Add bad-start harness recovery planning

This commit is contained in:
2026-06-26 16:44:24 +08:00
parent ce36cd79af
commit 92eb186006
4 changed files with 420 additions and 2 deletions

View File

@@ -1904,6 +1904,212 @@ class CoreFlowTests(unittest.TestCase):
)
self.assertNotIn("gpu-memory-utilization", proposal.config_patch.flag_patch)
def test_harness_brackets_down_from_bad_high_tp_start_before_runtime_tuning(self) -> None:
"""A no-LLM run that starts at the max TP should validate the adjacent lower
topology before spending trials on runtime micro-tuning."""
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": 50},
},
engine_overrides={
"base_flags": {
"host": "127.0.0.1",
"port": 8000,
"tensor-parallel-size": 8,
"data-parallel-size": 1,
"gpu-memory-utilization": 0.5,
"max-num-seqs": 8,
},
"tunable_flags": [
"tensor-parallel-size",
"data-parallel-size",
"gpu-memory-utilization",
"max-num-seqs",
],
"topology_constraints": {
"allowed_tensor_parallel_sizes": [1, 2, 4, 8],
"allowed_data_parallel_sizes": [1],
"allowed_tp_dp_products": [1, 2, 4, 8],
},
},
)
study = load_study_spec(study_path)
result_path = tmp_path / "trial-0001.json"
result_path.write_text(
json.dumps(
{
"status": "completed",
"best_sampling_u": 0.05,
"best_request_rate": 8.0,
"best_pass_rate": 0.96,
"probes": [
{
"threshold": 0.05,
"feasible": True,
"payload": {
"request_count": 300,
"pass_rate": 0.96,
"request_rate": 8.0,
"latency_summary": {"failed_reason_counts": {}},
},
},
{
"threshold": 0.08,
"feasible": False,
"payload": {
"request_count": 300,
"pass_rate": 0.5,
"request_rate": 10.0,
"early_stop_reason": "slo_pass_rate_unrecoverable",
"latency_summary": {
"failed_reason_counts": {"ttft_ms>4000.0": 120}
},
},
},
],
}
),
encoding="utf-8",
)
state = StudyState(
study_id=study.study_id,
best_trial_id="trial-0001",
best_request_rate=8.0,
best_request_rate_per_gpu=1.0,
trials=[
TrialSummary(
trial_id="trial-0001",
status="completed",
best_request_rate=8.0,
best_request_rate_per_gpu=1.0,
result_path=str(result_path),
config_patch={"env_patch": {}, "flag_patch": {}},
),
],
)
context = build_harness_context(
study=study,
window_summary={"prompt_tokens_p95": 6500},
state=state,
)
proposal = build_harness_guided_proposal(context)
self.assertIsNotNone(proposal)
self.assertEqual(
proposal.config_patch.flag_patch.get("tensor-parallel-size"), 4
)
self.assertNotIn("gpu-memory-utilization", proposal.config_patch.flag_patch)
self.assertNotIn("max-num-seqs", proposal.config_patch.flag_patch)
def test_harness_jumps_low_gpu_mem_util_to_nominal_floor_after_topology_settles(self) -> None:
"""A pathological gmu=0.5 start should jump to the normal operating floor
after topology is bracketed instead of wasting many 0.02 hill-climb trials."""
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": 50},
},
engine_overrides={
"base_flags": {
"host": "127.0.0.1",
"port": 8000,
"tensor-parallel-size": 2,
"data-parallel-size": 1,
"gpu-memory-utilization": 0.5,
},
"tunable_flags": [
"tensor-parallel-size",
"gpu-memory-utilization",
],
"topology_constraints": {
"allowed_tensor_parallel_sizes": [1, 2, 4],
"allowed_data_parallel_sizes": [1],
"allowed_tp_dp_products": [1, 2, 4],
},
},
)
study = load_study_spec(study_path)
result_path = tmp_path / "trial-0001.json"
result_path.write_text(
json.dumps(
{
"status": "completed",
"best_sampling_u": 0.07,
"best_request_rate": 2.4,
"best_pass_rate": 0.97,
"probes": [
{
"threshold": 0.07,
"feasible": True,
"payload": {
"request_count": 300,
"pass_rate": 0.97,
"request_rate": 2.4,
"latency_summary": {"failed_reason_counts": {}},
},
},
{
"threshold": 0.1,
"feasible": False,
"payload": {
"request_count": 300,
"pass_rate": 0.55,
"request_rate": 3.1,
"early_stop_reason": "slo_pass_rate_unrecoverable",
"latency_summary": {
"failed_reason_counts": {"tpot_ms>50.0": 90}
},
},
},
],
}
),
encoding="utf-8",
)
state = StudyState(
study_id=study.study_id,
best_trial_id="trial-0001",
best_request_rate=2.4,
best_request_rate_per_gpu=1.2,
trials=[
TrialSummary(
trial_id="trial-0001",
status="completed",
best_request_rate=2.4,
best_request_rate_per_gpu=1.2,
result_path=str(result_path),
config_patch={"env_patch": {}, "flag_patch": {}},
),
TrialSummary(
trial_id="trial-0002",
status="completed",
best_request_rate=2.2,
best_request_rate_per_gpu=0.55,
config_patch={
"env_patch": {},
"flag_patch": {"tensor-parallel-size": 4},
},
),
],
)
context = build_harness_context(
study=study,
window_summary={"prompt_tokens_p95": 1500},
state=state,
)
proposal = build_harness_guided_proposal(context)
self.assertIsNotNone(proposal)
self.assertEqual(
proposal.config_patch.flag_patch.get("gpu-memory-utilization"), 0.9
)
self.assertNotIn("tensor-parallel-size", proposal.config_patch.flag_patch)
def test_harness_continues_gpu_mem_util_after_tied_same_topology_probe(self) -> None:
"""After adjacent topology validation, gpu-memory-utilization should hill-climb
on the incumbent topology even if an earlier gmu step tied the incumbent and