[auto_docstring] needs to be only run on __doc__

[ArthurZucker]

What does this PR do?

This is mega long due I wanted to check benches.
Its not super super huge but a win is a win

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[ArthurZucker]

Benchmark Update 4 — Decoration speedup (warm process, without PyTorch)

Setup: same Python process, all imports and caches already warm (inspect signature cache, regex, auto-module). Both branches measured in the same process using explicit sys.path injection to bypass the editable install. 50 rounds × 3 real config classes.

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Decoration cost per class

	@auto_docstring call cost	what it does
branch	~0.35 µs / class	stores a _LazyDocClass closure
main	~1 106 µs / class	generates the full docstring eagerly
ratio	~3 160×	—

branch: 0.001 ms / 3 classes  =  0.35 µs/class   ← just stores a closure
main:   3.317 ms / 3 classes  = 1106 µs/class    ← full generation happens here

Cached cls.__doc__ access after generation: ~60 ns/class on both (identical).

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What this means for inference / training

	main	branch
from transformers import LlamaConfig	pays ~1 ms to generate doc immediately	pays ~0.35 µs to store a closure
model.forward(inputs)	__doc__ never touched	__doc__ never touched
LlamaConfig.__doc__ (explicit access)	~0 ns (already done)	~1 ms (generated once, then cached)
LlamaConfig.__doc__ again	~60 ns	~60 ns

Inference and training never read __doc__. On main, each from transformers import Xxx pays ~1 ms to generate the docstring whether or not it is ever used. On branch, that cost is deferred and only paid if .__doc__ is explicitly accessed.

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Why this does not show up in cold-process import benchmarks

The ~1 ms generation cost is negligible compared to Python startup (~200 ms) + transformers package init (~600 ms) + optional PyTorch import (~1 500 ms). The cold-process noise floor is ~50 ms, so a ~1–5 ms per-class saving is invisible there. The benefit accumulates across all decorated classes but is swamped by startup variance in single-class measurements.