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…ache manager MambaHybridCacheManagerV2 takes kda_token_states (default off). With the KDA replay caches it then also keeps the fp32 state after every draft of the last verify round per slot (kda_state_tok, [layers, slots, num_spec, H, V, K]), passes the layer's view in the speculative state and moves it with the slot. A verify kernel can start the next round from the state after the accepted drafts instead of replaying them. The cache factory enables it when the model's backbone sets kda_token_states. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
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…with per-token KDA states With per-token KDA states, kda_state_tok holds num_spec recurrent states per SSM state slot, outside the cache quota, addressed by the request's SSM slot (_allocate_pool_replay_buffers). V2 sizes the SSM pool group by the typical step's ratio, so its slot count, and kda_state_tok with it, grows with the quota. Without block reuse a slot past the min-slots floor (resident sequences plus reserved dummies) never holds a request, so those slots only add memory. On the Kimi K3 stack (7 DSpark drafts, TP16, max_seq_len 4096, free_gpu_memory_fraction 0.25) the pool got 183 slots: 32.4 GiB of kda_state_tok beside a 13.2 GiB quota. It ran out of device memory above fraction 0.28 at max_seq_len 4096, and above 0.16 at 512. With per-token KDA states and block reuse off, the typical step's request capacity is now raised until its attention pages alone take the quota. The ratio then gives the SSM pool group fewer slots than its floor, so the min-slots constraint sets its size and attention gets the rest. Without per-token states (replay caches only), or with block reuse, the ratio sizes the pool as before. test_v2_kda_token_states_keep_ssm_pool_at_live_floor builds the V2 storage for a 128 MiB quota with 2 MiB state slots: with per-token states the SSM pool keeps its floor (plus one for max_util_for_resume) and attention takes the rest of the quota; without them, or with block reuse, the pool keeps its ratio share. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 10 remain after this review. WalkthroughThe V2 hybrid Mamba cache manager can retain KDA per-token states when the model backbone enables them. The manager allocates, exposes, relocates, and accounts for these states in cache sizing. Tests cover option selection, allocation, cache operations, and memory sizing. ChangesKDA per-token state retention
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant Backbone as Model backbone
participant Util as _util.py
participant Manager as MambaHybridCacheManagerV2
participant LayerCache
Backbone->>Util: Provides kda_token_states setting
Util->>Manager: Passes option when enabled
Manager->>LayerCache: Supplies token records or legacy replay caches
Suggested reviewers: Merge Risk: 🔵 Low · up to Some tightly budgeted records-mode configurations may exceed their GPU cache quota during initialization. The risk is bounded, but owners should account for it before enabling the option. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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@tensorrt_llm/_torch/pyexecutor/kv_cache/mamba_cache_manager.py:
- Around line 3532-3537: Update _build_cache_config so the GPU quota reserved
for pools excludes the full kda_state_tok tensor size in the KDA token-state
live-floor path, or allocate that tensor from quota-managed storage. Extend
test_v2_kda_token_states_keep_ssm_pool_at_live_floor to assert that pool and
token-state bytes combined stay within quota.
