44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import time
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import torch
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import torch.utils.benchmark as benchmark
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from triton.testing import do_bench
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def benchmark_forward(fn, *inputs, repeats=10, desc='', verbose=True, **kwinputs):
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"""Use Pytorch Benchmark on the forward pass of an arbitrary function."""
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if verbose:
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print(desc, '- Forward pass')
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t = benchmark.Timer(
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stmt='fn(*inputs, **kwinputs)',
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globals={'fn': fn, 'inputs': inputs, 'kwinputs': kwinputs},
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num_threads=torch.get_num_threads(),
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)
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m = t.timeit(repeats)
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if verbose:
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print(m)
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return t, m
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torch.manual_seed(0)
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repeats = 30
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dtype = torch.float16
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device = 'cuda'
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verbose = False
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m, n = 8192, 8192
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tflops_matmul = {}
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tflops_matmul1 = {}
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for k in [512, 1024, 1536, 2048, 2560, 3072, 3584, 4096, 4608, 5120, 5632, 6144, 6656, 7168, 7680, 8192]:
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a = torch.randn(m, k, device=device, dtype=dtype)
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b = torch.randn(n, k, device=device, dtype=dtype).transpose(-1, -2)
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nFLOPS_matmul = 2 * m * n * k
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time.sleep(2) # to reduce power throttling
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timing = benchmark_forward(torch.matmul, a, b, desc='cuBLAS', verbose=verbose, repeats=repeats)[1]
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tflops_matmul[k] = nFLOPS_matmul / timing.mean * 1e-12
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print(f'[torch.utils.benchmark] cuBLAS, {m = }, {n = }, {k = }: {timing.mean * 1e3:.3f}ms, {tflops_matmul[k]:.1f} TFLOPS')
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time.sleep(2) # to reduce power throttling
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ms = do_bench(lambda: torch.matmul(a, b), warmup=10, rep=repeats)
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tflops_matmul1[k] = nFLOPS_matmul / ms * 1e-9
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print(f'[triton.test.do_bench] cuBLAS, {m = }, {n = }, {k = }: {ms:.3f}ms, {tflops_matmul1[k]:.1f} TFLOPS')
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