[None][feat] Unified kv cache manager foundation by allisonlim-nv · Pull Request #20178 · NVIDIA/TensorRT-LLM

allisonlim-nv · 2026-10-12T23:51:16Z

Dev Engineer Review

KVCacheManagerV2 now accepts rank-local KVCacheLayerSpec entries for layer ownership, dtype, KV geometry, buffer roles, attention windows, sink tokens, and reuse policy.
Explicit specs drive cache sizing, pool configuration, page tables, buffer views, and layer metadata. Omitting specs preserves the legacy path.
Buffer coalescing now uses both coalescing group and expanded size. Pointer-table and page-index handling also accounts for mixed KV layouts, including quantized data and scale pages.
The manager rejects unsupported combinations, including mixed layer-spec dtypes for cold-page compression and incompatible layouts for draft-token relocation.
Supplied CI comments report repeated pipeline failures but do not identify failing tests. They also report mixed bot-job outcomes, including an aborted run. Test execution results are unavailable.

QA Engineer Review

Modified tests cover cache sizing and quota conversion for per-layer geometry, sink tokens, reuse policies, and draft sizing; mixed-layout addressing, NVFP4 scale pages, pointer views, and dtype validation; and compatibility updates to existing fixtures and helper signatures.
test_helix_retention_uses_rank_local_ranges in test_kvcm2_integration.py is listed in tests/integration/test_lists/test-db/l0_b200.yml. No manual-QA listing was found.
Coverage verdict: needs follow-up. The tests cover important sizing and addressing paths, but no test results were supplied. The CI list contains one test from the modified integration test file.

Per-File QA Perspective

tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py: Verify explicit and legacy construction, ownership queries, sizing and quota calculations, buffer views, page indexing, and sink-token retention.
tests/unittest/_torch/executor/kv_cache/test_kvcm2_integration.py: Covers mixed-layout addressing, NVFP4 data and scale pages, pointer views, dtype selection, and validation. test_helix_retention_uses_rank_local_ranges is listed in test-db/l0_b200.yml.
cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/config.h: Verify that the coalescingGroup default preserves existing behavior for buffers in group 0.
cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storage/config.cpp: Verify that buffers coalesce only when their lifecycle, expanded size, and coalescing group match.
cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManagerV2.cpp: Verify that the optional coalescing_group argument and property match the C++ configuration.
cpp/tensorrt_llm/thop/attentionOp.cpp: Verify non-FP4 pointer-table reads for 2D and 3D layouts, and confirm that FP4 handling remains unchanged.
tensorrt_llm/_torch/attention/backends/flashinfer.py: Verify that page-index spaces separate layers with different KV factors and preserve the mixed-precision staging path.
tensorrt_llm/_torch/attention/backends/sparse/minimax_m3/cache_manager.py: Verify that the added kv_factor parameter preserves raw slot-ID behavior.
tensorrt_llm/runtime/kv_cache_manager_v2/__init__.pyi: Verify that the public BufferConfig stub exposes the new coalescing field and default.
tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py: Covers cache-cost and quota calculations, including per-layer sink and reuse distinctions and draft sizing. No integration-list entry is expected for this unit test.
tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py: Updates helper compatibility with both DSA cache-manager types. No integration-list entry is expected for this unit test.
tests/unittest/_torch/attention/sparse/glm_kpool/test_glm_kpool.py: Updates a fixture for runtime cache-cost checks. No integration-list entry is expected for this unit test.
tests/unittest/_torch/attention/sparse/msa/test_minimax_m3_shared_draft_layers.py: Updates fixtures for block-scale role tests. No integration-list entry is expected for this unit test.
tests/unittest/_torch/attention/test_fp4_mla.py: Updates a fixture for pipeline-slot runtime-sizing checks. No integration-list entry is expected for this unit test.
tests/unittest/_torch/executor/kv_cache/test_mamba_cache_manager.py: Updates fixtures for Mamba quota and allocation tests. No integration-list entry is expected for this unit test.
tests/unittest/disaggregated/test_minimax_m3_kv_transfer.py: Covers cache-index behavior with index scale, KV factor, and block limits. No integration-list entry is expected for this unit test.

