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📝 WalkthroughWalkthroughThe change adds shared GDN decay validation, storage-dtype-aware horizon analysis, updated DASC calibration guidance, separate metadata validation warnings, and serialization checks for unsupported GDN replacements. ChangesDASC validation and serialization
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix Merge Risk: 🔵 Low · up to Malformed storage-dtype input can produce an unexpected exception type from the public analysis API. Validate the input before lookup to preserve the documented error contract. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
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| # Use these storage-canonical horizons to derive the evaluated mask and the | ||
| # retained_heads/total_heads measurement geometry for every Wmax candidate. | ||
| horizons = mtss.analyze_gdn_decay( | ||
| model, | ||
| decay_parameter_storage_dtype=config["decay_parameter_storage_dtype"], | ||
| ) |
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[IMPORTANT Compatibility] The documented recipe only forwards one of the three knobs that determine calibration geometry, so it silently diverges from calibrate() for any non-default config.
build_dasc_policy computes its horizons via _analyze_gdn_modules(..., epsilon=config.epsilon, static_gate_input=config.static_gate_input, storage_dtype=_STORAGE_DTYPES[config.decay_parameter_storage_dtype]) (modelopt/torch/sparsity/state_sparsity/policy.py:381-385). analyze_gdn_decay re-declares its own defaults epsilon=1e-3 / static_gate_input=-0.3, which happen to match DASCConfig's defaults — so this snippet is only correct as long as the caller never overrides epsilon or static_gate_input. Both are public, documented DASCConfig fields.
Why it matters: a caller who sets e.g. "static_gate_input": 0.0 and follows this snippet verbatim derives their evaluated head mask and retained_heads from different horizons than the policy will encode. _validate_measurement_geometry (policy.py:329-345) only compares aggregate counts, not per-head identity, so the mismatch is not reliably caught — whenever the two head sets happen to have the same cardinality, calibrate() accepts the measurements and ships a policy whose retained_heads mask was never the one the quality evidence was measured on. That is exactly the class of drift the rest of this PR is trying to close. When the counts do differ, the error message points at the caller's measurements rather than at the two omitted kwargs, which is a confusing failure for the documented path.
Suggested fix — forward all three in the snippet (and say so in the prose at lines 192-193):
horizons = mtss.analyze_gdn_decay(
model,
epsilon=config.get("epsilon", 1e-3),
static_gate_input=config.get("static_gate_input", -0.3),
decay_parameter_storage_dtype=config["decay_parameter_storage_dtype"],
)Restating the defaults at the call site is itself fragile. Since the stated goal is "derive exactly the geometry consumed by calibration", the more robust option is to let the public analysis entry point take the config as its single source of truth — e.g. an overload/helper analyze_gdn_decay(model, config=config) that pulls epsilon, static_gate_input, and decay_parameter_storage_dtype off the validated DASCConfig — so a caller cannot get partway there. That would also remove the need for callers to know which subset of config fields feeds the horizon computation.
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Valid. Fixed in #2391. The documented config now makes epsilon and static_gate_input explicit and forwards them, along with decay_parameter_storage_dtype, to analyze_gdn_decay. The prose calls out all three as the measurement-geometry contract.
| *, | ||
| epsilon: float = 1e-3, | ||
| static_gate_input: float = -0.3, | ||
| decay_parameter_storage_dtype: Literal["float16", "bfloat16", "float32"] | None = None, |
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[SUGGESTION] This adds a fourth hand-maintained copy of the storage-dtype value set: _STORAGE_DTYPES keys (policy.py:44-48), this Literal, DASCConfig.decay_parameter_storage_dtype (config.py:89), and DASCPolicy.decay_parameter_storage_dtype (config.py:209). Adding or renaming a supported dtype now requires four coordinated edits, and a Literal that drifts out of sync with _STORAGE_DTYPES turns a static type error into the runtime ValueError below.
Consider a single shared alias next to _STORAGE_DTYPES and using it in all four places, e.g.:
DecayParameterStorageDtype = Literal["float16", "bfloat16", "float32"]
_STORAGE_DTYPES: dict[DecayParameterStorageDtype, torch.dtype] = {
"float16": torch.float16,
"bfloat16": torch.bfloat16,
"float32": torch.float32,
}Non-blocking — the runtime guard you added means a drifted Literal fails closed rather than silently.
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Addressed in #2391. A single internal _DecayParameterStorageDtype alias now types both config schemas, the public analysis argument, and the runtime mapping keys.
