ar_validate: fail loudly when every sample fails - #2288
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validate_ar() swallows per-sample exceptions into WARNING lines and returns whatever succeeded. When that list came back empty, the reporting block was guarded by `if results and ...`, so the script printed nothing and exited 0 -- a run where 100% of samples failed was indistinguishable from a successful one. Observed on a Cosmos3-Nano EAGLE3 checkpoint sharded by device_map="auto": all 80/80 samples died with "Expected all tensors to be on the same device", the job still exited 0, and the wrapper stamped PASS with no AL number anywhere. Raise instead, so the caller sees a non-zero exit. Signed-off-by: Ye Yu <yeyu@nvidia.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 6 remain after this review. 📝 WalkthroughWalkthroughThe AR validation script now validates the clamped effective sample count before checking results. It raises ChangesAR validation
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: ⚪ Minimal · up to This localized fix makes completely failed validation runs exit non-zero instead of appearing successful; no actionable merge-blocking risk remains beyond normal checks and review. Suggested reviewers: 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
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In `@examples/speculative_decoding/scripts/ar_validate.py`:
- Around line 126-128: Update validate_ar to compute and validate the effective
sample count after clamping args.num_samples to len(ds), rejecting zero or
negative values before validation. Use that effective count in the no-results
RuntimeError so the message distinguishes no samples from all-samples failure
and never reports the unclamped or invalid requested count.
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examples/speculative_decoding/scripts/ar_validate.py
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| if results and accelerator.is_main_process: | ||
| if not results: | ||
| raise RuntimeError( | ||
| f"AR validation produced no results: all {args.num_samples} samples failed. " |
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[SUGGESTION] The count in the message can overstate what was actually attempted.
validate_ar() clamps internally with num_samples = min(num_samples, len(ds)) (line 62), so the number of samples tried is min(args.num_samples, len(ds)), not args.num_samples. With the default (--num_samples 80) and the MT-Bench prompt split these coincide, but a user who passes --num_samples 200 and hits a total failure gets "all 200 samples failed" when only len(ds) were ever run — sending them looking for 120 warnings that don't exist while diagnosing exactly the kind of opaque failure this change exists to surface.
Also, the third sentence describes the rationale for the fix rather than telling the user anything actionable; per CONTRIBUTING that belongs in the PR description. Suggest tightening to:
| f"AR validation produced no results: all {args.num_samples} samples failed. " | |
| f"AR validation produced no results: all {min(args.num_samples, len(ds))} samples failed. " |
and dropping the "Exiting non-zero ..." line.
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Fixed in 638ad6a — the message now reports min(args.num_samples, len(ds)) rather than the raw request.
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| if results and accelerator.is_main_process: | ||
| if not results: |
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[SUGGESTION] The guard is all-or-nothing, so a near-total failure still exits 0 with a meaningless AR.
The reasoning in the PR description — "a run where 100% of samples failed was indistinguishable from a successful one" — applies just as well at 98.75%. If 79 of 80 samples die and one survives, avg_ar is computed from that single sample, printed as a normal result, and the script exits 0; a wrapper that greps for an AR number stamps PASS on noise. The motivating case (device_map="auto" device mismatch) happens to fail every sample, but a per-prompt failure mode — OOM on the longest MT-Bench prompts, a tokenizer edge case — fails a subset and lands squarely in this gap.
print(f" Samples: {len(results)}") does leave the evidence in the log, so this is a human-readable signal, not an automated one. Worth considering a failure-rate bound rather than an emptiness check, e.g. have validate_ar return the attempted/failed counts and fail when the failure fraction exceeds a threshold (a --max_failure_rate with a lenient default would keep flaky-but-usable runs green while still catching a collapse):
results, attempted, failures = validate_ar(...)
if failures / attempted > args.max_failure_rate:
raise RuntimeError(
f"AR validation failed for {failures}/{attempted} samples, above the "
f"--max_failure_rate of {args.max_failure_rate}. See the per-sample WARNING "
"lines above for the underlying error."