Description

Add the foundation for a unified target/draft KV-cache manager through explicit, rank-local KVCacheLayerSpec entries. Each spec defines layer ownership, dtype, KV geometry, buffer roles, attention window, and reuse policy.
Use these specs for cache construction, memory estimates, buffer views, and page-table/index handling. Preserve existing behavior when specs are omitted. Model integration will follow in subsequent PRs.

Test Coverage

Run existing KVCacheManagerV2 tests to check legacy construction, allocation, block reuse, and speculative decoding.

PR Checklist

Please review the following before submitting your PR:

PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
If PR introduces API changes, an appropriate PR label is added - either api-compatible or api-breaking. For api-breaking, include BREAKING in the PR title.
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.

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Signed-off-by: Allison Lim <allim@nvidia.com>

coderabbitai · 2026-10-13T00:05:49Z

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Walkthrough

KVCacheManagerV2 now accepts explicit per-layer cache specifications. It applies layer ownership, dimensions, roles, dtypes, windows, sink-token counts, and reuse policies to cache configuration, runtime views, and cache-size estimates. Buffer coalescing and page-index handling account for coalescing groups and per-layer KV factors.

Changes

KV cache layer specifications

Layer / File(s)	Summary
Define specs and configure cache layers `tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py`, `cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/config.h`, `cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storage/config.cpp`, `cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManagerV2.cpp`, `tensorrt_llm/runtime/kv_cache_manager_v2/__init__.pyi`	Adds validated layer-owner and layer-spec types. The manager uses explicit specs to configure layers and expose layer metadata. Buffer coalescing groups use both the coalescing group and expanded size.
Build per-layer cache views and indices `tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py`, `tensorrt_llm/_torch/attention/backends/flashinfer.py`, `tensorrt_llm/_torch/attention/backends/sparse/minimax_m3/cache_manager.py`, `cpp/tensorrt_llm/thop/attentionOp.cpp`, `tests/unittest/_torch/executor/kv_cache/test_kvcm2_integration.py`, `tests/unittest/disaggregated/test_minimax_m3_kv_transfer.py`	Cache views, page tables, pointer offsets, and page-index conversions use layer-specific roles, dtypes, KV factors, and strides. Cold-page codec creation uses the spec dtype and rejects mixed spec dtypes. Tests cover mixed layouts, NVFP4 scale pages, pointer views, and codec dtype selection.
Use specifications in cache sizing `tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py`, `tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py`	Static and runtime estimates use per-layer byte sizes, windows, sink counts, and reuse policies. Draft fixed costs round windowed-layer allocations separately. Tests check cache costs, quotas, per-request sizing, and draft sizing.
Validate estimates and compatible manager paths `tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py`, `tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py`, `tests/unittest/_torch/attention/sparse/glm_kpool/test_glm_kpool.py`, `tests/unittest/_torch/attention/sparse/msa/test_minimax_m3_shared_draft_layers.py`, `tests/unittest/_torch/attention/test_fp4_mla.py`, `tests/unittest/_torch/executor/kv_cache/test_mamba_cache_manager.py`	Adds estimate tests for explicit specs and updates legacy fixtures for the optional manager field. Cache-index tests cover explicit scale and KV-factor arguments.

Priority: Normal

Estimated code review effort: 4 (Complex) | ~60 minutes

Change: Feature

Possibly related issues

NVIDIA/Model-Optimizer#2482: The change adds explicit per-layer head dimensions and uses layer specs in cache sizing, which overlaps with the issue’s objective of preserving model-provided head dimensions for capacity estimates.
NVIDIA/TensorRT-LLM#17778: The change defines layer reuse policies and updates cache page-table and index handling. Route capture remains outside this change.

Suggested labels: api-compatible

Merge Risk: Low · up to 458b9

The remaining risk is limited to an optional draft-cache estimation path and does not affect normal manager construction.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 39.39% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 99 functions across 16 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly identifies the feature and matches the main change: establishing a unified KV cache manager foundation.
Description check		The description explains the purpose, implementation scope, compatibility behavior, and planned follow-up work. It includes the required sections and checklist. Test coverage is somewhat general and d…
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Full details: Description check

Explanation

The description explains the purpose, implementation scope, compatibility behavior, and planned follow-up work. It includes the required sections and checklist. Test coverage is somewhat general and does not list the specific new tests, but the description is mostly complete.

