Address DASC review feedback - #2387
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Signed-off-by: kaix-nv <kaix@nvidia.com>
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📝 WalkthroughWalkthroughDASC now supports storage-dtype-aware decay policies, stricter GDN discovery, recalibration through state replacement, stale-policy warnings, and expanded checkpoint and validation tests. Documentation describes the updated retention metric, adapter support, and policy lifecycle. ChangesDASC sparsity lifecycle
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~45 minutes Sequence Diagram(s)sequenceDiagram
participant calibrate
participant ModeloptStateManager
participant replace_dasc_mode
participant DASCPolicy
calibrate->>ModeloptStateManager: detect existing DASC state
calibrate->>replace_dasc_mode: pass normalized config and measurements
replace_dasc_mode->>DASCPolicy: rebuild policy
replace_dasc_mode->>ModeloptStateManager: replace DASC state and metadata
Merge Risk: 🟡 Moderate · up to DASC now records a checkpoint storage dtype for the decay parameters, but the retained-head selection is still computed at full precision. When float16 or bfloat16 storage is configured and a head sits exactly at the retention boundary, exporting the policy for a reloaded model can fail even though calibration succeeded. Checkpoint loading still works with a warning, so the impact is limited to policy export, but the boundary handling should be resolved or explicitly accepted before merge. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
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| retained = [head for head, horizon in enumerate(values) if horizon > policy.selected_wmax] | ||
| if retained != layer.retained_heads: | ||
| raise ApplyModeError( | ||
| f"DASC policy head mask does not match current decay parameters in layer {name!r}" | ||
| ) | ||
| lower, upper = _storage_cast_horizon_bounds( |
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[IMPORTANT Algorithm] The exact head-mask comparison here defeats the storage-cast tolerance the rest of this function is built to provide.
static_horizons gets a carefully composed inverse-rounding interval via _storage_cast_horizon_bounds (lines 440-450), but retained is re-derived with a strict horizon > policy.selected_wmax on the post-cast parameters and compared for exact list equality. A head whose calibration-time horizon sits within one storage ulp of selected_wmax will flip sides after a legitimate BF16/FP16 round-trip and export_policy() hard-fails — exactly the scenario decay_parameter_storage_dtype was added to accommodate. The test names acknowledge this gap (test_dtype_cast_preserves_policy_when_the_selected_mask_is_unchanged, ..._when_the_selected_mask_is_unchanged), but nothing covers the flip case and nothing documents it.
Why it matters: BF16 relative error is ~4e-3, so on a real Qwen3-Next-scale model (~48 GDN layers × 32+ heads) the chance that at least one head's horizon lands in the ~0.8%-wide ambiguity band around Wmax is non-trivial. When it happens the user gets "DASC policy head mask does not match current decay parameters in layer 'X'" — which reads as tampering/drift, not as sub-ulp rounding — and the only stated remedy is full recalibration. The deployed mask is authoritative (a serving backend reads policy.layers[...].retained_heads, not re-derived horizons), and a boundary head's horizon is ≈ Wmax either way, so the quality evidence still holds.
Suggested fix: decide the mask from the same bound interval instead of the point estimate — only reject when a head is unambiguously on the other side of selected_wmax:
lower, upper = _storage_cast_horizon_bounds(
modules[name],
epsilon=policy.epsilon,
static_gate_input=policy.static_gate_input,
storage_dtype=_STORAGE_DTYPES[policy.decay_parameter_storage_dtype],
)
declared_retained = set(layer.retained_heads)
for head in range(len(values)):
# A head is only a real mismatch when its whole admissible interval
# falls on the side opposite to the declared mask.
if head in declared_retained and upper[head] <= policy.selected_wmax:
raise ApplyModeError(...)
if head not in declared_retained and lower[head] > policy.selected_wmax:
raise ApplyModeError(...)If instead the strictness is intentional, please say so in the docstring and in the guide, and add a regression test that pins the flip-at-the-boundary failure so the contract is explicit rather than incidental.
