[BugFix] Fix KV cache sizing and allocation for hybrid Mamba/attention models

[lesj0610]

Summary

Fix KV cache sizing for hybrid Mamba/attention models, mainly the Qwen3.5/3.6 GDN path.

Mamba state in mamba_cache_mode="none" and "align" is per-request, not per-token. The old code handled it like normal attention KV, which wastes attention capacity and makes tensor sizing harder.

This separates request-constant Mamba/GDN groups into a compact pool. mamba_cache_mode="all" keeps the old shared-pool behavior.

Changes

• Add KV cache memory-model metadata for token-proportional and request-constant groups.
• Add a compact block pool for request-constant groups. Block id 0 is still reserved as the null block.
• Generate separate KV pool configs for attention KV and Mamba/GDN state KV.
• Make scheduler, manager, and worker reshape paths use the right pool/page size.
• Keep unsupported paths fail-closed: prefix caching, CPU offload, KV connector, and full cudagraph capture.
• Keep cudagraph memory profiling working with a minimal mixed-memory KV config.

Related PRs

• [Bugfix] Fix KV cache sizing and allocation for hybrid Mamba/attention models vllm-project/vllm#37429: same bug area, different approach. This uses explicit memory-model metadata and pool dispatch instead of Mamba-specific free lists.
• [Bugfix] Exclude O(1) Mamba groups from hybrid KV cache token capacity vllm-project/vllm#40384: fixes capacity reporting for O(1) Mamba groups. This branch also changes allocation, reshape/stride math, and unsupported-path handling.

Validation

Commands run on this branch:

.venv/bin/ruff check \
  vllm/v1/core/kv_cache_utils.py \
  vllm/v1/core/block_pool.py \
  vllm/v1/core/kv_cache_manager.py \
  tests/v1/core/test_kv_cache_utils.py \
  tests/v1/core/test_prefix_caching.py

.venv/bin/python -m pytest \
  tests/v1/core/test_kv_cache_utils.py \
  tests/v1/core/test_block_pool.py \
  tests/v1/core/test_prefix_caching.py \
  -q -k 'request_constant or mixed_memory_model or real_mamba_spec or compact_pool or token_proportional_capacity or num_blocks_override or take_events'

Result: ruff passed, and the focused pytest command passed with 13 passed, 131 deselected.

Other focused validation during branch work:

• 109 passed for block pool, KV cache invariants, coordinator, prefix-cache gate, config generation, and manager paths.
• 17 passed for mixed/request-constant KV config tests.
• CPU offload request-constant reject test passed.
• Cudagraph profiling override regression test passed.

Runtime checks were run in eager mode (enforce_eager=True). Full cudagraph execution with request-constant KV is not supported by this PR and fails closed with a clear error.

Model	TP	Before GPU KV	After GPU KV	Change
Qwen3.5-4B dense GDN	1	~250K tokens	~352K tokens	1.4x
Qwen3.5-9B dense GDN	1	~36K tokens	~49K tokens	1.3x
Qwen3.6-27B dense GDN	2	~284K tokens	~376K tokens	1.3x

Runtime runs loaded Qwen3_5ForConditionalGeneration and the Triton/FLA GDN prefill kernel. Qwen3.5-9B and Qwen3.6-27B also passed short English/Korean/Arabic answer checks with thinking disabled.

AI assistance

AI assistance was used for analysis and patch preparation. The listed validation was run locally before submission.

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[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Skip override math when no token-proportional pool exists

When a model has only REQUEST_CONSTANT groups (for example Mamba with mamba_cache_mode='none'/'align') and num_gpu_blocks_override is set, bytes_per_block becomes 0 and this line forces available_memory to exactly request_constant_bytes. The later mixed-model allocator rejects reserved_bytes >= available_memory, so startup fails with a ValueError even though the override should be a no-op in this configuration. This makes num_gpu_blocks_override unusable for request-constant-only models.

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[chatgpt-codex-connector]

Check connector incompatibility against full KV config

This guard only scans attn_groups, so it misses request-constant non-attention groups (e.g., Mamba) present in kv_cache_config. In hybrid Mamba+attention models, allocate_uniform_kv_caches can then continue instead of failing closed, and if tensor sizes happen to match it may build attention-layout views for non-attention layers; otherwise it trips a later assertion instead of the intended explicit NotImplementedError. The compatibility check should inspect kv_cache_config.kv_cache_groups (or pool metadata), not just attention groups.

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