GPUCodeForces/S1/uucoco_#7/run_code.py

78 lines
2.5 KiB
Python

import torch
import time
from MinkowskiDistance_torch import Model, get_inputs, get_init_inputs
from MinkowskiDistance_cuda import ModelNew
def run_benchmark():
if not torch.cuda.is_available():
print("CUDA 不可用")
return
device = torch.device("cuda")
# 准备输入数据
inputs = [x.cuda(device=device) for x in get_inputs()]
init_inputs = [x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in get_init_inputs()]
# 初始化模型
torch_model = Model(*init_inputs).cuda()
cuda_model = ModelNew(*init_inputs).cuda()
torch_model.eval()
cuda_model.eval()
print("-------------------- 精度对齐验证 --------------------")
with torch.no_grad():
# 预热GPU
_ = torch_model(*inputs)
_ = cuda_model(*inputs)
# 正式测试
output_torch = torch_model(*inputs)
output_cuda = cuda_model(*inputs)
# 精度验证
abs_diff = torch.abs(output_torch - output_cuda)
max_diff = torch.max(abs_diff).item()
mean_diff = torch.mean(abs_diff).item()
if max_diff < 1e-4 and mean_diff < 1e-5:
print(f"✅ 精度对齐:最大误差 {max_diff:.6f},平均误差 {mean_diff:.6f}")
precision_flag = True
else:
print(f"❌ 精度不一致:最大误差 {max_diff:.6f},平均误差 {mean_diff:.6f}")
precision_flag = False
print("\n-------------------- 性能加速比测试 --------------------")
num_iterations = 100
# 预热GPU
for _ in range(10):
_ = torch_model(*inputs)
_ = cuda_model(*inputs)
# PyTorch模型计时
torch.cuda.synchronize()
start_time = time.time()
for _ in range(num_iterations):
_ = torch_model(*inputs)
torch.cuda.synchronize()
torch_time = (time.time() - start_time) / num_iterations
# 自定义CUDA内核计时
torch.cuda.synchronize()
start_time = time.time()
for _ in range(num_iterations):
_ = cuda_model(*inputs)
torch.cuda.synchronize()
cuda_time = (time.time() - start_time) / num_iterations
print(f"PyTorch内置Swish平均执行时间: {torch_time:.6f}")
print(f"自定义CUDA Swish平均执行时间: {cuda_time:.6f}")
speedup = torch_time / cuda_time if cuda_time > 0 else 0
print(f"加速比 (Speedup): {speedup:.2f}x")
return precision_flag, speedup
if __name__ == "__main__":
precision_flag, speedup = run_benchmark()