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Valid. Fixed in #2388 with the combined contract: calibration uses storage-canonical values, while export accepts a mask only when the inverse cast interval can explain the boundary side. It still rejects a declared retained/omitted choice when the full admissible interval lies on the opposite side of strict horizon > Wmax.
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Inline comments:
In `@modelopt/torch/sparsity/state_sparsity/policy.py`:
- Around line 350-351: Update build_dasc_policy and its _analyze_gdn_modules
flow to canonicalize A_log and dt_bias using the configured storage dtype before
computing horizons, measurement geometry, and retained-head masks. Ensure
validation and export use the same cast-normalized values, or reject candidates
when casting changes the computed mask; preserve correct strict horizon >
selected_wmax behavior, with boundary coverage around selected_wmax.
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docs/source/guides/6_sparsity.rstmodelopt/torch/sparsity/state_sparsity/__init__.pymodelopt/torch/sparsity/state_sparsity/api.pymodelopt/torch/sparsity/state_sparsity/config.pymodelopt/torch/sparsity/state_sparsity/conversion.pymodelopt/torch/sparsity/state_sparsity/mode.pymodelopt/torch/sparsity/state_sparsity/policy.pytests/unit/torch/sparsity/state_sparsity/test_dasc.py
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| """Decay analysis and policy selection for GDN state sparsity.""" | ||
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| import hashlib | ||
| import importlib |
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[SUGGESTION] import importlib does not bind the importlib.util submodule — CPython's importlib/__init__.py never imports it, and the docs call this out explicitly. Line 58 (importlib.util.find_spec(root_module)) therefore only works because some other import in the process (torch, which this module imports) happens to have pulled in importlib.util as a side effect.
Why it matters: that call sits inside the except ModuleNotFoundError handler, so an AttributeError there would escape _supported_gdn_classes() rather than be caught by the sibling except Exception. The affected path — "megatron-core is not installed" — is the common case for most users, so the graceful-skip branch this code was written to provide is the one relying on an incidental third-party import. The test suite would not catch a regression either, since test_supported_class_resolution_uses_imported_module_identities also reaches through dasc_policy.importlib.util.
One-line fix:
| import importlib | |
| import importlib | |
| import importlib.util |
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Valid. Fixed in #2388 by importing importlib.util explicitly; the graceful missing-framework path no longer depends on a transitive import side effect.
| 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. |
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[SUGGESTION] "the default float32 keeps unconfigured policies strict" is not accurate for the most common deployment shape, and a wrong doc here is worse than none because it is the user's only guidance on when they must set this field.
_storage_rounding_radius always folds in tensor.dtype alongside storage_dtype (cast_dtypes = tuple(dict.fromkeys((storage_dtype, tensor.dtype)))). So for a model whose A_log/dt_bias are live in BF16 — the normal case for a Qwen3-Next checkpoint loaded with torch_dtype="bfloat16" — leaving decay_parameter_storage_dtype at the float32 default still yields a BF16-wide tolerance (~4e-3 relative), not a strict FP32 one. The default is only strict when the live tensors are also FP32.
Suggested rewording:
| 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. | |
| calibration. Policy validation allows only the rounding introduced by that declared storage dtype | |
| and the live tensor dtype, so the effective tolerance is the wider of the two. The default | |
| ``float32`` adds no storage slack of its own; a model whose decay tensors are live in BF16 or FP16 | |
| is still validated against that live dtype's rounding. |
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Valid. Fixed in #2388. The guide now states that effective validation tolerance includes both declared storage and live tensor dtypes, and that default float32 adds no storage slack of its own.
| try: | ||
| validate_dasc_model_structure(model, policy) | ||
| except ApplyModeError as error: | ||
| warnings.warn( | ||
| f"{error}. The restored DASC policy is stale; re-run calibrate() before deployment", | ||
| stacklevel=2, | ||
| ) | ||
| _attach_policy(model, policy) |
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[SUGGESTION] Downgrading validate_dasc_model_structure to a warning is the right call for structure drift, but the try also swallows the two "this is not even a GDN model" failures that _get_gdn_modules raises: "DASC found no supported GDN modules; expected one of: ..." and "DASC found GDN subclasses that are not ModelOpt dynamic modules at: ...".
