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[FRONTEND] Use PyTorch device_interface in do_bench #4470

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Merged
Jokeren merged 1 commit into from
Aug 9, 2024
Merged

[FRONTEND] Use PyTorch device_interface in do_bench #4470

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23 changes: 13 additions & 10 deletions python/triton/testing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -90,7 +90,8 @@ def do_bench_cudagraph(fn, rep=20, grad_to_none=None, quantiles=None, return_mod
return _summarize_statistics(torch.tensor(ret), quantiles, return_mode)


def do_bench(fn, warmup=25, rep=100, grad_to_none=None, quantiles=None, fast_flush=True, return_mode="mean"):
def do_bench(fn, warmup=25, rep=100, grad_to_none=None, quantiles=None, fast_flush=True, return_mode="mean",
device_type="cuda"):
"""
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.
Expand All @@ -113,34 +114,36 @@ def do_bench(fn, warmup=25, rep=100, grad_to_none=None, quantiles=None, fast_flu
assert return_mode in ["min", "max", "mean", "median"]
import torch

di = torch._dynamo.device_interface.get_interface_for_device(device_type)

fn()
torch.cuda.synchronize()
di.synchronize()

# We maintain a buffer of 256 MB that we clear
# before each kernel call to make sure that the L2 cache
# doesn't contain any input data before the run
cache_size = 256 * 1024 * 1024
if fast_flush:
cache = torch.empty(int(cache_size // 4), dtype=torch.int, device='cuda')
cache = torch.empty(int(cache_size // 4), dtype=torch.int, device=device_type)
else:
cache = torch.empty(int(cache_size), dtype=torch.int8, device='cuda')
cache = torch.empty(int(cache_size), dtype=torch.int8, device=device_type)

# Estimate the runtime of the function
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
start_event = di.Event(enable_timing=True)
end_event = di.Event(enable_timing=True)
start_event.record()
for _ in range(5):
cache.zero_()
fn()
end_event.record()
torch.cuda.synchronize()
di.synchronize()
estimate_ms = start_event.elapsed_time(end_event) / 5

# compute number of warmup and repeat
n_warmup = max(1, int(warmup / estimate_ms))
n_repeat = max(1, int(rep / estimate_ms))
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)]
start_event = [di.Event(enable_timing=True) for i in range(n_repeat)]
end_event = [di.Event(enable_timing=True) for i in range(n_repeat)]
# Warm-up
for _ in range(n_warmup):
fn()
Expand All @@ -159,7 +162,7 @@ def do_bench(fn, warmup=25, rep=100, grad_to_none=None, quantiles=None, fast_flu
fn()
end_event[i].record()
# Record clocks
torch.cuda.synchronize()
di.synchronize()
times = torch.tensor([s.elapsed_time(e) for s, e in zip(start_event, end_event)], dtype=torch.float)
return _summarize_statistics(times, quantiles, return_mode)

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