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Actionable comments posted: 2

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Inline comments:
Review comments at
@tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py:
- Around line 274-359: Add parametrized tests for KVCacheLayerSpec.__post_init__
validation and bytes_per_token across FP8, BF16, and NVFP4, including the
divisibility check. Cover spec-driven manager mismatch errors for pipeline
placement, heads, head_dim, kv_factor, and dtype, plus get_layer_owner and
draft_layer_ids legacy fallbacks. Verify
_get_layer_spec_cache_size_components(specs)[0] matches legacy
get_layer_bytes_per_token(Role.ALL) results for equivalent configurations,
especially NVFP4 scale bytes.
- Around line 1590-1593: Normalize explicit windows in the `layer_specs` path to
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allisonlim-nv · 2026-10-13T02:05:49Z

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🟠 Major · Count blocks with the layer’s KV factor. · kv_cache_manager_v2.py:3707

tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py:3707
🎯 Functional Correctness | 🟠 Major | ⚡ Quick win

Count blocks with the layer’s KV factor.

The explicit-spec path permits a key-only layer in a manager whose kv_cache_type has a manager-wide factor of two. For a single such layer, get_page_index_upper_bound counts key pages, but this division halves the reported free-block count. Use the selected layer’s factor, or derive the count from the pool’s slot geometry, so empty-cache sizing does not discard usable capacity.
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Review comment at
@tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py at line 3707:
Update get_page_index_upper_bound to calculate free blocks using the selected
layer’s KV factor or the pool’s slot geometry, rather than the manager-wide
kv_factor. Preserve all usable capacity for a key-only layer when sizing an
empty cache.

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Inline comments:
Review comments at
@tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py:
- Around line 2610-2612: Add a focused mixed-spec page-table conversion test
using one key-only layer and one key/value layer. After allocation, verify each
layer’s page-table indices match its buffer view, exercising the per-layer path
controlled by _use_per_layer_page_tables.
- Line 3504: Validate that head_dim is even when accepting an explicit NVFP4
spec, before get_buffers builds the buffer view; reject odd dimensions so the
view cannot have a zero-sized final dimension.
- Around line 1860-1863: Update the cold-page codec creation path to pass the
explicit layer dtype from the uniform `self.layer_specs` layout as
`runtime_dtype`, falling back to `self.dtype` when no layer specs are provided.
Locate the codec call near the `layer_specs` dtype-uniformity check.

---

Outside diff comments:
Review comments at
@tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py:
- Line 3707: Update get_page_index_upper_bound to calculate free blocks using
the selected layer’s KV factor or the pool’s slot geometry, rather than the
manager-wide kv_factor. Preserve all usable capacity for a key-only layer when
sizing an empty cache.

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mikeiovine

LGTM. Take a look at the coderabbit comment I replied to though.

Looks like we can rely on existing tests for this PR since it's just adding machinery that we will wire up in follow ups.

Signed-off-by: Allison Lim <allim@nvidia.com>

allisonlim-nv · 2026-10-13T21:15:41Z

/bot run

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SimengLiu-nv

Please add unit tests to coverage code changes and address the comments.

Signed-off-by: Allison Lim <allim@nvidia.com>

yuxianq · 2026-10-13T23:05:35Z

@yihwang-nv, could you help to review this PR as trt-llm-torch-attention-devs? Thanks~

Assignment explanation

Components	runtime/kv-cache/layout-io (60%); runtime/kv-cache/lifecycle (20%); backends/flashinfer (10%); integration/attention-tests (10%)
Tags	features:paged-kv (50%); features:speculative-decoding (30%); features:quantization (20%)
PR effort	8 points — Rank-local layer mapping, dtype-dependent costing, ownership/reuse rules and backend indexing require deep contract review across legacy and explicit-spec paths. This supports 8 points despite the localized file count.
Importance	Important — Introduces a broad optional cache-construction contract spanning layer identity, heterogeneous storage and draft ownership. FlashInfer page-index sharing now depends on KV factor, a correctness-sensitive distinction between K-only and KV views.
Affinity (0–1)	0.416 component/tag match; 0.312 after PIC preference
Outstanding points at selection	47 → 55 (+8)
Points already reserved on this PR	0
Effective weight	1
Completed review points (preceding 168 hours)	20
Combined points after assignment	75
Demand-derived target	56.00 points (soft share)
Additional fair share before assignment	0.00 points
Share explanation	Whole PR / qualification or coverage constraint exceeds a soft share; assignment is allowed.
Selection rule	affinity with half-share guard

