Preserve NaN category validation with full-graph compilation by aw471 · Pull Request #1303 · NVIDIA/structured-data-models

aw471 · 2026-10-29T22:03:45Z

Summary

Validate category dictionaries when fitting or loading processor state, keeping NaN scans and CPU synchronization out of prediction.

Description of the change

Both AddCategoryCounts and ImputeMode check floating-point dictionaries during fit and after load_state_dict. Prediction retains the existing shared-dictionary shortcut and comparison for different dictionaries. Refit after changing category vocabularies; do not modify fitted vocabularies in place.

Validation can synchronize at fit/load boundaries. It adds no prediction-time NaN tensors, CPU copies, caches, custom operations, or fitted buffers. Compilation targets PyTorch 2.14 only. One successful float-state roundtrip test covers both processors.
Minimal reproduction

On main 7b365d16b, this input is silently accepted. With the fix it raises the category-validation error. Verified on CPU and NVIDIA L4, PyTorch 2.14 (CUDA 13.0 on L4).
import torch
from sdm import CategoricalTensor, TableTensor
from sdm.processing import AddCategoryCounts

table = TableTensor(
    columns={"categorical": ("city",)},
    categorical=CategoricalTensor(
        code=torch.tensor([[0], [1]], dtype=torch.int32, device="cuda"),
        categories=(torch.tensor([torch.nan, 1.0], device="cuda"),),
    ),
)
processor = AddCategoryCounts().fit(table)
processor.transform(table)
Expected error after the fix:
ValueError: Categorical column 'city' contains NaN in its category vocabulary.
Replace CUDA devices with CPU to reproduce there.
Validation

Manual check Result

CPU Inductor, shared float dictionaries Finite and empty dictionaries pass; NaN mutation rejected without recompilation

L4 Inductor, fullgraph=True Int/string/float checks each capture one graph; eager and compiled NaN errors verified; CUDA remains healthy after errors

L4 KumoTabular prediction with #1302 One full graph, zero breaks, exact eager parity

The prediction check used the standard small classification architecture, default recipe, pretrained=False, 128 context rows and 16 query rows after eager fit. It checks compiler integration. #1302 remains a separate requirement for the tested prediction path; this change does not enable whole-model compiled fit by itself.

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codecov · 2026-10-29T22:07:07Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.70%. Comparing base (a3516ba) to head (972e88b).
✅ All tests successful. No failed tests found.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1303   +/-   ##
=======================================
  Coverage   82.70%   82.70%           
=======================================
  Files         177      177           
  Lines       11541    11541           
=======================================
  Hits         9545     9545           
  Misses       1996     1996

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coderabbitai · 2026-10-29T22:09:55Z

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📝 Summary

Summary by CodeRabbit

Bug Fixes
- Floating-point category vocabularies containing NaN values are now rejected during fitting and when loading saved processor state, with a clear error in regular execution and compilation.
- Restored categorical processors now preserve their fitted behavior when transforming data.
Documentation
- Guidance now recommends refitting categorical processors after category vocabularies change and avoiding in-place changes to those vocabularies.

Category processors now check floating-point category vocabularies during fitting and after state loading. The check detects NaNs and can be disabled with SDM_ENABLE_CATEGORY_CHECKS="0". Tests compare transformed outputs after restoring processor state.

Changes: Category vocabulary validation

Layer / File(s)	Summary
Shared category validation `sdm/processing/categorical/_categorical.py`	The shared helper checks floating-point vocabularies for NaNs. It uses `torch._assert_async` during compilation and raises `ValueError` otherwise. `_CategoryList` validates loaded category buffers.
Processor fitting and category storage `sdm/processing/categorical/count.py`, `sdm/processing/categorical/impute.py`, `test/processing/categorical/test_impute.py`	`AddCategoryCounts` and `ImputeMode` validate categories during fitting and store fitted vocabularies in `_CategoryList`. Their documentation advises refitting after vocabulary changes. Tests compare transformed tables after restoring processor state.

Priority: Normal

Estimated code review effort: 2 (Simple) | ~12 minutes

Possibly related PRs

NVIDIA/structured-data-models#1301: Adds an identity shortcut to _check_categories in the same vocabulary-check path.
NVIDIA/structured-data-models#1302: Uses torch._assert_async for compiled categorical-code validation, a pattern also used by the category-value check.

