forked from ccf-ai-infra/GPUCodeForces
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
import torch
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import time
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from matmul_torchcode import Model, get_inputs, get_init_inputs
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from matmul_cudacode import ModelNew
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def run_benchmark():
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if not torch.cuda.is_available():
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print("CUDA is not available")
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return
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device = torch.device("cuda")
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# Prepare input data
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inputs = [x.cuda(device=device) for x in get_inputs()]
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init_inputs = [x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in get_init_inputs()]
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# Initialize models
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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("-------------------- Precision Check --------------------")
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with torch.no_grad():
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# Warm-up GPU
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_ = torch_model(*inputs)
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_ = cuda_model(*inputs)
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# Formal test
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output_torch = torch_model(*inputs)
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output_cuda = cuda_model(*inputs)
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# Precision validation
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abs_diff = torch.abs(output_torch - output_cuda)
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max_diff = torch.max(abs_diff).item()
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mean_diff = torch.mean(abs_diff).item()
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if max_diff < 1e-4 and mean_diff < 1e-5:
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print(f"✅ Precision aligned: max error {max_diff:.6f}, mean error {mean_diff:.6f}")
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precision_flag = True
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else:
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print(f"❌ Precision mismatch: max error {max_diff:.6f}, mean error {mean_diff:.6f}")
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precision_flag = False
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print("\n-------------------- Performance Speedup Test --------------------")
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num_iterations = 100
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# Warm-up GPU
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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 model timing
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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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# Custom CUDA kernel timing
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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 built-in MatMul average execution time: {torch_time:.6f}s")
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print(f"Custom CUDA MatMul average execution time: {cuda_time:.6f}s")
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speedup = torch_time / cuda_time if cuda_time > 0 else 0
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print(f"Speedup: {speedup:.2f}x")
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return precision_flag, speedup
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if __name__ == "__main__":
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precision_flag, speedup = run_benchmark() |