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[TESTING] use torch.int for autotuning cache (#840)

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For stupid reasons, ops on int8 are 3 times slower than on int, and for
another set of stupid reasons we are not using cudaMemset for `zero_`,
so using `int8` buffer in `do_bench` makes it slow.

Co-authored-by: Philippe Tillet <phil@openai.com>
This commit is contained in:
Natalia Gimelshein
2022-11-04 18:05:16 -07:00
committed by GitHub
parent 77bc5187b5
commit 0d7e753227
2 changed files with 10 additions and 3 deletions
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2
python/test/regression/test_performance.py
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@@ -128,7 +128,7 @@ elementwise_data = {
1024 * 16: 0.0219,
1024 * 64: 0.0791,
1024 * 256: 0.243,
1024 * 1024: 0.534,
1024 * 1024: 0.530,
1024 * 4096: 0.796,
1024 * 16384: 0.905,
1024 * 65536: 0.939,

11
python/triton/testing.py
View File

@@ -115,7 +115,9 @@ def nvsmi(attrs):
return ret
def do_bench(fn, warmup=25, rep=100, grad_to_none=None, percentiles=[0.5, 0.2, 0.8], record_clocks=False):
def do_bench(fn, warmup=25, rep=100, grad_to_none=None,
percentiles=(0.5, 0.2, 0.8),
record_clocks=False, fast_flush=False):
"""
Benchmark the runtime of the provided function. By default, return the median runtime of :code:`fn` along with
the 20-th and 80-th performance percentile.
@@ -130,6 +132,8 @@ def do_bench(fn, warmup=25, rep=100, grad_to_none=None, percentiles=[0.5, 0.2, 0
:type grad_to_none: torch.tensor, optional
:param percentiles: Performance percentile to return in addition to the median.
:type percentiles: list[float]
:param fast_flush: Use faster kernel to flush L2 between measurements
:type fast_flush: bool
"""
# Estimate the runtime of the function
@@ -151,7 +155,10 @@ def do_bench(fn, warmup=25, rep=100, grad_to_none=None, percentiles=[0.5, 0.2, 0
# doesn't contain any input data before the run
start_event = [torch.cuda.Event(enable_timing=True) for i in range(n_repeat)]
end_event = [torch.cuda.Event(enable_timing=True) for i in range(n_repeat)]
cache = torch.empty(int(256e6), dtype=torch.int8, device='cuda')
if fast_flush:
cache = torch.empty(int(256e6 // 4), dtype=torch.int, device='cuda')
else:
cache = torch.empty(int(256e6), dtype=torch.int8, device='cuda')
# Warm-up
for _ in range(n_warmup):
fn()