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Signed-off-by: Kai Xu <kaix@nvidia.com>
📝 WalkthroughWalkthroughDASC policy handling now resolves supported GDN classes by import path, validates decay horizons against storage casting, refreshes mode state conditionally, and treats structure mismatches during restoration as stale-policy warnings. ChangesDASC policy flow
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: 🔵 Low · up to Unsupported-model errors do not identify which supported implementation path is expected, making configuration and dependency troubleshooting unnecessarily ambiguous. This is a small diagnostic fix and does not block core DASC behavior. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
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| def _storage_rounding_radius(tensor: torch.Tensor) -> torch.Tensor: | ||
| """Bound one cast-to-storage rounding step around the represented tensor values.""" | ||
| values = tensor.detach().to(device="cpu", dtype=torch.float64) | ||
| dtype_info = torch.finfo(tensor.dtype) | ||
| unit_roundoff = dtype_info.eps / 2.0 | ||
| subnormal_slack = dtype_info.tiny * dtype_info.eps | ||
| return values.abs() * (unit_roundoff / (1.0 - unit_roundoff)) + subnormal_slack |
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[IMPORTANT Compatibility] The rounding radius is derived from the tensor's current dtype, so the tolerance collapses to ~0 exactly in the round-trip this replacement was meant to survive.
torch.finfo(tensor.dtype) only reflects a real storage step when the module is still holding the narrow dtype (which is the only case test_dtype_cast_preserves_policy_when_the_selected_mask_is_unchanged exercises — it does model.to(dtype) and leaves it there). The common deployment path is different:
calibrate()on an FP32 model →static_horizonsderived from FP32A_log/dt_bias.- Save in BF16 (
save_pretrained(dtype=torch.bfloat16), or a BF16torch.saveof the state dict). - Reload and materialize in FP32 (
from_pretrained(dtype=torch.float32),.float()for CPU/debug, or an FP32 optimizer master-weight copy).
Now the parameters are FP32 tensors holding BF16-rounded values. unit_roundoff is FP32's (~6e-8), but the actual horizon deviation is one BF16 step (~4e-3 relative). stored falls outside [lower, upper] and validate_dasc_decay_parameters raises → export_policy() fails and mto.save() emits the "stale policy" warning for a checkpoint that is perfectly valid. The old fixed rtol=0.05 was dtype-agnostic and covered this, so this is a regression, not just a tightening.
The file's own provenance digest already encodes the intended tolerance model — _decay_parameters (line 150) canonicalizes to BF16 precisely because "one BF16 storage step" is the accepted loss. Bound against the coarsest plausible storage dtype rather than the current one, taking an elementwise max so a genuinely FP16-resident tensor still gets FP16's (much larger) subnormal slack:
_STORAGE_CAST_DTYPES = (torch.float16, torch.bfloat16)
def _storage_rounding_radius(tensor: torch.Tensor) -> torch.Tensor:
"""Bound one cast-to-storage rounding step around the represented tensor values."""
values = tensor.detach().to(device="cpu", dtype=torch.float64).abs()
radius = torch.zeros_like(values)
for dtype in (tensor.dtype, *_STORAGE_CAST_DTYPES):
dtype_info = torch.finfo(dtype)
unit_roundoff = dtype_info.eps / 2.0
radius = torch.maximum(
radius,
values * (unit_roundoff / (1.0 - unit_roundoff)) + dtype_info.tiny * dtype_info.eps,
)
return radius(torch.finfo is only valid for floating dtypes, so guard or skip when tensor.dtype is integral — not expected for these parameters, but worth an assert.) It would also be worth extending the parametrized cast test with a save-in-bf16 → load-in-fp32 case, since that is the shape of the failure the current test cannot see.
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Addressed in #2382. The rounding interval now takes the elementwise maximum over the current dtype, FP16, and BF16, so a BF16 storage round trip remains valid after reload into FP32. A regression test performs exactly FP32 calibration to BF16 storage rounding to FP32 reload and confirms policy export.
| @lru_cache(maxsize=1) | ||
| def _supported_gdn_classes() -> tuple[type[nn.Module], ...]: | ||
| """Resolve installed GDN implementations without making either framework mandatory.""" | ||
| classes = [] | ||
| for module_name, class_name in _SUPPORTED_GDN_CLASS_PATHS: | ||
| try: | ||
| candidate = getattr(importlib.import_module(module_name), class_name) | ||
| except (AttributeError, ImportError): | ||
| continue | ||
| if isinstance(candidate, type) and issubclass(candidate, nn.Module): | ||
| classes.append(candidate) | ||
| return tuple(classes) |
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[IMPORTANT ModeState] Resolution failures are silent and the real paths are never exercised, so a wrong/moved module path degrades into a misleading user-facing error.