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Claude review — DASC analysis and lifecycle contracts
Scope: full review. 4 files changed (+54/-15): modelopt/torch/sparsity/state_sparsity/policy.py, modelopt/torch/sparsity/state_sparsity/conversion.py, docs/source/guides/6_sparsity.rst, tests/unit/torch/sparsity/state_sparsity/test_dasc.py. All four reviewed, plus surrounding context in policy.py (_analyze_gdn_modules, _get_gdn_modules, build_dasc_policy, both validators), config.py (field defaults), and api.py (export_policy).
Findings
CRITICAL: 0 | IMPORTANT: 1 | SUGGESTION: 1
| Severity | Location | Issue |
|---|---|---|
| IMPORTANT Compatibility | docs/source/guides/6_sparsity.rst:149-154 |
Documented recipe forwards only decay_parameter_storage_dtype; epsilon and static_gate_input also determine calibration geometry |
| SUGGESTION | modelopt/torch/sparsity/state_sparsity/policy.py:219 |
Fourth hand-maintained copy of the storage-dtype Literal set |
Most impactful finding
The new analyze_gdn_decay(decay_parameter_storage_dtype=...) parameter and its plumbing through _analyze_gdn_modules are correct, and the test now exercises the real public path instead of hand-casting tensors. The gap is in the contract the docs establish around it.
build_dasc_policy derives horizons from three config fields (policy.py:381-385): epsilon, static_gate_input, and decay_parameter_storage_dtype. analyze_gdn_decay re-declares its own defaults for the first two, which coincidentally match DASCConfig, so the documented snippet is correct only while the caller leaves both at their defaults. Because _validate_measurement_geometry (policy.py:329-345) compares aggregate retained/total counts rather than per-head identity, a caller who overrides static_gate_input or epsilon and follows the doc verbatim can pass validation while the mask their quality evidence was measured on differs from the mask the policy encodes — the same provenance drift the rest of this PR is closing. When counts do differ, the resulting error blames the measurements rather than the two omitted kwargs.
What checks out
- Save/restore symmetry is genuinely fail-closed and symmetric.
restore_dasc_modelandupdate_dasc_metadataboth narrow theirexceptto_DASCModelStructureMismatchError, so the plainApplyModeErrors from_get_gdn_modules("no supported GDN modules", incomplete decay tensors, unconverted subclasses) propagate on both paths. The new test covers the save direction. - No partial metadata mutation on the fail-closed path: the raise happens before
metadata.clear(), so a failedmodelopt_state()leaves the prior metadata intact rather than half-written. - Recoverable-drift path is coherent: geometry mismatch warns and skips decay validation (matching the pre-existing sequential behavior, where a structure failure already short-circuited it), stays serializable, and
export_policy(api.py:85-87) still runs both validators so deployment remains blocked until recalibration. Docs prose matches the code. - Numerical equivalence of the rewritten boundary test:
_analyze_gdn_modulescasts to CPU plus storage dtype andcompute_gdn_decay_horizonsthen upcasts to float64, identical to the previous explicit.to(torch.float16). - Plugin laziness is preserved —
_supported_gdn_classes()is untouched and still resolves Megatron/transformers behindfind_specguards. - No
modelopt_stateschema change:metadataremains a singlepolicyfield, and the new parameter is keyword-only with aNonedefault, so existing callers and checkpoints are unaffected.
Risk assessment
Low. The code changes are small, tighten rather than loosen the lifecycle contract, and are backward compatible. The one IMPORTANT finding is a documentation/API-ergonomics gap in the very path this PR adds, and it only bites callers who override epsilon or static_gate_input — but for those callers it can silently ship a policy whose evidence does not match its mask, so it is worth fixing before merge.
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Inline comments:
In `@modelopt/torch/sparsity/state_sparsity/policy.py`:
- Line 225: Update analyze_gdn_decay’s lookup of decay_parameter_storage_dtype
in _STORAGE_DTYPES to catch both KeyError and TypeError, then raise the existing
ValueError for invalid values; add a regression test covering an unhashable
input such as a list.
