[Model Loader] Add FastSafetensors sharded-state loader

[yonghengbit]

Motivation

In multi-node tensor-parallel deployments, distributing or propagating model
weights across nodes can be a substantial part of startup cost. If a checkpoint
is already stored in each rank's final sharded layout, every worker should be
able to read only its rank-local files instead of loading weights elsewhere and
sending them over the interconnect.

vLLM already supports FastSafetensors and sharded-state checkpoints separately.
This PR combines those paths so a TP worker can submit all of its rank-local
shard files in one FastSafetensors device-copy operation, using GDS where
available or the buffered NoGDS path otherwise.

What this PR changes

• Adds the fastsafetensors_sharded load format.
• Reuses the existing sharded-state filename pattern and rank selection.
• Loads all files for the current TP rank together with
  SafeTensorsFileLoader(SingleGroup()).
• Keeps FastSafetensors resources alive until parameter copies on the current
  accelerator stream have completed.
• Automatically retries with nogds=true when GDS initialization fails before
  any parameter copy begins; explicit nogds=false still makes GDS mandatory.
• Exposes pattern, nogds, bbuf_size_kb, max_threads,
  max_copy_block_size, and debug_log through
  --model-loader-extra-config.
• Adds focused unit tests and user documentation.

This is opt-in and does not change existing loader defaults. It currently
requires a CUDA-like platform, consistent with direct FastSafetensors GPU
loading.

Performance and model validation

An end-to-end measurement of the original implementation on vLLM 0.15.1 used
Qwen3-14B with tensor parallelism 2 on a DCU system:

Loader	Weight-loading time
sharded_state	8.83 s
fastsafetensors_sharded (GDS)	6.42 s
fastsafetensors_sharded (nogds=true)	6.96 s

The generated output matched across these loading paths. Additional local
testing also showed fastsafetensors_sharded loading materially faster than
runai_streamer_sharded; the comparable raw RunAI timing was not retained, so
this PR intentionally does not claim a numeric ratio for that comparison.

The implementation in this PR was ported to current main; the loader-focused
tests below were rerun after the final rebase. A new main-branch end-to-end GPU
benchmark was not run in the current WSL environment.

Validation

Run in WSL Ubuntu 24.04 with Python 3.12:

.venv/bin/python -m pytest \
  tests/model_executor/model_loader/test_sharded_state_loader.py \
  -k fastsafetensors_sharded -v

.venv/bin/pre-commit run --files \
  docs/models/extensions/fastsafetensor.md \
  tests/model_executor/model_loader/test_sharded_state_loader.py \
  vllm/config/load.py \
  vllm/model_executor/model_loader/__init__.py \
  vllm/model_executor/model_loader/sharded_state_loader.py

Results:

• Focused pytest: 3 passed, 5 deselected.
• All applicable pre-commit hooks passed, including Ruff, markdownlint, mypy,
  SPDX, forbidden-import, accelerator API, and configuration validation.

The tests cover load-format routing and configuration validation, submitting all
rank-local files in one transfer, accelerator synchronization/resource lifetime,
and GDS-to-NoGDS fallback before the first parameter copy.

The branch is rebased on upstream main commit 1f7427bc0. The rebase retains
the deterministic batching fix from #51736 in the shared sharded-state test.

Duplicate check and related work

I searched the open vLLM PRs for fastsafetensors sharded and
fastsafetensors_sharded and found no duplicate read-side loader PR.

#51496 is related but complementary: it fixes saving the logical sharded state
before post-processing. This PR is intentionally limited to loading existing
rank-local sharded-state checkpoints and does not duplicate that save-side
change.

AI assistance disclosure

OpenAI Codex was used to help port the older implementation, draft focused
tests and documentation, resolve the latest upstream conflict, update deprecated
CUDA-specific calls to the accelerator API, and run the reported checks. The
human submitter supplied the original implementation, benchmark results, and
requirements, requested this rebase, and will maintain the contribution and
respond to review feedback.

[mergify]

Documentation preview: https://vllm--51554.org.readthedocs.build/en/51554/

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @yonghengbit.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @yonghengbit.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork