[#19527][fix] Account for beam width when sizing CUDA graph warmup KV requests

[LiRunGuo]

Dev Engineer Review

KVCacheManager.get_num_available_tokens now accounts for max_beam_width when estimating warmup capacity. The estimate includes shared fully covered prompt blocks and per-beam partial prompt and appended-token blocks. add_dummy_requests skips multi-beam warmup requests that exceed available blocks, while excluding VSWA pools from that block-count check. CUDA graph warmup passes the beam width to target and draft KV managers and skips a batch when the resulting capacity is below one token. KVCacheManagerV2 accepts the new argument for interface parity; its capacity calculation remains unchanged because it supports beam width 1.

QA Engineer Review

Changed tests cover beam-aware block counts and capacity, insufficient-pool skip behavior, exact-fit allocation and release, draft-token reservations, and warmup capacity checks for target and draft managers. Coverage verdict: sufficient for the described warmup behavior. Test results were not provided as independently verified evidence.

Per-File QA Perspective

• tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py — Verify callers can pass max_beam_width and that V2 capacity remains unchanged.
• tensorrt_llm/_torch/pyexecutor/model_engine.py — Verify CUDA graph warmup passes the configured beam width to target and draft cache managers, and skips batches when capacity is below one token.
• tensorrt_llm/_torch/pyexecutor/resource_manager.py — Verify beam-aware capacity estimates and insufficient-pool handling, including the VSWA exclusion and block release behavior.
• tests/unittest/_torch/executor/test_resource_manager.py — Tests beam-aware allocation, capacity, insufficient-pool behavior, and exact-fit release. Integration test-list status does not apply to this unit test.
• tests/unittest/_torch/executor/test_pytorch_model_engine_warmup.py — Tests warmup capacity queries, skip behavior, and block release for beam search. Integration test-list status does not apply to this unit test.

Description

Fixes #19527.

With beam search and a small KV cache pool, executor startup fails during CUDA graph warmup with RuntimeError: No free block found. This shouldn't happen!.

With beam search, only KV blocks fully covered by the prompt are shared among beams. The partial last prompt block, and every block allocated while appending tokens, are allocated once per beam. The V1 KVCacheManager ignored this when sizing the warmup dummy requests. get_num_available_tokens reported free_blocks * tokens_per_block, so the max-length warmup request could need more blocks than were free. For example, with beam width 32 and 32 free blocks it reported 1024 tokens, but a 1023-token request needs 31 shared blocks plus 32 per-beam tail blocks.

Changes:

• KVCacheManager.get_num_available_tokens takes max_beam_width. For beam width > 1 it returns a length such that every sequence up to it fits. Block usage is not monotonic in the length (a block-aligned prompt shares all of its blocks), and the caller clamps the result further (to max_seq_len - 1), so it bounds the per-beam tail by its worst case. Beam width 1 is unchanged.
• KVCacheManager.add_dummy_requests counts the blocks that beam-search dummy requests need. If they cannot fit, it returns None, the existing "skip" signal its callers handle, instead of failing inside the block manager. VSWA pools are excluded because this count does not model per-window pools.
• The CUDA graph warmup passes max_beam_width. KVCacheManagerV2.get_num_available_tokens accepts the argument for interface parity; V2 only supports a beam width of 1.
• With beam search, the CUDA graph warmup skips a batch size when the reported capacity is below one token, because add_dummy_requests does not size VSWA pools per beam and cannot catch this itself. The check only applies to beam width > 1, so beam-width-1 warmup is unchanged.

A request that genuinely cannot fit still fails, but with the executor's clear per-request error ("requires N KV cache blocks ... exceeds its GPU-primary capacity") instead of an executor startup failure.

Test Coverage

New unit tests in tests/unittest/_torch/executor/test_resource_manager.py (in l0_a10.yml):

• test_dummy_request_block_count_matches_beam_search_allocation: the block-count model matches what the C++ manager actually allocates for block-aligned and unaligned lengths.
• test_get_num_available_tokens_accounts_for_beam_width: every length up to the reported capacity fits with beam width 4, and beam width 1 is unchanged.
• test_add_dummy_requests_beam_search_returns_none_when_pool_too_small: oversized beam-search dummies return None and leak no blocks, and an exact fit still succeeds and returns every block.
• test_beam_search_dummy_requests_with_draft_tokens: the same checks with 3 reserved draft tokens.

New unit tests in tests/unittest/_torch/executor/test_pytorch_model_engine_warmup.py (the unittest/_torch/executor directory is in l0_h100.yml), built on real V1 KVCacheManagers with max_beam_width=4:

• test_cuda_graph_warmup_request_fits_beam_search_kv_blocks: the CUDA graph warmup batch fits a 16-block pool.
• test_cuda_graph_warmup_request_passes_beam_width_to_kv_cache_managers: both the target and the draft get_num_available_tokens receive max_beam_width.
• test_cuda_graph_warmup_request_skips_beam_search_batch_below_one_token: a capacity below one token skips the batch size and frees the warmup requests already added.

With main's sources, all 7 new tests fail; the engine-level fit test fails with the original No free block found error. Without the below-one-token check, only the last test fails.

