perf(utils): simpler and faster numpy padding fallback, test numba in CI

[AmitMY]

Audit item #1 — but the conclusion is the opposite of what that audit proposed, now that I have measurements.

The audit was wrong about numba; here are the numbers

I originally suspected numba was an unnecessary dependency. With it actually installed, it wins decisively:

case	numba	numpy fallback (old)	simple loop
16 short	2.4 µs	7.2	7.1
64 mixed	4.6 µs	37.1	25.1
256 mixed	16.3 µs	321.2	97.0
8 very ragged	7.2	35.2	4.9

5–20× faster, one-time JIT compile 179 ms. The fast extra earns its place. Retracting that part of the audit.

What actually needed fixing: the fallback

The no-numba path built per-element row and column index arrays (repeat / cumsum / searchsorted) to perform a single vectorised scatter. Filling row by row is shorter and much faster — numpy already copies each row at C speed, so the index arrays were pure overhead:

-        batch_size = len(lengths)
-        row_indices = np.repeat(np.arange(batch_size), lengths)
-        cumsum = lengths.cumsum()
-        positions = np.arange(len(all_values))
-        groups = np.searchsorted(cumsum, positions, side='right')
-        prev_cumsum = np.empty(batch_size, dtype=np.uint32)
-        prev_cumsum[0] = 0
-        prev_cumsum[1:] = cumsum[:-1]
-        col_indices = positions - prev_cumsum[groups]
-        output[row_indices, col_indices] = all_values
+        offset = 0
+        for row, length in enumerate(lengths):
+            output[row, :length] = all_values[offset:offset + length]
+            offset += length

4.7× faster at 256 rows (321 → 69 µs), 7× on ragged input (35 → 4.9 µs), 15 lines → 6.

The bigger find: the fast path was never tested

CI ran uv pip install ".[dev]" and never ".[fast]". So numba — the path this package advertises for speed — was not executed by CI at all. Added an extras axis so both padding paths run against both transformers versions:

        transformers: ["<5", ">=5"]
        # "dev" exercises the pure-numpy padding path, "dev,fast" the numba one
        extras: ["dev", "dev,fast"]

Four jobs instead of two.

README

Since numba is a 5–20× win, it should not be buried in an optional extra nobody discovers:

pip install "utf8-tokenizer[fast]"

...with a note that the plain install still works and falls back to numpy.

One honest caveat

The new fallback is algorithmically close to pad_bytearrays_to_tensor_loop, which test_utils uses as a reference — so in the plain dev leg, those 16 comparisons are less independent than before. Two things keep it honest: the new dev,fast leg means pad_bytearrays_to_tensor goes through numba there, making the comparison meaningful again, and test_utils has 9 assertions against an independent torch.nn.utils.rnn.pad_sequence reference that cover the plain leg.

Verification

• 241 passed with numba installed and 241 passed without it (locally: numba+transformers 5.14.1, and no-numba+transformers 4.57.6).
• All three implementations asserted byte-identical on every benchmark case before timing.
• ruff check . clean.

🤖 Generated with Claude Code

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Note

Low Risk
Performance and test-matrix changes in padding utilities and docs; no auth or API contract changes.

Overview
Replaces the no-numba padding fallback in _fill_padded with a short row-by-row copy loop instead of building scatter index arrays, improving performance when numba is not installed.

CI now runs four jobs (transformers <5 / >=5 × dev / dev,fast) so tests exercise both the pure-numpy and numba padding paths; install uses ".[${{ matrix.extras }}]".

README recommends pip install "utf8-tokenizer[fast]" and notes that the plain install still works with the numpy fallback.

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