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fanchao yue (i25168)
2025-08-29 15:32:38 +08:00
parent 9cbd9c7ff5
commit 18ea3be3c0
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#include "test_utils.h"
#include "performance_utils.h"
#include "yaml_reporter.h"
#include <iostream>
#include <vector>
#include <iomanip>
// ============================================================================
// 实现标记宏 - 参赛者修改实现时请将此宏设为0
// ============================================================================
#ifndef USE_DEFAULT_REF_IMPL
#define USE_DEFAULT_REF_IMPL 1 // 1=默认实现, 0=参赛者自定义实现
#endif
#if USE_DEFAULT_REF_IMPL
#include <thrust/reduce.h>
#include <thrust/device_vector.h>
#include <thrust/execution_policy.h>
#include <thrust/functional.h>
#endif
// 误差容忍度
constexpr double REDUCE_ERROR_TOLERANCE = 0.005; // 0.5%
// ============================================================================
// ReduceSum算法实现接口
// 参赛者需要替换Thrust实现为自己的高性能kernel
// ============================================================================
template <typename InputT = float, typename OutputT = float>
class ReduceSumAlgorithm {
public:
// 主要接口函数 - 参赛者需要实现这个函数
void reduce(const InputT* d_in, OutputT* d_out, int num_items, OutputT init_value) {
#if !USE_DEFAULT_REF_IMPL
// ========================================
// 参赛者自定义实现区域
// ========================================
// TODO: 参赛者在此实现自己的高性能归约算法
// 示例：参赛者可以调用1个或多个自定义kernel
// blockReduceKernel<<<grid, block>>>(d_in, temp_results, num_items, init_value);
// finalReduceKernel<<<1, block>>>(temp_results, d_out, grid.x);
#else
// ========================================
// 默认基准实现
// ========================================
auto input_ptr = thrust::device_pointer_cast(d_in);
auto output_ptr = thrust::device_pointer_cast(d_out);
// 直接使用thrust::reduce进行归约
*output_ptr = thrust::reduce(
thrust::device,
input_ptr,
input_ptr + num_items,
static_cast<OutputT>(init_value)
);
#endif
}
// 获取当前实现状态
static const char* getImplementationStatus() {
#if USE_DEFAULT_REF_IMPL
return "DEFAULT_REF_IMPL";
#else
return "CUSTOM_IMPL";
#endif
}
private:
// 参赛者可以在这里添加辅助函数和成员变量
// 例如：中间结果缓冲区、多阶段归约等
};
// ============================================================================
// 测试和性能评估
// ============================================================================
bool testCorrectness() {
std::cout << "ReduceSum 正确性测试..." << std::endl;
TestDataGenerator generator;
ReduceSumAlgorithm<float, float> algorithm;
bool allPassed = true;
// 测试不同数据规模
for (int i = 0; i < NUM_TEST_SIZES && i < 2; i++) { // 限制测试规模
int size = std::min(TEST_SIZES[i], 10000);
std::cout << " 测试规模: " << size << std::endl;
// 测试普通数据
{
auto data = generator.generateRandomFloats(size, -10.0f, 10.0f);
float init_value = 1.0f;
// CPU参考计算
double cpu_result = cpuReduceSum(data, static_cast<double>(init_value));
// GPU计算
float *d_in;
float *d_out;
MACA_CHECK(mcMalloc(&d_in, size * sizeof(float)));
MACA_CHECK(mcMalloc(&d_out, sizeof(float)));
MACA_CHECK(mcMemcpy(d_in, data.data(), size * sizeof(float), mcMemcpyHostToDevice));
algorithm.reduce(d_in, d_out, size, init_value);
float gpu_result;
MACA_CHECK(mcMemcpy(&gpu_result, d_out, sizeof(float), mcMemcpyDeviceToHost));
// 验证误差
double relative_error = std::abs(gpu_result - cpu_result) / std::abs(cpu_result);
if (relative_error > REDUCE_ERROR_TOLERANCE) {
std::cout << " 失败: 误差过大 " << relative_error << std::endl;
allPassed = false;
} else {
std::cout << " 通过 (误差: " << relative_error << ")" << std::endl;
}
mcFree(d_in);
mcFree(d_out);
}
// 测试特殊值 (NaN, Inf)
if (size > 100) {
std::cout << " 测试特殊值..." << std::endl;
auto data = generator.generateSpecialFloats(size);
float init_value = 0.0f;