Two coupled problems:
-
Silent skip.
except (AttributeError, ImportError): continueswallows a path that no longer exists._supported_gdn_classes()then returns()(or a partial tuple),_is_gdn_modulereturnsFalsefor every module, and the user sees"DASC found no supported GDN modules; expected one of: GatedDeltaNet, Qwen3NextGatedDeltaNet"on a model that does containGatedDeltaNet. That message actively misdirects debugging. The previous name-based check was immune to module-path drift;megatron.corein particular reorganizes submodules across releases, andtransformers' modular-model tooling relocates/duplicates modeling modules. -
No automated guard. The autouse
_register_test_gdn_classfixture monkeypatches_supported_gdn_classesin every test, andtest_supported_class_resolution_uses_imported_module_identitiesmonkeypatches_SUPPORTED_GDN_CLASS_PATHSto synthetic modules. So no test ever asserts that either entry in_SUPPORTED_GDN_CLASS_PATHSresolves against a real install — the only validation is the manual CPU smoke run in the PR description, which won't catch a future dependency bump.
Also note except (AttributeError, ImportError) is narrower than this repo's optional-dependency convention (modelopt.torch.utils.import_plugin, which catches ModuleNotFoundError and then broad Exception). A Megatron/TE import that raises OSError or RuntimeError on a partially-configured install currently propagates out of _is_gdn_module and breaks DASC for transformers-only users.
Suggested fix: broaden the catch and make the miss visible, e.g.
@lru_cache(maxsize=1)
def _supported_gdn_classes() -> tuple[type[nn.Module], ...]:
"""Resolve installed GDN implementations without making either framework mandatory."""
classes = []
for module_name, class_name in _SUPPORTED_GDN_CLASS_PATHS:
try:
candidate = getattr(importlib.import_module(module_name), class_name)
except ModuleNotFoundError:
continue # framework not installed
except Exception as error: # installed but unusable, or path moved upstream
warnings.warn(f"DASC could not resolve {module_name}.{class_name}: {error!r}")
continue
if isinstance(candidate, type) and issubclass(candidate, nn.Module):
classes.append(candidate)
return tuple(classes)and add a test that skips rather than fakes, so the real identity is checked wherever the dependency exists:
@pytest.mark.parametrize(("module_name", "class_name"), dasc_policy._SUPPORTED_GDN_CLASS_PATHS)
def test_declared_gdn_paths_resolve_when_installed(module_name, class_name):
module = pytest.importorskip(module_name)
assert issubclass(getattr(module, class_name), nn.Module)(that test needs to opt out of the autouse fixture, or read _SUPPORTED_GDN_CLASS_PATHS directly as written above).
Separately, lru_cache caches a failed resolution for the whole process; with the warning above at least the user learns why every subsequent DASC call fails.
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Addressed in #2382. Missing optional frameworks remain silent, but resolution failures for installed frameworks now warn with the fully qualified path and exception. The tests cover missing, broken, invalid, and valid resolution, and separately verify each declared real path wherever its framework is installed.
| def _is_gdn_module(module: nn.Module) -> bool: | ||
| """Accept supported GDN implementations and their ModelOpt dynamic subclasses.""" | ||
| supported_classes = _supported_gdn_classes() | ||
| module_class = type(module) | ||
| is_supported_class = module_class in supported_classes or ( | ||
| isinstance(module, DynamicModule) | ||
| and any(base in supported_classes for base in module_class.__mro__) | ||
| ) | ||
| return ( | ||
| any(base.__name__ in _SUPPORTED_GDN_CLASS_NAMES for base in type(module).__mro__) | ||
| is_supported_class | ||
| and isinstance(getattr(module, "A_log", None), torch.Tensor) | ||
| and isinstance(getattr(module, "dt_bias", None), torch.Tensor) | ||
| ) |
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[SUGGESTION] Requiring isinstance(module, DynamicModule) for any non-exact class means a plain subclass of a supported GDN is now rejected. The new UnsupportedSubclass test shows this is deliberate, and I agree name-based matching was too loose — but the rejection surfaces through _get_gdn_modules as "DASC found no supported GDN modules", which reads as "your model has no GDN layers" rather than "your GDN subclass isn't recognized."
Real cases that hit this: a trust_remote_code model whose modeling file subclasses Qwen3NextGatedDeltaNet, or a downstream research fork that subclasses Megatron's GatedDeltaNet to tweak the gate. Those have identical A_log/dt_bias semantics, so the policy would be valid, but the user gets no hint that subclassing is the cause.
