* 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.
*/
#include <iostream>
#include <fstream>
#include <cmath>
#include <string.h>
#include <stdint.h>
#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 "experiment_ops.h"
#include "nn_other.h"
#include "../../op_graph/stateless_random_proto.h"
#define FAILED -1
#define SUCCESS 0
using namespace ge;
using std::map;
using std::string;
using std::vector;
#define ADD_INT_INPUT(intputIndex, intputName, intputDtype, inputShape, value) \
vector<int64_t> placeholder##intputIndex##_shape = inputShape; \
auto placeholder##intputIndex = op::Data("placeholder" + intputIndex).set_attr_index(0); \
TensorDesc placeholder##intputIndex##_desc = TensorDesc(ge::Shape(placeholder##intputIndex##_shape), FORMAT_ND, \
intputDtype); \
placeholder##intputIndex##_desc.SetPlacement(ge::kPlacementHost); \
placeholder##intputIndex##_desc.SetFormat(FORMAT_ND); \
Tensor tensor_placeholder##intputIndex; \
ret = GenOnesDataInt64(placeholder##intputIndex##_shape, tensor_placeholder##intputIndex, \
placeholder##intputIndex##_desc, value); \
if (ret != SUCCESS) { \
printf("%s - ERROR - [XIR]: Generate input data failed\n", GetTime().c_str()); \
return FAILED; \
} \
placeholder##intputIndex.update_input_desc_x(placeholder##intputIndex##_desc); \
input.push_back(tensor_placeholder##intputIndex); \
graph.AddOp(placeholder##intputIndex); \
add1.set_input_##intputName(placeholder##intputIndex); \
inputs.push_back(placeholder##intputIndex)
#define ADD_INPUT_ATTR(attrName, attrValue) add1.set_attr_##attrName(attrValue)
#define ADD_OUTPUT(outputIndex, outputName, outputDtype, outputShape) \
TensorDesc outputName##outputIndex##_desc = TensorDesc(ge::Shape(outputShape), FORMAT_ND, outputDtype); \
add1.update_output_desc_##outputName(outputName##outputIndex##_desc)
#define ADD_CONST_INPUT(intputIndex, intputName, intputDtype, inputShape, constValues) \
vector<int64_t> placeholder##intputIndex##_shape = inputShape; \
auto placeholder##intputIndex = op::Const("placeholder" + intputIndex); \
TensorDesc placeholder##intputIndex##_desc = TensorDesc(ge::Shape(placeholder##intputIndex##_shape), FORMAT_ND, \
intputDtype); \
placeholder##intputIndex##_desc.SetPlacement(ge::kPlacementHost); \
placeholder##intputIndex##_desc.SetFormat(FORMAT_ND); \
Tensor tensor_placeholder##intputIndex; \
ret = GenConstDataInt64(placeholder##intputIndex##_shape, tensor_placeholder##intputIndex, \
placeholder##intputIndex##_desc, constValues); \
if (ret != SUCCESS) { \
printf("%s - ERROR - [XIR]: Generate input data failed\n", GetTime().c_str()); \
return FAILED; \
} \
placeholder##intputIndex.SetAttr("value", tensor_placeholder##intputIndex); \
placeholder##intputIndex.update_output_desc_y(placeholder##intputIndex##_desc); \
graph.AddOp(placeholder##intputIndex); \
add1.set_input_##intputName(placeholder##intputIndex); \
add1.update_input_desc_##intputName(placeholder##intputIndex##_desc); \
inputs.push_back(placeholder##intputIndex)
#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)
{
uint32_t oneByte = 1;
uint32_t twoByte = 2;
uint32_t fourByte = 4;
uint32_t eightByte = 8;
if (dt == ge::DT_FLOAT) {
return fourByte;
} else if (dt == ge::DT_FLOAT16) {
return twoByte;
} else if (dt == ge::DT_BF16) {
return twoByte;
} else if (dt == ge::DT_INT32) {
return fourByte;
} else if (dt == ge::DT_INT64) {
return eightByte;
} else if (dt == ge::DT_INT16) {
return twoByte;
} else if (dt == ge::DT_INT8) {
return oneByte;
} else if (dt == ge::DT_UINT8) {
return oneByte;
} else if (dt == ge::DT_BOOL) {