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… the GPU quota at the SSM live floor With per-token KDA states and block reuse off, the SSM pool is held at its live floor and the typical step's attention pages fill the GPU quota. kda_state_tok, num_spec fp32 states per SSM slot, is allocated beside the pools afterwards. So the pools took the whole quota and the states came on top of it: initialization could run out of device memory although _minimum_live_gpu_quota() passed. At the floor the SSM pool keeps ceil(floor / max_util_for_resume) slots: the floor's min-slots constraint, as the runtime scales it. _build_cache_config now takes the token states of that many slots out of the GPU tier quota before the pools are sized, and logs the split. The runtime rounds each pool up to whole allocation grains, which can add SSM slots when a slot is smaller than a grain; their states are not reserved. The quota for max_tokens and the minimum quota count the token states of all those slots (_state_quota), not only the floor's. Otherwise the attention pages could get less than max_tokens needs: on Kimi K3 (DSpark with 7 drafts, TP16, max_batch_size 8: a floor of 9 slots, 10 kept) the estimation quota for max_tokens 16896 would leave the KV pool about 13.5K tokens. With it the quota is 2.56 GiB and the pool about 20.6K tokens (arithmetic from the logged 2.38 GiB quota at 27648 bytes per token). test_v2_kda_token_states_keep_ssm_pool_at_live_floor now also checks that the pools and the token states of every SSM slot fit in the quota, with attention taking the rest of it to within one grain. test_v2_kda_token_states_reserve_quota_before_sizing checks the reserved bytes, and that the minimum quota and the quota for max_tokens exceed those of the same manager without token states by the states of every kept slot. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
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… slots as the runtime does The runtime holds max_util_for_resume as a C++ float, and _build_cache_config passed that value to _live_floor_token_state_slots, while _state_quota (behind _minimum_live_gpu_quota and _get_quota_from_max_tokens) passed the Python double. When the floor divided by the ratio is a whole number, the two ceilings differ by one slot: at 0.95, a floor of 19 slots keeps 21 slots (19 / float32(0.95) is just above 20), while the double gave 20. The minimum and the quota for max_tokens then counted one slot's token states fewer than the build reserves. _live_floor_token_state_slots now rounds the ratio through float32, as kv_cache_manager_v2.py does for its own floor. The new test checks that the minimum quota and the build reserve the same 21 slots at a floor of 19 and a ratio of 0.95. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
…SSM live floor kda_state_tok is allocated for every slot the SSM pool keeps, beside the V2 pools. Its bytes come out of the GPU quota only at the live floor (block reuse off, no pool_ratio, attention layers on the rank), where the slot count is known before the pools are sized. With block reuse, a pool_ratio, or no attention layers, the ratio-sized pool kept many more slots and kda_state_tok took their states outside the quota. The K3 targets only refused block reuse at their first forward, after the manager had allocated. _build_cache_config now raises ValueError for per-token states off the live floor. test_v2_kda_token_states_refuse_a_pool_off_the_live_floor covers the three cases. The ratio-sizing case of test_v2_kda_token_states_keep_ssm_pool_at_live_floor now runs without the states, since that combination is refused. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
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54b87a2 adds two small fixes to the KV cache manager V2 quota for the per-token KDA states, with unit tests. @juney-nvidia could you run CI on it when you have a moment? Thanks. |
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@juney-nvidia could you please |
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Correct me if I'm misunderstanding something, but doesn't caching every intermediate draft token state defeat the purpose of SSM replay? The point is to reduce memory traffic by trading compute. This PR seems to be an improvement on the legacy replay-off path; do you have numbers comparing this to replay on? |
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Thanks, fair question. The PR text invites it, because it describes the buffer as more than it is used for. In short: the buffer's one user, the K3 verify kernels, still replays drafts. They do not cache per-draft states. The buffer is sized as if they did, though, and that part should change. What the buffer holds in practice.
Numbers vs replay-on. One GB200, TP16 per-rank shapes (6 heads x 128), 8 verify tokens per request, CUDA graphs with PDL. Each value is the µs a KDA layer adds after a PDL predecessor, over 12 layer copies, so the weights are HBM-cold. Both arms start from the same projection rows and end at the gated core:
What should change here: the footprint.
