Harden DASC recalibration lifecycle - #2379
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Signed-off-by: Kai Xu <kaix@nvidia.com>
📝 WalkthroughWalkthroughDASC calibration now replaces existing mode state during recalibration. GDN detection supports approved base classes and dynamic subclasses. Policy validation tolerates FP16 and BF16 storage casts while checking horizons and retained-head masks. ChangesDASC calibration updates
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Sequence Diagram(s)sequenceDiagram
participant Caller
participant calibrate
participant ModeloptStateManager
participant replace_dasc_mode
Caller->>calibrate: provide model, config, and measurements
calibrate->>ModeloptStateManager: inspect model state
calibrate->>replace_dasc_mode: replace existing DASC mode
replace_dasc_mode->>ModeloptStateManager: update serialized mode entries
replace_dasc_mode-->>calibrate: return recalibrated model
calibrate-->>Caller: return model
Merge Risk: 🟡 Moderate · up to Unrelated modules can be treated as supported GDN implementations, potentially producing invalid DASC calibration behavior. Use supported class identities before merging. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
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In `@modelopt/torch/sparsity/state_sparsity/policy.py`:
- Line 68: Update _is_gdn_module to validate supported GDN class identities
rather than matching __name__, while still accepting ModelOpt-generated
subclasses through their supported base classes and retaining the A_log/dt_bias
checks. Add a regression test using an unrelated same-name class to confirm it
is rejected.
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docs/source/guides/6_sparsity.rstmodelopt/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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| policy = get_attached_dasc_policy(model) | ||
| validate_dasc_model_structure(model, policy) | ||
| try: | ||
| validate_dasc_model_structure(model, policy) |
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[IMPORTANT Compatibility] Save is now lenient about structural staleness, but restore_dasc_model is still strict — so this change lets ModelOpt write a checkpoint that can never be loaded again.
Moving validate_dasc_model_structure inside the try means mto.save() succeeds with only a warning when the calibrated GDN geometry has changed (exactly what test_structure_staleness_does_not_block_checkpoint_save asserts). But the persisted metadata is the stale policy, and restore_dasc_model (line 93) still calls validate_dasc_model_structure unconditionally and raises ApplyModeError. Since restore_from_modelopt_state replays every mode in order and does not catch per-mode failures, that one raise aborts the whole restore — including any unrelated modes stored alongside DASC in the same modelopt_state.
Why it matters: the stated goal is that "a stale policy remains serializable so it cannot block saving or composing other ModelOpt modes." As written, DASC no longer blocks saving other modes, but it does block restoring them, and the failure now surfaces at load time (possibly a different machine, days later) instead of fail-fast at save. The new test only covers the save half — nothing asserts what happens when that checkpoint is read back.
Suggested fix — make restore consistent with save, and let export_policy remain the single strict gate (it already is, per api.py:86-87):
def restore_dasc_model(model: nn.Module, config: DASCConfig, metadata: MetadataDict) -> nn.Module:
...
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)
return modelIf restore must stay strict instead, then the save-side warning should say the DASC entry will not restore, and a round-trip test (mto.save → mto.restore) should pin whichever behavior you pick.
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Addressed in #2380. Restore now attaches structurally stale policy metadata with an actionable warning, so a saved stale checkpoint remains recoverable. Export remains strict and rejects deployment until recalibration; the save/restore/export lifecycle is tested.
|
| # Covers ordinary FP16/BF16 storage casts; the exact retained-head mask below is the semantic gate. | ||
| _HORIZON_DTYPE_CAST_RTOL = 0.05 |
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[IMPORTANT Compatibility] The exact-digest check was removed and _HORIZON_DTYPE_CAST_RTOL = 0.05 is now the sole numerical gate on horizon drift — but it is a hand-tuned constant with a thin margin, and the new float16/bfloat16 test cannot exercise it.
Two connected problems:
1. The tolerance is not derived from dtype precision. Horizon error is dominated by softplus(dt_bias + static_gate_input); for strongly negative x, softplus(x) ≈ exp(x), so the relative horizon error is roughly the absolute rounding error of dt_bias, which grows with |dt_bias| under a fixed-mantissa cast. Real Qwen3-Next dt_bias is initialized via inverse-softplus of dt ∈ [1e-3, 0.1], i.e. dt_bias ≈ -7 … -2.3. At |dt_bias| ≈ 12 a bf16 cast (8 significant bits → step 2^-4 at that magnitude) contributes ~3% on its own, and A_log ≈ 2.7 adds ~0.8% via exp(A_log) — roughly 4% against a 5% budget. That margin is undocumented and not much more than 1x, so a legitimate model.to(torch.bfloat16) on a slow-decay checkpoint can trip "DASC policy horizons do not match current decay parameters" at export_policy(), which is the primary deployment path.
