* 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_lamb_apply_optimizer_assign.cpp
* \brief
*/
#include <iostream>
#include <fstream>
#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 "../op_graph/lamb_apply_optimizer_assign_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) \
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, 2); \
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); \
placeholder##intputIndex.update_output_desc_y(placeholder##intputIndex##_desc); \
input.push_back(tensor_placeholder##intputIndex); \
graph.AddOp(placeholder##intputIndex); \
lambOp1.set_input_##intputName(placeholder##intputIndex); \
inputs.push_back(placeholder##intputIndex)
#define ADD_OUTPUT(outputIndex, outputName, outputDtype, outputShape) \
TensorDesc outputName##outputIndex##_desc_ = TensorDesc(ge::Shape(outputShape), FORMAT_ND, outputDtype); \
lambOp1.update_output_desc_##outputName(outputName##outputIndex##_desc)
#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 dilation = 1;
if (dt == ge::DT_FLOAT) {
dilation = 4;
} else if (dt == ge::DT_FLOAT16 || dt == ge::DT_BF16) {
dilation = 2;
} else if (dt == ge::DT_INT32) {
dilation = 4;
}
return dilation;
}
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 * 4;
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);
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(
DataType inDtype, std::vector<ge::Tensor>& input, std::vector<Operator>& inputs, std::vector<Operator>& outputs,
Graph& graph)
{
Status ret = SUCCESS;
auto lambOp1 = op::LambApplyOptimizerAssign("lambApplyOptimizerAssign1");
std::vector<int64_t> fullShape = {2, 16};
std::vector<int64_t> scalarShape = {1};
ADD_INPUT(1, grad, inDtype, fullShape);
ADD_INPUT(2, inputv, inDtype, fullShape);
ADD_INPUT(3, inputm, inDtype, fullShape);
ADD_INPUT(4, input3, inDtype, fullShape);
ADD_INPUT(5, mul0_x, inDtype, scalarShape);
ADD_INPUT(6, mul1_x, inDtype, scalarShape);
ADD_INPUT(7, mul2_x, inDtype, scalarShape);
ADD_INPUT(8, mul3_x, inDtype, scalarShape);
ADD_INPUT(9, add2_y, inDtype, scalarShape);
ADD_INPUT(10, steps, inDtype, scalarShape);
ADD_INPUT(11, do_use_weight, inDtype, scalarShape);
ADD_INPUT(12, weight_decay_rate, inDtype, scalarShape);
outputs.push_back(lambOp1);
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{};
DataType inDtype = DT_FLOAT;
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());
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());
ge::AscendString error_msg = ge::GEGetErrorMsgV2();
std::cout << "Error message: " << std::string(error_msg.GetString()) << std::endl;
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;
}