[None][perf] Fill the KVCM2 host tier progressively

[lowsfer]

Summary

Makes the KVCM2 host tier allocate progressively: the address range is
reserved up front, a worker thread commits it in the background, and allocation
blocks only on the chunk it actually needs. Serving starts as soon as the first
chunk is resident rather than after the whole tier is.

The tier is populated by faulting pages in through the kernel and handing them
to the driver with cuMemHostRegister, rather than allocating them inside the
driver with cuMemCreate. That choice is the subject of most of this
description, because it is a deliberate trade rather than a free win.

Why progressive

A host tier is sized from host memory, so it is routinely hundreds of GiB.
Making all of it resident before the first request is served is time the process
spends doing nothing useful, and it scales with the tier, so the larger the
configured cache the worse the startup.

Committing incrementally needs a base address that does not move while the tier
grows, and a way to make more memory resident without touching what is already
there. The range is reserved once at the tier's max_quota and committed as the
tier grows, so the base address is fixed for the object's lifetime. max_quota
defaults to the host memory the OS reports; a resize past the reservation is
refused in adjustCacheLevel, before any pool group has been touched.

Committing runs on a worker thread that publishes a watermark. The wait against
it is tight because fresh slot ids ascend from zero and recycled slots come
first: the Nth fresh allocation blocks only on the chunk containing it, and once
the fill completes every wait is a relaxed load. The watermark is a plain
std::atomic and the wait is atomic::wait, so a blocked allocation costs a
futex wait rather than a condition variable and a lock.

The unit the fill commits in and the unit memory is allocated in are deliberately
separate. The first paces progress and is 512 MiB; the second decides how finely
a later resize can hand memory back, and is a multiple of the slot size so that
no copy ever spans two registrations.

Why registered memory rather than driver allocation

The earlier revision of this PR carried two backings behind an
IHostMemBacking interface, selecting cuMemCreate on PCIe and mmap on
coherent platforms. That abstraction is gone: there is one backing, and it
registers.

The reason is executor starvation. cuMemCreate allocates and zeroes pages
inside the driver, and the ioctl path it uses takes locks in the nvidia kernel
module that are shared across every process on the machine. While a rank fills
its tier, unrelated CUDA calls on that rank's executor thread queue behind the
allocation work of all the other ranks.

Measured on an 8-GPU B200 host, 8 ranks filling 64 GiB each concurrently, with a
probe thread timing cudaPointerGetAttributes throughout — the call a reviewer
observed an executor thread stuck in:

	fill	probe blocked	worst probe stall
cuMemCreate	6.4–10.4 GiB/s	96–100%	4.6–34 s
registered	5.2–5.6 GiB/s	30–46%	0.3–2.6 s

The register path is slower to fill and leaves the executor thread able to run.
That is the trade this PR makes, stated plainly: it costs fill throughput to
stop a one-time startup fill from freezing the process for tens of seconds.

Two cheaper fixes were tried first and do not work

Both target the existing cuMemCreate path and both were measured on the same
8 ranks × 64 GiB:

mitigation	single process	8 ranks
nanosleep(1us) between driver calls	12 → 253 probe iterations, worst 366 → 1.3 ms	98% → 96% blocked
split cuMemSetAccess per granule	worst ~200 → 26 ms	98% → 96.7% blocked

Both work beautifully with one process and neither survives contention, which
is what the kernel-side locking predicts: releasing a user-space mutex more
often cannot help when the queue that matters is a global one inside the driver.
sched_yield() does nothing even single-process — the window it opens is too
short on an idle many-core box; only a real deschedule helps.

Registration boundaries

cuMemHostRegister means a copy cannot span two registered ranges, which is the
constraint a previous revision removed and this one reintroduces. It is
contained rather than leaked: the commit unit is rounded to a multiple of the
slot size, so a slot never straddles a registration and batchedPageCopy keeps
the copy-engine path unconditionally.

Staging buffers and upload batches do not go through HostMem at all. They are
sized once and released once, so they use StaticHostMem — a single
cuMemHostAlloc, one registration, no fill worker or watermark — and a copy may
span the whole range.

Transparent huge pages now default off

TLLM_KV_CACHE_MANAGER_V2_THP defaults to off. MADV_HUGEPAGE opts a region's
faults into direct compaction wherever
/sys/kernel/mm/transparent_hugepage/defrag is madvise or always, and what
that costs depends on how fragmented the node's free memory is.

