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