Net effect: mto.restore_from_modelopt_state(resnet, dasc_state) now succeeds with a UserWarning and attaches a DASC policy to a model that has no GDN layers at all. That is a different class of error from "the checkpoint's layer geometry moved" — the first is an unrecoverable architecture mismatch that no amount of recalibration on this model fixes, the second is the stale-policy case this PR intentionally made survivable. Warnings are also easy to lose under -W ignore or a library that filters them, so the mismatch can go unnoticed until export_policy() much later.
Consider separating the two, e.g. resolve the modules up front and let a hard "no GDN modules" failure propagate while only geometry drift warns:
try:
_get_gdn_modules(model) # architecture-level check: fail closed
except ApplyModeError:
raise
...or give validate_dasc_model_structure a flag / distinct exception subclass so restore_dasc_model can tell "wrong architecture" from "stale geometry". Non-blocking, but it would make the relaxation targeted rather than blanket.
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Valid. Fixed in #2388 by distinguishing recoverable GDN geometry drift from unsupported architecture. Restore now warns only for the former and fails closed when the target has no supported GDN modules; a regression covers restoring onto nn.Linear.
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Claude review — DASC review-feedback consolidation
Scope: trigger comment was a bare /claude review, so this is a full pass. All 8 changed files reviewed (741+/68-): the 6 modelopt/torch/sparsity/state_sparsity/ files in full, plus tests/unit/torch/sparsity/state_sparsity/test_dasc.py and docs/source/guides/6_sparsity.rst. Nothing was skipped.
Findings — CRITICAL: 0 · IMPORTANT: 1 · SUGGESTION: 3
| Severity | File | Issue |
|---|---|---|
| IMPORTANT Algorithm | policy.py:433 |
Exact head-mask equality defeats the storage-cast tolerance built in the same function |
| SUGGESTION | policy.py:19 |
importlib.util used without importing it; latent AttributeError on the megatron-absent path |
| SUGGESTION | 6_sparsity.rst:186 |
"default float32 keeps unconfigured policies strict" is inaccurate for BF16-live models |
| SUGGESTION | conversion.py:95 |
Restore warning also swallows "not a GDN model at all" |
Most impactful
The head-mask check is stricter than the horizon check it sits next to. validate_dasc_decay_parameters computes a composed inverse-rounding interval for static_horizons (_storage_cast_horizon_bounds) — the math there is right, including the reversed((storage, live)) cast ordering, the monotonic ±radius box on exp(A_log) * softplus(dt_bias + gate), and the subnormal term. But two lines earlier the retained-head set is re-derived with a hard horizon > selected_wmax and compared for exact list equality. Any head whose horizon sits within one storage ulp of Wmax flips after a legitimate BF16 round-trip and export_policy() fails — the exact case decay_parameter_storage_dtype was introduced to support. Both new dtype tests are scoped to "...when the selected mask is unchanged", so the flip is untested and undocumented. Either widen the mask check to the same interval, or make the strictness an explicit, tested contract.
Verified as correct (no action needed)
- Mode/state composition.