2. Nothing in the suite validates the lenient path. test_dtype_cast_preserves_policy_when_the_selected_mask_is_unchanged now parametrizes over bfloat16 and float16, but the fixture uses A_log = torch.zeros(...) and dt_bias = torch.tensor([-2.0, 2.0]). 0.0, -2.0, and 2.0 are all exactly representable in both dtypes, so model.to(dtype) is bit-exact for the horizon computation and the test passes without ever going through a lossy cast. The PR's central claim — "validate FP16/BF16 casts by re-derived horizons plus the exact selected-head mask" — is therefore unverified in either direction (accepts a real cast / rejects real drift).
Suggested fix: make the tolerance dtype-aware rather than a magic 5%, e.g. scale it from torch.finfo(param.dtype).eps and the observed |A_log| / |dt_bias + static_gate_input| maxima, so the bound tracks the actual storage precision instead of assuming a parameter range. And give the fixture values that are not representable in fp16/bf16 (e.g. A_log = torch.tensor([0.1, 0.7]), dt_bias = torch.tensor([-2.3, 1.7])) so the parametrized test actually distinguishes an accepted cast from rejected drift. Keeping the exact retained_heads mask comparison as the semantic gate is the right call — it's the tolerance and its coverage that need shoring up.
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Addressed in #2380. The fixed 5% tolerance is replaced by an analytic interval derived from one rounding step in the current storage dtype. Tests use values that demonstrably change under both FP16 and BF16 casts.
| """Accept supported GDN implementations and their ModelOpt dynamic subclasses.""" | ||
| return ( | ||
| type(module).__name__ in _SUPPORTED_GDN_CLASS_NAMES | ||
| any(base.__name__ in _SUPPORTED_GDN_CLASS_NAMES for base in type(module).__mro__) |
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[SUGGESTION] The MRO walk accepts any subclass of a supported GDN class, which is broader than the stated intent and silently re-opens the case the old check fenced off.
The docstring and the doc update say "ModelOpt-generated dynamic subclasses," but type(module).__mro__ matches every user-defined subclass too. That's exactly the case the test used to assert as unsupported — UnsupportedGatedDeltaNet(GatedDeltaNet) was rejected before and had to be rewritten as a non-subclass to keep the test passing. A user subclass that overrides how A_log/dt_bias map to decay (or reparameterizes dt_bias) now silently passes the gate, and compute_gdn_decay_horizons will apply the base-class formula -exp(A_log) * softplus(dt_bias + g) anyway — producing a plausible-looking but wrong policy rather than failing closed.
If the goal is specifically to survive ModelOpt dynamic conversion, gate the subclass allowance on that rather than on subclassing in general:
from modelopt.torch.opt.dynamic import DynamicModule
def _is_gdn_module(module: nn.Module) -> bool:
"""Accept supported GDN implementations and their ModelOpt dynamic subclasses."""
cls = type(module)
is_supported_class = cls.__name__ in _SUPPORTED_GDN_CLASS_NAMES or (
isinstance(module, DynamicModule)
and any(base.__name__ in _SUPPORTED_GDN_CLASS_NAMES for base in cls.__mro__)
)
return (
is_supported_class
and isinstance(getattr(module, "A_log", None), torch.Tensor)
and isinstance(getattr(module, "dt_bias", None), torch.Tensor)
)If accepting arbitrary subclasses is a deliberate choice, the docs at docs/source/guides/6_sparsity.rst:175-177 should say so plainly rather than implying only ModelOpt-generated subclasses are covered — a reader currently gets a stronger fail-closed guarantee than the code provides.
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Addressed in #2380. Ordinary subclasses are no longer accepted. Only exact supported class identities, or ModelOpt DynamicModule subclasses whose MRO contains one of those exact identities, pass. Arbitrary subclass and same-name lookalike tests were added.
| model = unwrap_model(model, force_unwrap=True) | ||
| policy = build_dasc_policy(model, config, measurements) | ||
| manager = ModeloptStateManager(model) | ||
| manager.update_last_state_before_new_mode(model) |
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[SUGGESTION] update_last_state_before_new_mode is doing net-negative work here: no new mode is being appended, and the entry it refreshes is overwritten eight lines later.
