Delete S1/12/.ipynb_checkpoints/run_code-checkpoint.py

S1/12/.ipynb_checkpoints/run_code-checkpoint.py

@@ -1,88 +0,0 @@
-###########################################################
-# 性能和精度验证程序
-###########################################################
-import torch
-import torch.nn as nn
-import time
-from tripletmarginloss_torch import Model, get_inputs, get_init_inputs
-from tripletmarginloss_cuda import ModelNew
-
-def run_benchmark():
-    # 检查 CUDA 是否可用
-    if not torch.cuda.is_available():
-        print("CUDA 不可用，请确保您有可用的 NVIDIA GPU 并已正确安装 PyTorch CUDA 版本。")
-        return
-    else:
-        device = torch.device("cuda")
-
-    # 初始化模型
-    init_inputs = get_init_inputs()
-    init_inputs = [
-        x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs
-    ]
-    inputs = get_inputs()
-    inputs = [
-        x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs
-    ]
-
-    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():
-        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 = 1000  # 增加迭代次数以获得更准确的时间测量
-    
-    # Warm up
-    for _ in range(100):
-        _ = 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 (matmul + relu) 平均执行时间: {torch_time:.6f} 秒")
-    print(f"自定义 CUDA ReLU 平均执行时间: {cuda_time:.6f} 秒")
-    speedup = 0
-    if cuda_time > 0:
-        speedup = torch_time / cuda_time
-        print(f"加速比 (Speedup): {speedup:.2f}x")
-    else:
-        print("CUDA 内核执行时间为0，无法计算加速比。")
-    return precision_flag, speedup
-
-if __name__ == "__main__":
-    precision_flag, speedup = run_benchmark()