The two NUMA nodes of a single B200 host differ by an order of magnitude. Eight
ranks filling 32 GiB each:

node	free order-9 blocks	huge pages	ordinary pages
0	~29,600	4.6 GiB/s	3.0 GiB/s
1	~330	0.32 GiB/s	2.5 GiB/s

Every rank must finish before the system serves, so the figure that matters is
the slowest: 100 s with huge pages against 13 s without. Fragmentation
accumulates with uptime while the benefit does not, so ordinary pages bound the
worst case at 1.6× off ideal rather than 7.8×.

Transfer bandwidth does not pay for it — over a registered region
host-to-device is PCIe-bound and identical either way, and device-to-host is no
slower. cudaHostAlloc reports no huge pages at all, so this also makes the
tier consistent with every other pinned buffer rather than exceptional. The
disaggregated test harness already set this variable to 0 after hitting the same
effect on CI nodes (nvbugs/6888251).

NUMA placement and allow_remote_numa_fallback

Host pages are placed on the NUMA node the GPU attaches to. mbind attaches the
policy to the address range so it holds whichever thread faults a page, and the
fill worker is bound to that node's CPUs so the prefault writes are local;
neither substitutes for the other.

	true (default)	false
placement	MPOL_PREFERRED; first touch by the bound fill worker if mbind is denied	MPOL_BIND; construction fails if mbind is denied

allow_remote_numa_fallback defaults to true because strict binding would cap
a tier at one node's memory while max_quota defaults to total system memory —
so the default takes a remote page, which the GPU reaches more slowly, over
failing the allocation. Nothing unprivileged reproduces MPOL_BIND, so a strict
request that cannot be applied is refused rather than silently downgraded.

Containers: mbind, set_mempolicy and get_mempolicy sit behind
CAP_SYS_NICE in Docker's default seccomp profile. Without it the backing falls
back to first touch, and refuses outright in strict mode.

Dead code removed alongside

launchCopyEngine and copyPageIndicesToDevice carried #if CUDA_VERSION
fallbacks for toolkits older than 12.8 and for the 12.x cuMemcpyBatchAsync
signature. cpp/CMakeLists.txt requires CUDA 13.0, so none of it could be
compiled; the kernel fallback and its only test (whose single TEST was itself
inside #if CUDA_VERSION < 12080, so that binary ran zero tests) are gone with
it. What remains is one unconditional cuMemcpyBatchAsync per site, with the
CU_MEMCPY_SRC_ACCESS_ORDER_STREAM requirement stated where it matters.

Fixes surfaced along the way

• The fill worker ran on device 0 regardless of the creator's selection. A
  new thread starts on device 0 whatever the creator chose, so any process not
  using GPU 0 was placing and mapping host memory against the wrong one.
• KVCacheManagerConfig::validate() never visited the cache tiers. Every
  per-tier check was unreachable from C++ despite the manager constructor
  calling validate(): a zero quota and a disk path that is not a directory
  both passed silently. Those checks predate this change and are enforced now.
• A host tier maximum below what a level needs is rejected rather than
  silently satisfying less than the user configured.
• The fill worker is joined, and the backing kept, across a poisoned
  teardown, so a corrupted manager does not leak the thread or the mapping.

Test

kvCacheManagerV2HostMemBackingTest covers reserve/commit/decommit/destroy,
data preservation across a commit, rejection of a copy spanning a unit boundary,
and GPU round trips.

kvCacheManagerV2HostMemNumaTest (C++, multi-GPU stage) and
test_host_numa_placement.py (Python, l0_gb200_multi_gpus) both need two GPUs
on different NUMA nodes to mean anything: they pin to one node and allocate
against a GPU on the other, so an unbound range would land on the thread's node
and the case would fail. The Python test reads placement from
/proc/self/numa_maps, an ordinary file read rather than a gated syscall, so it
asserts placement with or without CAP_SYS_NICE.

Measured

Verified on an 8-GPU B200 host (umbriel-b200-043), built against main at
c226e2d77c: 7 KVCM2 binaries / 156 tests pass.

The contention figures above come from the same host: 8 ranks, one process per
GPU, both arms run alternately within each rank so that drift on a shared
machine lands on both equally, with a probe thread timing
cudaPointerGetAttributes throughout and reporting the fraction of the fill it
spent blocked rather than only its worst case.

Not reachable in CI

Two paths compile but no stage exercises them, because each needs a condition CI
does not create: the strict-mode refusal (mbind denied while
allow_remote_numa_fallback=false), and the NUMA-fragmentation regime that
motivates the THP default, which depends on a node's free-block distribution.