Consider distinguishing the two cases in the error — e.g. have _get_gdn_modules note when it found modules whose MRO contains a supported class but which failed the DynamicModule gate, and say so ("found N GDN subclass(es) that are not ModelOpt dynamic modules; DASC requires the exact supported class"). Cheap to compute on the failure path only, and it turns a dead end into an actionable message.
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Addressed in #2382. If an ordinary subclass has a supported GDN identity in its MRO but is not a ModelOpt DynamicModule, the failure now identifies its module path and explains that an exact supported class is required.
|
| epsilon=policy.epsilon, | ||
| static_gate_input=policy.static_gate_input, | ||
| ) | ||
| modules = _get_gdn_modules(model) |
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[SUGGESTION] analyze_gdn_decay (line 109) already calls _get_gdn_modules internally, so this is a second full named_modules() walk plus a second _is_gdn_module check on every module — and _is_gdn_module now touches the lru_cached importer as well. Not hot-path critical (validation only, small module counts), but it's redundant work and, more importantly, a second independent resolution: if the two walks ever disagree the modules[name] lookup below becomes an unhandled KeyError rather than an ApplyModeError.
Resolving once and passing the dict down (e.g. an internal _analyze_gdn_decay(modules, ...) that both analyze_gdn_decay and this function use) keeps a single source of truth for which modules DASC is validating.
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Addressed in #2382. Module discovery is now performed once, with an internal analysis helper accepting the resolved module mapping. Policy construction and validation no longer repeat named_modules traversal or class resolution.
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Inline comments:
In `@modelopt/torch/sparsity/state_sparsity/policy.py`:
- Line 104: Update the supported-class list construction in the policy
validation flow to retain both module_name and class_name from
_SUPPORTED_GDN_CLASS_PATHS, formatting each entry as the fully qualified
module-and-class path so the resulting error identifies unambiguous supported
classes.
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Claude review — Harden DASC policy identity and restore
Scope: full review of the 3 changed files (modelopt/torch/sparsity/state_sparsity/{conversion,policy}.py, tests/unit/torch/sparsity/state_sparsity/test_dasc.py). Small PR, no files skipped. No prior Claude review on this PR to reconcile against.
Findings
CRITICAL: 0 · IMPORTANT: 2 · SUGGESTION: 2
| # | Severity | Location | Issue |
|---|---|---|---|
| 1 | IMPORTANT Compatibility | policy.py:168-174 |
Storage-rounding radius keyed to the current dtype, not the storage dtype |
| 2 | IMPORTANT ModeState | policy.py:43-54 |
Silent path-resolution failure + no automated guard on the real class paths |
| 3 | SUGGESTION | policy.py:84-96 |
Subclass rejection surfaces as a misleading "no supported GDN modules" |
| 4 | SUGGESTION | policy.py:341 |
Second redundant _get_gdn_modules walk; modules[name] can KeyError |
Most impactful
#1 — the analytic bound doesn't cover the case it replaced. _storage_rounding_radius reads torch.finfo(tensor.dtype), which only describes a real rounding step while the parameter is still resident in the narrow dtype. That is the only shape the parametrized test covers (model.to(dtype) and stays there). The realistic path — calibrate in FP32, save in BF16, reload/materialize in FP32 (from_pretrained(dtype=torch.float32), .float(), or an FP32 master-weight copy) — leaves FP32 tensors holding BF16-rounded values. The radius then uses FP32's unit roundoff (~6e-8) against a deviation of one BF16 step (~4e-3), so validate_dasc_decay_parameters raises: export_policy() fails and mto.save() warns "stale policy" for a valid checkpoint.
The removed rtol=0.05 was dtype-agnostic and did cover this, so it's a regression in the exact direction the PR set out to fix. Note the file's own provenance digest (_decay_parameters, line 150) already canonicalizes to BF16 — "one BF16 storage step" is the module's stated tolerance model, and the new bound should be derived from that (an elementwise max over {current, fp16, bf16} also keeps FP16's much larger subnormal slack). The inline comment has a concrete patch.
#2 — hardcoded module paths fail silently and are never tested. except (AttributeError, ImportError): continue turns a moved-upstream path into (), and the user then gets "DASC found no supported GDN modules; expected one of: GatedDeltaNet, Qwen3NextGatedDeltaNet" on a model that plainly contains one. The autouse _register_test_gdn_class fixture patches the resolver in every test, and test_supported_class_resolution_uses_imported_module_identities patches _SUPPORTED_GDN_CLASS_PATHS to synthetic modules — so nothing in CI asserts that either real path resolves. The only validation is the manual smoke run in the PR description, which won't survive a megatron-core / transformers bump. The catch is also narrower than this repo's import_plugin convention: a Megatron/TE import raising OSError or RuntimeError on a partial install propagates out of _is_gdn_module.