Merge Risk: Moderate · previous review 75b47

Malformed category state can still be accepted or produce misleading errors; these validation paths should be corrected before merge.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 33.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly and concisely describes preserving NaN category validation under full-graph compilation, which is the main change.
Description check		The description directly explains category validation during fitting and state loading, prediction-time constraints, compilation behavior, tests, and validation results.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Full details: Docstring Coverage

Explanation

Docstring coverage is 33.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 3 functions across 4 files.

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Write docstrings for the functions missing them to satisfy the coverage threshold.

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Follow-up Comments

Report NaNs in the shared vocabulary.

75-75: Minor · Functional Correctness · Quick win

Report NaNs in the shared vocabulary.

When this check fails, the error says the vocabulary does not match the fitted values and recommends AlignCategories. The vocabulary is the fitted object; the failure is a NaN. Use a NaN-specific message for both eager and compiled failures.

As per coding guidelines, “errors … should describe public operations, inputs, outputs, and values.” Based on learnings, exception messages must accurately describe the triggering condition.

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Review comment at @sdm/processing/categorical/_categorical.py at line 75:
Update the message passed to _check_no_nan for the shared vocabulary so it
identifies NaNs as the triggering condition instead of reporting a vocabulary
mismatch or recommending AlignCategories; ensure the NaN-specific message is
used for both eager and compiled failures.
Sources: Coding guidelines, Learnings

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Follow-up Comments

Add regression tests for shared-vocabulary NaN validation.

75-75: Trivial · Maintainability & Code Quality · Quick win

Add regression tests for shared-vocabulary NaN validation.

The existing categorical tests do not cover a shared floating-point or complex vocabulary mutated to NaN. Add eager and fullgraph=True tests that assert the column-specific error and verify that the compiled call does not recompile.

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Review comment at @sdm/processing/categorical/_categorical.py at line 75:
Add regression tests around _check_no_nan for shared floating-point and complex
vocabularies mutated to NaN; cover both eager execution and fullgraph=True,
asserting the column-specific error and that repeated compiled calls do not
trigger recompilation.

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Review comments at @sdm/processing/categorical/_categorical.py:
- Line 37: Update the NaN-validation guard to include complex category
vocabularies alongside floating-point vocabularies, and revise the associated
docstring to describe both types; preserve the existing early return for other
category types.

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Review comments at @sdm/processing/categorical/_categorical.py:
- Line 65: Update the category-buffer loading flow around _check_category_values
so checkpoint values are validated before copying into the existing
module._categories buffers, preserving their original dtype during validation.
Reject non-finite floating-point checkpoint categories before integer conversion
can hide them.

Review comments at @test/processing/categorical/test_impute.py:
- Line 24: Add categorical NaN rejection tests in the existing
test_float_category_state_roundtrip area: verify AddCategoryCounts and
ImputeMode fit raise ValueError for a float vocabulary containing NaN,
load_state_dict raises ValueError for a restored NaN vocabulary, and the
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aw471 · 2026-10-30T00:19:46Z

/ok to test a6903ff

aw471 · 2026-10-30T01:23:11Z

Addressed by #1301 and #1302.

aw471 marked this pull request as ready for review October 29, 2026 22:04

aw471 requested review from RBendias, akihironitta and rusty1s as code owners October 29, 2026 22:04

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Comment thread sdm/processing/categorical/_categorical.py Resolved

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Comment thread sdm/processing/categorical/_categorical.py Resolved

Comment thread test/processing/categorical/test_impute.py Resolved

Preserve NaN category values during alignment

1cb6b44

aw471 force-pushed the compile/category-vocabulary-nan branch from a6903ff to 1cb6b44 Compare October 30, 2026 00:59

aw471 added 2 commits October 29, 2026 18:00

Remove test changes from the NaN category fix

35f9156

Remove out-of-scope NaN behavior changes

972e88b

aw471 closed this Oct 30, 2026

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Preserve NaN category validation with full-graph compilation - #1303

Preserve NaN category validation with full-graph compilation#1303
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Manual check	Result
CPU Inductor, shared float dictionaries	Finite and empty dictionaries pass; NaN mutation rejected without recompilation
L4 Inductor, `fullgraph=True`	Int/string/float checks each capture one graph; eager and compiled NaN errors verified; CUDA remains healthy after errors
L4 KumoTabular prediction with #1302	One full graph, zero breaks, exact eager parity

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