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Walkthrough

This change adds mmap and CUDA VMM host-memory backings, progressive commitment, and KV cache tier quota and NUMA options. It adds host-memory placement introspection and tests, updates cold-page copy paths, and changes a dependency patch command.

Changes

Host-memory backing and KV cache integration

Layer / File(s)	Summary
Host-memory backings and backing tests cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/utils/hostMemBacking.*, cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/CMakeLists.txt, cpp/tests/unit_tests/batch_manager/kvCacheManagerV2HostMemBackingTest.cpp, cpp/tests/unit_tests/kernels/CMakeLists.txt	Adds the backing interface, mmap and VMM implementations, backing selection, and factory functions. Unit tests cover backing operations.
Reserved and progressively committed HostMem cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/utils/hostMem.*, cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storage/core.*, cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/stagingBuffer.*, cpp/tests/unit_tests/batch_manager/kvCacheManagerV2HostMemTest.cpp	HostMem reserves an address range, commits memory in chunks, and provides readiness waits and bounded resize. Host pools wait for allocated ranges to become usable. Tests cover commitment, resizing, address stability, and concurrent waits.
Tier configuration and storage integration cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/config.*, cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storage/*, cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storageManager.*, cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManagerV2.cpp, tensorrt_llm/runtime/kv_cache_manager_v2/__init__.pyi, cpp/tests/unit_tests/batch_manager/kvCacheManagerV2ColdPageTest.cpp	Tier validation methods are renamed to validate(). Host tier configuration adds optional maxQuota and allowRemoteNumaFallback fields. Storage passes tier-specific backing options and enforces the configured maximum quota.
NUMA placement probe and validation cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/introspection.*, cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManagerV2.cpp, tensorrt_llm/runtime/kv_cache_manager_v2/_introspection.py, cpp/tests/unit_tests/multi_gpu/*HostMemNumaTest.cpp, tests/unittest/kv_cache_manager_v2_tests/test_host_numa_placement.py, tests/integration/defs/cpp/test_multi_gpu.py, tests/integration/test_lists/test-db/*	Adds native and Python host-memory placement probes. C++ and Python tests check page placement and NUMA binding policies.

Cold-page and batched-copy paths

Layer / File(s)	Summary
Cold-page configuration and batched copies cpp/tensorrt_llm/batch_manager/kv_cache_compression/nativeColdPageCodec.cpp, cpp/tests/unit_tests/batch_manager/coldPageCodecTest.cpp, cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/batchedPageCopy.*, cpp/tests/unit_tests/batch_manager/*ColdPage*, cpp/tests/unit_tests/batch_manager/CMakeLists.txt	Cold-page configuration no longer rejects lossless fallback lifecycles based on chunked registration. Batched copies use cuMemcpyBatchAsync without older CUDA-version fallbacks. Cold-page tests use host-aligned slot sizes, and the fallback-copy test is removed.

Dependency patch command

Layer / File(s)	Summary
Patch-state verification 3rdparty/CMakeLists.txt	The patch command uses shell conditionals to attempt the forward patch and then require a successful reverse dry-run.

Priority: ➖ Normal

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

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant HostMem
  participant FillWorker
  participant IHostMemBacking
  participant Caller
  HostMem->>FillWorker: start asynchronous fill
  FillWorker->>IHostMemBacking: commit memory range
  Caller->>HostMem: waitUsable(endOffset)
  HostMem-->>Caller: return or rethrow fill failure

Loading

Merge Risk: 🟡 Moderate · up to f3d25

Several earlier concerns remain open. A partial host-tier fill failure can leave every later allocation throwing, and the new CI NUMA test may error on setup. Resolve these before merging. The libnuma include-path question for the kernel test is still unconfirmed.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 38.04% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 163 functions across 25 files. (2 skipped…	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
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.
Title check		The title follows the repository format and clearly identifies the progressive KVCM2 host-tier filling change.
Description check		The description clearly explains the progressive allocation design, rationale, trade-offs, NUMA behavior, fixes, and test coverage. It omits the template's PR Checklist section and uses Summary and Te…

Full details: Description check

Explanation

The description clearly explains the progressive allocation design, rationale, trade-offs, NUMA behavior, fixes, and test coverage. It omits the template's PR Checklist section and uses Summary and Test headings instead of Description and Test Coverage, but the required change and validation details are substantially complete.