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The Kimi K3 verify kernels keep, per draft of the last verify round, the row innovations vn [H, V], beta * k and the decay [H, K] in kda_state_tok, and replay the accepted drafts from them. They never write a draft's full state. The buffer was sized and documented as the full state after every draft, [layers, slots, num_spec, H, V, K] fp32, so ~98 % of it was never used; at the SSM live floor those bytes came out of the KV cache quota. kda_state_tok is now [layers, slots, 3, num_spec, H, K] fp32, in the kernels' record order (vn, then beta * k, then the decay). The per-slot budget and the live-floor quota count those bytes. V = K holds wherever the replay caches exist, since their [Q | K | V] sections are equal. For Kimi K3 at TP16 with 7 drafts, a slot's records take 4.45 MB over the 69 KDA layers instead of 190 MB. The token-state tests now expect the records' shape and bytes. The runtime rounds the pools' quota up to whole 2 MiB grains, and the live-floor test's records are now less than one grain, so that test allows the pools and the records up to one grain over the quota. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
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Thanks, that makes sense. I jumped to that conclusion by looking at the buffer size, but looks like we need to shrink that buffer before landing this one anyways. Also, with this new mechanism on, k3_kda_verify doesn't read kda_qkg_cache / kda_v_cache / kda_beta_cache, do we still need them? |
…place With kda_token_states, the verify kernels replay the accepted drafts from the drafts' records (kda_state_tok). Nothing then reads the per-draft replay caches kda_qkg_cache, kda_v_cache and kda_beta_cache: their only reader is the built-in fused verify (kda_mtp_decode), which a model that asks for the records replaces. They are no longer allocated in that mode. The extended conv caches stay, since the records' verify reads them too. has_kda_replay_caches now follows the conv caches, so the replay path stays on with either the replay caches or the records. Slot relocation, the disaggregated-generation slot seeding and the per-layer view handle the absent caches. Without kda_token_states nothing changes. For Kimi K3 at TP16 with 7 drafts this frees 86,240 B per slot and KDA layer, 59.5 MB per rank at 10 SSM slots. The tests check that the caches are not allocated with the records, that the view, relocation and seeding work without them, and that the replay path holds in both modes. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
…he KDA quotas At the live floor the runtime keeps ceil(floor / max_util_for_resume) SSM slots, and kda_state_tok has an entry for each. The max_tokens and minimum quotas counted the states of the floor's slots only and the drafts' records of all of them, so the slots past the floor were left without their states. The full-state token buffers' share of the 1 / max_util_for_resume headroom used to cover that; the records do not. For Kimi K3 at batch 8 (9 slots, 10 kept), the max_tokens 16,896 estimation pool could resume about 16,469 attention tokens. The quotas now count every slot the floor keeps with its states and its records. For that K3 configuration the estimation pool resumes about 17,485 tokens (arithmetic on the logged sizes). The pools that serving sizes from the memory fraction, and models without the records, do not change. The quota tests now expect the extra slots' states. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
With the Kimi K3 verify kernels' draft records (kda_state_tok, NVIDIA#19817), the per-draft replay caches kda_qkg_cache, kda_v_cache and kda_beta_cache are not allocated. The suspend-time commit indexed them per layer and the slot relocation asserted them, so both would fail on such a manager. The commit now passes None for absent caches (the records' replay never reads them), and the relocation moves the buffers that exist, the drafts' records included. With the replay caches nothing changes. The new test builds a manager with the records and without the caches. It compares the suspend-time commit bit for bit with the records' replay and the conv store run on their own, and checks that relocation moves a slot's conv caches, records and draft count. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
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Follow-up to my earlier comment:
One difference from my earlier comment, which said Footprint (Kimi K3, TP16, 7 drafts, 69 KDA layers): 4.45 MB per slot instead of 190 MB, so 42.5 MiB instead of 1.77 GiB per rank at batch 8 (10 SSM slots). In the K3 serving config below ( Checks
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Good catch: with the records on, nothing reads
Tests, each failing on the previous head and passing with this commit: with the records the caches are not allocated, and |
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[by Claude Code] @VALLIS-NERIA Could you review this PR? Thanks! |
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Superseded by the consolidated draft PR: #19989. Source commits and follow-ups are retained; prior reviews remain historical evidence, not approval of the replacement heads. |
Description
KDA speculative verification on the V2 hybrid cache manager keeps per-slot replay caches (conv windows, q/k/g, v,
beta of the last round's drafts). The next round replays the accepted drafts from those caches to rebuild the
recurrent state. A verify kernel can instead keep a compact record per draft (the row innovations vn, beta * k and
the decay) and replay the accepted drafts (
prev_num_accepted_tokens) from those records at the start of its nextlaunch,
S = decay * S + vn (beta k), with no q/k/g, v or beta caches.This PR adds the cache side of that option, off by default:
MambaHybridCacheManagerV2(kda_token_states=False). With the KDA replay path enabled and the option on, themanager allocates
kda_state_tok: fp32[mamba layers, slots, 3, num_spec, H, K], zero-initialised: per draftof the slot's last verify round, vn, then beta * k, then the decay (V = K on this path; the SSM pool keeps the
state after the round's first, non-draft token). The q/k/g, v and beta replay caches are then not allocated; the
extended conv caches stay. A verify kernel replays the first n records when the round accepted n > 0 drafts
(
prev_num_accepted_tokens); a slot reset for a new request has n = 0, so a previous owner's records are neverread and no reset copy is needed.
