forked from ccf-ai-infra/GPUCodeForces
101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
###########################################################
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# 性能和精度验证程序
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###########################################################
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import torch
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import torch.nn as nn
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import time
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from batchnorm1d_torch import Model as TorchModel, get_inputs, get_init_inputs
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from batchnorm1d_cuda import ModelNew as CudaModel
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def run_benchmark():
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# 检查 CUDA 是否可用
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if not torch.cuda.is_available():
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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。")
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return
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else:
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device = torch.device("cuda")
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# 初始化模型
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# 获取 torch 版本的初始化参数
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torch_init_inputs = get_init_inputs()
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weight = torch_init_inputs[0].cuda(device=device)
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# 为 BatchNorm 准备参数
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batch_size = 16
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input_dim = 1024
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output_dim = 2048
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bn_weight = torch.ones(output_dim, device=device, dtype=torch.float32)
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bn_bias = torch.zeros(output_dim, device=device, dtype=torch.float32)
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# 初始化输入数据
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inputs = get_inputs()
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inputs = [x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs]
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# 初始化两个模型
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track_bool = True
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torch_model = TorchModel(weight.clone(), num_features=output_dim, eps=1e-5, track_running_stats=track_bool).cuda()
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cuda_model = CudaModel(weight.clone(), bn_weight.clone(), bn_bias.clone(), eps=1e-5, track_running_stats=track_bool).cuda()
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torch_model.eval()
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cuda_model.eval()
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print("-------------------- 精度对齐验证 --------------------")
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with torch.no_grad():
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output_torch = torch_model(*inputs)
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output_cuda = cuda_model(*inputs)
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# 更严格的精度检查
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abs_diff = (output_torch - output_cuda).abs()
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max_diff = abs_diff.max().item()
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mean_diff = abs_diff.mean().item()
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print(f"最大差异: {max_diff:.6f}")
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print(f"平均差异: {mean_diff:.6f}")
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precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-03, atol=1e-03)
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if precision_flag:
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print("✅ 精度对齐：两个模型的输出结果非常接近。")
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else:
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print("❌ 精度不一致！")
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print("\n-------------------- 性能加速比测试 --------------------")
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print(f"track_running_stats = {track_bool}")
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num_iterations = 1000 # 增加迭代次数以获得更准确的时间测量
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# Warm up
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print("预热中...")
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for _ in range(100):
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with torch.no_grad():
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_ = torch_model(*inputs)
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_ = cuda_model(*inputs)
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# PyTorch 模型计时
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torch.cuda.synchronize()
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start_time = time.time()
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with torch.no_grad():
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for _ in range(num_iterations):
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_ = torch_model(*inputs)
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torch.cuda.synchronize()
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torch_time = (time.time() - start_time) / num_iterations
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# 自定义 CUDA 内核计时
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torch.cuda.synchronize()
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start_time = time.time()
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with torch.no_grad():
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for _ in range(num_iterations):
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_ = cuda_model(*inputs)
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torch.cuda.synchronize()
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cuda_time = (time.time() - start_time) / num_iterations
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print(f"PyTorch 平均执行时间: {torch_time*1000:.4f} 毫秒")
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print(f"自定义 CUDA BatchNorm 平均执行时间: {cuda_time*1000:.4f} 毫秒")
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speedup = 0
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if cuda_time > 0:
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speedup = torch_time / cuda_time
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print(f"加速比 (Speedup): {speedup:.2f}x")
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else:
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print("CUDA 内核执行时间为0，无法计算加速比。")
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return precision_flag, speedup
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if __name__ == "__main__":
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precision_flag, speedup = run_benchmark() |