* Add fast fast_inference GRPO smoke test for the vLLM LoRA rollout path
Covers the vLLM >= 0.25.0 LoRA collision path (unsloth#7283, fixed in
unsloth-zoo#919) with all seven attention and MLP projections as LoRA targets so
both fused families (qkv_proj, gate_up_proj) are exercised. Kept tiny: the
ungated unsloth/Qwen2.5-0.5B-Instruct, max_steps=1 (the collision triggers on the
first rollout), short prompts/completions, and enforce_eager=True to skip CUDA
graph capture. Runs in ~89s cold and ~37s on a warm torch.compile cache.
Wrapped as a pytest test that skips without CUDA and still runs as a script; a
length-based reward gives non-zero GRPO advantages; asserts the vLLM engine is
attached at load and still bound on the trainer. Heavy imports are deferred into
the test so CPU-only collection stays import-free.
Co-authored-by: JoshuaL3000 <joshua.jian.ern.liew@intel.com>
* Assert GRPO metrics and pin seed in fast_inference test
Switch to unsloth/Qwen3-0.6B, disable vLLM torch.compile
(compilation_config=0) and run 3 steps so the updated LoRA adapter is
re-synced into vLLM on every step, not just loaded once.
Pin GRPOConfig(seed=...), which TRL forwards to vLLM SamplingParams, so
the run is reproducible, and assert per-step metrics (loss, grad_norm,
completion length, reward, reward spread, kl) instead of only checking
that train() returned. Verified across seeds 42/123/2024/7.
* Correct the seed comment and drop the pytest return
GRPOConfig(seed=...) does not reach vLLM SamplingParams: TRL's
generation_kwargs carries no seed key. Reproducibility comes from the
Trainer's set_seed pinning the global RNG the colocated sampler draws
from, so describe that instead.
Returning a value from a test triggers PytestReturnNotNoneWarning, which
pytest intends to make an error; the value was unused.
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Co-authored-by: danielhanchen <unslothai@gmail.com>
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