ModeloptStateManager.state_dict()returns the liveself._statelist, soreplace_dasc_mode's in-placestate[first_index] = (...)and reverse-orderdelof duplicate entries do take effect, and the{"config": ..., "metadata": ...}shape matches whatadd_modewrites. Replacing atfirst_indexpreserves restore ordering relative to other modes. - Backward compatibility of the state schema.
decay_parameter_storage_dtypedefaults to"float32"on bothDASCConfigandDASCPolicy, so pre-PR checkpoints satisfy theexpected_configcomparison inrestore_dasc_model. Thedecay_parameters_sha256semantics did change (float64 to storage-canonicalized float32), but that digest is no longer validated anywhere, so old checkpoints do not break. Thelegacy_statetest covers this. - Pydantic config merge.
model_config = ConfigDict(protected_namespaces=())onDASCConfig/DASCPolicymerges with rather than replacesModeloptBaseConfig'sextra="forbid", validate_assignment=True(pydantic v2 merges basemodel_configdicts), so fail-closed validation is retained. validate_dasc_model_structure._model_structureemits exactly{"name", "num_heads"}, matching the reconstructedpolicy_structure, and both sides sort by name — the new dict-equality check does not spuriously fail alongside the digest check.- Return-value semantics. The new
unwrap_model(model, force_unwrap=True)incalibratedoes not change what callers get back:apply_modealready unwrapped and returned the inner module. - Relaxed
perplexity_retention. Removing thele=1.0bound is consistent with the documenteddense_ppl / dasc_ppldefinition, and the matching relaxation ofmin_perplexity_retentionkeeps config and measurement domains aligned. - Plugin laziness is otherwise handled correctly — neither megatron nor transformers is hard-imported, resolution is
lru_cached, and absent roots skip silently.
Risk assessment
Low-to-moderate. This is metadata-only: DASC does not alter execution, quantize state, or touch any export path, so there is no TRT-LLM/HF/ONNX exposure and no released-checkpoint migration risk (the base is the unreleased feature/dasc-state-sparsity branch). Test coverage of the new behaviour is genuinely good — lifecycle, tamper, lossy-cast, cross-dtype reload, wrapper handling, mixed-layer rejection, and legacy-state restore are all pinned. The one IMPORTANT finding is a false-rejection risk at deployment time rather than a silent-corruption risk, so it fails safe; it should still be resolved (fixed or documented) because it undercuts a headline goal of the PR.
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## feature/dasc-state-sparsity #2387 +/- ##
===============================================================
+ Coverage 78.71% 78.78% +0.07%
===============================================================
Files 548 548
Lines 64088 64243 +155
===============================================================
+ Hits 50446 50615 +169
+ Misses 13642 13628 -14
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Addresses the fresh full-diff review findings on #2387. Changes: - derive calibration horizons, geometry, and masks from decay parameters canonicalized to the declared checkpoint storage dtype - validate boundary masks with inverse storage/live-dtype intervals while preserving the strict `horizon > Wmax` rule - fail closed when restoring DASC metadata onto a model with no supported GDN modules, while retaining stale-policy warnings for recoverable GDN geometry drift - import `importlib.util` explicitly and correct the storage-tolerance documentation - add FP16 boundary-flip and wrong-architecture restore regressions Validation: - `python -m pytest -q tests/unit/torch/sparsity/state_sparsity/test_dasc.py`: 24 passed, 1 skipped (optional Megatron dependency) - state + weight + attention sparsity tests: 302 passed, 1 skipped - pre-commit on all touched files: passed - real Transformers `Qwen3NextGatedDeltaNet` CPU smoke: BF16-declared calibration, BF16 reload, and policy export round-trip 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 sparsity policy validation to account for rounding in both stored and live tensor data types. * Policies now tolerate valid decay-related rounding differences while rejecting masks that are inconsistent with current parameters. * Improved handling and messaging for unsupported model structures during state restoration. * **Tests** * Added coverage for storage-precision threshold behavior and unsupported model structures. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: kaix-nv <kaix@nvidia.com>
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Summary
Consolidates the complete reviewed fix stack for #2375 into one DCO-safe commit:
This consolidated PR supersedes the mechanically stacked review-fix PRs #2377, #2378, #2379, #2380, #2382, #2383, #2384, and #2385. Its tree is byte-identical to the independently reviewed leaf commit from #2386.
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New Features
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