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📝 WalkthroughWalkthroughDASC policy validation now uses storage-dtype decay horizons and interval bounds. Restoration distinguishes model-structure mismatches from other application errors. Tests cover storage-induced threshold changes and unsupported model structures. ChangesDASC policy validation
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🟡 Moderate · up to Malformed decay parameters can yield invalid exported DASC policies or inconsistent validation failures. Restore the established input validation before merge. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
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| modules, | ||
| epsilon=config.epsilon, | ||
| static_gate_input=config.static_gate_input, | ||
| storage_dtype=_STORAGE_DTYPES[config.decay_parameter_storage_dtype], |
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[IMPORTANT Compatibility] Calibration geometry is now storage-canonical, but the public helper callers use to reproduce it is not.
build_dasc_policy now derives horizons from parameters cast to decay_parameter_storage_dtype, and those same horizons feed _validate_measurement_geometry (line 372), which compares them against the caller-supplied measurement.retained_heads / total_heads. But analyze_gdn_decay — the documented public entry point (__all__, re-exported from state_sparsity/__init__.py) — still takes only epsilon / static_gate_input and calls _analyze_gdn_modules with storage_dtype=None, i.e. it keeps returning live-dtype horizons.
Why it matters: with decay_parameter_storage_dtype="float16" and live FP32 params, a head whose live horizon is 7.01 and storage-canonical horizon is 6.99 is retained according to analyze_gdn_decay but omitted in the policy. A caller who sized their paired evaluation off analyze_gdn_decay (or off the mask they actually evaluated) now reports a retained_heads count that is off by one and calibrate() hard-fails with DASC measurement geometry for Wmax=... does not match the model. There is no public way to reproduce the new geometry short of manually casting each A_log/dt_bias and calling compute_gdn_decay_horizons — which is exactly what the new test does (test_calibration_uses_storage_canonical_mask_at_wmax_boundary hand-sets measurement["retained_heads"] = 0). The guide's example measurement block still shows a bare "retained_heads": 40 with no hint that it must be derived under the declared storage dtype.
Secondary consequence worth a note in the guide: at the boundary the reported quality metrics were measured against the live mask, so they describe a slightly different head set than the policy encodes.
Suggested fix: give analyze_gdn_decay an optional storage-dtype argument that forwards to _analyze_gdn_modules (keeping the current default so existing callers are unaffected), and state in docs/source/guides/6_sparsity.rst that retained_heads/total_heads must be derived under decay_parameter_storage_dtype.
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Valid. Fixed in #2389. The public analyze_gdn_decay API now accepts the same optional decay_parameter_storage_dtype used by calibration, and the guide requires deriving evaluated masks plus retained/total counts with it. The FP16 boundary regression now exercises this public path.
| try: | ||
| validate_dasc_model_structure(model, policy) | ||
| except ApplyModeError as error: | ||
| except _DASCModelStructureMismatchError as error: |
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[IMPORTANT Compatibility] Narrowing this except makes the save and restore paths disagree, so a checkpoint that saves with a warning can become unloadable.
validate_dasc_model_structure calls _get_gdn_modules, which raises a plain ApplyModeError for three distinct conditions (policy.py:169-181): no supported GDN modules, supported identities missing A_log/dt_bias, and unconverted GDN subclasses. Only the geometry digest mismatch now raises _DASCModelStructureMismatchError, so all three _get_gdn_modules failures propagate out of restore_dasc_model.
Meanwhile update_dasc_metadata (line 108-118, the save path) still catches broad ApplyModeError and downgrades the same three conditions to a warning. The asymmetry is reachable: if a model reaches one of those states, mto.modelopt_state(model) emits "The saved DASC policy is stale" and writes a checkpoint that still contains the dasc entry — but mto.restore() on that checkpoint then raises. Because DASC state lives in the shared modelopt_state list, that failure aborts the whole restore, taking down the other modes in the same checkpoint with it, even though DASC is metadata-only and does not change execution (per the guide).