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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-review-boundary #2389 +/- ##
===============================================================================
+ Coverage 78.77% 78.78% +0.01%
===============================================================================
Files 548 548
Lines 64204 64243 +39
===============================================================================
+ Hits 50576 50615 +39
Misses 13628 13628
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Closes the remaining review-contract and cleanup findings from #2389 and #2390. Changes: - make the documented measurement recipe forward all three horizon inputs: epsilon, static gate input, and decay-parameter storage dtype - use one shared storage-dtype type alias across config, policy metadata, public analysis, and runtime dtype mapping - split validation-only checks from CPU float64 materialization - finish the storage/live cast-bound documentation and clarify float32 contribution - give the NaN export regression its own accurately named test Validation: - focused DASC tests: 25 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 decay-parameter validation across storage and live tensor data types, including more accurate rounding bounds. - Added validation for non-finite decay parameters during policy export. - **Documentation** - Updated sparsity guidance with epsilon and static gate input configuration examples. - Clarified how dtype conversions and rounding affect policy validation. - **Refactor** - Improved consistency and reliability of decay analysis and policy validation without changing supported dtype options or defaults. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: kaix-nv <kaix@nvidia.com>
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>
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Inline comments:
In `@modelopt/torch/sparsity/state_sparsity/policy.py`:
- Around line 225-231: Update analyze_gdn_decay to validate
decay_parameter_storage_dtype before indexing _STORAGE_DTYPES, ensuring
unhashable or otherwise invalid runtime values raise the documented ValueError
rather than leaking TypeError. Preserve the existing mapping lookup and handling
for valid storage dtype values.
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| storage_dtype = None | ||
| if decay_parameter_storage_dtype is not None: | ||
| try: | ||
| storage_dtype = _STORAGE_DTYPES[decay_parameter_storage_dtype] |
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[SUGGESTION] analyze_gdn_decay now validates one of its three keyword arguments at the public boundary and leaves the other two to be validated deep inside the per-module loop, so the same function reports caller mistakes with two different exception types and one misleading message.
An invalid storage dtype raises ValueError("decay_parameter_storage_dtype must be one of: ...") here. An invalid epsilon falls through to compute_gdn_decay_horizons (policy.py:111), whose ValueError is then wrapped by _analyze_gdn_modules (policy.py:214-217) into ApplyModeError("Invalid GDN decay parameters in module 'linear_attn': epsilon must be in (0, 1)") — an ApplyModeError blaming a specific model module for what is purely a bad argument. Callers writing except ValueError around the analysis call catch one and not the other, and the message points at the model instead of the kwarg.
This is pre-existing behavior for epsilon, but this PR is the one establishing the argument-validation contract for this entry point, so it is the natural place to make it uniform. CONTRIBUTING's "validate external input once at the interface boundary" points the same way: hoist the range check here and let the internal path trust it.
def analyze_gdn_decay(
model: nn.Module,
*,
epsilon: float = 1e-3,
static_gate_input: float = -0.3,
decay_parameter_storage_dtype: _DecayParameterStorageDtype | None = None,
) -> dict[str, list[float]]:
"""Return per-head horizons, optionally canonicalized to a checkpoint storage dtype."""
if not 0.0 < epsilon < 1.0:
raise ValueError("epsilon must be in (0, 1)")
storage_dtype = None
...There was a problem hiding this comment.
Fixed in #2394. epsilon and static_gate_input are now validated at analyze_gdn_decay() before model discovery, with nonnumeric values normalized to the public ValueError contract. Added finite/range and unhashable-input regressions.
| if not a_log.dtype.is_floating_point or not dt_bias.dtype.is_floating_point: | ||
| raise ValueError("GDN A_log and dt_bias must use floating-point dtypes") | ||
| if storage_dtype is not None: | ||
| _validate_gdn_decay_tensors(a_log, dt_bias) |
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[SUGGESTION] This _validate_gdn_decay_tensors call is redundant with the one now inside compute_gdn_decay_horizons (policy.py:113), and being conditional on storage_dtype is not None makes the two branches of this function look like they validate differently when they don't.
Every path through this loop reaches compute_gdn_decay_horizons, which validates unconditionally, so the storage_dtype is None branch is already covered. The only thing the extra call buys is validating the pre-cast tensors — but it does not actually deliver that guarantee: a finite FP32 A_log that overflows during .to(torch.float16) still passes here and then fails inside compute_gdn_decay_horizons with "GDN decay parameters must be finite", pointing at the storage-canonical tensor rather than the real cause.
Either drop the call and let the single validator in compute_gdn_decay_horizons own it, or, if pre-cast validation is deliberate, hoist it out of the if so it applies to both branches and add a one-line comment naming why the pre-cast values need their own check. As written the duplication is the kind that drifts once one of the two validators changes.
