Align DASC numeric validation with scalar API - #2397
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Signed-off-by: kaix-nv <kaix@nvidia.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 0 remain after this review. 📝 WalkthroughWalkthroughThe change tightens decay-analysis input validation, rejects tensor-valued epsilon and static gate inputs, uses explicit CPU ChangesState sparsity validation
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~10 minutes Change: Bug fix Merge Risk: ⚪ Minimal · up to Horizon computation now keeps epsilon on CPU regardless of PyTorch's configured default device, so the prior runtime device mismatch is resolved. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
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Inline comments:
In `@modelopt/torch/sparsity/state_sparsity/policy.py`:
- Line 125: Update the tensor construction in the horizons calculation to
explicitly place the epsilon scalar on the CPU, matching the device used for
decay and avoiding default-device mismatches.
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modelopt/torch/sparsity/state_sparsity/config.pymodelopt/torch/sparsity/state_sparsity/policy.pytests/unit/torch/sparsity/state_sparsity/test_dasc.py
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| decay = -torch.exp(a_log_cpu) * F.softplus(dt_bias_cpu + static_gate_input) | ||
| horizons = torch.log(torch.as_tensor(epsilon, device="cpu", dtype=torch.float64)) / decay | ||
| horizons = torch.log(torch.tensor(epsilon, dtype=torch.float64)) / decay |
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[IMPORTANT Compatibility] This "restore the original scalar conversion" also drops the explicit device="cpu" pin, which is a separate concern from the tensor-rejection change.
What: torch.tensor(...) is a factory function and therefore honors torch.set_default_device(). Under torch.set_default_device("cuda") this allocates the epsilon scalar on CUDA, while decay two lines above is deliberately pinned to CPU (a_log.detach().to(device="cpu", ...)). A 0-dim CUDA tensor divided by a multi-element CPU tensor is not covered by PyTorch's CPU-scalar promotion rule, so the division raises RuntimeError: Expected all tensors to be on the same device....
Why it matters: the function's docstring promises "one static retention horizon per GDN head in CPU float64", and the previous torch.as_tensor(epsilon, device="cpu", ...) guaranteed that regardless of ambient global state. torch.set_default_device("cuda") is a real pattern in this repo (examples/deepseek/deepseek_v4/ptq.py:274), and this failure mode is invisible in the unit tests, which never set a default device.
Fix: now that _validate_analysis_arguments guarantees epsilon is a finite numbers.Real in (0, 1), no tensor is needed at all — math.log gives a device-free Python float, and float / tensor dispatches to the tensor's __rtruediv__:
| horizons = torch.log(torch.tensor(epsilon, dtype=torch.float64)) / decay | |
| horizons = math.log(epsilon) / decay |
This is device-independent by construction and removes a per-call tensor allocation. It needs import math at the top of policy.py if not already present.
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Fixed in #2398 with the suggested device-free math.log(epsilon) calculation. Added a regression using a non-CPU ambient torch.device context to assert that the documented CPU output contract is preserved.
✅ Action performedFull review finished. |
| try: | ||
| epsilon_is_valid = math.isfinite(epsilon) and 0.0 < epsilon < 1.0 | ||
| epsilon_is_valid = ( | ||
| isinstance(epsilon, Real) and math.isfinite(epsilon) and 0.0 < epsilon < 1.0 | ||
| ) | ||
| except (TypeError, ValueError, OverflowError): | ||
| epsilon_is_valid = False | ||
| if not epsilon_is_valid: | ||
| raise ValueError("epsilon must be finite and in (0, 1)") | ||
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| try: | ||
| static_gate_input_is_valid = math.isfinite(static_gate_input) | ||
| static_gate_input_is_valid = isinstance(static_gate_input, Real) and math.isfinite( | ||
| static_gate_input | ||
| ) | ||
| except (TypeError, ValueError, OverflowError): |
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[SUGGESTION] Two small follow-ups on the new numbers.Real gate — both non-blocking.
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boolpasses asReal.bool→int→Integral→Real, sostatic_gate_input=Trueis silently accepted and flows through as1.0rather than being rejected as a type error.epsilonis incidentally saved by the0.0 < epsilon < 1.0range check (True == 1), but that's coincidence, not intent.validate_wmax_candidatesa few lines below already establishes this file's convention of excluding bools explicitly (isinstance(value, bool) or not isinstance(value, int)), so it's worth being consistent:epsilon_is_valid = ( isinstance(epsilon, Real) and not isinstance(epsilon, bool) and math.isfinite(epsilon) and 0.0 < epsilon < 1.0 )
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The
exceptclauses are now wider than reachable. Withisinstance(..., Real)short-circuiting first,math.isfiniteonly ever sees a value that implements__float__(thenumbers.RealABC requires it), soTypeError/ValueErrorare no longer reachable here —OverflowError(the10**1000case) is the only live path. Narrowing toexcept OverflowError:documents exactly which input class needs normalization and keeps the handler from quietly swallowing a future real bug.