Suggested fix: pick one boundary and apply it on both sides. Either raise the same subset on save so an unrestorable checkpoint is never written, or keep the _get_gdn_modules structural errors recoverable on restore and reserve fail-closed for the specific "this policy belongs to a different architecture" case. If the asymmetry is intentional, a one-line comment here naming why restore is stricter than save would keep the next reader from re-broadening the except.
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Valid. Fixed in #2389 by making the save boundary match restore: unsupported/missing GDN architecture errors now propagate on both paths, while only recoverable GDN geometry or decay drift remains serializable with a warning. A regression verifies modelopt_state fails after replacing the GDN layer with nn.Linear.
| declared_retained = set(layer.retained_heads) | ||
| for head, (head_lower, head_upper) in enumerate(zip(lower, upper)): | ||
| retained_is_impossible = ( | ||
| head in declared_retained and head_upper <= policy.selected_wmax | ||
| ) | ||
| omitted_is_impossible = ( | ||
| head not in declared_retained and head_lower > policy.selected_wmax | ||
| ) | ||
| if retained_is_impossible or omitted_is_impossible: | ||
| raise ApplyModeError( | ||
| "DASC policy head mask does not match current decay parameters in layer " | ||
| f"{name!r}" | ||
| ) |
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[SUGGESTION] This per-head interval check looks unreachable, so the error it raises can never surface.
Two invariants already hold by the time this loop runs:
DASCPolicy.validate_policy(config.py:241-248) enforcesretained_heads == [h for h, v in enumerate(static_horizons) if v > selected_wmax]on every construction path — restore builds the policy viaDASCPolicy(**metadata["policy"]), andbuild_dasc_policyreturns a validated instance — so the declared mask is always exactly the mask derived fromstatic_horizons.- The bounds check immediately below accepts only
lower * (1 - slack) <= stored <= upper * (1 + slack).
Combining them: head in declared_retained implies static_horizons[head] > selected_wmax, and (2) implies static_horizons[head] <= upper * (1 + slack), so head_upper <= selected_wmax requires upper and wmax to sit within 32 * eps(float64) of each other. The omitted branch is symmetric. In other words neither retained_is_impossible nor omitted_is_impossible can fire outside a ~1e-14 relative window, and the "horizons do not match" error below fires first for any real drift.
Not a correctness bug — the relaxation this PR wants (accepting a legitimate storage-boundary flip) is achieved correctly by the bounds check alone. But per the "remove dead code" / simplicity guidance in CONTRIBUTING, dropping the loop would leave validate_dasc_decay_parameters with one comparison to reason about instead of two overlapping ones. If it is kept deliberately as defense-in-depth against a future policy schema that no longer ties the mask to static_horizons, a short comment saying so would be worth more than the code itself.
| Set ``decay_parameter_storage_dtype`` to the checkpoint dtype for ``A_log`` and ``dt_bias`` before | ||
| calibration. Policy validation allows only the rounding introduced by that declared storage dtype | ||
| and the live tensor dtype; the default ``float32`` keeps unconfigured policies strict. | ||
| and the live tensor dtype, so the effective tolerance is the wider of the two. The default |
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[SUGGESTION] "the wider of the two" doesn't match what the code computes.
_storage_rounding_radius (policy.py:263-273) iterates over both dtypes and applies each one's eps/2 inflation in sequence:
for dtype in reversed(cast_dtypes):
...
upper = (upper + smallest_subnormal) / (1.0 - unit_roundoff)so the resulting relative tolerance is the composition (1 + u_storage)(1 + u_live) - 1 ≈ u_storage + u_live, not max(u_storage, u_live). The two coincide numerically only when one dtype dominates (e.g. declared FP32 storage with BF16-live tensors, where the BF16 term swamps the FP32 one) — which is probably why this reads as correct in the common case, but it is wrong for FP16 storage with FP16-live tensors, where the true tolerance is roughly double what this sentence promises.
Since correcting this paragraph is one of the PR's stated goals, consider "so the tolerances of the two casts compose" (or just "so both casts' rounding is allowed") instead of "so the effective tolerance is the wider of the two".
