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[https://nvbugs/6506920][fix] Add stderr, stderr_margin_sigmas fields to HypothesisTestingParams… #16858

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[https://nvbugs/6506920][fix] Add stderr, stderr_margin_sigmas fields to HypothesisTestingParams… #16858

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c94a85c
[nvbugs/6506920][fix] Teach accuracy framework about lm-eval per-metr…
HandongLi-01 Jul 24, 2026
5ce3400
[nvbugs/6506920][chore] Remove stale waiver after fix
HandongLi-01 Jul 28, 2026
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24 changes: 22 additions & 2 deletions tests/integration/defs/accuracy/accuracy_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,9 @@ def compute_threshold(num_samples: int,
return ref_accuracy - z_alpha * scale


STDERR_MARGIN_SIGMAS = 2.0

@coderabbitai coderabbitai Bot Jul 25, 2026

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🗄️ Data Integrity & Integration | 🟠 Major | ⚡ Quick win

Implement the advertised stderr_margin_sigmas parameter.

The PR contract describes stderr_margin_sigmas as configurable, but this class only exposes the hard-coded STDERR_MARGIN_SIGMAS = 2.0; reference entries cannot select another margin.

Suggested fix 
 class HypothesisTestingParams:
     stderr: Optional[float] = None
+    stderr_margin_sigmas: float = STDERR_MARGIN_SIGMAS

     def __post_init__(self) -> None:
         self.ref_accuracy_anchor = self.ref_accuracy
         if self.stderr is not None and self.stderr > 0:
             sign = -1 if self.higher_is_better else 1
-            self.ref_accuracy += sign * STDERR_MARGIN_SIGMAS * self.stderr
+            self.ref_accuracy += sign * self.stderr_margin_sigmas * self.stderr

Also forward entry.get("stderr_margin_sigmas", STDERR_MARGIN_SIGMAS) from get_hypothesis_testing_params().

Based on the PR objectives, stderr_margin_sigmas is part of the intended HypothesisTestingParams API.

Also applies to: 84-96, 204-213

🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tests/integration/defs/accuracy/accuracy_core.py` at line 72, Make
stderr_margin_sigmas configurable in the HypothesisTestingParams API instead of
relying only on STDERR_MARGIN_SIGMAS. Update the relevant parameter construction
and reference-entry handling, including get_hypothesis_testing_params(), to
forward entry.get("stderr_margin_sigmas", STDERR_MARGIN_SIGMAS) so entries can
override the default while preserving 2.0 when unspecified.



@dataclass(slots=True)
class HypothesisTestingParams:
ref_accuracy: float
Expand All @@ -78,10 +81,19 @@ class HypothesisTestingParams:
beta: float = 0.2
sigma: float = 50.0
higher_is_better: bool = True
# lm-eval-style per-metric stderr. When set, ref_accuracy is treated as an
# anchor and the strict floor is offset by STDERR_MARGIN_SIGMAS * stderr so
# single-run noise does not trip the outer assertion (nvbugs/6506920).
stderr: Optional[float] = None
ref_accuracy_anchor: float = field(init=False)
theta: float = field(init=False)
threshold: float = field(init=False)

def __post_init__(self) -> None:
self.ref_accuracy_anchor = self.ref_accuracy
if self.stderr is not None and self.stderr > 0:
sign = -1 if self.higher_is_better else 1
self.ref_accuracy += sign * STDERR_MARGIN_SIGMAS * self.stderr
Comment on lines +87 to +96

@coderabbitai coderabbitai Bot Jul 30, 2026

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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win

Reject invalid stderr values instead of silently ignoring them.

Negative or NaN values fail the > 0 check and silently disable the adjustment, potentially restoring flaky strict-gate failures. Validate that stderr is finite and non-negative before applying the margin.

🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tests/integration/defs/accuracy/accuracy_core.py` around lines 87 - 96,
Update __post_init__ to validate stderr whenever it is provided, rejecting
negative or non-finite values such as NaN before calculating the accuracy
adjustment. Preserve the existing margin behavior for finite, non-negative
stderr values and leave ref_accuracy unchanged only when stderr is None.

self.theta = compute_theta(self.num_samples,
sigma=self.sigma,
alpha=self.alpha,
Expand All @@ -95,6 +107,13 @@ def __post_init__(self) -> None:

def report(self, accuracy: Optional[float] = None) -> str:
metric_name = self.metric_name.upper()
if self.stderr is not None and self.stderr > 0:
stderr_line = (
f"\nAnchor {self.metric_name}: {self.ref_accuracy_anchor:.3f}"
f" (adjusted by {STDERR_MARGIN_SIGMAS:.1f}sigma * stderr={self.stderr:.3f})"
)
else:
stderr_line = ""
report = f"""===========================================================
= {metric_name} HYPOTHESIS TESTING
===========================================================
Expand All @@ -104,7 +123,7 @@ def report(self, accuracy: Optional[float] = None) -> str:
#Samples: {self.num_samples}
Higher is better: {self.higher_is_better}
Theta (Minimum detectable effect): {self.theta:.3f}
Reference {self.metric_name}: {self.ref_accuracy:.3f}
Reference {self.metric_name}: {self.ref_accuracy:.3f}{stderr_line}
Threshold: {self.threshold:.3f}
==========================================================="""
if accuracy is not None:
Expand Down Expand Up @@ -190,7 +209,8 @@ def get_hypothesis_testing_params(self,
sigma=entry.get("sigma", self.SIGMA),
num_samples=entry.get("num_samples", self.NUM_SAMPLES),
higher_is_better=entry.get("higher_is_better",
self.HIGHER_IS_BETTER))
self.HIGHER_IS_BETTER),
stderr=entry.get("stderr"))

def evaluate(self,
llm: Union[PyTorchLLM, AutoDeployLLM],
Expand Down
11 changes: 8 additions & 3 deletions tests/integration/defs/accuracy/references/gsm8k.yaml
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Expand Up @@ -156,11 +156,16 @@ deepseek-ai/DeepSeek-V4-Pro:
spec_dec_algo: MTP
accuracy: 96.0
# Full GSM8K DEP8 DSpark path: TP=8, EP=8, attention DP, MegaMoE DeepGEMM,
# and max_draft_len=5. Measured 96.475 on 1319 samples; floor set to 96.0
# for run-to-run margin.
# and max_draft_len=5. Author-anchor 96.475 on 1319 samples with lm-eval
# reporting per-metric stderr ~0.55; observed spread across runs
# 95.94..96.55 tracks that stderr. accuracy_core subtracts 2*stderr from
# the anchor for the strict floor (95.375), so single-run tails no longer
# trip the outer gate (nvbugs/6506920). The hypothesis-testing threshold
# (~92.3) remains the primary regression gate.
- quant_algo: FP8_BLOCK_SCALES
spec_dec_algo: DSpark
accuracy: 96.0
accuracy: 96.475
stderr: 0.55
Qwen3/Qwen3-4B:
- spec_dec_algo: Eagle
accuracy: 85.823
Expand Down
1 change: 0 additions & 1 deletion tests/integration/test_lists/waives.txt
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Expand Up @@ -184,7 +184,6 @@ full:B300/disaggregated/test_auto_scaling.py::test_service_discovery[http-kv_cac
full:B300/disaggregated/test_disaggregated.py::test_disaggregated_ctxpp2_genpp2[TinyLlama-1.1B-Chat-v1.0] SKIP (https://nvbugs/6322073)
full:B300/test_e2e.py::test_qwen_e2e_cpprunner_large_new_tokens[DeepSeek-R1-Distill-Qwen-1.5B-DeepSeek-R1-Distill-Qwen-1.5B] SKIP (https://nvbugs/6414760)
full:DGX_B200/accuracy/test_disaggregated_serving.py::TestQwen3NextInstruct::test_auto_dtype[use_py_transceiver=True] SKIP (https://nvbugs/6501837)
full:DGX_B200/accuracy/test_llm_api_pytorch.py::TestDeepSeekV4ProDSpark::test_gsm8k_dep8_megamoe_deepgemm SKIP (https://nvbugs/6506920)
full:DGX_H200/accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[disable_skip_indexer] SKIP (https://nvbugs/6476233)
full:DGX_H200/accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[latency_default] SKIP (https://nvbugs/6476233)
full:GB200/accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_auto_dtype[False-True-True-True] SKIP (https://nvbugs/6525893)
Expand Down
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