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Constrain harness topology by visible GPUs

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This commit is contained in:
Gahow Wang
2026-05-13 01:25:31 +08:00
parent fb6d74a18c
commit 5c2958e6c1
3 changed files with 143 additions and 7 deletions
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26
docs/harness-ablation/profile-driven-harness-implementation-20260512.md
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@@ -104,3 +104,29 @@ Started on `dash0` (`11.73.2.172`) at commit `e3ed775`.
- monitor: read-only subagent `Wegener`
Acceptance for this run is based on end-to-end trial results, not unit tests. If the first four trials lag the min-prompt no-harness baseline (`0.0650`, `0.1992`, `0.2696`, then failed/NA), the run should be treated as a failed harness iteration and the harness should be optimized again.
## V2 Result And Failure
V2 was stopped early after four trials because it did not improve the no-harness baseline and made a preventable launch-risk proposal.
Raw `request_rate/GPU`:
| Variant | iter1 | iter2 | iter3 | iter4 |
| --- | ---: | ---: | ---: | --- |
| no-harness min-prompt | 0.0650 | 0.1992 | 0.2696 | 0.2696 |
| harness v2 | 0.0650 | 0.1992 | 0.2696 | failed |
Harness v2 did correctly diagnose the first bottleneck and proposed:
- iter2: `tensor-parallel-size=2`, raw `0.1992 req/s/GPU`;
- iter3: `tensor-parallel-size=4`, raw `0.2696 req/s/GPU`.
However, iter4 proposed `tensor-parallel-size=8` and failed at engine launch. The study's `hardware.gpu_count` is 8, but the launch environment sets `CUDA_VISIBLE_DEVICES=0,1,2,4,5,6,7`, which exposes only 7 GPUs. Therefore TP=8 should not have been considered launch-safe.
This is a general harness bug: topology planning must use the effective visible GPU count from the execution profile, not just the nominal hardware count.
Fix:
- parse `engine.base_envs.CUDA_VISIBLE_DEVICES`;
- compute effective GPU count as `min(hardware.gpu_count, visible_device_count)`;
- filter topology candidates and adjacent TP frontier candidates by the effective GPU count.

30
src/aituner/harness.py
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@@ -1210,17 +1210,18 @@ def _legal_topology_points(
) -> list[dict[str, Any]]:
constraints = study.engine.topology_constraints
tunable = set(study.engine.tunable_flags)
effective_gpu_count = _effective_gpu_count(study)
if constraints is not None and constraints.allowed_tensor_parallel_sizes:
tp_values = sorted(set(constraints.allowed_tensor_parallel_sizes))
elif "tensor-parallel-size" in tunable:
tp_values = [value for value in [1, 2, 4, 8] if value <= study.hardware.gpu_count]
tp_values = [value for value in [1, 2, 4, 8] if value <= effective_gpu_count]
else:
tp_values = [current_tp]
if constraints is not None and constraints.allowed_data_parallel_sizes:
dp_values = sorted(set(constraints.allowed_data_parallel_sizes))
elif "data-parallel-size" in tunable:
dp_values = [value for value in [1, 2, 4, 8] if value <= study.hardware.gpu_count]
dp_values = [value for value in [1, 2, 4, 8] if value <= effective_gpu_count]
else:
dp_values = [current_dp]
@@ -1243,13 +1244,15 @@ def _legal_topology_points(
continue
if (
constraints.require_tp_dp_product_equals_gpu_count
and tp_dp_product != study.hardware.gpu_count
and tp_dp_product != effective_gpu_count
):
continue
elif tp_dp_product > study.hardware.gpu_count:
elif tp_dp_product > effective_gpu_count:
continue
if constraints is not None and not constraints.allowed_tp_dp_products:
if tp_dp_product > study.hardware.gpu_count:
if tp_dp_product > effective_gpu_count:
continue
if tp_dp_product > effective_gpu_count:
continue
for ep in ep_values:
enable_ep = current_enable_ep or ep > 1
@@ -1330,6 +1333,18 @@ def _normalized_topology_flags(flags: dict[str, Any]) -> dict[str, Any]:
}
def _effective_gpu_count(study: StudySpec) -> int:
visible = str(study.engine.base_envs.get("CUDA_VISIBLE_DEVICES") or "").strip()
if not visible:
return study.hardware.gpu_count
if visible.lower() in {"none", "void", "-1"}:
return 0
devices = [item.strip() for item in visible.split(",") if item.strip()]
if not devices:
return study.hardware.gpu_count
return min(study.hardware.gpu_count, len(devices))
def _score_topology_candidate(
top_bottleneck: str,
bottleneck_hypotheses: list[dict[str, Any]],
@@ -1621,6 +1636,7 @@ def _round_up_to_multiple(value: int, multiple: int) -> int:
def _next_allowed_tp(study: StudySpec, *, current_tp: int, current_dp: int) -> int | None:
constraints = study.engine.topology_constraints
effective_gpu_count = _effective_gpu_count(study)
if constraints is not None and constraints.allowed_tensor_parallel_sizes:
candidates = sorted({int(item) for item in constraints.allowed_tensor_parallel_sizes})
else:
@@ -1629,7 +1645,7 @@ def _next_allowed_tp(study: StudySpec, *, current_tp: int, current_dp: int) -> i
if candidate <= current_tp:
continue
tp_dp_product = candidate * current_dp
if tp_dp_product > study.hardware.gpu_count:
if tp_dp_product > effective_gpu_count:
continue
if constraints is not None:
if (
@@ -1639,7 +1655,7 @@ def _next_allowed_tp(study: StudySpec, *, current_tp: int, current_dp: int) -> i
continue
if (
constraints.require_tp_dp_product_equals_gpu_count
and tp_dp_product != study.hardware.gpu_count
and tp_dp_product != effective_gpu_count
):
continue
return candidate