double cpu_result = cpuReduceSum(data, static_cast<double>(init_value));
float *d_in;
float *d_out;
MACA_CHECK(mcMalloc(&d_in, size * sizeof(float)));
MACA_CHECK(mcMalloc(&d_out, sizeof(float)));
MACA_CHECK(mcMemcpy(d_in, data.data(), size * sizeof(float), mcMemcpyHostToDevice));
algorithm.reduce(d_in, d_out, size, init_value);
float gpu_result;
MACA_CHECK(mcMemcpy(&gpu_result, d_out, sizeof(float), mcMemcpyDeviceToHost));
// 对于包含特殊值的情况，检查是否正确处理
if (std::isfinite(cpu_result) && std::isfinite(gpu_result)) {
double relative_error = std::abs(gpu_result - cpu_result) / std::abs(cpu_result);
if (relative_error > REDUCE_ERROR_TOLERANCE) {
std::cout << " 失败: 特殊值处理错误" << std::endl;
allPassed = false;
} else {
std::cout << " 通过 (特殊值处理)" << std::endl;
}
} else {
std::cout << " 通过 (特殊值结果)" << std::endl;
}
mcFree(d_in);
mcFree(d_out);
}
}
return allPassed;
}
void benchmarkPerformance() {
PerformanceDisplay::printReduceSumHeader();
TestDataGenerator generator;
PerformanceMeter meter;
ReduceSumAlgorithm<float, float> algorithm;
const int WARMUP_ITERATIONS = 5;
const int BENCHMARK_ITERATIONS = 10;
// 用于YAML报告的数据收集
std::vector<std::map<std::string, std::string>> perf_data;
for (int i = 0; i < NUM_TEST_SIZES; i++) {
int size = TEST_SIZES[i];
// 生成测试数据
auto data = generator.generateRandomFloats(size);
float init_value = 0.0f;
// 分配GPU内存
float *d_in;
float *d_out;
MACA_CHECK(mcMalloc(&d_in, size * sizeof(float)));
MACA_CHECK(mcMalloc(&d_out, sizeof(float)));
MACA_CHECK(mcMemcpy(d_in, data.data(), size * sizeof(float), mcMemcpyHostToDevice));
// Warmup阶段
for (int iter = 0; iter < WARMUP_ITERATIONS; iter++) {
algorithm.reduce(d_in, d_out, size, init_value);
}
// 正式测试阶段
float total_time = 0;
for (int iter = 0; iter < BENCHMARK_ITERATIONS; iter++) {
meter.startTiming();
algorithm.reduce(d_in, d_out, size, init_value);
total_time += meter.stopTiming();
}
float avg_time = total_time / BENCHMARK_ITERATIONS;
// 计算性能指标
auto metrics = PerformanceCalculator::calculateReduceSum(size, avg_time);
// 显示性能数据
PerformanceDisplay::printReduceSumData(size, avg_time, metrics);
// 收集YAML报告数据
auto entry = YAMLPerformanceReporter::createEntry();
entry["data_size"] = std::to_string(size);
entry["time_ms"] = std::to_string(avg_time);
entry["throughput_gps"] = std::to_string(metrics.throughput_gps);
entry["data_type"] = "float";
perf_data.push_back(entry);
mcFree(d_in);
mcFree(d_out);
}
// 生成YAML性能报告
YAMLPerformanceReporter::generateReduceSumYAML(perf_data, "reduce_sum_performance.yaml");
PerformanceDisplay::printSavedMessage("reduce_sum_performance.yaml");
}
// ============================================================================
// 主函数
// ============================================================================
int main(int argc, char* argv[]) {
std::cout << "=== ReduceSum 算法测试 ===" << std::endl;
// 检查参数
std::string mode = "all";
if (argc > 1) {
mode = argv[1];
}
bool correctness_passed = true;
bool performance_completed = true;
try {
if (mode == "correctness" || mode == "all") {
correctness_passed = testCorrectness();
}
if (mode == "performance" || mode == "all") {
if (correctness_passed || mode == "performance") {
benchmarkPerformance();
} else {
std::cout << "跳过性能测试，因为正确性测试未通过" << std::endl;
performance_completed = false;
}
}
std::cout << "\n=== 测试完成 ===" << std::endl;
std::cout << "实现状态: " << ReduceSumAlgorithm<float, float>::getImplementationStatus() << std::endl;
if (mode == "all") {
std::cout << "正确性: " << (correctness_passed ? "通过" : "失败") << std::endl;
std::cout << "性能测试: " << (performance_completed ? "完成" : "跳过") << std::endl;
}
return correctness_passed ? 0 : 1;
} catch (const std::exception& e) {
std::cerr << "测试出错: " << e.what() << std::endl;
return 1;
}
}