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Fixed in #2394. _analyze_gdn_modules() validates the live decay tensors before any checkpoint-storage cast, so casting cannot hide an invalid integer source dtype; compute_gdn_decay_horizons still validates the storage-canonical tensors after casting.
| try: | ||
| validate_dasc_model_structure(model, policy) | ||
| validate_dasc_decay_parameters(model, policy) | ||
| except ApplyModeError as error: | ||
| except _DASCModelStructureMismatchError as error: | ||
| warnings.warn( | ||
| f"{error}. The saved DASC policy is stale; re-run calibrate() before deployment", | ||
| stacklevel=2, | ||
| ) | ||
| else: | ||
| try: |
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[SUGGESTION] Narrowing the caught type here is the right call for the architecture-replacement case the new test covers, but it makes the save path fail closed on every non-structure-mismatch ApplyModeError from _get_gdn_modules, not just "the supported GDN architecture was replaced."
validate_dasc_model_structure → _get_gdn_modules can also raise a plain ApplyModeError from _reject_incomplete_gdn_modules (policy.py:133-147) when a supported GDN identity is present but getattr(module, "A_log", None) is not a Tensor. That is exactly what FSDP with use_orig_params=False produces: the module keeps its class identity but the original parameter attributes are deregistered in favor of a flat param. In that state mto.modelopt_state(model) / mto.save(model) now raises instead of warning, so the whole checkpoint — including any other ModelOpt modes composed with DASC — becomes unsaveable, with no escape hatch short of removing the DASC mode.
I have not confirmed DASC is expected to be saved under FSDP, so this is plausible rather than demonstrated — but the fix is cheap and keeps the fail-closed guarantee the PR is after. Distinguish "architecture is gone" (fail closed, as tested) from "decay tensors are temporarily not materialized" (warn and stay serializable), e.g. by giving _reject_incomplete_gdn_modules its own _DASCModelStructureMismatchError subclass, or by catching it explicitly on this path:
try:
validate_dasc_model_structure(model, policy)
except _DASCModelStructureMismatchError as error:
warnings.warn(
f"{error}. The saved DASC policy is stale; re-run calibrate() before deployment",
stacklevel=2,
)
else:
...with _reject_incomplete_gdn_modules raising a recoverable error type. Whichever shape you pick, the restore_dasc_model path (conversion.py:99-105) should stay symmetric with it.
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Fixed in #2394. A supported GDN identity whose decay tensors are temporarily unavailable is now a typed recoverable-staleness condition on both save and restore. Removing/replacing the supported architecture remains a hard ApplyModeError. The lifecycle boundary is documented and covered by a save/restore/export regression.
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Claude review — DASC analysis and lifecycle contracts
Scope: full review (trigger comment was a bare /claude review). 5 files changed (+132/-33), all reviewed: modelopt/torch/sparsity/state_sparsity/{policy.py,conversion.py,config.py}, docs/source/guides/6_sparsity.rst, tests/unit/torch/sparsity/state_sparsity/test_dasc.py. Also read surrounding context in policy.py (_get_gdn_modules, _analyze_gdn_modules, build_dasc_policy, validate_dasc_model_structure, validate_dasc_decay_parameters, _storage_cast_horizon_bounds) and the restore_dasc_model path in conversion.py.
Findings
CRITICAL: 0 | IMPORTANT: 0 | SUGGESTION: 3
| Severity | Location | Issue |
|---|---|---|
| SUGGESTION | policy.py:234 |
analyze_gdn_decay validates decay_parameter_storage_dtype at the boundary but lets a bad epsilon surface as ApplyModeError blaming a specific module |
| SUGGESTION | policy.py:206 |
Pre-cast _validate_gdn_decay_tensors duplicates the validator now inside compute_gdn_decay_horizons, and does not actually catch cast-induced overflow |
| SUGGESTION | conversion.py:113-121 |
Save now fails closed on any non-structure ApplyModeError, including _reject_incomplete_gdn_modules (reachable under FSDP use_orig_params=False), not just architecture replacement |
None of the three block merge.
