Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add coefficient of variance to the bench mark report. #1554

Merged
merged 1 commit into from
Jul 8, 2024
Merged

Add coefficient of variance to the bench mark report. #1554

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 15 additions & 5 deletions benchmarks/xetla_benchmark/benchmark_testing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -102,6 +102,11 @@ def extract_kernels(funcs):
times = torch.tensor([sum([k.duration for k in ks]) * 1e-3 for ks in kernels], dtype=torch.float)
if quantiles is not None:
ret = torch.quantile(times, torch.tensor(quantiles, dtype=torch.float)).tolist()
# add coefficient of the variance.
std = torch.std(times)
mean = torch.mean(times)
cv = std / mean
ret.extend([mean.tolist(), cv.tolist()])
if len(ret) == 1:
ret = ret[0]
return ret
Expand Down Expand Up @@ -240,6 +245,7 @@ def _run(self, bench: Benchmark, save_path: str, show_plots: bool, print_data: b
y_min = [f'{x}-{label}-min' for x in bench.line_names]
y_max = [f'{x}-{label}-max' for x in bench.line_names]
y_vals += y_mean + y_min + y_max
y_vals += [f'{x}-CV' for x in bench.line_names]
x_names = list(bench.x_names)
df = pd.DataFrame(columns=x_names + y_vals)
for x in bench.x_vals:
Expand All @@ -252,11 +258,11 @@ def _run(self, bench: Benchmark, save_path: str, show_plots: bool, print_data: b
x_args = dict(zip(x_names, x))

row_vals = {}
for label in bench.ylabel:
for label in itertools.chain(bench.ylabel, ["CV"]):
row_vals[label] = ([], [], [])
for y in bench.line_vals:
ret = self.fn(**x_args, **{bench.line_arg: y}, **bench.args, **kwrags)
for id, label in enumerate(bench.ylabel):
for id, label in enumerate(itertools.chain(bench.ylabel, ["CV"])):
try:
y_mean, y_min, y_max = ret[id]
except TypeError:
Expand All @@ -266,9 +272,13 @@ def _run(self, bench: Benchmark, save_path: str, show_plots: bool, print_data: b
row_vals[label][2].append(y_max)
rows = []
for label in bench.ylabel:
rows += row_vals[label][0]
rows += row_vals[label][1]
rows += row_vals[label][2]
if len(row_vals[label][0]) > 0:
rows += row_vals[label][0]
if len(row_vals[label][1]) > 0:
rows += row_vals[label][1]
if len(row_vals[label][2]) > 0:
rows += row_vals[label][2]
rows += row_vals["CV"][0]
df.loc[len(df)] = list(x) + rows

if bench.plot_name:
Expand Down
18 changes: 10 additions & 8 deletions benchmarks/xetla_benchmark/fused_softmax.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -130,28 +130,30 @@ def benchmark(M, N, provider):
x = torch.randn(M, N, device='xpu', dtype=torch.bfloat16)
quantiles = [0.5, 0.0, 1.0]
if provider == 'torch-native':
ms, min_ms, max_ms = benchmark_suit.do_bench(lambda: torch.softmax(x, axis=-1), quantiles=quantiles, warmup=10,
rep=10)
ms, min_ms, max_ms, mean, cv = benchmark_suit.do_bench(lambda: torch.softmax(x, axis=-1), quantiles=quantiles,
warmup=10, rep=10)
if provider == 'triton':
triton_fn = lambda: softmax(x)
torch_fn = lambda: torch.softmax(x, axis=-1)
benchmark_suit.assert_close(triton_fn(), torch_fn(), err_msg="triton to torch")
ms, min_ms, max_ms = benchmark_suit.do_bench(triton_fn, quantiles=quantiles, warmup=10, rep=10)
ms, min_ms, max_ms, mean, cv = benchmark_suit.do_bench(triton_fn, quantiles=quantiles, warmup=10, rep=10)

if provider == 'torch-jit':
ms, min_ms, max_ms = benchmark_suit.do_bench(lambda: naive_softmax(x), quantiles=quantiles, warmup=10, rep=10)
ms, min_ms, max_ms, mean, cv = benchmark_suit.do_bench(lambda: naive_softmax(x), quantiles=quantiles, warmup=10,
rep=10)

if provider == 'xetla':
name = "softmax_shape_{}_{}".format(M, N)
func = getattr(xetla_kernel, name)
xetla_fn = lambda: func(x, 0)
torch_fn = lambda: torch.softmax(x, axis=-1)
# benchmark_suit.assert_close(xetla_fn(), torch_fn(), err_msg="xetla to torch")
ms, min_ms, max_ms = benchmark_suit.do_bench(xetla_fn, quantiles=quantiles, warmup=10, rep=10)
ms, min_ms, max_ms, mean, cv = benchmark_suit.do_bench(xetla_fn, quantiles=quantiles, warmup=10, rep=10)

gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3)
tflops = lambda ms: 4 * x.nelement() * 1e-12 / (ms * 1e-3) # reduce-max, reduce-sum, elem-wise sub, elem-wise div
return (gbps(ms), gbps(max_ms), gbps(min_ms)), (tflops(ms), tflops(max_ms), tflops(min_ms))
gbps = lambda mean: 2 * x.nelement() * x.element_size() * 1e-9 / (mean * 1e-3)
tflops = lambda mean: 4 * x.nelement() * 1e-12 / (mean * 1e-3
) # reduce-max, reduce-sum, elem-wise sub, elem-wise div
return (gbps(mean), gbps(max_ms), gbps(min_ms)), (tflops(mean), tflops(max_ms), tflops(min_ms)), cv


if __name__ == "__main__":
Expand Down