Two concrete consequences:
-
Misleading warning. It routes into
update_dasc_metadata, which warns"The saved DASC policy is stale; re-run calibrate() before deployment"— emitted from insidecalibrate(), the call that is clearing the staleness.test_export_rejects_changed_decay_parameters_and_restore_rejects_structurecurrently pins that warning, so it reads as intentional, but the advice it gives the user is already being followed. -
Recalibration can be blocked by the state it replaces.
update_dasc_metadatastarts withget_attached_dasc_policy(model), which raisesApplyModeError("Model has no valid attached DASC policy")if_modelopt_dasc_policyis absent or unparseable. That attribute is a plain Python attribute, not a buffer, so it does not travel withstate_dict()and is not copied byModeloptStateManager.transfer_state_dict. A model carrying DASC state but no attached policy therefore cannot be recalibrated at all — even thoughpolicy(line 120) has already been successfully rebuilt and is about to replace everything.
Since replace_dasc_mode writes both config and metadata for the DASC entry itself, dropping the call is the simplest fix. If the intent is to keep a non-DASC trailing mode's metadata fresh, guard it so it only runs when the last mode isn't the DASC entry being replaced:
policy = build_dasc_policy(model, config, measurements)
manager = ModeloptStateManager(model)
state = manager.state_dict()
dasc_indices = [index for index, (mode, _) in enumerate(state) if mode == "dasc"]
if not dasc_indices:
raise ApplyModeError("Cannot replace DASC mode because the model has no DASC state")
if dasc_indices[-1] != len(state) - 1:
# a later mode owns the tail of the state; refresh it before we rewrite ours
manager.update_last_state_before_new_mode(model)Note the ordering dependency if you keep the call as-is: it must stay before the state = manager.state_dict() read only by accident — state_dict() returns the live list, so it happens to work either way. Making that independence explicit (or removing the call) would be clearer.
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Addressed in #2380. In-place DASC recalibration skips update_last_state_before_new_mode when DASC is already the trailing mode, so missing or stale attached metadata cannot block replacement. Refresh occurs only when a non-DASC mode trails DASC; both paths are tested.
| @property | ||
| def next_prohibited_modes(self) -> set[str]: | ||
| """Route repeat calibration through the replacing public API.""" | ||
| return {"dasc"} | ||
|
|
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[SUGGESTION] This routes repeat calibration away from apply_mode, but the resulting error loses the actionable message that convert_dasc_model carefully provides.
assert_compatibility_as_next_mode_of (modelopt/torch/opt/mode.py:266-269) enforces this with a bare assert, so a generic mto.apply_mode(model, mode=[("dasc", cfg)]) on an already-calibrated model now fails with a framework-level AssertionError: Cannot add dasc after dasc! dasc does not allow dasc to be its next mode. — no mention of calibrate(). That's a step back from the ApplyModeError at conversion.py:50-53, which does name the public entrypoint. The new test asserts the property value but not the raise, so nothing pins the user-facing behavior.
Also worth noting: next_prohibited_modes is only checked against last_mode and the top of the export stack, so dasc → some_other_mode → dasc still slips through and appends a second entry. replace_dasc_mode's duplicate collapsing covers that, so this is consistent — just be aware the guard is not a global "at most one DASC entry" invariant.
Consider documenting the intended recovery in the property docstring, or raising the actionable error from convert_dasc_model by detecting existing DASC state there (which would also survive python -O, where assert is stripped entirely).
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Claude review — 2 IMPORTANT, 3 SUGGESTION, 0 CRITICAL.
Scope: full review (the trigger comment carried no scoping instructions). All 7 changed files reviewed — api.py, config.py, conversion.py, mode.py, policy.py under modelopt/torch/sparsity/state_sparsity/, plus tests/unit/torch/sparsity/state_sparsity/test_dasc.py and docs/source/guides/6_sparsity.rst — with supporting context read from modelopt/torch/opt/conversion.py, modelopt/torch/opt/mode.py, modelopt/torch/opt/config.py, and modelopt/torch/utils/network.py.