)Reasonable to defer as out of scope for a targeted bug fix — the current change is a strict improvement either way.
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Agreed the gap is real — 79/80 failing still prints an AR from one sample and exits 0, and the argument I make in the description does apply there. Postponing rather than folding it in here: a failure-rate bound changes behavior for runs that currently pass, so it deserves its own PR with a default chosen deliberately (and probably a --max_failure_rate flag rather than a hardcoded threshold). This PR stays scoped to the 100%-failure case, which is unambiguous.
Separately, thanks for the eagle_utils.py catch — I verified it: validate_ar() returns (category, ar) tuples and eagle_utils.py:415/417 does sum(ars) / len(ars), which raises TypeError straight into the surrounding bare except Exception. So in-training AR validation prints "AR validation not available." and never logs to W&B. Real bug, same silent-failure family, but a different file and call path than this PR touches — filing separately.
Claude Review SummaryScope: Full review. 1 file changed (+8/-1), Findings — CRITICAL: 0, IMPORTANT: 0, SUGGESTION: 2 The change itself is correct and does what the description claims. I verified the mechanics:
Suggestions (non-blocking)
Out of scope, but worth knowing (not a finding against this PR)While tracing callers of ```python
AssessmentLow risk. Small, surgical, strictly-better-than-before change to an example script with no API surface, no state/mode interaction, and no export path. Approving. |
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2288 +/- ##
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+ Coverage 79.02% 79.05% +0.02%
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Files 525 525
Lines 61104 61104
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+ Hits 48287 48304 +17
+ Misses 12817 12800 -17
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validate_ar() clamps to min(num_samples, len(ds)), so the failure message interpolated a count that could exceed what actually ran when --num_samples is larger than the dataset. Report the clamped value. Signed-off-by: Ye Yu <yeyu@nvidia.com>
validate_ar() accepts a non-positive num_samples and returns [] without any sample having failed, so the new guard would have reported 'all 0 samples failed' (or a negative count). Check the effective count first and raise a distinct, accurate error for that case. Signed-off-by: Ye Yu <yeyu@nvidia.com>
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/claude review |
| # validate_ar() clamps to len(ds), so report what was actually attempted rather than the | ||
| # requested --num_samples, which can be larger than the dataset. A non-positive count means | ||
| # nothing ran at all -- distinct from "everything ran and failed", so say so separately. | ||
| attempted = min(args.num_samples, len(ds)) | ||
| if attempted <= 0: | ||
| raise ValueError( | ||
| f"No samples to validate: --num_samples={args.num_samples} with a dataset of " | ||
| f"{len(ds)} prompts. Pass a positive --num_samples." | ||
| ) |
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[SUGGESTION] The non-positive-count guard runs after validate_ar(), so --num_samples 0 still pays for the full model load and accelerator.prepare() across all GPUs before erroring out. It also duplicates the min(num_samples, len(ds)) clamp that already lives at line 63, so the two can drift apart if either side changes.
Both go away if the block moves up to just after ds is loaded and before the validate_ar() call — len(ds) is available there, and it becomes a genuine "reject before processing" check rather than a post-hoc one:
ds = load_dataset("HuggingFaceH4/mt_bench_prompts")["train"]
# validate_ar() clamps to len(ds), so track what will actually be attempted rather than the
# requested --num_samples, which can be larger than the dataset. A non-positive count means
# nothing will run at all -- distinct from "everything ran and failed", so say so separately.
attempted = min(args.num_samples, len(ds))
if attempted <= 0:
raise ValueError(
f"No samples to validate: --num_samples={args.num_samples} with a dataset of "
f"{len(ds)} prompts. Pass a positive --num_samples."
)
results = validate_ar(
model,
tokenizer,
ds,
args.steps,
args.osl,
args.num_samples,
accelerator.device,
)
if not results:
raise RuntimeError(
f"AR validation produced no results: all {attempted} samples failed. "
"See the per-sample WARNING lines above for the underlying error. "
"Exiting non-zero so this is not mistaken for a successful validation."