What I verified as correct
- Horizon bound monotonicity.
lower/upperin_storage_cast_horizon_boundscorrectly invert-log(eps) / (exp(A_log) * softplus(dt_bias + gate)): bothexpandsoftplusare increasing, so+radiuson both terms yields the max denominator (min horizon) and-radiusthe min denominator (max horizon). Theu/(1-u)form is the right inverse-rounding bound (recovering pre-castxfrom post-castv = x(1+delta)), andtiny * epsis a conservative 2x the subnormal half-spacing. Reordering the retained-head mask check ahead of the horizon check is behavior-preserving — both raiseApplyModeError. replace_dasc_modereordering.manager.state_dict()returns the liveself._state, so the in-placestate[first_index] = ...still persists. Gatingupdate_last_state_before_new_modeondasc_indices[-1] != len(state) - 1is the right root-cause fix: when DASC is the trailing mode, that call dispatched to DASC's ownupdate_dasc_metadata, which validated the previous calibration's attached policy and emitted a spurious stale warning before being overwritten two lines later. Movingbuild_dasc_policybelow thedasc_indicesemptiness check also means a model without DASC state now fails fast instead of paying for a full policy build first.- Lenient restore still fails closed at the boundary.
restore_dasc_modelwarning instead of raising does not leak an invalid policy into export:export_policyandupdate_dasc_metadataboth runvalidate_dasc_model_structurefirst, and that comparison includes layer names — sovalidate_dasc_decay_parameters'policy.layers[name]lookup is only reached once names match, and theKeyErrorI went looking for is unreachable on that path.warningsis already imported atconversion.py:19. - Test-coverage delta. The
pytest.warns("saved DASC policy is stale")assertion dropped fromtest_export_rejects_changed_decay_parameters_and_restore_rejects_structureis still covered at four other sites, and thedelattr(model, "_modelopt_dasc_policy")added there is a sound negative control — an accidental refresh would raiseApplyModeErrorrather than pass quietly. Rebuilding the dynamic-module test viatype("_DynamicGatedDeltaNet", (DynamicModule, GatedDeltaNet), {})correctly mirrors whatDynamicModule.convertsynthesizes.
Risk assessment
Low-to-moderate. Well contained to one sub-package, with no mode-registration, config-schema, or public-API changes, and both remaining issues fail closed (spurious rejection / misleading error) rather than silently emitting a wrong policy — so there is no checkpoint-corruption or export-correctness risk. #1 is the one worth fixing before merge: it makes a legitimate BF16-save / FP32-load checkpoint un-exportable, a user-visible regression against the rtol=0.05 behavior this PR replaces.
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## feature/dasc-state-sparsity-lifecycle-fixes #2380 +/- ##
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+ Coverage 78.74% 78.76% +0.01%
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## Summary Consolidates the complete reviewed fix stack for #2375 into one DCO-safe commit: - harden package exports, measurement semantics, wrapper handling, and actionable calibration errors - validate exact installed GDN identities plus ModelOpt dynamic subclasses; reject lookalikes, ordinary subclasses, incomplete layers, and partial layer sets - make stale checkpoints saveable and restorable while keeping deployment export strict - make DASC recalibration replace and deduplicate existing mode state without stale-metadata refresh - record the declared decay-parameter checkpoint storage dtype and use derived FP16/BF16/FP32 rounding bounds - preserve BF16/FP16 storage and wider/cross-dtype reload compatibility without globally widening FP32 tolerance - add installed Transformers path coverage, optional Megatron gating, lifecycle, tamper, lossy-cast, and mixed-layer regressions - document the explicit storage-dtype contract 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. ## Validation - focused DASC suite: 23 passed, 1 absent optional Megatron skip - DASC plus weight sparsity plus attention sparsity compatibility suite: 134 passed, 1 optional skip - DASC package coverage: 408/408 statements, 100% - full pre-commit on all touched files: passed - real Transformers Qwen3NextGatedDeltaNet BF16 storage to FP32 reload smoke: passed - commit author and Signed-off-by identity both use kaix-nv <kaix@nvidia.com> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added support for configuring decay-parameter storage precision with FP16, BF16, or FP32. * Added safer recalibration that replaces existing DASC state. * Expanded compatibility with supported GDN adapter classes and model wrappers. * Added improved validation for sparsity policies, measurements, model structure, and decay parameters. * Added support for perplexity-retention values above 1. * **Documentation** * Clarified evaluation responsibilities, recalibration behavior, stale-policy handling, supported adapters, and dtype requirements. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: kaix-nv <kaix@nvidia.com>
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Summary
Follow-up to #2379 that closes the latest automated review findings:
This PR is intentionally stacked on #2379 because repository rules protect a PR head branch after creation.
Validation
Summary by CodeRabbit