94
tests/test_core_flow.py
View File

@@ -1175,6 +1175,100 @@ class CoreFlowTests(unittest.TestCase):
self.assertIsNotNone(proposal)
self.assertEqual(proposal.config_patch.flag_patch, {"tensor-parallel-size": 2})
def test_harness_excludes_topology_above_visible_gpu_count(self) -> None:
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
study_path = _write_study_assets(
tmp_path,
engine_overrides={
"base_envs": {"CUDA_VISIBLE_DEVICES": "0,1,2,4,5,6,7"},
"tunable_flags": ["tensor-parallel-size"],
"topology_constraints": {
"allowed_tensor_parallel_sizes": [1, 2, 4, 8],
"allowed_tp_dp_products": [1, 2, 4, 8],
},
},
)
result_path = tmp_path / "trial-0003.json"
result_path.write_text(
json.dumps(
{
"status": "completed",
"best_request_rate": 1.078,
"best_pass_rate": 0.958,
"probes": [
{
"threshold": 0.039,
"feasible": False,
"payload": {
"request_count": 100,
"pass_rate": 0.8,
"request_rate": 1.10,
"early_stop_reason": "slo_pass_rate_unrecoverable",
"latency_summary": {
"failed_reason_counts": {"tpot_ms>25.0": 20}
},
},
}
],
}
),
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-0003",
best_request_rate=1.078,
best_request_rate_per_gpu=0.2695,
trials=[
TrialSummary(
trial_id="trial-0001",
status="completed",
best_request_rate=0.065,
best_request_rate_per_gpu=0.065,
config_patch={"env_patch": {}, "flag_patch": {}},
),
TrialSummary(
trial_id="trial-0002",
status="completed",
best_request_rate=0.398,
best_request_rate_per_gpu=0.199,
config_patch={
"env_patch": {},
"flag_patch": {"tensor-parallel-size": 2},
},
),
TrialSummary(
trial_id="trial-0003",
status="completed",
best_request_rate=1.078,
best_request_rate_per_gpu=0.2695,
result_path=str(result_path),
config_patch={
"env_patch": {},
"flag_patch": {"tensor-parallel-size": 4},
},
),
],
),
)
candidates = context["candidate_actions"]
self.assertFalse(
any(
action["config_patch"]["flag_patch"].get("tensor-parallel-size") == 8
for action in candidates
)
)
proposal = build_harness_guided_proposal(context)
self.assertTrue(
proposal is None
or proposal.config_patch.flag_patch.get("tensor-parallel-size") != 8
)
def test_harness_stop_blocked_until_slo_driven_topology_frontier_is_measured(self) -> None:
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)