return oneByte;
}
return oneByte;
}
int32_t GenOnesDataInt64(vector<int64_t> shapes, Tensor& input_tensor, TensorDesc& input_tensor_desc, int64_t value)
{
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 * sizeof(int64_t);
int64_t* pData = new (std::nothrow) int64_t[size];
for (size_t i = 0; i < size; ++i) {
*(pData + i) = value;
}
input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t*>(pData), data_len);
delete[] pData;
return SUCCESS;
}
int32_t GenConstDataInt64(vector<int64_t> shapes, Tensor& input_tensor, TensorDesc& input_tensor_desc,
const vector<int64_t>& values)
{
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 * sizeof(int64_t);
int64_t* pData = new (std::nothrow) int64_t[size];
for (size_t i = 0; i < size; ++i) {
*(pData + i) = (i < values.size()) ? values[i] : 0;
}
input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t*>(pData), data_len);
delete[] pData;
return SUCCESS;
}
int32_t WriteDataToFile(string bin_file, uint64_t data_size, uint8_t* inputData)
{
FILE* fp = fopen(bin_file.c_str(), "w");
fwrite(inputData, sizeof(uint8_t), data_size, fp);
fclose(fp);
return SUCCESS;
}
int CreateOppInGraph(std::vector<ge::Tensor>& input, std::vector<Operator>& inputs, std::vector<Operator>& outputs,
Graph& graph)
{
Status ret = SUCCESS;
auto add1 = op::StatelessRandom("stateless_random");
std::vector<int64_t> shapeInputShape = {2};
std::vector<int64_t> shapeValues = {4, 8};
std::vector<int64_t> scalarShape = {1};
std::vector<int64_t> outShape = {4, 8};
ADD_CONST_INPUT(1, shape, ge::DT_INT64, shapeInputShape, shapeValues);
ADD_INT_INPUT(2, seed, ge::DT_INT64, scalarShape, 42);
ADD_INT_INPUT(3, offset, ge::DT_INT64, scalarShape, 0);
ADD_INT_INPUT(4, from, ge::DT_INT64, scalarShape, 0);
ADD_INT_INPUT(5, to, ge::DT_INT64, scalarShape, 100);
ADD_INPUT_ATTR(dtype, ge::DT_INT32);
ADD_OUTPUT(1, y, ge::DT_INT32, outShape);
outputs.push_back(add1);
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 - 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{};
if (argc > 1) {
std::cout << argv[1] << std::endl;
}
ret = CreateOppInGraph(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 = {};
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());
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 - 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());
printf("\n========== INPUT SUMMARY ==========\n");
printf("Total inputs: %zu\n", input.size());
int input_num = input.size();
for (int i = 0; i < input_num; i++) {
printf("---------- Input %d ----------\n", i);
DataType dt = input[i].GetTensorDesc().GetDataType();
ge::Shape inShape = input[i].GetTensorDesc().GetShape();
size_t inDimNum = inShape.GetDimNum();
int64_t inShapeSize = inShape.GetShapeSize();
uint32_t elemSize = GetDataTypeSize(dt);
uint32_t dataBytes = inShapeSize * elemSize;
printf(" dtype : %d\n", dt);
printf(" shape : [");
for (size_t d = 0; d < inDimNum; d++) {
printf("%ld", inShape.GetDim(d));
if (d + 1 < inDimNum)
printf(", ");
}
printf("] (dims=%zu, elements=%ld)\n", inDimNum, inShapeSize);
printf(" format : %d\n", input[i].GetTensorDesc().GetFormat());
printf(" data size : %u bytes (%ld elems * %u bytes/elem)\n", dataBytes, inShapeSize, elemSize);
uint8_t* inData = input[i].GetData();
if (inData != nullptr && inShapeSize > 0) {
printf(" values : ");
if (dt == ge::DT_INT64) {
int64_t* vals = (int64_t*)inData;
for (int64_t j = 0; j < inShapeSize && j < 16; j++) {
printf("%ld", vals[j]);
if (j + 1 < inShapeSize && j + 1 < 16)
printf(", ");
}
}
if (inShapeSize > 16)
printf(" ... (%ld more)", inShapeSize - 16);
