[BUG] Fix FP64 Gumbel precision coverage

[tianyu-z]

The existing --use-fp64-gumbel flag only covered the explicit Triton Gumbel sampler. V1 sampling and spec decode also use the equivalent exponential-race form q.exponential_(); probs / q; argmax, so those paths still used fp32 exponential noise even when the precision flag was enabled.

Thread use_fp64_gumbel through the Python V1 sampler, TopKTopPSampler, rejection sampler recovery sampling, and LLM draft proposer sampling. When enabled, these paths now draw Exp(1) noise in float64 and compute the race scores in float64, while preserving the existing fp32 fast path by default.

Add regression coverage for the fp64 paths and a CUDA proof script. On H100 with PyTorch 2.9.1+cu126, 200M fp32 exponential samples had min exactly 2^-24 and zero samples below 2^-24, while float64 produced samples below that cutoff. In the many-tail race with trials=100000, tail_tokens=262144, gap=20.5, expected tail hits were 32.76; fp32 produced 0 hits and float64 produced 32 hits.

Purpose

Summary

This PR extends use_fp64_gumbel coverage to the V1 sampling paths that use the exponential-race form of Gumbel-max sampling.

Concretely, it makes use_fp64_gumbel=True apply to:

• V1 top-k/top-p sampling via q.exponential_(); probs / q; argmax
• spec decode recovered-token sampling from residual probabilities
• LLM draft proposer sampling
• the V1 sampler wiring from ModelConfig into Sampler / RejectionSampler

The default path is unchanged: use_fp64_gumbel=False still uses the existing fp32 fast paths.

Why

q.exponential_(); probs.div(q).argmax() is mathematically equivalent to Gumbel-max sampling:

q ~ Exp(1) = -log(U)
argmax(probs / q)
= argmax(log(probs) - log(q))
= argmax(log(probs) + Gumbel)

So the same fp32 tail-truncation issue that affects explicit Gumbel sampling also affects these exponential-race sampling paths.

On CUDA, fp32 random draws cannot represent the very small lower-tail events that fp64 can. For ordinary single-token AR sampling this is usually tiny, but for wide distributions / many parallel categorical races, the missing tail can become observable and can systematically remove rare-token wins.

Test Plan

python tools/gumbel_precision/prove_exponential_race_precision.py

Test Result

On an H100, the script showed:
torch.float32: samples=200000000 count(q < 2^-24)=0 min=5.960464477539062500e-08
torch.float64: samples=200000000 count(q < 2^-24)=8 min=1.963535558298479652e-09

many-tail race: trials=100000 num_tail_tokens=262144 gap=20.5 expected_tail_hits=32.7613
torch.float32: tail_hits=0
torch.float64: tail_hits=32

This demonstrates both pieces:

fp32 exponential noise has a lower-tail cutoff around 2^-24.
In an exponential-race sampler, those missing lower-tail events can change actual categorical outcomes.

Impacted Paths

This PR updates the following paths to honor use_fp64_gumbel=True:

vllm/v1/sample/ops/topk_topp_sampler.py

• random_sample
• native top-k/top-p sampling
• CPU/native fallback sampling
• CUDA/FlashInfer fallback behavior when fp64 is requested
• ROCm/aiter fallback behavior when fp64 is requested

vllm/v1/sample/rejection_sampler.py

• spec decode recovered-token sampling from residual distributions
• Triton kernel accumulator dtype for fp64 exponential-race scores

vllm/v1/spec_decode/llm_base_proposer.py

• draft-token sampling path

vllm/v1/sample/sampler.py

• threads use_fp64_gumbel into TopKTopPSampler

vllm/v1/worker/gpu_model_runner.py

• threads ModelConfig.use_fp64_gumbel into the V1 sampler

vllm/config/model.py

• updates the config docstring to mention both explicit Gumbel-max and equivalent exponential-race sampling

Tests

Added targeted tests for:

wiring Sampler(use_fp64_gumbel=True) into TopKTopPSampler
fp64 exponential-race sampling in random_sample
fp64 recovered-token sampling in rejection sampling
fp64 draft-token sampling in compute_probs_and_sample_next_token

Validation run on H100:

python -m pytest -q \
  tests/v1/sample/test_topk_topp_sampler.py::test_sampler_threads_fp64_gumbel_to_topk_topp_sampler \
  tests/v1/sample/test_topk_topp_sampler.py::test_random_sample_uses_fp64_exponential_race_when_requested \
  tests/v1/sample/test_rejection_sampler.py::test_sample_recovered_tokens_uses_fp64_exponential_race_when_requested \
  tests/v1/spec_decode/test_llm_base_proposer_sampling.py::test_compute_probs_and_sample_next_token_uses_fp64_exponential_race

---

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[gemini-code-assist]

Code Review

This pull request implements support for use_fp64_gumbel across the V1 sampling architecture, including the standard sampler, rejection sampler, and speculative decoding proposer. These changes ensure that lower-tail sampling events are preserved by using FP64 precision for random noise generation when requested, addressing potential truncation issues in FP32. The update includes modifications to Triton kernels, the introduction of helper functions for exponential noise sampling, and the addition of a statistical proof tool and unit tests to verify the implementation. I have no feedback to provide as there were no review comments to assess.

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[mergify]

Hi @tianyu-z, the pre-commit checks have failed. Please run:

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pre-commit run --all-files

Then, commit the changes and push to your branch.

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Is mypy failing?

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