[Attention] Support FlashInfer re-paging for packed BLHNC KV caches

[leonardHONG]

Purpose

Enable FlashInfer dense GQA to use packed BLHNC KV caches with BF16/FP16 storage.

Manager blocks remain intact while FlashInfer reads smaller kernel pages through remapped indices and shared-storage views. This enables the existing packed grouping for configurations such as Qwen3.6 + DFlash, reducing KV groups from 46 to 5. The change also aligns ordinary sliding-window manager blocks with the primary attention block size.

Addresses question 2 in #58638. This complements #58380's layout selection and the sparse MLA work in #55219/#57169; it adds the dense FlashInfer read path without depending on #57169.

Test Plan

.venv/bin/python -m pytest -q \
  tests/kernels/attention/test_flashinfer.py \
  tests/v1/core/test_contiguous_kv_packing.py \
  tests/v1/worker/test_attn_utils.py \
  -k 'packed_flashinfer or sliding_window_keeps_manager_size or packed_allocation_retains_manager_blocks or clear_layer_kv_caches_releases_flashinfer_read_views'

Serving setup:

• 1× B300, PyTorch 2.13.0+cu130, FlashInfer 0.7.0.
• Qwen3.6-35B-A3B-FP8 + z-lab/Qwen3.6-35B-A3B-DFlash.
• MRv2, TP=1, FlashInfer, BF16 activation/KV/Mamba states, 7 speculative tokens.
• Max model length 262144, max sequences 32, max batched tokens 8192, block size 128, GPU memory utilization 0.8.
• Chunked prefill enabled; prefix caching disabled for throughput tests.
• OMP_NUM_THREADS=1, VLLM_USE_DEEP_GEMM=0.

Both layouts use this branch. Each layout was tested on two fresh servers in alternating order. The workloads below used 8/8/32 warmup requests and 256/64/256 measured requests. Performance uses synthetic acceptance length 4; accuracy uses real speculative verification.

Test Result

8 focused regression cases passed, covering graph replay, packed/dense read equivalence, manager-sized allocation, cache cleanup and SWA block sizing. Ruff and format checks passed.

Runtime checks confirmed that target and draft attention use the new path: 640-token manager blocks, 128-token kernel pages, shared storage.

Serving results, averaged over two independent runs per layout:

Workload	Metric	LBHNC	BLHNC	Change
8192 input / 1 output, c1	req/s	6.419	7.015	+9.3%
1024 input / 1024 output, c1	output tok/s	286.56	427.01	+49.0%
1024 input / 1024 output, c32	output tok/s	5115.53	7221.04	+41.2%
8192 input / 1 output, c1	p99 TTFT, ms	160.59	148.02	−7.8%
1024 input / 1024 output, c1	p99 TPOT, ms	3.478	2.328	−33.1%
1024 input / 1024 output, c32	p99 TPOT, ms	6.038	4.274	−29.2%

The two c32 runs measured 7258.52 and 7183.57 tok/s, improving over their paired LBHNC runs by 42.0% and 40.3%.

KV groups dropped 46→5. Reported KV capacity increased 6,640,889→6,789,922 tokens (+2.2%), with comparable peak PyTorch allocated memory: 214.281→214.263 GiB.

NSYS, 32 steady decode steps:

Per-step measurement	LBHNC	BLHNC
Target metadata scope	8.948 ms	1.632 ms
Scheduler scope	3.575 ms	0.789 ms
GPU activity gaps	16.542 ms	7.521 ms

The trace shows that enabling packed grouping reduces repeated metadata construction and scheduling work, allowing the GPU to spend less time waiting between operations.

Correctness:

• GSM8K, 250 questions, 5-shot chat, thinking disabled: 244/250 (97.6%) for both layouts, with identical response texts.
• Real mean acceptance length: 5.297733 for both layouts.
• Fixed-prompt cold-cache and block-reuse checks: exact logits and greedy-token matches. Prefix-hit checks also produced identical greedy tokens.
• Two-GPU DCP kernel validation: 12 cases passed, including BF16/FP16, empty local shards, partial pages and graph replay. Maximum output/LSE errors were 0.00862 / 1.91e-6.

E2E/profiling measurements used fb780b9b0; the final guard simplification passed six routing-equivalence checks.

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• Purpose and related issue.
• Test plan and commands.
• Test results, before/after measurements and model evaluation.
• Documentation changes considered; no new model or CLI option.

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[mergify]

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

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, @leonardHONG.

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