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Fix autotuner oom #2442

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Merged
yzh119 merged 2 commits into from
Feb 3, 2026
+74 −57
Merged

Fix autotuner oom #2442

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5a70f90
fix: graceful OOM fallback during autotuning
zack041 Jan 30, 2026
2c43050
style: apply pre-commit formatting
zack041 Jan 30, 2026
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131 changes: 74 additions & 57 deletions flashinfer/autotuner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -466,64 +466,81 @@ def choose_one(
}

for p in profiles:
tensors = self._prepare_input_tensors(p, inputs)
is_cache_hit, runner_id, tactic, _ = self.search_cache(
custom_op, runners, p.get_opt_shapes(), tuning_config
)
if not is_cache_hit:
min_time = float("inf")
# Initialize runner and tactic as None in case of no valid tactic or runners are found
runner_id, tactic = None, None
for r_id, r in enumerate(runners):
# TODO: use FakeTensor here.
valid_tactics = r.get_valid_tactics(tensors, p)
runner_arg_names = runner_arg_names_map[r]
if "do_preparation" in runner_arg_names and len(valid_tactics) > 0:
r(tensors, tactic=-1, do_preparation=True, **kwargs)
for tac in valid_tactics:
try:
time_measured = self._profile_single_kernel(
r, tensors, tac, **kwargs
)
except Exception as e:
shapes = self._get_input_sizes(tensors)
logger.warning(
f"[Autotuner]: Skipping tactic {r} {tac}, due to failure while profiling: {e}"
)

# Log stacktrace as debug to not spam log
logger.debug(
f"[Autotuner]: Failed when profiling {r} {tac}, shapes={shapes}. Error occurred: {e}"
)

# Record the failed profiling combinations
if custom_op not in self.stats.failed_profiling_count:
self.stats.failed_profiling_count[custom_op] = set()
self.stats.failed_profiling_count[custom_op].add(
AutoTuner._get_cache_key(
custom_op, r, p.get_opt_shapes(), tuning_config
try:
tensors = self._prepare_input_tensors(p, inputs)
is_cache_hit, runner_id, tactic, _ = self.search_cache(
custom_op, runners, p.get_opt_shapes(), tuning_config
)
if not is_cache_hit:
min_time = float("inf")
# Initialize runner and tactic as None in case of no valid tactic or runners are found
runner_id, tactic = None, None
for r_id, r in enumerate(runners):
# TODO: use FakeTensor here.
valid_tactics = r.get_valid_tactics(tensors, p)
runner_arg_names = runner_arg_names_map[r]
if (
"do_preparation" in runner_arg_names
and len(valid_tactics) > 0
):
r(tensors, tactic=-1, do_preparation=True, **kwargs)
for tac in valid_tactics:
try:
time_measured = self._profile_single_kernel(
r, tensors, tac, **kwargs
)
)

# Set time_measured to inf to notify the failure of the tactic. This can happen when `get_valid_tactics` mistakenly return wrong tactics
# or some runtime error occurs during profiling.
time_measured = float("inf")
if time_measured < min_time:
min_time = time_measured
runner_id, tactic = r_id, tac
if runner_id is not None:
# At least one valid (runner, tactic) pair is found
cache_key = AutoTuner._get_cache_key(
custom_op, runners[runner_id], p.get_opt_shapes(), tuning_config
)
# inspect call stack
self.profiling_cache[cache_key] = (runner_id, tactic, p)
self.stats.tuned_op_successful_configs[custom_op] = (
self.stats.tuned_op_successful_configs.get(custom_op, 0) + 1
)
logger.debug(
f"[Autotuner]: profiling chosen runner: {runners[runner_id]} {tactic} for {cache_key}"
)
except torch.cuda.OutOfMemoryError:
raise
except Exception as e:
shapes = self._get_input_sizes(tensors)
logger.warning(
f"[Autotuner]: Skipping tactic {r} {tac}, due to failure while profiling: {e}"
)

# Log stacktrace as debug to not spam log
logger.debug(
f"[Autotuner]: Failed when profiling {r} {tac}, shapes={shapes}. Error occurred: {e}"
)

# Record the failed profiling combinations
if custom_op not in self.stats.failed_profiling_count:
self.stats.failed_profiling_count[custom_op] = set()
self.stats.failed_profiling_count[custom_op].add(
AutoTuner._get_cache_key(
custom_op, r, p.get_opt_shapes(), tuning_config
)
)

# Set time_measured to inf to notify the failure of the tactic. This can happen when `get_valid_tactics` mistakenly return wrong tactics
# or some runtime error occurs during profiling.
time_measured = float("inf")
if time_measured < min_time:
min_time = time_measured
runner_id, tactic = r_id, tac

if runner_id is not None:
# At least one valid (runner, tactic) pair is found
cache_key = AutoTuner._get_cache_key(
custom_op,
runners[runner_id],
p.get_opt_shapes(),
tuning_config,
)
# inspect call stack
self.profiling_cache[cache_key] = (runner_id, tactic, p)
self.stats.tuned_op_successful_configs[custom_op] = (
self.stats.tuned_op_successful_configs.get(custom_op, 0) + 1
)
logger.debug(
f"[Autotuner]: profiling chosen runner: {runners[runner_id]} {tactic} for {cache_key}"
)

except torch.cuda.OutOfMemoryError:
torch.cuda.empty_cache()
logger.warning(
"[Autotuner]: OOM detected, falling back to default tactic"
)
return runners[0], -1

# Get the best runner and tactic from cache
# If no valid tactic is found, the fallback runner and tactic will be used
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