Affinity and workload are snapshots at selection, not live counters. Existing reservations on this PR are counted once.

allisonlim-nv · 2026-10-24T07:00:02Z

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NVIDIA#20178 foundation) Squashed net change of PR NVIDIA#20486 at head 714916d relative to the main it last merged (0829689), ported onto feat/musespark-open-agentx; unrelated main commits are not included. NVIDIA#20486 carries the unified KV cache manager foundation of PR NVIDIA#20178 through eea6dcb (NVIDIA#20178's later 61248ec, 77d4eae and 636715a are not part of the NVIDIA#20709 stack and are not ported). Commits: 3bc36a5 [None][feat] add unified KV cache layout foundation dad9f1f [None][refactor] simplify unified KV cache layer specs 6ea5ad8 [None][test] remove unified KV cache foundation tests 090cbc0 [None][docs] remove unified KV cache design document 511975f [None][feat] support per-layer storage layouts in KVCacheManagerV2 bfc8bc7 fix: normalize explicit KV cache spec windows 18c71c1 fix: use explicit KV cache dtype for cold-page codec ae64dca test: cover explicit KV cache dtype for cold-page codec 701abea fix: honor physical slot strides in KV cache views 623816c test: cover mixed KV cache odd-slot addressing 7cde28e fix: restore KV cache subclass and test compatibility 4a3bb25 [None][test] fix mixed KV cache slot address arithmetic 9d0107b [None][fix] align NVFP4 data and scale page layouts e273a98 [None][fix] accept mixed KV cache pointer table layouts f6b1d08 [None][fix] preserve sink and reuse policy in KV cache costing b6db612 [TRTLLM-20434][feat] Dflash Unified KVCache 39d814d [None][fix] share the DFlash draft KV cache in disagg only with speculative_config in both sections 22cf25c [None][fix] gate the disagg shared draft KV cache on matching tensor_parallel_size feed06a [None][test] cover the disagg shared draft KV gate for generation-only speculative_config and mismatched TP 0319dc3 test: use default timeout for unified DFlash KV test 1f39128 fix: restore DFlash private arena fallback for separate KV caches 97fb214 fix: seed unified DFlash context lengths from bounded token counts caf4c45 test: cover DFlash mixed-precision block tables 875eff2 fix: require matching attention DP for shared draft KV 212fdab test: enable shared draft KV in DFlash integration test 1302d43 fix: keep mixed-window DFlash caches separate Port conflict resolutions (base = main 0829689, ours = agentx b7979ca + !10912): - kv_cache_manager_v2.py: our joint_kv_cache_reuse kwarg and the PR's layer_specs kwarg land at the same spot in _get_per_request_layer_tokens, get_cache_bytes_per_request and get_cache_quota_per_request (5 hunks): keep both. The generic layer_configs loop moves under the PR's `else:`; main's kv_pool_rebalance_config (NVIDIA#19077) is not on agentx and is dropped. - _util.py: agentx's _per_manager_cache_cost has no tokens_per_block parameter (main-only); keep the PR's unified layer_specs injection. - dflash.py: keep agentx's windowed VANILLA arena (_ctx_base) branch; ctx_len_gen (PR) equals self._ctx_len[slots]. - rank_info.py: drop main's bounded_replay_on_generation (not on agentx); keep num_draft_layers. - tests: drop main-only imports (PyTorchModelEngine, KvCacheRetentionConfig). !10912 stays intact: validate_block_attention_windows() is not re-added. Signed-off-by: Allison Lim <allim@nvidia.com> Signed-off-by: Zheyu Fu <zheyuf@nvidia.com>

…indings The ported NVIDIA#20178/NVIDIA#20486/NVIDIA#20709 Python assumes main's KV cache manager bindings. Two pieces are not on agentx: - Per-layer AttentionReusePolicy (main NVIDIA#19727 C++). The stack only ever uses REQUIRED, which is what agentx does for every layer, so the reuse_policy field, its AttentionLayerConfig argument and its place in the scratch and pool-group keys are dropped. The one test that needs PRIVATE is skipped. - BufferConfig.coalescing_group (the stack's own C++, which the agentx image does not carry). Group 0 is what the prebuilt bindings do implicitly, so the argument is passed only for nonzero (NVFP4) groups, which fail loudly unless the bindings are rebuilt from this branch's cpp/. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com>