kda_state_tokis allocated per SSM pool slot, outside the V2 pools. The per-slot state budget(
_mamba_state_bytes_per_slot, used for the quota computed frommax_tokensand for the live-quota check)counts these records, and
shutdown()releases them with the other replay buffers.quota, and
kda_state_tokwith it. Without block reuse a slot past the min-slots floor (residentsequences plus reserved dummy slots) never holds a request. So with the records and block reuse off,
the SSM pool keeps only its floor:
ceil(floor / max_util_for_resume)slots, the floor's min-slots constraintas the runtime scales it. The records of those slots are taken out of the GPU cache quota before the pools
are sized, and the typical step's request capacity is raised until its attention pages take the rest. The quota
computed from
max_tokensand the minimum quota count every one of those slots with its states and its records,so the attention pages still fit
max_tokensatmax_util_for_resume. Without the records, or with block reuse,the ratio sizes the pool as before.
SpeculativeState.kda_state_tok), and slot relocation moves itwith the other replay buffers.
has_kda_replay_cachesfollows the conv caches, so the replay path holds witheither the replay caches or the records; relocation, the disaggregated-generation slot seeding and the per-layer
view handle the absent caches.
keeps_kda_token_statesreports whether the manager has it (the base class says False)._create_kv_cache_managerpasseskda_token_states=Trueonly when the KDA replay caches are on, the manager isthe V2 one, and the model's backbone sets
kda_token_states. No model sets it in this PR, so nothing changes forany existing model.
Memory, when enabled:
layers x slots x 3 x num_spec x H x K x 4bytes beside the V2 pools, in place of the q/k/g,v and beta replay caches. With block reuse off the slots are the floor (the resident sequences plus the reserved
dummy slots) over
max_util_for_resume, and the bytes come out of the cache quota. The runtime rounds the pools'quota up to whole allocation grains (2-32 MiB), so the pools can take back less than one grain of it, and when the
SSM slots are smaller than a grain the pool can keep a slot more, whose records are not reserved. For example
Kimi K3 at TP16 (69 KDA layers, 6 heads of 128 per rank, 7 drafts) needs 4.45 MB of records per slot, and the
replay caches it no longer allocates took 5.95 MB per slot (arithmetic).
The records need the SSM live floor (block reuse off, no
pool_ratio, attention layers on the rank), where theslot count is known before the pools are sized.
_build_cache_configraisesValueErrorforkda_token_statesoffthe floor: a ratio-sized pool would keep many more slots, and their records would sit outside the quota. The floor's
slot count rounds
max_util_for_resumethrough float32, as the runtime does, so the minimum quota and the quotafor
max_tokenscount the same slots the build reserves.The first user is the Kimi K3 decode path (#19832's
k3_kda_verify/k3_kda_attn, CuTe DSL verify kernels thatwrite these records and replay the accepted drafts from them); it lands separately.