)Non-blocking — the current ordering is functionally correct (the empty loop yields no results, and attempted <= 0 is checked before not results, so the two failure modes still get distinct messages). This only saves a wasted load on a typo'd flag and keeps the clamp in one place.
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Claude Review Summary Scope: Full review — the trigger comment was a bare Findings — CRITICAL: 0, IMPORTANT: 0, SUGGESTION: 1 What changed since the last review
I re-verified the mechanics on the current file:
Suggestion (non-blocking)
Still open from the prior review (not re-raised as a finding) My earlier suggestion 2 — that the guard is all-or-nothing, so 79/80 samples failing still prints an AR from one sample and exits 0 — is not addressed in Assessment Low risk. A small, surgical fix to an example script with no public API surface, no mode/state or |
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@ChenhanYu could you take a look when you get a chance? You're on Small PR:
One thing worth flagging separately: the review surfaced a real pre-existing bug in the other caller. |
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Claude Review Summary
Scope: Full review. The trigger comment was a request to @ChenhanYu rather than a scoping instruction, so I reviewed everything. 1 file changed (+18/-1), examples/speculative_decoding/scripts/ar_validate.py. HEAD is still 638ad6a, identical to what I approved in my last pass, so rather than re-verify the same lines a third time I spent this round tracing the fix outward to the callers that consume the exit code. That turned up one new, verified issue.
Findings: CRITICAL 0, IMPORTANT 1, SUGGESTION 0 new
[IMPORTANT Compatibility] The launcher path still stamps PASS and still exits 0
tools/launcher/common/specdec/ar_eval_mtbench.sh is the wrapper described in the PR body ("the wrapper stamped it PASS"). After this change ar_validate.py does exit non-zero, but that status is discarded before it reaches the job result. Line 28 registers trap 'error_handler $0 $LINENO' ERR, line 59 runs the Python, and line 64 runs report_result "PASS: MT-Bench AR evaluation".
Three things line up against the fix:
- The script has no
set -e. error_handler(tools/launcher/common/service_utils.sh:27-36) prints the ERROR line, emits a REPORT line, setsFAIL=1andFAIL_EXIT=1, and then returns. It never exits.exit_handler(service_utils.sh:38-42) is the piece that turnsFAIL_EXIT=1intoexit 1, and it is never trapped in this script.grep -rn exit_handler tools/launcher/shows it registered indflash_online_training.sh,specdec_bench/run.sh, andspecdec_bench/upload_to_s3.sh, but not inar_eval_mtbench.sh.
So execution continues past the failed Python, the PASS line at :64 runs unconditionally, and the exit status of the script is the exit status of report_result, which is 0. Net effect on the path you actually hit: a 100%-failed run now emits both an ERROR line and a PASS: MT-Bench AR evaluation report marker, and still exits 0.
Why this is worth flagging rather than waving through: the sibling script already carries this exact fix, with a comment naming this exact failure mode. At dflash_online_training.sh:54-57 you will find trap 'error_handler $0 $LINENO' ERR followed by trap 'exit_handler' EXIT, and the comment between them reads "Without this, the ERR trap sets FAIL_EXIT=1 but the script still exits 0, masking training failures (e.g. CUDA OOM) as Slurm SUCCEEDED." That is the same bug class this PR is fixing, one layer out, already diagnosed and fixed next door.
Suggested change in ar_eval_mtbench.sh: add trap 'exit_handler' EXIT alongside the existing ERR trap, and gate the PASS marker so it is not emitted on failure. The EXIT trap alone corrects the exit code but still prints PASS, which matters if the harness greps the REPORT markers rather than reading the exit status. Something like:
if ! CUDA_VISIBLE_DEVICES=0 python3 .../ar_validate.py --model_path "$MODEL_PATH" --per_category "$@" ; then
report_result "FAIL: MT-Bench AR evaluation"
exit 1
fi
report_result "PASS: MT-Bench AR evaluation"Entirely reasonable to split this into its own PR, since it is a different file and a different layer. But I would not merge this one believing the silent-PASS symptom is closed, because on the launcher path it is not. If you do split it, worth noting in the PR body so the next person reading "this bit us on a real run" does not assume the wrapper is covered.
