Add Qwen 3.5 0.8B VLM pruning campaign - #2290
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Share one pinned final-evaluation contract between an FFN-only campaign and a bounded regression, with resumable student-teacher measurements. Signed-off-by: Johannes Rausch <jrausch@nvidia.com>
Keep the bounded quality regression independent from the larger campaign while composing both from one pinned evaluator specification. Signed-off-by: Johannes Rausch <jrausch@nvidia.com>
Signed-off-by: Johannes Rausch <jrausch@nvidia.com>
Signed-off-by: Johannes Rausch <jrausch@nvidia.com>
Signed-off-by: Johannes Rausch <jrausch@nvidia.com>
Signed-off-by: Johannes Rausch <jrausch@nvidia.com>
Signed-off-by: Johannes Rausch <jrausch@nvidia.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughThe PR adds modality-specific Qwen 3.5 VLM campaign setup, resumable evaluation, repeated end-to-end quality evaluation, checkpoint provenance checks, and pipeline execution updates. It also adds configurations, documentation, and tests for these flows. ChangesPuzzletron VLM workflows
Estimated code review effort: 4 (Complex) | ~60 minutes Merge Risk: ⚪ Minimal · up to This PR adds an opt-in VLM pruning campaign and related guided setup and evaluation configuration without any supplied actionable merge-blocking risk; it is merge-ready after normal checks and review. Sequence Diagram(s)sequenceDiagram
participant SetupWizard
participant RecommendedFlow
participant VLMEvaluator
participant CompletedRunRecord
SetupWizard->>RecommendedFlow: pass text or multimodal modality
RecommendedFlow->>VLMEvaluator: configure post-MIP quality evaluation
VLMEvaluator->>CompletedRunRecord: load matching completion
alt no valid completion
VLMEvaluator->>VLMEvaluator: run evaluation repetition
VLMEvaluator->>CompletedRunRecord: persist result atomically
end
Caution Pre-merge checks failedPlease resolve all errors before merging. Addressing warnings is optional.
❌ Failed checks (1 error, 1 warning)
✅ Passed checks (4 passed)
Full details: Security Anti-PatternsExplanation CRITICAL security anti-patterns were introduced in production Python changes. The feature-range diff adds Resolution Remove all added
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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Inline comments:
In `@examples/puzzletron/evaluation/vlm/evaluator.py`:
- Around line 87-88: Update _load_completed_run so a non-matching payload
identity returns None instead of raising RuntimeError, allowing the evaluation
repetition to be recomputed; preserve the existing completed-run return path
when the identities match.
In `@examples/puzzletron/evaluation/vlm/preflight.py`:
- Line 224: Change the log_samples configuration in the evaluation runner to
False so per-document prompt, target, and output logging is disabled by default;
retain support for explicit opt-in through the existing configuration or flag.
In `@tests/unit/torch/puzzletron/evaluation/test_checkpoint.py`:
- Around line 78-82: Update the monkeypatched
checkpoint.importlib.metadata.distribution in the test to raise
PackageNotFoundError for “lmms-eval” instead of returning _distribution(None),
so the test exercises the missing-distribution path for an imported checkout
without package metadata.
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Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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📒 Files selected for processing (29)
examples/puzzletron/README.mdexamples/puzzletron/configs/families/qwen3_5/qwen3p5_0p8b/runs/e2e_vlm_quality_comparison.yamlexamples/puzzletron/configs/families/qwen3_5/qwen3p5_0p8b/runs/vlm_campaign.yamlexamples/puzzletron/configs/families/qwen3_5/qwen3p5_0p8b/vlm_quality_evaluation.yamlexamples/puzzletron/configs/families/qwen3_5/setup_v2_defaults.yamlexamples/puzzletron/docs/qwen3p5_0p8b_vlm_smoke.mdexamples/puzzletron/docs/setup_wizard.mdexamples/puzzletron/evaluation/checkpoint.pyexamples/puzzletron/evaluation/vlm/evaluator.pyexamples/puzzletron/evaluation/vlm/model.pyexamples/puzzletron/evaluation/vlm/post_mip.pyexamples/puzzletron/evaluation/vlm/preflight.pyexamples/puzzletron/evaluation/vlm/run.pyexamples/puzzletron/evaluation/vlm/suites.pyexamples/puzzletron/run_post_mip_node.pymodelopt/torch/puzzletron/post_mip/filters.pymodelopt/torch/puzzletron/stages/pipeline.pypuzzletron_setup/bundle.pypuzzletron_setup/v2/post_mip.pypuzzletron_setup/v2/wizard.pytests/unit/torch/puzzletron/evaluation/test_checkpoint.pytests/unit/torch/puzzletron/evaluation/vlm/test_run.pytests/unit/torch/puzzletron/test_post_mip_runner.pytests/unit/torch/puzzletron/test_qwen3p5_0p8b_full_smoke_plan.pytests/unit/torch/puzzletron/test_qwen3p5_0p8b_full_vlm_smoke_plan.pytests/unit/torch/puzzletron/test_scoring_parent.pytests/unit/torch/puzzletron/test_setup_v2_post_mip.pytests/unit/torch/puzzletron/test_setup_v2_quick.pytests/unit/torch/puzzletron/test_sparse_runtime_stats.py
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## feature/puzzletron_v2 #2290 +/- ##
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+ Coverage 50.80% 51.23% +0.43%
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Files 709 709
Lines 92478 92488 +10
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+ Hits 46981 47386 +405
+ Misses 45497 45102 -395
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Treat stale completion markers as cache misses and require explicit opt-in for sample logging. Signed-off-by: Johannes Rausch <jrausch@nvidia.com>
1 participant
What does this PR do?
Type of change: new example, new tests, and bug fix.
Extends the Qwen 3.5 0.8B pruning workflow with an opt-in VLM campaign and guided setup from model selection through VLM evaluation.
A follow-up will evaluate additional pruning axes at 5–15% whole-model pruning, longer distillation, and a wider low-cost candidate screen before full evaluation of finalists.
Testing
Focused unit tests cover campaign composition, guided setup, checkpoint-aware VLM evaluation resume, student-versus-teacher comparison, and checkpoint handoff. Repository hooks passed.
Before your PR is "Ready for review"
CONTRIBUTING.md?: N/A. No dependency was added and no external code was copied.Summary by CodeRabbit
New Features
Bug Fixes
Documentation