forked from ccf-ai-infra/GPUCodeForces
87 lines
3.0 KiB
Python
87 lines
3.0 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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# --- 修改点 1: 更新 import 语句，指向我们新的文件 ---
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from focalloss_fused_torchcode import Model, get_inputs, get_init_inputs
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from focalloss_fused_cudacode import ModelNew
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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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init_inputs = get_init_inputs()
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init_inputs = [
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x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs
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]
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inputs = get_inputs()
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inputs = [
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x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs
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]
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torch_model = Model(*init_inputs).cuda()
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cuda_model = ModelNew(*init_inputs).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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precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-03, atol=1e-05)
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max_diff = torch.max(torch.abs(output_torch - output_cuda)).item()
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mean_diff = torch.mean(torch.abs(output_torch - output_cuda)).item()
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if precision_flag:
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print(f"✅ 精度对齐：两个模型的输出结果非常接近。")
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print(f"最大误差: {max_diff:.8f}, 平均误差: {mean_diff:.8f}")
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else:
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print(f"❌ 精度不一致！最大误差: {max_diff:.8f}, 平均误差: {mean_diff:.8f}")
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print("\n-------------------- 性能加速比测试 --------------------")
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num_iterations = 100
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# GPU 预热
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for _ in range(10):
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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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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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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 基准 Focal Loss 平均执行时间: {torch_time:.6f} 秒")
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print(f"自定义 Fused Focal Loss 平均执行时间: {cuda_time:.6f} 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:.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()
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