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Fix harness SLO early-stop diagnosis

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This commit is contained in:
Gahow Wang
2026-05-12 22:20:01 +08:00
parent ef359c8eea
commit e3ed775afd
2 changed files with 92 additions and 0 deletions
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1
src/aituner/harness.py
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@@ -607,6 +607,7 @@ def _active_bottleneck(probe: dict[str, Any] | None) -> str:
if not str(k).startswith("ttft") if not str(k).startswith("ttft")
and not str(k).startswith("tpot") and not str(k).startswith("tpot")
and not str(k).startswith("probe_elapsed_s>") and not str(k).startswith("probe_elapsed_s>")
and str(k) != "slo_pass_rate_unrecoverable"
) )
if ttft_count == 0 and tpot_count == 0 and request_failed_count == 0: if ttft_count == 0 and tpot_count == 0 and request_failed_count == 0:
return "none_obvious" return "none_obvious"

91
tests/test_core_flow.py
View File

@@ -1084,6 +1084,97 @@ class CoreFlowTests(unittest.TestCase):
{"max-num-seqs": 32}, {"max-num-seqs": 32},
) )
def test_slo_unrecoverable_does_not_mask_latency_bottleneck(self) -> None:
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
study_path = _write_study_assets(
tmp_path,
slo_overrides={
"ttft_rule": {"kind": "fixed_ms", "threshold_ms": 4000},
"tpot_rule": {"kind": "fixed_ms", "threshold_ms": 25},
},
engine_overrides={
"tunable_flags": [
"tensor-parallel-size",
"max-num-seqs",
],
"topology_constraints": {
"allowed_tensor_parallel_sizes": [1, 2, 4],
"allowed_tp_dp_products": [1, 2, 4],
},
},
)
result_path = tmp_path / "trial-0001.json"
result_path.write_text(
json.dumps(
{
"status": "completed",
"best_request_rate": 0.065,
"best_request_rate_per_gpu": 0.065,
"best_pass_rate": 1.0,
"probes": [
{
"threshold": 0.015625,
"feasible": False,
"payload": {
"request_count": 290,
"pass_rate": 0.041,
"request_rate": 0.483,
"early_stop_reason": "slo_pass_rate_unrecoverable",
"latency_summary": {
"failed_reason_counts": {
"ttft_ms>4000.0": 2,
"tpot_ms>25.0": 14,
"slo_pass_rate_unrecoverable": 263,
}
},
},
},
{
"threshold": 0.001953125,
"feasible": True,
"payload": {
"request_count": 39,
"pass_rate": 1.0,
"request_rate": 0.065,
"latency_summary": {"failed_reason_counts": {}},
},
},
],
}
),
encoding="utf-8",
)
study = load_study_spec(study_path)
context = build_harness_context(
study=study,
window_summary={"prompt_tokens_p95": 7628, "prompt_tail_ratio_p95_p50": 3.8},
state=StudyState(
study_id=study.study_id,
best_trial_id="trial-0001",
best_request_rate=0.065,
best_request_rate_per_gpu=0.065,
trials=[
TrialSummary(
trial_id="trial-0001",
status="completed",
best_request_rate=0.065,
best_request_rate_per_gpu=0.065,
best_pass_rate=1.0,
result_path=str(result_path),
config_patch={"env_patch": {}, "flag_patch": {}},
)
],
),
)
self.assertNotEqual(
context["bottleneck_hypotheses"][0]["name"],
"admission_or_queueing",
)
proposal = build_harness_guided_proposal(context)
self.assertIsNotNone(proposal)
self.assertEqual(proposal.config_patch.flag_patch, {"tensor-parallel-size": 2})
def test_harness_stop_blocked_until_slo_driven_topology_frontier_is_measured(self) -> None: def test_harness_stop_blocked_until_slo_driven_topology_frontier_is_measured(self) -> None:
with tempfile.TemporaryDirectory() as tmp: with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp) tmp_path = Path(tmp)