Local results (H200, devel:1.3.0rc27 container):

• On 134fa24: test_resource_manager.py + test_pytorch_model_engine_warmup.py, 73 passed.
• kv_cache/test_mamba_cache_manager.py: 34 tests fail on current main both with and without this PR (verified on 134fa24), with AttributeError: 'types.SimpleNamespace' object has no attribute 'mapping'. The test's mock model_config has no mapping, and get_kv_cache_manager_cls now reads it (added in [None][feat] Helix speculative verify groups: fp8 + fp4 MLA and DSpark #19273). On the earlier base 0d4304a, test_resource_manager.py + test_mamba_cache_manager.py gave 232 passed with this change.
• End to end with the repro script from [Bug]: Beam search with a small KV cache pool fails CUDA graph warmup with "No free block found" #19527 (Qwen2.5-0.5B-Instruct, max_beam_width=32, KvCacheConfig(max_tokens=8192), max_seq_len=1024, max_batch_size=8) on 134fa24: without the fix, startup fails with No free block found; with it, all 32 beams are generated. On the earlier base, beam 64 with max_tokens=16384 also completes, and beam 16/32/64 on a large pool are unchanged.

PR Checklist

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Walkthrough

The PR adds beam-width-aware KV-cache capacity calculations and dummy-request checks. CUDA graph warmup passes beam width to capacity queries for target and draft caches. Tests cover allocation demand, capacity limits, failed allocations, exact-fit allocation, and resource release.

Changes

Beam-aware KV-cache capacity

Layer / File(s)	Summary
Capacity interface and warmup wiring tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py, tensorrt_llm/_torch/pyexecutor/model_engine.py, tests/unittest/_torch/executor/test_pytorch_model_engine_warmup.py	KVCacheManagerV2.get_num_available_tokens accepts max_beam_width for interface parity; its capacity calculation remains unchanged. CUDA graph warmup passes beam width to target and draft cache queries. When multi-beam capacity is below one token, warmup frees its requests and returns None. Tests check capacity queries and resource release.
Beam-aware capacity and dummy allocation tensorrt_llm/_torch/pyexecutor/resource_manager.py, tests/unittest/_torch/executor/test_resource_manager.py	KVCacheManager models shared prompt blocks and per-beam allocations when calculating token capacity and dummy-request block demand. For non-VSWA multi-beam requests that exceed available blocks, add_dummy_requests returns None. Tests cover block demand, capacity limits, oversized and exact-fit allocations, and resource release.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~20 minutes

Change: Bug fix · Severity of issue fixed: Medium

Suggested reviewers: juney-nvidia, eopxd, lori-ren, bowenfu

Merge Risk: ⚪ Minimal · up to 9949e

No unresolved issue is established for beam-search CUDA graph warmup; the PR is mergeable after normal checks.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 75.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 20 functions across 5 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	The changes meet the coding requirements in [#19527]. V1 get_num_available_tokens accounts for beam width, and add_dummy_requests returns None when beam-search dummy requests exceed the availabl…
Out of Scope Changes check	✅ Passed	The reported changes are connected to [#19527]. The V2 signature update provides interface parity, and the warmup guard addresses the documented VSWA limitation. The implementation and tests remain wi…
Title check	✅ Passed	The title follows the required issue-and-type format and clearly summarizes the beam-aware CUDA graph warmup change.
Description check	✅ Passed	The description includes the required Description, Test Coverage, and PR Checklist sections. It explains the issue, solution, and relevant tests; the checklist is marked complete.

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• Resolve merge conflict in branch fix/beam-search-cuda-graph-warmup-kv-sizing

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Inline comments:
In `@tensorrt_llm/_torch/pyexecutor/model_engine.py`:
- Around line 3338-3347: Add a focused warmup test around the model-engine
cache-capacity flow using distinct target and draft cache-manager spies, a
max_beam_width greater than one, and assertions that both
get_num_available_tokens calls receive the configured beam width and expected
arguments. Keep the existing request-construction coverage unchanged and ensure
the draft-cache path is exercised.

In `@tensorrt_llm/_torch/pyexecutor/resource_manager.py`:
- Around line 1874-1884: Add a boundary-focused beam-search test with
max_num_draft_tokens set to a positive value, covering the non-CROSS cache path.
Verify the expected capacity from get_num_available_tokens, then confirm
add_dummy_requests returns None and leaves the free-block count unchanged.
- Around line 1874-1885: In the warmup flow, after applying the draft manager
limit to available_tokens and before calculating token_num or creating the final
dummy request, return early when available_tokens is below one. Call
free_warmup_requests() before returning None, preserving existing behavior for
capacities of at least one token.

In `@tests/unittest/_torch/executor/test_resource_manager.py`:
- Around line 1143-1144: Extend the exact-fit allocation test after the request
cleanup loop to assert that kv_cache_manager.get_num_free_blocks() equals the
original total_free count, ensuring all allocated blocks are returned and leaks
cannot pass unnoticed.

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[LiRunGuo]

@karljang Thanks for approving #19527. I've addressed the CodeRabbit review in 9949eb1: a beam-search-only check in the CUDA graph warmup plus more unit tests, with details in each thread. Could you please trigger /bot run when you have a moment? I'm happy to rebase onto the latest main or adjust anything else.