Most impactful findings
1. Save/restore asymmetry creates unrestorable checkpoints (conversion.py:102, IMPORTANT)
Moving validate_dasc_model_structure inside the try makes structural staleness a warning at save time, but restore_dasc_model:93 still raises ApplyModeError unconditionally. Because restore_from_modelopt_state replays modes without per-mode error handling, a checkpoint that mto.save() happily writes can never be loaded back — and that failure takes down any unrelated modes sharing the same modelopt_state. This partially inverts the PR's own goal: DASC no longer blocks saving other modes, but it now blocks restoring them, and the failure moved from fail-fast to load time. The new test_structure_staleness_does_not_block_checkpoint_save covers only the save half; there is no mto.save then mto.restore round-trip.
2. The new numerical gate is a hand-tuned constant, and its test cannot exercise it (policy.py:34-35, IMPORTANT)
With the exact digest check removed, _HORIZON_DTYPE_CAST_RTOL = 0.05 is the only tolerance on horizon drift. It is not derived from dtype precision: horizon error is dominated by softplus(dt_bias + g), whose relative error tracks the absolute rounding error of dt_bias and therefore grows with the magnitude of dt_bias. For realistic Qwen3-Next parameterizations a bf16 cast lands around ~4% against the 5% budget — a thin, undocumented margin on the main model.to(bfloat16) then export_policy() path. Meanwhile the parametrized float16/bfloat16 test is a no-op: the fixture uses A_log = zeros and dt_bias = [-2.0, 2.0], all exactly representable in both dtypes, so the cast is bit-exact and the lenient path is never actually traversed. The PR's central claim — validating low-precision casts via re-derived horizons plus the exact head mask — is currently unverified in both directions.
Suggestions (non-blocking)
policy.py:68— the MRO walk accepts any subclass, not just ModelOpt dynamic ones; a user subclass that reparameterizes decay now passes the gate and gets the base-class formula applied silently. Concreteisinstance(module, DynamicModule)alternative in the inline comment.conversion.py:122—update_last_state_before_new_modeinreplace_dasc_moderefreshes an entry that is overwritten immediately after; it emits "re-run calibrate()" from insidecalibrate(), and can hard-block recalibration viaget_attached_dasc_policywhen the (non-serialized) policy attribute is missing.mode.py:51-55—next_prohibited_modesis enforced by a bareassert, so repeatapply_modenow fails with a generic framework message instead of the actionableApplyModeErrorthatconvert_dasc_modelprovides (and is stripped underpython -O).
Verified as correct
- The
config.pyPydantic narrowing is safe. Reducingmodel_configtoConfigDict(protected_namespaces=())does not dropextra="forbid"/validate_assignment=True: Pydantic v2 mergesmodel_configacross the MRO, andModeloptBaseConfigsets both atmodelopt/torch/opt/config.py:74. Worth pinning with an explicit "extra keys rejected" assertion, since nothing in the suite currently guards this inheritance. - The
unwrap_model(model, force_unwrap=True)added tocalibrateis not a behavior change.apply_modealready unwraps identically (opt/conversion.py:384) and returns the unwrapped module, so wrapped-model return types are unchanged. - Validator ordering avoids a latent
KeyError.validate_dasc_decay_parametersindexespolicy.layers[name]without a membership check, but bothexport_policyand the new combinedtryinupdate_dasc_metadatarunvalidate_dasc_model_structurefirst, which rejects added or removed GDN layers — so the unguarded lookup is never reached with an unknown name. - Duplicate collapsing in
replace_dasc_modeis correct:state_dict()returns the liveself._statelist, and reverse-order deletion keeps indices valid.
Risk assessment
Moderate. The diff is small and well-scoped, and the mode/state plumbing is sound. The risk is concentrated in the two lifecycle boundaries this PR deliberately loosened: the save-lenient/restore-strict split (finding 1) is the one I would fix before merge, since it can silently produce dead checkpoints that also strand co-resident modes. Finding 2 is lower blast radius but leaves the PR's headline behavior untested.
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## feature/dasc-state-sparsity-parity-gate #2379 +/- ##
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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>
1 participant
Follow-up to #2377 addressing its Claude inline lifecycle review.
Changes:
Validation:
Signed-off-by: Kai Xu kaix@nvidia.com
Summary by CodeRabbit
New Features
Bug Fixes
Documentation