[MRV2] Add reduced sampling for tensor-parallel decoding

[hzx55906]

Purpose

This PR adds an opt-in reduced sampling path that avoids gathering full-vocabulary logits during supported tensor-parallel decoding workloads.

With --enable-reduced-sampling:

• Greedy sampling gathers one local (max_logit, global_token_id) pair per TP rank.
• Bounded top-k sampling gathers only local top-k candidate values and global token IDs.
• Greedy speculative decoding verifies draft tokens using reduced target argmaxes.
• Unsupported cases automatically fall back to the existing full-logits path.

The feature is disabled by default and requires Model Runner V2 with tensor_parallel_size > 1.

Related RFC/issue: #54333

Implementation

The implementation is model-agnostic and does not modify individual model files.

• LogitsProcessor exposes shard-local logits and vocabulary metadata when the standard vocab-parallel head supports it.
• Model Runner V2 decides whether to keep local logits or perform the normal full-vocabulary all-gather.
• Sampler implements reduced greedy and bounded top-k sampling.
• RejectionSampler adds reduced verification for all-greedy speculative decoding.
• Configuration and tests cover supported cases and fail-closed behavior.

For greedy sampling, communication is reduced from O(B × V) to O(B × TP).

For bounded top-k sampling, communication becomes O(B × K × TP). The reduced path is used only when its candidate payload is smaller than the full local-logits payload.

Compatibility and fallback

Reduced sampling supports:

• All-greedy decoding.
• Random sampling with bounded top_k.
• Bounded top-k combined with top-p.
• Eligible mixed greedy/random batches.
• Greedy target plus greedy draft speculative decoding.
• Standard FP16, BF16, and FP32 vocab-parallel heads.

The batch falls back to full logits when it uses features requiring the complete vocabulary distribution, including:

• Logit bias, allowed tokens, bad words, or penalties.
• Thinking budgets, min_p, grammar, or structured output.
• Output logprobs or explicit logprob token IDs.
• NaN counting or sampling-distribution replay.
• Random sampling without bounded top-k or with an explicit seed.
• Probabilistic speculative decoding.
• Unsupported vocabulary-shard metadata or model logits postprocessing.
• Cases where candidate communication would not be smaller.

The configuration is rejected when used with Model Runner V1, TP=1, batch-sharded sampling, LoRA, diffusion models, or custom samplers.

Known limitations

• Exact ties at the top-k cutoff may not preserve every token retained by the full threshold-based implementation.
• Random reduced sampling is distributionally equivalent outside tie cases, but not guaranteed to be bit-for-bit identical to every full-logits backend.
• Out-of-tree models that modify the captured logits tensor in place may require a future explicit local-logits contract.
• Fallback currently applies to the whole sampler batch rather than individual requests.

Test Plan

.venv/bin/python -m pytest tests/test_config.py -k reduced_sampling -v

.venv/bin/python -m pytest \
  tests/v1/sample/test_head_dtype.py \
  -k compute_local_logits -v

.venv/bin/python -m pytest \
  tests/v1/worker/test_gpu_sampler_flags.py \
  -k reduced -v

Multi-GPU validation:

• Compare greedy outputs with reduced sampling enabled and disabled.
• Compare greedy speculative acceptance and output tokens.
• Validate bounded top-k sampling and all full-logits fallback cases.
• Measure latency for TP=2+.

Test Result

Test configuration

• GPU: L20X
• Number of GPUs: equal to the tensor-parallel size
  • TP=2: 2× L20X
  • TP=4: 4× L20X
• Sequence length: 8K
• Latency unit: milliseconds; lower is better
• Sampling configurations:
  • Greedy: temperature=0
  • Top-k/top-p: temperature=0.6, top_k=20, top_p=0.95

Performance results

Model	Hardware	Sampling	Batch	Speculative	Baseline E2E	Reduced E2E	E2E reduction	Baseline sampling	Reduced sampling	Sampling reduction
Qwen3.6	2× L20X, TP=2	Greedy	512	None	34.55	33.65	3%	1.065	0.122	89%
Qwen3.6	2× L20X, TP=2	Top-k/top-p	512	None	36.90	34.90	5%	3.240	1.326	59%
Qwen3.6	2× L20X, TP=2	Greedy	256	None	21.43	21.00	2%	0.554	0.097	82%
Qwen3.6	2× L20X, TP=2	Greedy	256	MTP-1	26.30	24.70	6%	1.045	0.114	89%
Qwen3.6	2× L20X, TP=2	Top-k/top-p	256	None	22.39	21.56	4%	1.657	0.724	56%
Qwen3-235B	4× L20X, TP=4	Top-k/top-p	128	None	34.90	33.70	3%	0.611	0.235	62%
Qwen3-235B	4× L20X, TP=4	Greedy	128	None	33.65	33.432	1%	0.224	0.060	73%

Across the measured configurations:

• Sampling latency is reduced by 56%–89%.
• End-to-end latency is reduced by 1%–6%.
• Greedy sampling achieves a sampling-latency reduction of 73%–89%.
• Top-k/top-p sampling achieves a sampling-latency reduction of 56%–62%.
• Greedy MTP-1 speculative decoding reduces sampling latency by 89% and end-to-end latency by 6%.
• Performance improvements are observed on both TP=2 and TP=4.

The end-to-end improvement is smaller than the sampling improvement because sampling is only one component of the decode step. Model execution and other runtime overhead remain unchanged.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @hzx55906.

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