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Claude review — DASC storage-boundary validation
Scope: full review of the PR (4 files, +90/-23): modelopt/torch/sparsity/state_sparsity/policy.py, conversion.py, docs/source/guides/6_sparsity.rst, and tests/unit/torch/sparsity/state_sparsity/test_dasc.py. Small enough that nothing was skipped. I also read state_sparsity/config.py (unchanged) because the mask-validation change only makes sense against the DASCPolicy pydantic invariants.
Findings: CRITICAL: 0, IMPORTANT: 2, SUGGESTION: 2
What holds up
The core fix is sound. Deriving horizons from storage-canonicalized A_log/dt_bias in build_dasc_policy makes the persisted mask agree with the values that actually land in the checkpoint, and the interval form of _storage_cast_horizon_bounds is correct: horizon = -log(eps) / (exp(A_log) * softplus(dt_bias + g)) is monotonically decreasing in both parameters, so perturbing by +radius gives the lower bound and -radius the upper, and softplus stays positive so no sign flip is possible. Ordering the floating-point-dtype guard in validate_dasc_decay_parameters before _storage_cast_horizon_bounds is deliberate and necessary — _storage_rounding_radius calls torch.finfo(tensor.dtype), which raises TypeError on integer dtypes. The importlib.util import genuinely matters: _supported_gdn_classes calls importlib.util.find_spec, and relying on importlib.import_module to have populated the submodule was a latent AttributeError. The new tests exercise real code paths rather than mocks, including a genuine FP16 boundary flip with hand-computed values.
IMPORTANT (2)
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Calibration geometry is now storage-canonical, but the public helper that reproduces it is not (
policy.py:370)._validate_measurement_geometrycompares the caller-suppliedretained_heads/total_headsagainst storage-canonical horizons, whileanalyze_gdn_decay— the documented public entry point — still returns live-dtype horizons. At theWmaxboundary those disagree, so a caller who sized their paired evaluation off the public helper getscalibrate()rejecting valid measurements, with no public way to reproduce the new geometry. The new test works around this by hand-settingretained_headsto 0; the guide's example measurement block still shows a bareretained_heads: 40with no hint that it must be derived under the declared storage dtype. -
Narrowing the
exceptinrestore_dasc_modeldesynchronizes save from restore (conversion.py:101)._get_gdn_modulesraises plainApplyModeErrorfor three conditions beyond the geometry digest mismatch. Restore now propagates all of them whileupdate_dasc_metadatastill downgrades the same three to a warning — so a model in one of those states writes a checkpoint that then cannot be restored, and because DASC shares themodelopt_statelist the failure aborts loading the other modes too, even though DASC is metadata-only. The fail-closed intent is right; the two paths just need to agree on where the line is.
SUGGESTION (2)
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The new per-head
retained_is_impossible/omitted_is_impossibleloop (policy.py:458-470) appears unreachable:DASCPolicy.validate_policyalready pins the mask tostatic_horizons, and the bounds check below pinsstatic_horizonsinto the same interval, so neither branch can fire outside a ~1e-14 relative window. Not a bug — the intended relaxation is achieved by the bounds check alone — but worth either dropping or annotating. -
The guide's "the effective tolerance is the wider of the two" does not match
_storage_rounding_radius, which composes both casts.
Correction to that last thread: the FP16-storage-with-FP16-live example I gave there is wrong — tuple(dict.fromkeys(...)) dedups the equal case, so only one inflation applies. The imprecision is real but narrower than I wrote: composition only occurs when the two dtypes differ, and the worst case is BF16 storage with FP16-live tensors (or the reverse) at 2^-9 + 2^-11, about 25% wider than "the wider of the two" promises. Please read that finding with this magnitude in mind; the suggested wording change still stands.
Risk
Low-to-moderate. No algorithmic or numerical defect in the validation math, and no modelopt_state schema change, so existing DASC checkpoints keep loading. Both IMPORTANT findings are about the contract at the edges rather than the fix itself: finding 1 can reject legitimate calibration runs at the boundary, and finding 2 can make a savable checkpoint unloadable. Both are contained within the DASC feature, which is experimental and metadata-only.
