import torch
import time
from huberloss_torch import Model, get_inputs, get_init_inputs
from huberloss_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)

    # 更严格的精度检查
    abs_diff = (output_torch - output_cuda).abs()
    max_diff = abs_diff.max().item()
    mean_diff = abs_diff.mean().item()
    
    print(f"最大差异: {max_diff:.6f}")
    print(f"平均差异: {mean_diff:.6f}")
    
    precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-05, atol=1e-05)
    if precision_flag:
        print("✅ 精度对齐：两个模型的输出结果非常接近。")
    else:
        print("❌ 精度不一致！")

    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HuberLoss平均执行时间: {torch_time:.6f}秒")
    print(f"自定义CUDA HuberLoss平均执行时间: {cuda_time:.6f}秒")
    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()