[aw471]

Summary

Record each string column's maximum byte length when it is loaded, so the follow-up PRs can replace cuDF in string operations with PyTorch code that runs under torch.compile(fullgraph=True): sorting (#1272) and category lookup (#1275).

Why

torch.compile needs tensor sizes as plain numbers when it compiles. How much work a string operation does depends on the longest string, which is only known on the GPU, and reading it from there inside compiled code breaks the graph. Recording it once at load time avoids that. Without it, code must assume a string could be as long as the whole column: sorting 200k URLs then takes 7.76 ms and 126 MiB instead of 2.91 ms and 71 MiB.

How the follow-up PRs use it:

• Sorting (Replace cuDF string sorting with native PyTorch on CUDA #1272) sorts on at most the first 21 bytes, in 7-byte words. The bound sets how many of those words to sort on, and whether strings that tie on the prefix need a second pass over their remaining bytes.
• Category lookup (Compile categorical string lookup with native PyTorch operations #1275) packs each string into ceil(bound / 8) 64-bit words, so matching strings against a vocabulary compares fixed-width keys.
• Entry permutation (Add native variable-length entry permutation #1271) does not read the bound. It passes it on to the reordered strings so the operations above still have it.

Description of the change

• Length bound. StringTensor.from_arrow and from_cudf record max_length, an upper bound on the byte length of every string, or None if unknown. It is also a constructor argument. Copies, views, torch.cat (largest bound), compilation and pickling keep it; old pickles load with None. The empty text block of TableTensor and from_tensor also record a bound, so concatenating with them keeps it.
• Scope. The bound covers only the tensor's own strings. A slice of a larger Arrow array or cuDF column shares its parent's buffers, but views and copies of the slice cannot reach strings outside it, so scanning only the slice is enough and keeps the bound tight.
• Rounding. The bound is rounded up to the next of 1, 3, 7, 15, …, 127, 255, …. torch.compile compiles a separate graph (~17 s) for each bound value, so rounding lets similar columns share one. The bound can then be up to about twice the real longest length.

Nothing in this PR reads the bound, so behavior is unchanged. Operations given an unknown bound still work, with larger buffers.

Validation

Tests cover bound rounding, unknown bounds, Arrow and cuDF loading, slices, copies, views, concatenation, compilation and pickling.

Costs on one NVIDIA L4: recording the bound adds 22 µs to from_arrow for 40k strings and 0.08 ms to from_cudf. Loading a 1,000-row slice of a 5M-row Arrow array takes 0.013 ms; scanning the parent's whole buffer took 0.66 ms. Rounding costs compiled sorting (#1272) at most 0.2 ms and 9 MiB on the measured inputs.

Additional details

Each distinct bound compiles a separate graph, and with fullgraph=True, exceeding Dynamo's recompile limit (8 per function) is an error. Batches that are views or gathers of one loaded table share that table's bound, so they compile once. Batches loaded separately can land in different buckets.

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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.97%. Comparing base (2b1696f) to head (c7e37c9).
✅ All tests successful. No failed tests found.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1274      +/-   ##
==========================================
+ Coverage   82.93%   82.97%   +0.03%     
==========================================
  Files         177      177              
  Lines       11459    11467       +8     
==========================================
+ Hits         9504     9515      +11     
+ Misses       1955     1952       -3     

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📝 Summary

Summary by CodeRabbit

• New Features
  • String tensors now track an optional upper bound on string byte length, including when created from Arrow or cuDF data. Bounds are rounded up to a supported size, and empty inputs have a bound of zero.
  • The bound is preserved through common tensor operations, conversions, and serialization. Concatenation accounts for appended data and uses the largest known bound; if any input bound is unknown, the result remains unknown.

String tensors now carry an optional upper bound on stored string byte lengths. Arrow and cuDF ingestion derive this bound from offsets. Tensor transformations and serialization preserve it, while concatenation combines known bounds or returns None if any input bound is unknown.

Changes: String tensor maximum-length bounds

Layer / File(s)	Summary
Define and derive length bounds sdm/tensor/var_len.py, sdm/tensor/string.py, sdm/tensor/table.py, test/tensor/test_string.py	VarLenTensor accepts an optional maximum-length bound. Arrow and cuDF ingestion derive bounds from offsets and round them to the smallest covering value of the form 2**k - 1. Empty string inputs use a bound of zero. Tests cover rounding and empty and sliced cuDF inputs.
Preserve bounds across tensor operations sdm/tensor/var_len.py, test/tensor/test_string.py	Flattening, unflattening, pickling, tensor transformations, and materialization preserve the bound. Concatenation uses the maximum input bound when all bounds are known; otherwise, it uses None. Tests cover propagation through tensor operations and serialization.

Priority: Low

Estimated code review effort: 3 (Moderate) | ~20 minutes

Possibly related PRs

• NVIDIA/structured-data-models#1292: Both changes update VarLenTensor state carried through compilation, flattening, and unflattening.

Merge Risk: Minimal · up to c7e37

No actionable current-head defect was established in the bound derivation or propagation paths.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 37.50% 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 the main change: recording string length bounds for compiled string operations.
Description check		The description directly explains the max_length feature, its purpose, propagation behavior, rounding, scope, and validation for the changeset.
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 37.50% 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 16 functions across 4 files.

Resolution

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

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Actionable comments posted: 1

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• 🪄 Fix comments: Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @sdm/tensor/_string_keys.py:
- Around line 32-33: Update the `pack` fallback for a `None` bound so it does
not use the entire `_data.numel()` buffer as the width or allocate `present`,
`index`, and `symbols` proportional to rows × total bytes. Use a
memory-efficient strategy that preserves the existing unknown-bound behavior and
output expected by consumers; keep rejection of an explicitly undersized width
unchanged.

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Commits

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Files selected for processing: 4

• sdm/tensor/_string_keys.py
• sdm/tensor/string.py
• sdm/tensor/var_len.py
• test/tensor/test_string_keys.py

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