) : 1U;
}
void PrintOutputData(const uint8_t *output_data, int64_t output_shape, DataType data_type)
{
if (data_type == DT_COMPLEX128) {
const double *result = reinterpret_cast<const double *>(output_data);
for (int64_t j = 0; j < output_shape; j++) {
LOG_PRINT("result[%ld] is: (%f, %f)\n", j, result[kComplexValueParts * j],
result[kComplexValueParts * j + 1]);
}
return;
}
if (data_type == DT_COMPLEX64) {
const float *result = reinterpret_cast<const float *>(output_data);
for (int64_t j = 0; j < output_shape; j++) {
LOG_PRINT("result[%ld] is: (%f, %f)\n", j, result[kComplexValueParts * j],
result[kComplexValueParts * j + 1]);
}
return;
}
if (data_type == DT_DOUBLE) {
const double *result = reinterpret_cast<const double *>(output_data);
for (int64_t j = 0; j < output_shape; j++) {
LOG_PRINT("result[%ld] is: %f\n", j, result[j]);
}
return;
}
if (data_type == DT_FLOAT) {
const float *result = reinterpret_cast<const float *>(output_data);
for (int64_t j = 0; j < output_shape; j++) {
LOG_PRINT("result[%ld] is: %f\n", j, result[j]);
}
return;
}
if (data_type == DT_FLOAT16 || data_type == DT_BF16) {
const uint16_t *result = reinterpret_cast<const uint16_t *>(output_data);
for (int64_t j = 0; j < output_shape; j++) {
LOG_PRINT("result[%ld] 16bit bits is: 0x%04x\n", j, result[j]);
}
}
}
int32_t GenSampleData(vector<int64_t> shapes, Tensor &input_tensor, TensorDesc &input_tensor_desc,
DataType data_type, const vector<double> &values)
{
input_tensor_desc.SetRealDimCnt(shapes.size());
size_t size = 1;
for (uint32_t i = 0; i < shapes.size(); i++) {
size *= static_cast<size_t>(shapes[i]);
}
size_t expected_value_count = size;
if (data_type == DT_COMPLEX128 || data_type == DT_COMPLEX64) {
expected_value_count *= kComplexValueParts;
}
if (values.size() != expected_value_count) {
return FAILED;
}
uint32_t data_len = static_cast<uint32_t>(size * GetDataTypeSize(data_type));
std::vector<uint8_t> data(data_len, 0);
if (data_type == DT_FLOAT) {
float *data_ptr = reinterpret_cast<float *>(data.data());
for (size_t i = 0; i < size; ++i) {
data_ptr[i] = static_cast<float>(values[i]);
}
} else if (data_type == DT_DOUBLE) {
double *data_ptr = reinterpret_cast<double *>(data.data());
for (size_t i = 0; i < size; ++i) {
data_ptr[i] = values[i];
}
} else if (data_type == DT_COMPLEX128) {
double *data_ptr = reinterpret_cast<double *>(data.data());
for (size_t i = 0; i < expected_value_count; ++i) {
data_ptr[i] = values[i];
}
} else if (data_type == DT_COMPLEX64) {
float *data_ptr = reinterpret_cast<float *>(data.data());
for (size_t i = 0; i < expected_value_count; ++i) {
data_ptr[i] = static_cast<float>(values[i]);
}
} else {
return FAILED;
}
input_tensor = Tensor(input_tensor_desc, data);
return SUCCESS;
}
int32_t WriteDataToFile(string bin_file, uint64_t data_size, uint8_t *inputData)
{
FILE *fp = fopen(bin_file.c_str(), "wb");
if (fp == nullptr) {
return FAILED;
}
size_t write_size = fwrite(inputData, sizeof(uint8_t), data_size, fp);
fclose(fp);
return write_size == data_size ? SUCCESS : FAILED;
}
int CreateOppInGraph(DataType inDtype, std::vector<ge::Tensor> &input, std::vector<Operator> &inputs,
std::vector<Operator> &outputs, Graph &graph)
{
Status ret = SUCCESS;
auto acosh1 = op::Acosh("acosh1");
std::vector<int64_t> xShape = {2, 3};
std::vector<double> xData = {1.5, 0.0, 2.0, 0.5, 3.0, 0.0, 5.0, 1.0, 1.2, -1.0, 4.0, 0.25};
ADD_INPUT(1, x, inDtype, xShape, xData);
outputs.push_back(acosh1);
return SUCCESS;
}
int main(int argc, char *argv[])
{
(void)argc;
(void)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\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_COMPLEX128;
ret = CreateOppInGraph(inDtype, input, inputs, outputs, graph);
if (ret != SUCCESS) {
printf("%s - ERROR - [XIR]: Create ir session using build options failed\n", GetTime().c_str());
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());
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);
printf("%s - INFO - [XIR]: Session add ir compute graph to ir session success\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());
int input_num = static_cast<int>(input.size());
for (int i = 0; i < input_num; i++) {
string input_file = "./tc_ge_irrun_test_acosh_npu_input_" + std::to_string(i) + ".bin";
uint8_t *input_data_i = input[i].GetData();
int64_t input_shape = input[i].GetTensorDesc().GetShape().GetShapeSize();
uint32_t data_size = static_cast<uint32_t>(input_shape) * GetDataTypeSize(input[i].GetTensorDesc().GetDataType());
(void)WriteDataToFile((const char *)input_file.c_str(), data_size, input_data_i);
}
int output_num = static_cast<int>(output.size());
for (int i = 0; i < output_num; i++) {
string output_file = "./tc_ge_irrun_test_acosh_npu_output_" + std::to_string(i) + ".bin";
uint8_t *output_data_i = output[i].GetData();
int64_t output_shape = output[i].GetTensorDesc().GetShape().GetShapeSize();
uint32_t data_size = static_cast<uint32_t>(output_shape) * GetDataTypeSize(output[i].GetTensorDesc().GetDataType());
(void)WriteDataToFile((const char *)output_file.c_str(), data_size, output_data_i);
PrintOutputData(output_data_i, output_shape, output[i].GetTensorDesc().GetDataType());
}
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());
return SUCCESS;
}
