* Copyright (c) 2025-2026 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_uniform_proto.h"
#define FAILED -1
#define SUCCESS 0
using namespace ge;
using std::map;
using std::string;
using std::vector;
#define ADD_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 = GenOnesDataFloat32(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_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_DOUBLE_INPUT(intputIndex, intputName, 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, ge::DT_DOUBLE); \
placeholder##intputIndex##_desc.SetPlacement(ge::kPlacementHost); \
placeholder##intputIndex##_desc.SetFormat(FORMAT_ND); \
Tensor tensor_placeholder##intputIndex; \
ret = GenOnesDataDouble(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_DOUBLE) {
return eightByte;
} else if (dt == ge::DT_INT32) {
return fourByte;
} else if (dt == ge::DT_INT64) {
return eightByte;
} else if (dt == ge::DT_UINT64) {
return eightByte;
} else if (dt == ge::DT_INT8) {
return oneByte;
}
return oneByte;
}
int32_t GenOnesDataFloat32(vector<int64_t> shapes, Tensor &input_tensor, TensorDesc &input_tensor_desc, float 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(float);
float *pData = new (std::nothrow) float[size];
for (size_t i = 0; i < size; ++i) {
*(pData + i) = value;
}
input_tensor = Tensor(input_tensor_desc, (uint8_t *)pData, data_len);
delete[] pData;
return SUCCESS;
}
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 GenOnesDataDouble(vector<int64_t> shapes, Tensor &input_tensor, TensorDesc &input_tensor_desc, double 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(double);
double *pData = new (std::nothrow) double[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::StatelessUniform("add1");
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, 12345);
ADD_INT_INPUT(3, offset, ge::DT_INT64, scalarShape, 0);
ADD_DOUBLE_INPUT(4, from, scalarShape, 0.0);
ADD_DOUBLE_INPUT(5, to, scalarShape, 1.0);
ADD_INPUT_ATTR(dtype, 0);
ADD_OUTPUT(1, y, ge::DT_FLOAT, 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 - 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{};
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 - 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());
printf("\n========== INPUT SUMMARY ==========\n");
printf("Total inputs: %zu\n", input.size());
int input_num = input.size();
const char* dtypeNames[] = {
"FLOAT(0)", "FLOAT16(1)", "INT8(2)", "INT32(3)", "UINT8(4)", "",
"INT16(6)", "UINT16(7)", "UINT32(8)", "INT64(9)", "UINT64(10)",
"DOUBLE(11)", "BOOL(12)", "", "UINT1(14)", "", "", "", "", "", "",
"", "", "", "", "", "", "BF16(27)"
};
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", dt);
if (dt >= 0 && dt <= 27 && strlen(dtypeNames[dt]) > 0) {
printf(" (%s)", dtypeNames[dt]);
}
printf("\n");
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(", ");
}
} else if (dt == ge::DT_DOUBLE) {
double *vals = (double*)inData;
for (int64_t j = 0; j < inShapeSize && j < 16; j++) {
printf("%.6f", vals[j]);
if (j + 1 < inShapeSize && j + 1 < 16) printf(", ");
}
} else if (dt == ge::DT_FLOAT) {
float *vals = (float*)inData;
for (int64_t j = 0; j < inShapeSize && j < 16; j++) {
printf("%.6f", vals[j]);
if (j + 1 < inShapeSize && j + 1 < 16) printf(", ");
}
} else if (dt == ge::DT_INT32) {
int32_t *vals = (int32_t*)inData;
for (int64_t j = 0; j < inShapeSize && j < 16; j++) {
printf("%d", 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", dt);
if (dt >= 0 && dt <= 27 && strlen(dtypeNames[dt]) > 0) {
printf(" (%s)", dtypeNames[dt]);
}
printf("\n");
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_FLOAT && output_data_i != nullptr && outShapeSize > 0) {
float *resultData = (float*)output_data_i;
float minVal = resultData[0], maxVal = resultData[0];
double sum = 0.0;
int nanCount = 0, infCount = 0;
for (int64_t j = 0; j < outShapeSize; j++) {
float v = resultData[j];
if (std::isnan(v)) { nanCount++; continue; }
if (std::isinf(v)) { infCount++; continue; }
if (v < minVal) minVal = v;
if (v > maxVal) maxVal = v;
sum += v;
}
printf(" --- Statistics ---\n");
printf(" min : %.6f\n", minVal);
printf(" max : %.6f\n", maxVal);
printf(" mean : %.6f\n", sum / outShapeSize);
printf(" range check : all in [0, 1) ? %s\n",
(minVal >= 0.0f && maxVal < 1.0f && nanCount == 0) ? "YES" : "NO");
if (nanCount > 0) printf(" NaN count : %d\n", nanCount);
if (infCount > 0) printf(" Inf count : %d\n", infCount);
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);
}
} else if (dt == ge::DT_FLOAT16 && output_data_i != nullptr && outShapeSize > 0) {
printf(" --- Values (fp16 raw hex) ---\n");
uint16_t *fp16Data = (uint16_t*)output_data_i;
for (int64_t j = 0; j < outShapeSize && j < 32; j++) {
printf(" result[%ld] = 0x%04X\n", j, fp16Data[j]);
}
}
}
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 - 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;
}