On the diff itself
Unchanged from 638ad6a, which I verified in detail last round and still stand behind: guard ordering puts attempted <= 0 ahead of not results, so --num_samples 0 cannot produce "all 0 samples failed"; attempted genuinely equals the loop trip count because validate_ar() clamps identically at line 63; len(ds) is safe on the train split; rank-0-only reporting is preserved, and there is no collective between accelerator.prepare() and either raise, so no rank can hang on a peer that already died.
I also checked every in-repo invocation for a backward-compat break from the new ValueError. tests/examples/speculative_decoding/test_eagle.py:177 passes --num_samples 5, tests/regression/torch/speculative/test_dflash.py:160 passes 3, and train_eagle3_and_export.sh:53, README.md:140, and the launcher all take the default 80. Nothing passes 0 or relies on 0-means-all, so the new guard cannot break a currently-working caller.
Carried over, not re-raised as findings
- My earlier suggestion about the non-positive guard running after the multi-GPU model load, and duplicating the
min(num_samples, len(ds))clamp across lines 63 and 129 where it can drift, stands as a non-blocking nit. The CONTRIBUTING guidance to validate external input once at the interface boundary argues for hoisting it above thevalidate_ar()call, but it is cosmetic. - The partial-failure rate bound (79/80 failing still exits 0): agreed it deserves its own PR, since it changes behavior for currently-passing runs.
- The
eagle_utils.pytuple-summing bug is confirmed real, and out of scope here as you say. One correction for the codeowner: it is at line 435, not 415, and the same expression is repeated in thewandb.logcall on line 437. Both sit inside thetrywhoseexcept Exceptionprints "AR validation not available." and swallows theTypeError, so in-training AR validation never reports a number and never reaches W&B. A fix needs a generator over the tuple values, an empty-list guard, and a narrower except clause.
Assessment
Low risk to merge. The diff is small, surgical, has no public API, mode-state, modelopt_state, or export surface, and is strictly better than main. My one blocking-flavored finding is not a defect in these lines; it is that the fix stops one layer short of the symptom it was written to eliminate. I am raising it as IMPORTANT rather than a suggestion because the motivating incident behind this PR is not resolved by the PR as it stands, and the adjacent script shows the missing piece is already understood in this repo. Happy to see it handled here or in an immediate follow-up.
1 participant
What does this PR do?
Type of change: Bug fix
validate_ar()catches per-sample exceptions, prints aWARNING, and returns whatever succeeded. When every sample failed it returned an empty list, and the reporting block was guarded byif results and accelerator.is_main_process:— so the script printed no results and exited 0. A run where 100% of samples failed was indistinguishable from a successful one.This bit us on a real run: an EAGLE3 checkpoint loaded with
device_map="auto"was sharded across 8 GPUs, every one of the 80 samples died withExpected all tensors to be on the same device, and the job still exited 0 with no AR number anywhere in the log — the wrapper stamped it PASS.Now it raises, so the caller sees a non-zero exit. Any previously "passing" run that printed no AR number was never meaningful.
Usage
No API change. Existing invocations are unaffected when at least one sample succeeds:
python examples/speculative_decoding/scripts/ar_validate.py \ --model_path <ckpt> --steps 3 --osl 1024 --num_samples 80Testing
Reproduced the silent-pass on a Cosmos3-Nano EAGLE3 checkpoint (80/80 samples failing): before this change the job exited 0 and stamped PASS; after it, the job exits non-zero with the sample failures visible. Confirmed the normal path is unchanged by a subsequent run that completed 80/80 and printed AR 3.42.
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