Full details: Docstring Coverage

Explanation

Docstring coverage is 38.04% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 163 functions across 25 files. (2 skipped: 2 unsupported.)

Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

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🧹 Nitpick comments (1)

cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/config.cpp (1)

63-67: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick win

Add regression coverage for tier validation at config construction.

The native KVCacheManagerConfig constructor now validates every tier. Zero quotas and invalid disk paths therefore fail before KVCacheManager is created. Existing tests cover an empty tier list and valid tier configurations, but they do not pin the new tier failures or their Python exception type. Add focused tests for zero quotas and a missing disk directory. Define the expected exception consistently for both backends if the tests are shared.

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Review comment at @cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/config.cpp
around lines 63 - 67:
Add focused regression tests for the KVCacheManagerConfig constructor validating
tiers: verify zero quotas and a nonexistent disk directory fail during config
construction, before KVCacheManager creation, and assert the expected Python
exception type consistently across both backends when tests are shared.

---

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Inline comments:
Review comments at
@cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/stagingBuffer.cpp:
- Around line 52-54: Update the mmap backing override in the staging-memory
selection path to apply the same ATS and coherent-link checks as the default
selector, or reject the override when those checks fail. Ensure kPinnedHost
staging for disk transfers cannot select unregistered mmap memory on unsupported
links.

Review comments at
@cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/utils/hostMem.cpp:
- Around line 164-178: Add an atomic mStopFill flag to HostMem and check it at
the start of each commitRange loop iteration. Set the flag before destroy()
joins the fill worker; for shrinking in resize(), request a stop and join before
proceeding, using mFilledChunks as the committed-prefix watermark.
- Around line 195-202: Keep the committed chunk count separate from fill failure
state in the fill worker, and ensure waiters wake to recheck when failure is
recorded. Update waitUsable() to rethrow only when the requested offset exceeds
the committed prefix, and let resize() shrink to or below that prefix before
clearing the failure. Add a failure-injection case to
kvCacheManagerV2HostMemTest.cpp covering usable offsets below the prefix,
failure above it, and successful shrink.

Review comments at
@cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManagerV2.cpp:
- Around line 1525-1541: In the bindings for GpuCacheTierConfig,
HostCacheTierConfig, and DiskCacheTierConfig, preserve the Python assert_valid
API by binding it to each class’s existing validate method alongside validate.
Keep the current validate bindings and copy behavior unchanged.

Review comments at
@cpp/tests/unit_tests/batch_manager/kvCacheManagerV2ColdPageTest.cpp:
- Around line 701-715: In ResizeBeyondMaxQuotaIsRefused, record the cold-level
quota after the successful resize to kMaxQuota and before the refused request,
then assert the quota is unchanged afterward using equality rather than an
upper-bound check.

Review comments at
@cpp/tests/unit_tests/batch_manager/kvCacheManagerV2HostMemTest.cpp:
- Around line 314-319: The ResizeShrinkKeepsRetainedContents test depends on a
CUDA context created by earlier tests, making it order-dependent. Call
cudaSetDevice(0) and assert success at the start of this test, before
constructing HostMem.

---

Nitpick comments:
Review comments at
@cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/config.cpp:
- Around line 63-67: Add focused regression tests for the KVCacheManagerConfig
constructor validating tiers: verify zero quotas and a nonexistent disk
directory fail during config construction, before KVCacheManager creation, and
assert the expected Python exception type consistently across both backends when
tests are shared.

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• 3rdparty/CMakeLists.txt
• cpp/include/tensorrt_llm/common/waitableAtomic.h
• cpp/tensorrt_llm/batch_manager/kv_cache_compression/nativeColdPageCodec.cpp
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/CMakeLists.txt
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/batchedPageCopy.cu
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/config.cpp
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/config.h
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/stagingBuffer.cpp
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/stagingBuffer.h
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storage/core.cpp
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storage/core.h
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storageManager.cpp
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/storageManager.h
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/utils/hostMem.cpp
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/utils/hostMem.h
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/utils/hostMemBacking.cpp
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/utils/hostMemBacking.h
• cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManagerV2.cpp
• cpp/tests/unit_tests/batch_manager/CMakeLists.txt
• cpp/tests/unit_tests/batch_manager/coldPageCodecTest.cpp
• cpp/tests/unit_tests/batch_manager/kvCacheManagerV2ColdPageTest.cpp
• cpp/tests/unit_tests/batch_manager/kvCacheManagerV2HostMemBackingTest.cpp
• cpp/tests/unit_tests/batch_manager/kvCacheManagerV2HostMemTest.cpp
• cpp/tests/unit_tests/common/CMakeLists.txt
• cpp/tests/unit_tests/common/waitableAtomicTest.cpp
• cpp/tests/unit_tests/multi_gpu/CMakeLists.txt
• cpp/tests/unit_tests/multi_gpu/kvCacheManagerV2HostMemNumaTest.cpp
• tensorrt_llm/runtime/kv_cache_manager_v2/__init__.pyi
• tests/integration/defs/cpp/test_multi_gpu.py