printf("\n");
}
string input_file = "./tc_ge_irrun_test_npu_input_" + std::to_string(i) + ".bin";
WriteDataToFile((const char*)input_file.c_str(), dataBytes, inData);
printf(" saved to : %s\n", input_file.c_str());
}
printf("\n========== OUTPUT SUMMARY ==========\n");
printf("Total outputs: %zu\n", output.size());
int output_num = output.size();
for (int i = 0; i < output_num; i++) {
printf("---------- Output %d ----------\n", i);
DataType dt = output[i].GetTensorDesc().GetDataType();
ge::Shape outShape = output[i].GetTensorDesc().GetShape();
size_t outDimNum = outShape.GetDimNum();
int64_t outShapeSize = outShape.GetShapeSize();
uint32_t elemSize = GetDataTypeSize(dt);
uint32_t dataBytes = outShapeSize * elemSize;
printf(" dtype : %d\n", dt);
printf(" inferred shape : [");
for (size_t d = 0; d < outDimNum; d++) {
printf("%ld", outShape.GetDim(d));
if (d + 1 < outDimNum)
printf(", ");
}
printf("] (dims=%zu, elements=%ld)\n", outDimNum, outShapeSize);
printf(" format : %d\n", output[i].GetTensorDesc().GetFormat());
printf(" data size : %u bytes (%ld elems * %u bytes/elem)\n", dataBytes, outShapeSize, elemSize);
string output_file = "./tc_ge_irrun_test_npu_output_" + std::to_string(i) + ".bin";
uint8_t* output_data_i = output[i].GetData();
WriteDataToFile((const char*)output_file.c_str(), dataBytes, output_data_i);
printf(" saved to : %s\n", output_file.c_str());
if (dt == ge::DT_INT32 && output_data_i != nullptr && outShapeSize > 0) {
int32_t* resultData = (int32_t*)output_data_i;
int32_t minVal = resultData[0], maxVal = resultData[0];
for (int64_t j = 0; j < outShapeSize; j++) {
if (resultData[j] < minVal)
minVal = resultData[j];
if (resultData[j] > maxVal)
maxVal = resultData[j];
}
printf(" --- Statistics ---\n");
printf(" min : %d\n", minVal);
printf(" max : %d\n", maxVal);
printf(" range check : all in [0, 100) ? %s\n", (minVal >= 0 && maxVal < 100) ? "YES" : "NO");
printf(" --- Values ---\n");
for (int64_t j = 0; j < outShapeSize && j < 64; j++) {
printf(" result[%ld] = %d\n", j, resultData[j]);
}
if (outShapeSize > 64) {
printf(" ... (%ld more values, see .bin file)\n", outShapeSize - 64);
}
} else if (dt == ge::DT_FLOAT && output_data_i != nullptr && outShapeSize > 0) {
float* resultData = (float*)output_data_i;
float minVal = resultData[0], maxVal = resultData[0];
double sum = 0.0;
for (int64_t j = 0; j < outShapeSize; j++) {
if (resultData[j] < minVal)
minVal = resultData[j];
if (resultData[j] > maxVal)
maxVal = resultData[j];
sum += resultData[j];
}
printf(" --- Statistics ---\n");
printf(" min : %.6f\n", minVal);
printf(" max : %.6f\n", maxVal);
printf(" mean : %.6f\n", sum / outShapeSize);
printf(" --- Values ---\n");
for (int64_t j = 0; j < outShapeSize && j < 64; j++) {
printf(" result[%ld] = %.8f\n", j, resultData[j]);
}
if (outShapeSize > 64) {
printf(" ... (%ld more values, see .bin file)\n", outShapeSize - 64);
}
}
}
printf("\n========== DIAGNOSTICS ==========\n");
ge::AscendString error_msg = ge::GEGetErrorMsgV2();
std::string error_str(error_msg.GetString());
printf("Error message : %s\n", error_str.empty() ? "(none)" : error_str.c_str());
ge::AscendString warning_msg = ge::GEGetWarningMsgV2();
std::string warning_str(warning_msg.GetString());
printf("Warning message: %s\n", warning_str.empty() ? "(none)" : warning_str.c_str());
printf("%s - INFO - [XIR]: Start to finalize ir graph session\n", GetTime().c_str());
delete session;
ret = ge::GEFinalize();
if (ret != SUCCESS) {
printf("%s - ERROR - [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;
}