Test Coverage
tests/unittest/_torch/executor/kv_cache/test_mamba_cache_manager.py:test_kimi_explicit_v2_manager_kda_token_states_follow_the_model[True|False](CPU): the factory asks for thestates only when the backbone does.
test_kimi_explicit_v2_manager_enables_kda_replay(CPU): nokda_token_statesby default.test_v2_kda_token_states_replace_the_replay_caches[2|None](GPU): the records' shape[slots, 3, num_spec, H, K], dtype, the per-layer view and zero init; no q/k/g, v or beta caches, the conv caches kept andhas_kda_replay_cachestrue; nothing allocated without the replay path.test_v2_kda_replay_allocates_logical_slot_caches(GPU): the default manager has the replay caches, no records,and
has_kda_replay_caches.test_v2_kda_token_states_relocate_with_their_slot(GPU): without the replay caches, slot moves copy a slot'sconv caches, records and draft count bit for bit, and
shutdown()releases them.test_v2_kda_token_states_seed_disaggregated_generation_slots(CPU): seeding a transferred request's slot withoutthe replay caches sets its conv caches and clears its draft count, and leaves its records alone.
test_kda_replay_path_stays_on_with_the_drafts_records(CPU):has_kda_replay_cachesholds with the replaycaches and with the records, and not without the replay path.
test_v2_kda_token_states_count_in_the_per_slot_budget(GPU): with the option on, a slot's budget grows byexactly the records: three fp32
[H, K]per draft and layer.test_v2_kda_token_states_keep_ssm_pool_at_live_floor[True-False-True|False-False-False|False-True-False](GPU):the V2 storage for a 128 MiB quota with 2 MiB state slots. With the records and no block reuse the SSM pool keeps
its floor (plus one for
max_util_for_resume), and the pools plus the records of every SSM slot fill the quota towithin one 2 MiB grain: the runtime rounds the reduced quota up to whole grains. Without the records, or with
block reuse, the pool keeps its ratio share (more than 10x the floor).
test_v2_kda_token_states_reserve_quota_before_sizing(no GPU work): at the floor the GPU tier quota handed tothe runtime is reduced by the records of
ceil(3 / max_util_for_resume)= 4 slots, and the minimum quota and thequota for
max_tokensexceed those of the same manager without the records by the records of all 4 and the 4thslot's states.
test_v2_kda_token_states_reserve_the_slots_the_runtime_keeps(no GPU work): at a floor of 19 slots and a ratio of0.95 the minimum quota and the build both count 21 slots (the runtime's float32 ratio), the minimum quota with the
2 extra slots' states.
test_v2_kda_token_states_refuse_a_pool_off_the_live_floor[block_reuse|pool_ratio|no_attention]: the recordsoff the live floor raise
ValueError. The ratio-sizing case of..._keep_ssm_pool_at_live_floornow runswithout the states, since that combination is refused.
The Kimi stub model config in that file gains
mapping=Noneandspec_config=None. Since #19273get_kv_cache_manager_clsreadsmodel_config.mapping, so every Kimi factory test failed on the stub; on main thosetests are waived under nvbugs/6818710. With the fields, all Kimi factory tests pass. Their waive entries are left to
that bug's owner.
Results on GB200: main
ca37c9f725with this PR on one GPU, and main alone on another GPU of the same node, at thesame time (same container image and build):
executor/kv_cache/test_mamba_cache_manager.pyexecutor/kv_cache/modules/kimi_kda/test_mamba_cache_manager.py:mapping(
test_hybrid_cache_manager_factory_*,test_qwen3_gdn_replay_uses_v2_preference). Main waives them undernvbugs/6818710; this PR does not touch their stubs.
test_kimi_explicit_v2_manager_*,test_kimi_disagg_*,test_kimi_without_v2_preference_uses_mixed_manager). They pass with the stub fields above.executor/kv_cache/: the same 20 tests fail on main. 18 have stale stubs or mocks (11SimpleNamespacewithout_max_cuda_graph_batch_size, 5Mockused in arithmetic orlen, 2KvCacheCreatorwithout
_cache_transceiver_config). The other 2 hit the 300 s timeout on these nodes(
test_world_ranks_converge_on_hostless_fallback,test_multi_rank_syncs_to_fleet_min).The follow-up commits on GB200 (same image and build):
test_v2_kda_token_states_keep_ssm_pool_at_live_floor[True-False-True]fails onthe first commit (the SSM pool holds 49 slots for a floor of 3) and passes with the follow-up. The whole file has
3 more passing tests (the new cases) and the same failing tests as the first commit, run in the same environment.