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• cpp/tensorrt_llm/batch_manager/kv_cache_compression/nativeColdPageCodec.cpp
• cpp/tensorrt_llm/batch_manager/kv_cache_manager_v2/stagingBuffer.h
• cpp/tests/unit_tests/batch_manager/coldPageCodecTest.cpp

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Inline comments:
Review comments at
@cpp/tests/unit_tests/batch_manager/kvCacheManagerV2HostMemTest.cpp:
- Line 377: In the waiter lambda around waitUsable, capture the loop index i and
write to a distinct byte within the waited-for range for each thread, rather
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@PerkzZheng, could you help to review this PR as trt-llm-torch-attention-devs? Thanks~

Assignment explanation

Components	runtime/kv-cache/lifecycle (55%); runtime/kv-cache/layout-io (15%); runtime/metadata-workspaces (8%); native-ops/bindings (4%); runtime/parallelism (5%); integration/attention-tests (11%); integration/build-config (2%)
Tags	features:paged-kv (100%)
PR effort	8 points — Progressive host-tier allocation introduces a memory-lifecycle contract requiring deep reasoning across residency, slot granularity, registration boundaries, staging lifetime and distributed failures. Eight points reflects these coupled correctness and performance concerns; implementation truncation limits precision.
Importance	Important — Sensitive cache-memory and transfer contracts change: the HostMem header specifies progressive residency with stable addresses, maxQuota bounds resizing, copy dispatch drops the chunk-registration condition, and the cold-codec registration guard is removed. Python configuration and distributed initialization statuses also change.
Retained GitHub review requests	schetlur-nv: preserve other-group coverage: NVIDIA/trt-llm-multi-gpu-ci-review, NVIDIA/trt-llm-kernels-devs, NVIDIA/trt-llm-runtime-devs, NVIDIA/trt-llm-kv-cache-manager-devs, NVIDIA/trt-llm-qa-function, NVIDIA/trt-llm-devs
Affinity (0–1)	0.449 component/tag match; 0.337 after PIC preference
Outstanding points at selection	45 → 53 (+8)
Points already reserved on this PR	0
Effective weight	1
Completed review points (preceding 168 hours)	17
Combined points after assignment	70
Demand-derived target	70.00 points (soft share)
Additional fair share before assignment	8.00 points
Share explanation	Within demand-derived share.
Selection rule	affinity with 0.3-share guard
Assignment scope (PerkzZheng)	all

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

[tensorrt-cicd]

PR_Github #78667 [ run ] completed with state FAILURE. Commit: 1dc346c
/LLM/main/L0_MergeRequest_PR pipeline #64814 completed with status: 'FAILURE'

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• If you cannot view the failures, ask the CI triggerer to share details
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CI Agent Failure Analysis

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/bot run --disable-fail-fast

[tensorrt-cicd]

PR_Github #78688 [ run ] triggered by Bot. Commit: 1dc346c Link to invocation

[tensorrt-cicd]

PR_Github #78688 [ run ] completed with state FAILURE. Commit: 1dc346c
/LLM/main/L0_MergeRequest_PR pipeline #64834 completed with status: 'FAILURE'

CI Report

⚠️ Action Required:

• Please check the failed tests and fix your PR
• If you cannot view the failures, ask the CI triggerer to share details
• Once fixed, request an NVIDIA team member to trigger CI again

CI Agent Failure Analysis

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[lowsfer]

/bot run --disable-fail-fast

[tensorrt-cicd]

PR_Github #78708 [ run ] triggered by Bot. Commit: 1dc346c Link to invocation

[tensorrt-cicd]

PR_Github #78708 [ run ] completed with state SUCCESS. Commit: 1dc346c
/LLM/main/L0_MergeRequest_PR pipeline #64849 completed with status: 'SUCCESS'

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