What I verified
_dtype_exactly_containsis numerically sound.max >=/eps <=/tiny*eps <=correctly encodes "exponent range ⊇ and mantissa ≥" for IEEE-like binary formats. Spot-checked all four relevant pairs: fp16 ⊂ fp32 ✓, bf16 ⊂ fp32 ✓ (bf16max3.3895e38 ≤ fp32 3.4028e38, so themaxcomparison does not falsely reject), fp16 ⊄ bf16 and bf16 ⊄ fp16 ✓ — so the mutually-incomparable case still composes both bounds andtest_cross_dtype_reload_accumulates_both_rounding_boundsis unaffected. Self-comparison returnsTrue, reproducing the olddict.fromkeysdedupe forstorage == live.- The tightening only drops provably-zero slack, and in the right direction. For live ⊂ storage the storage cast is an exact widening; for storage ⊂ live the load cast is. In both cases the dropped term contributes no reachable error, so
_storage_cast_horizon_boundscannot start rejecting a legitimate reload.test_bf16_storage_round_trip_loaded_in_fp32_preserves_policy(bf16 storage, fp32 live) now uses the bf16 term alone, which is exactly the reachable rounding. - Perturbation directions in
_storage_cast_horizon_boundsremain correct with the radius as a magnitude:softplusis monotonic, sodt_bias ± dt_radiusmoves the horizon the intended way for both signs ofdt_bias. _validate_gdn_decay_tensorsextraction preserves semantics. Moving the finiteness check from the post-float64copy to the original tensor is equivalent (widening casts preserve finiteness). Only the precedence of theepsiloncheck vs. the tensor checks flips, which no test depends on. The int64 regression intest_bf16_storage_round_trip_loaded_in_fp32_preserves_policystill matches under the new wrapped message.update_dasc_metadatamutates nothing on the fail-closed path — the raise precedesmetadata.clear(), so a failedmodelopt_state()leaves prior metadata intact.- Skipping decay validation after a structure warning is necessary, not just tidy:
validate_dasc_decay_parametersindexespolicy.layers[name], which wouldKeyErroron drifted structure. The newelsebranch is the correct sequencing. - The save/restore asymmetry is deliberate and right.
restore_dasc_modelvalidates structure but not decay parameters, which is correct given the usualbuild → restore_from_modelopt_state → load_state_dictorder — weights are not loaded yet at restore time, so decay validation there would be premature.export_policystill runs both, keeping deployment blocked until recalibration. - Docs now match the code. The prior review's IMPORTANT finding (recipe forwarding only
decay_parameter_storage_dtype) is addressed — the snippet now passesepsilonandstatic_gate_inputtoo, so a caller who overrides either can no longer measure their quality evidence on a different mask than the policy encodes. The composition prose ("a duplicate dtype or an exact widening cast contributes no additional slack") is an accurate description of both_dtype_exactly_containsbranches. - Prior SUGGESTION resolved:
_DecayParameterStorageDtypeis now the single source of truth — grep confirmsconfig.py:33is the only remaining copy of the literal set, and_STORAGE_DTYPESis typed against it. - No compatibility surface moved: the new kwarg is keyword-only with a
Nonedefault,modelopt_statemetadata is still a singlepolicyfield, and plugin laziness in_supported_gdn_classes()is untouched.
CodeRabbit's finding on the unhashable-input TypeError at policy.py:225 is valid and I have not duplicated it.
Risk assessment
Low. The changes tighten the lifecycle contract rather than loosening it, the numerical tightening is provably slack-free, and the newly hard-failing save path is a documented, intentional change on an unreleased mode. The one finding with real teeth is the FSDP-shaped concern on conversion.py — plausible rather than demonstrated, and cheap to close by giving the incomplete-tensor case a recoverable error type.
Claude review passed — no blocking issues found. LGTM
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## Summary Addresses the remaining review findings on #2389: - validate public analysis arguments uniformly before model traversal, including unhashable storage-dtype inputs - validate original decay tensors before checkpoint-storage casting can mask invalid dtypes - treat temporarily unavailable decay tensors on an otherwise supported GDN identity as recoverable stale policy state on save and restore - retain fail-closed behavior when the supported GDN architecture is absent or replaced - document and test the checkpoint lifecycle boundary ## Validation - `python -m pytest -q tests/unit/torch/sparsity/state_sparsity/test_dasc.py` (34 passed, 1 optional Megatron skip) - `python -m pytest -q tests/unit/torch/sparsity/state_sparsity tests/unit/torch/sparsity/weight_sparsity tests/unit/torch/sparsity/attention_sparsity` (312 passed, 1 optional Megatron skip) - `pre-commit run --files ...` on all changed files - signed commit with DCO sign-off <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Bug Fixes** - Improved handling of temporarily unavailable decay data, allowing affected policies to be restored with a warning. - Added clearer validation for decay analysis settings, including storage types, tolerance values, and gate inputs. - Policies with unsupported architecture changes continue to fail safely during save and restore. - Stale policies remain blocked from export until recalibration is completed. - **Documentation** - Updated sparsity guidance to clarify recoverable versus unsupported policy changes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: kaix-nv <kaix@nvidia.com>
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Addresses the follow-up review findings on #2388.
Changes:
decay_parameter_storage_dtypecanonicalization through publicanalyze_gdn_decay, so callers can derive exactly the geometry consumed by calibrationValidation:
Commit is ED25519-signed and carries a matching Signed-off-by trailer.
Summary by CodeRabbit
New Features
Bug Fixes
Documentation