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…#20486 and #20709) (#20806) Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> Co-authored-by: Allison Lim <allim@nvidia.com>

… under disaggregated serving (#25) * [None][feat] Unified kv cache manager foundation (NVIDIA#20178) Signed-off-by: Allison Lim <allim@nvidia.com> (cherry picked from commit fce28f0) Backport to this branch: the cpp/ part (BufferConfig.coalescing_group, the attentionOp 3-D pool-pointer table) is not included; _coalescing_group_kwargs passes the group only when it is nonzero, so layer specs without NVFP4 buffers run on the existing bindings, and the two mixed NVFP4/BF16 pool tests skip on bindings without coalescing_group. Hunks for main-only features (mixed-precision KV, pool rebalancing) are dropped, and layer_specs is declared on the class for managers built without __init__. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [TRTLLM-16722][feat] Dflash Unified KVCache (NVIDIA#20486) Port of NVIDIA#20486 at 219e26e (net diff against the main it merges, df95eda). Signed-off-by: Allison Lim <allim@nvidia.com> Backport to this branch: conflicts resolved against the separate draft pool's disagg window transfer (RankInfo keeps both draft fields), the drafter slot parking and seeds in DFlashSpecMetadata.prepare (the position seeding applies to the unified cache only), and the fused block-table refresh (skipped when the layer has a block offset). Hunks for code this branch does not have (spec_tail_kernels, model-level layer specs) are dropped; Gemma4 hybrid geometry stands in for _has_variable_kv_cache_geometry. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][feat] Per View KV head transfer (NVIDIA#20709) Port of NVIDIA#20709 at 300a671 (net diff against the main it merges, 0746a23). Signed-off-by: Allison Lim <allim@nvidia.com> Backport to this branch: import and registration-check conflicts resolved by keeping both sides. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][fix] Bound DFlash's shared context by the allocation's extent DFlash took the number of valid block-table entries as the pages a request holds. On a unified cache with per-layer page tables (e.g. an FP8 target with a BF16 drafter), a sliding-window draft layer's evicted pages stay BAD_PAGE_INDEX in front of its live tail, so the count stopped at about one window (66 blocks for window 2048, page 32) whatever the context length. The decode-time allocation check then trapped once a context passed ~2.1K tokens, and prefill skipped the drafter's context store for later chunks and for prefix hits past the window. Use the extent of the allocation instead, the last valid entry + 1. Reads below the window are never issued and writes land in the live tail; for a full-attention drafter, and on the cpp backend, the extent equals the count. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> (cherry picked from commit ff11364) * [None][fix] Re-seed DFlash generation slots in CUDA-graph warmup CUDA-graph warmup builds every batch from dummy requests whose ids repeat across batches (0..bs-1), so a reused id keeps its drafter slot and the previous batch's context length, which the replay after each capture advances for good. A later batch can give the same id far fewer pages, and on a unified KV cache the drafter's allocation check then trapped at server start: during the advanced-sampling capture pass the filler rows reached 31..79 positions against a 24-position allocation. Only warmup runs a CUDA-graph batch eagerly, so there re-seed each generation row's slot from this batch's positions, as prepare() already seeds a new request. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> (cherry picked from commit 2b23449) * [None][feat] MiniMax-M3.1 DSpark: keep the drafter's KV in the target's manager under disagg On GB300 a disaggregated generation worker gets the target's prompt KV but not the DSpark drafter's, which lives in a separate pool: the drafter starts from an empty window at block 0 and acceptance collapses on long prompts. Hosting the draft layers in the target's KVCacheManagerV2 lets the transceiver move them with the target's pages. - MiniMaxM3KVCacheManagerV2 hosts explicit draft layer specs: index-K buffers follow the spec-built ones, dense target layers stay FP8, size-only coalescing keeps the target pools, draft layers use the base view. - KvCacheCreator unifies a standalone DSpark drafter with such a manager under disaggregated serving when the drafter reads the target's page size. Agg keeps the separate pool paired with block reuse, and a model that ships that pool's window (F0a) keeps it in disagg. TRTLLM_DISAGG_SHARED_DRAFT_KV=0 opts out. - An NVFP4 target transfers with a BF16 draft held in pools of its own. - A windowed draft lifecycle gets the hot-tier bytes of every admissible sequence's window plus one context chunk, instead of the bytes-per-token split that handed it a quarter of the KV cache. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][feat] Reuse a unified DSpark drafter's KV only where its window is complete With the drafter's layers in the target's KV cache manager, the draft lifecycle's REQUIRED policy let the drafter's window constrain every prefix match: a request branching off a shared prefix found no committed window there and re-prefilled its whole prompt. - A windowed drafter's layer specs are OPTIONAL. The target alone sets the match, and a new cache's first resume selects the drafter's layers where their window is complete (page-aligned matches only). - Where the window is missing, prepare_context caps the claim so the prefill writes everything the drafter reads (from L + 2 - W on): at L + 1 - W, or one window before a branch point within W of the prompt end, whose window the request then publishes. A partial match first retries at its last full page. - The commit gate for unstored drafter context, its unwritten-prefix marks and branch-point publication cover a manager that hosts the drafter's layers, not only a paired draft pool. A disagg generation request needs no cap: the KV transfer requests an unselected OPTIONAL group in full. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][perf] MiniMax-M3.1 DSpark: copy only the unified drafter's block offsets per step A hybrid NVFP4 MiniMax-M3 cache is pool-less: no target layer reads its attention-op block offsets. With explicit draft layers it kept every layer's pool, so each step converted the whole per-layer table (layers x sequence slots x max_seq_len / tokens_per_block) on the device: about 3 ms per decode step at max_seq_len 1M on TP2. Keep one attention-op pool, the first draft layer's, which is the only one the unified drafter reads, and fill it from the draft lifecycle's rows with the copy a separate draft pool uses. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][fix] Allocate the whole block of a drafter that shares the target's KV cache A unified DFlash/DSpark drafter appends its block after the request's context, through the request's own pages. The manager sized every request with num_extra_kv_tokens = K-1 beyond its history, but the DSpark shift_label block is K wide (DFlash K+1). When the allocation ended on a page boundary, at the prompt end or after a step that accepts all K+1 tokens, the block's last slot had no page: the drafter then truncated the advertised context and its scratch overwrote the last context position in the draft layers, silently. When DRAFT-owner layers live in the manager, size each request beyond its history by max(num_extra_kv_tokens, drafter block width) at every allocation site and in the generation headroom. The creator passes the checkpoint-resolved width (shift_label). Adapted from NVIDIA/TensorRT-LLM Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> NVIDIA#20664 (76c4cef); num_extra_kv_tokens keeps its position-side meaning. * [None][fix] Fail at startup when TRTLLM_DISAGG_SHARED_DRAFT_KV=1 cannot be honored An explicit TRTLLM_DISAGG_SHARED_DRAFT_KV=1 on a disaggregated worker whose target and drafter cannot share one KV cache manager silently kept the separate draft pool, whose KV is not transferred. Raise at KV cache creation instead, as NVIDIA/TensorRT-LLM NVIDIA#20664's _check_shared_draft_kv_request does. disaggregated_mpi_worker now writes "auto" rather than "1" when both sections pass its coarse checks, so its automatic choice can still fall back where a worker's finer checks refuse; the creator shares under "1" and "auto" alike. The unset default is unchanged. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][fix] Let disaggregated_mpi_worker share a DSpark drafter's KV The launcher wrote TRTLLM_DISAGG_SHARED_DRAFT_KV=0 for any drafter other than DFlash, and for any page size other than 16, 32 or 64, so a MiniMax-M3.1 DSpark deployment started through it (128-token pages) kept the separate draft pool, whose KV is not transferred. Accept a standalone DSpark drafter with attention_backend unset, AUTO or TRTLLM, as NVIDIA/TensorRT-LLM NVIDIA#20664 does, and leave its page size to each worker's KvCacheCreator: the launcher cannot know whether the drafter reads the target's page. It writes "auto"; if the two sides still disagree, the peers' draft-layer check fails loudly. An explicit TRTLLM_DISAGG_SHARED_DRAFT_KV=0 is now kept. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][fix] Keep attention-DP dummy generation rows on DFlash's dummy slot A generation row with the attention-DP dummy id must never inherit a real slot, because the C++ offset copy maps placeholders to page 0: a slot that a context warmup stored under that id would let the dummy row write its drafter K/V past its own pages, onto another request's page. As in NVIDIA/TensorRT-LLM NVIDIA#20664, such rows now always take the dummy slot. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][perf] DFlash: size a unified MiniMax-M3.1 drafter's context stores on the host DFlash bounds each context store by the row's allocation, which it reads back from the device unless the drafter's block-table rows are known to be written in full. That was only recognized for a separate draft pool, so with the unified drafter every context chunk waited for the target's forward before the store was issued. Let the KV cache manager say whether it rewrites copied rows in full, and check the target manager's own table for a unified drafter. The MiniMax-M3 manager's drafter-only pool is copied in full. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][feat] KVCacheManagerV2: reuse an OPTIONAL group at a partial endpoint An OPTIONAL lifecycle group was checked only at a page-aligned endpoint, so a partial prefix match always left it incomplete. A unified DSpark drafter then lost its window's partial page and the caller cut its claim back to the last full page, recomputing up to a page per request. Check an OPTIONAL group at any endpoint with the REQUIRED coverage rule: the last page needs only the matched prefix. The first resume copies a selected group's partial page into a private page, as for REQUIRED, and counts it as a partial reuse. Partial snapshots now publish OPTIONAL pages as well, so a continuation of a prompt that ended mid-page finds them. A match whose partial page is missing still falls back to the last full page. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> * [None][fix] Give the block-table refresh test's stub the extent columns The stock DFlashWorker._refresh_ctx_block_tables reads _ctx_block_cols since the allocation-extent fix; the test's object.__new__ stub never set it. Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> --------- Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> Co-authored-by: allisonlim-nv <allim@nvidia.com>