of pools plus 16 MiB of token states for a 128 MiB quota), and
test_v2_kda_token_states_reserve_quota_before_sizingfails (the minimum quota counts 3 slots of token states, not4). Both pass with the fix. The whole file has 1 more passing test (the new one) and the same 24 failing tests as
the previous commit, run at the same time on the same node.
token states' 1.77 GiB now sit inside the cache quota. The estimation quota from
max_tokensis 2.57 GiB (2.38before), and the final quota is 15.35 GiB as before, of which the pools get 13.58 GiB. The 8 fixed prompts give
the same outputs as the previous commit in the same session, and decode throughput is unchanged (665.0 vs 664.8
tokens/s).
states sat outside the quota, 13.58 instead of 15.35 GiB per rank on that K3 configuration. The records
follow-ups below shrink the reservation to 0.04 GiB.
CI lists: no change needed. The file is collected through
unittest/_torch/executor(for examplel0_h100.ymlandl0_b300.yml); it carries nocpu_onlymark, sol0_cpudoes not collect it.The two newest follow-up commits (
[None][fix] KV cache manager V2: count the live-floor KDA token-state slots as the runtime does,[None][fix] KV cache manager V2: refuse per-token KDA states off the SSM live floor), on GB200:slot short at a floor of 19) and 1 passes. All 5 pass with the fixes.
test_mamba_cache_manager.pywhole: 175 passed / 24 failed against 171 / 24 before. The failing set is the same,and the 4 new tests pass.
block reuse off.
The records follow-ups (
dc903ae9d6kda_state_toksized for the drafts' records,c05a0c3a45no replay cachesbeside the records,
de2f7e12efevery live-floor SSM slot in the KDA quotas), on GB200 (same image and build):test_mamba_cache_manager.pywhole, after each commit: the same 24 failing tests as the published head (thehybrid factory stubs above), and the new or updated tests pass. Before each commit they fail: 5 (the records'
shape, budget and live floor, the two quota tests), 4 (the caches not allocated, relocation and seeding without
them,
has_kda_replay_caches) and 2 (the quotas without the extra slot's states).k3_kda_verifyat 1 / 3 / 5 / 7drafts and 1-8 requests (and with the external gate at 1 / 4 / 8 requests) and
k3_kda_attn, 4 verify roundseach: outputs, SSM states, conv caches and records identical in 36 / 36 cases.
[None][fix] KV cache manager V2: keep the KDA replay state across a suspend; refuse a pool rebalance with it #19846's follow-ups, interleaved with the published heads (sessions 7777282 and 7779408):
to 15.13 GiB (about 495k to 578k tokens at 27,648 B per token);
15.14 GiB.
configuration the
max_tokensestimation pool now resumes about 17,484 tokens formax_tokens16,896, insteadof 16,469 (arithmetic on the logged sizes).
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-compatibleorapi-breaking. Forapi-breaking, includeBREAKINGin the PR title. (No LLM API change.)Any new dependencies have been scanned for license and vulnerabilities (No new dependencies.)
CODEOWNERS updated if ownership changes (No ownership change.)
Documentation updated as needed (No user-facing change: the option is off by default and no model turns it on; the docstrings describe it.)
Update tava architecture diagram if there is a significant design change in PR. (No significant design change.)
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Please check this after reviewing the above items as appropriate for this PR.
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The current diff changes import formatting and line wrapping in
_util.pyandmamba_cache_manager.py. It does not change runtime behavior. The KDA token-state implementation is present in the files, but it is not part of this diff.QA Engineer Review
No test changes.
Per-File QA Perspective
tensorrt_llm/_torch/pyexecutor/_util.py: Changes are formatting-only. They introduce no observable behavior change that requires new QA coverage.tensorrt_llm/_torch/pyexecutor/kv_cache/mamba_cache_manager.py: Changes are formatting-only. They introduce no observable behavior change that requires new QA coverage.