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Inline comments:
In `@docs/source/guides/6_sparsity.rst`:
- Around line 186-188: Update the configuration guidance near the live tensor
dtype discussion to explain that validation composes the storage-dtype and
live-dtype rounding bounds in sequence, rather than selecting only the wider
individual tolerance. Clarify that _storage_rounding_radius provides the
combined bound used by _storage_cast_horizon_bounds for A_log and dt_bias
validation, while preserving the existing float32 and BF16/FP16 behavior
details.
In `@modelopt/torch/sparsity/state_sparsity/policy.py`:
- Around line 450-451: Restore full decay-parameter validation before
_storage_cast_horizon_bounds: require non-empty, one-dimensional, equal-shape,
finite floating-point A_log and dt_bias tensors. Extract or reuse a shared
validator from compute_gdn_decay_horizons so both paths enforce identical shape,
cardinality, dtype, and finiteness checks, and raise the established
ApplyModeError for invalid inputs before calculating bounds or allowing
export_policy() to proceed.
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## feature/dasc-state-sparsity-review-final #2388 +/- ##
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Also addressed both review-summary suggestions: the composed-bound wording is corrected in #2390. The per-head impossibility check is intentionally retained before the numeric provenance check because it preserves the more actionable mask-mismatch diagnosis for self-consistent tampered policy metadata; |
Addresses the completed CodeRabbit review findings on #2388. Changes: - extract one decay-tensor validator shared by horizon analysis and export validation - reject empty, non-1D, unequal-shape, non-floating, or non-finite decay tensors before inverse-bound calculations - add a NaN export regression - document the actual sequential composition of distinct storage/live dtype rounding bounds and single counting when dtypes match Validation: - focused DASC tests: 24 passed, 1 skipped (optional Megatron dependency) - pre-commit on all touched files: passed Commit is ED25519-signed and carries a matching Signed-off-by trailer. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * Improved validation of sparsity decay parameters, including checks for valid shapes, floating-point types, and finite values. * Deployment validation now rejects invalid or non-finite decay parameters. * **Documentation** * Clarified rounding-bound behavior when storage and live tensor data types differ. * **Tests** * Added coverage for rejecting calibrated models with non-finite decay parameters. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: kaix-nv <kaix@nvidia.com>
Addresses the follow-up review findings on #2388. Changes: - expose optional `decay_parameter_storage_dtype` canonicalization through public `analyze_gdn_decay`, so callers can derive exactly the geometry consumed by calibration - document that evaluated masks and measurement head counts must use the same declared storage dtype - make unsupported-architecture save and restore both fail closed - keep GDN geometry/decay drift serializable with a warning and deployment-blocked until recalibration - add public analysis and save/restore symmetry regressions Validation: - focused DASC tests: 24 passed, 1 skipped (optional Megatron dependency) - state + weight + attention sparsity tests: 302 passed, 1 skipped - pre-commit on all touched files: passed Commit is ED25519-signed and carries a matching Signed-off-by trailer. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Calibration now accounts for checkpoint storage formats (float16, bfloat16, and float32) when analyzing decay behavior. - Redundant precision-rounding slack is excluded when storage formats are compatible. - Invalid decay parameters and storage-format selections are clearly rejected. - **Bug Fixes** - Recoverable structure or decay mismatches now allow metadata updates to continue with warnings. - Checkpoint serialization fails safely when a calibrated GDN layer is replaced with an unsupported module. - **Documentation** - Clarified calibration parameter requirements and sequential handling of rounding bounds. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: kaix-nv <kaix@nvidia.com>
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Addresses the fresh full-diff review findings on #2387.
Changes:
horizon > Wmaxruleimportlib.utilexplicitly and correct the storage-tolerance documentationValidation:
python -m pytest -q tests/unit/torch/sparsity/state_sparsity/test_dasc.py: 24 passed, 1 skipped (optional Megatron dependency)Qwen3NextGatedDeltaNetCPU smoke: BF16-declared calibration, BF16 reload, and policy export round-trip passedCommit is ED25519-signed and carries a matching Signed-off-by trailer.
Summary by CodeRabbit
Bug Fixes
Tests