allisonlim-nv added 4 commits October 12, 2026 16:01

[None][feat] add unified KV cache layout foundation

3bc36a5

Signed-off-by: Allison Lim <allim@nvidia.com>

[None][refactor] simplify unified KV cache layer specs

dad9f1f

Signed-off-by: Allison Lim <allim@nvidia.com>

[None][test] remove unified KV cache foundation tests

6ea5ad8

Signed-off-by: Allison Lim <allim@nvidia.com>

[None][docs] remove unified KV cache design document

090cbc0

Signed-off-by: Allison Lim <allim@nvidia.com>

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Comment thread tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py Resolved

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allisonlim-nv changed the title ~~Feat/unified kv cache manager foundation~~ [None][feat] Unified kv cache manager foundation Oct 13, 2026

[None][feat] support per-layer storage layouts in KVCacheManagerV2

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Comment thread tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py Resolved

fix: normalize explicit KV cache spec windows

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allisonlim-nv added 3 commits October 13, 2026 14:43

fix: use explicit KV cache dtype for cold-page codec

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Signed-off-by: Allison Lim <allim@nvidia.com>

test: cover explicit KV cache dtype for cold-page codec

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fix: honor physical slot strides in KV cache views

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Merge branch 'main' into feat/unified-kv-cache-manager-foundation

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Merge branch 'main' into feat/unified-kv-cache-manager-foundation

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Merge branch 'main' into feat/unified-kv-cache-manager-foundation

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allisonlim-nv merged commit fce28f0 into NVIDIA:main Oct 26, 2026
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This was referenced Oct 26, 2026

[None][feat] Per View KV head transfer #20709

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[None][feat] DSpark Unified KVCache standalone #20664

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[None][feat] Rubin merge back: MLA, DSpark, DWDP, Helix #21067

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zheyuf added a commit that referenced this pull request Oct 27, 2026

[None][feat] Unified target/draft KV cache for DFlash (port of #20178, …

0f6cff3

…#20486 and #20709) (#20806) Signed-off-by: Zheyu Fu <zheyuf@nvidia.com> Co-authored-by: Allison Lim <allim@nvidia.com>

zheyuf mentioned this pull request Oct 29, 2026

[None][feat] MiniMax-M3.1 DSpark: unified target and drafter KV cache under disaggregated serving brb-nv/TensorRT-LLM#25

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