benchmarks/conv2d.py [6:23]:
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@torch.inference_mode()
def benchmark_torch_function(iters, f, *args):
    f(*args)
    if torch.cuda.is_available():
        torch.cuda.synchronize()
        start_event = torch.cuda.Event(enable_timing=True)
        end_event = torch.cuda.Event(enable_timing=True)
        start_event.record()
    else:
        t0 = time.time()
    for _ in range(iters):
        f(*args)
    if torch.cuda.is_available():
        end_event.record()
        torch.cuda.synchronize()
        return start_event.elapsed_time(end_event)
    else:
        return (time.time() - t0) * 1e3
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benchmarks/linear.py [6:23]:
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@torch.inference_mode()
def benchmark_torch_function(iters, f, *args):
    f(*args)
    if torch.cuda.is_available():
        torch.cuda.synchronize()
        start_event = torch.cuda.Event(enable_timing=True)
        end_event = torch.cuda.Event(enable_timing=True)
        start_event.record()
    else:
        t0 = time.time()
    for _ in range(iters):
        f(*args)
    if torch.cuda.is_available():
        end_event.record()
        torch.cuda.synchronize()
        return start_event.elapsed_time(end_event)
    else:
        return (time.time() - t0) * 1e3
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