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Both suggestions are fixed in #2398. bool is explicitly excluded for epsilon and static_gate_input, tests cover both public paths, and the handlers now catch only the reachable OverflowError case.
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Claude review — 1 IMPORTANT, 1 SUGGESTION
Scope: full review. All 3 changed files (+15/-20) reviewed: modelopt/torch/sparsity/state_sparsity/config.py, modelopt/torch/sparsity/state_sparsity/policy.py, tests/unit/torch/sparsity/state_sparsity/test_dasc.py. Nothing deliberately skipped.
Findings
- CRITICAL: 0
- IMPORTANT: 1 —
policy.py:125drops the explicitdevice="cpu"pin - SUGGESTION: 1 —
config.py:44-57boolpasses asReal;exceptclauses now wider than reachable
Most impactful
Reverting torch.as_tensor(epsilon, device="cpu", dtype=torch.float64) to torch.tensor(epsilon, dtype=torch.float64) bundles two independent changes: dropping as_tensor (correct — tensors are now rejected upstream, so it is no longer needed) and dropping the explicit CPU pin (an unintended regression). torch.tensor is a factory function and honors torch.set_default_device(), so under torch.set_default_device("cuda") the epsilon scalar lands on CUDA while decay is deliberately CPU-pinned one line earlier. A 0-dim CUDA tensor divided by a multi-element CPU tensor falls outside PyTorch's CPU-scalar promotion rule and raises a device-mismatch RuntimeError — contradicting the function's documented "CPU float64" contract. That global-device pattern is in use in this repo (examples/deepseek/deepseek_v4/ptq.py:274), and no unit test sets a default device, so this is silent in CI.
Since validation now guarantees epsilon is a finite Real in (0, 1), math.log(epsilon) / decay is the cleaner fix — device-free by construction and one fewer tensor allocation per call. math is already imported at policy.py:22, so it is a one-line drop-in.
What looks right
- Tensor rejection is sound.
torch.Tensoris not registered with thenumbersABCs, soisinstance(x, Real)rejects every tensor — including 0-dim ones — before any scalar conversion. That genuinely closes the multi-elementRuntimeErrorleak rather than papering over it, which is the right root-cause fix. numbers.Realwidening matches the annotated API. NumPy real scalars register withReal, sonp.float64/np.float32epsilon values are accepted as intended, and theOverflowErrornormalization for10**1000is preserved.- Config path unaffected.
validate_epsilon/validate_static_gate_inputaremode="after"validators, so they receive an already-coerced Pythonfloat; the newisinstancegate cannot reject previously-validDASCConfiginputs. Nomodelopt_stateschema or checkpoint-compat impact. - Removing
test_analysis_accepts_tensor_scalar_argumentsis appropriate. It requiredtype: ignore[arg-type]against afloat-annotated parameter, i.e. it pinned behavior that was never part of the public contract, and the base branch is an unreleased feature branch — no user-facing break. - New test coverage hits both public entry points (
analyze_gdn_decayandcompute_gdn_decay_horizons) for both arguments.
Risk
Low. Tightly scoped validation change with no mode-registration, state-schema, or export surface touched. The one IMPORTANT finding is a latent device-placement regression that only fires under a non-default global device — worth fixing before merge, but it does not affect the default path.
🤖 Generated with Claude Code
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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-validation-source #2397 +/- ##
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+ Coverage 68.76% 78.78% +10.02%
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Files 548 548
Lines 64243 64243
=========================================================================================
+ Hits 44174 50615 +6441
+ Misses 20069 13628 -6441
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## Summary Addresses every finding on #2397: - compute the scalar log with `math.log`, avoiding ambient PyTorch default-device allocation entirely - explicitly reject booleans despite their `numbers.Real` inheritance - narrow normalization handlers to the reachable oversized-integer `OverflowError` - add boolean regressions through both public APIs - add a CPU-result regression under a non-CPU ambient default-device context ## Validation - focused DASC suite: 51 passed, 1 optional Megatron skip - pre-commit hooks 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 validation for sparsity analysis arguments, including rejection of boolean values. - Preserved expected error handling for invalid or non-finite inputs. - Ensured decay horizon calculations work correctly regardless of the ambient PyTorch device. - **Tests** - Expanded coverage for invalid boolean arguments and CPU-based horizon computation. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: kaix-nv <kaix@nvidia.com>
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feature/dasc-state-sparsity-review-validation-source
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Summary
Addresses both Claude findings on #2396 while retaining CodeRabbit’s oversized-integer fix:
numbers.Realinputs, including NumPy real scalarsValidation
Summary by CodeRabbit