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WalkthroughThe router now includes LoRA adapter IDs in KV-cache hashing. Cache-salt IDs use SHA-256 derivation shared with KVCacheManagerV2. Tests compare router hashes with worker-side hashes across supported algorithms and request options. ChangesKV cache routing consistency
Priority: Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Bug fix · Severity of issue fixed: Medium Sequence Diagram(s)sequenceDiagram
participant Request
participant KvCacheAwareRouter
participant RouterHashing
participant KVCacheManagerV2
Request->>KvCacheAwareRouter: provide tokens, cache salt, and LoRA request
KvCacheAwareRouter->>RouterHashing: pass tokens, salt ID, and LoRA task ID
RouterHashing->>RouterHashing: compute V1 and V2 block hashes
KVCacheManagerV2-->>RouterHashing: provide matching worker-side hash values
RouterHashing-->>KvCacheAwareRouter: return routing hashes
Merge Risk:
 |
| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Linked Issues check |  |
The PR implements the hash-parity and propagation requirements in #18156. It carries lora_id through router hashing and ReuseScope, shares the V2 SHA-256 salt derivation, preserves the engine deri… |
When lora_task_id is set, route block_key_hasher through the Python hash_v1_block_key path, including for unsalted requests. Update the native-path test to require the LoRA fallback and retain parity coverage for the resulting hashes. |
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Check name Status Explanation Out of Scope Changes check The changed production files and tests relate to the router reuse-namespace keying in #18156. The shared salt helper removes duplicate derivation, the V2 delegation preserves the existing engine key s…Title check The title clearly identifies the main change: carrying the LoRA ID through the KV-aware router. It uses the required issue and type format. Description check The description explains the issue, implementation, test coverage, test results, and checklist status. It provides sufficient detail for review and follows the required template sections. Full details: Out of Scope Changes check
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Explanation
The changed production files and tests relate to the router reuse-namespace keying in
#18156. The shared salt helper removes duplicate derivation, the V2 delegation preserves the existing engine key space, and the documentation records the V1 limitation. No unrelated change is demonstrated.
-
Full details: Linked Issues check
-
Explanation
The PR implements the hash-parity and propagation requirements in
#18156. It carrieslora_idthrough router hashing andReuseScope, shares the V2 SHA-256 salt derivation, preserves the engine derivation path, and adds parity and propagation tests. However,#18156requires skipping the native fast path when LoRA is set.block_key_hasheruses_NativeBlockKeyHasherwhencache_salt_idisNone, even whenlora_task_idis set. The newtest_block_key_hasher_python_fallback_only_for_cache_saltexplicitly enforces this contrary behavior.Resolution
When
lora_task_idis set, routeblock_key_hasherthrough the Pythonhash_v1_block_keypath, including for unsalted requests. Update the native-path test to require the LoRA fallback and retain parity coverage for the resulting hashes.
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In `@tests/unittest/disaggregated/test_router.py`:
- Line 531: Add a -> None return annotation to each newly added test function,
including test_cache_salt_id_derivation_matches_worker_path and the other test
functions in the same change, while leaving their behavior unchanged.
- Around line 618-637: The test must exercise
KvCacheAwareRouter._routing_key_sync rather than only calling
_get_request_lora_id and _compute_block_hashes directly. Add a routing-key case
using a request with LoRA metadata, then assert the hashes produced by
_routing_key_sync match the corresponding worker event hashes while preserving
the existing salt and hash-algorithm coverage.
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tensorrt_llm/_torch/pyexecutor/kv_cache_manager_v2.pytensorrt_llm/inputs/utils.pytensorrt_llm/runtime/kv_cache_hash.pytensorrt_llm/serve/router.pytensorrt_llm/serve/router_utils.pytests/unittest/disaggregated/test_router.py
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Pushed a follow-up commit for both review comments. The call-site gap was the right catch: the round-trip test pinned I also dropped the dict fallback in One thing I should be explicit about: the earlier numbers in this PR came from a run against the |
| """ | ||
| if cache_salt is None: | ||
| return None | ||
| digest = hashlib.sha256(cache_salt.encode("utf-8")).digest() | ||
| return int.from_bytes(digest[:8], "little") | ||
| return get_cache_salt_id(cache_salt) |
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Should we fix the v2 c++ manager instead?
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There is no salt derivation in the V2 C++ manager to fix — it consumes an id that Python has already derived, so this line is the layer where the two disagreeing derivations actually live.
Tracing it: EventManager::hashV1BlockKey takes std::optional<std::uint64_t> cacheSaltId (eventManager.h:189, mixed at eventManager.cpp:588-590), ReuseScope on the C++ side holds std::optional<std::uint64_t> salt and its own comment says it mirrors Python's (blockRadixTree.h:50-59), and castReuseScope accepts None / ReuseScope / an int lora id / an object with lora_id and salt — never a string (nanobind/batch_manager/kvCacheManagerV2.cpp:679-699). The only salt string under kv_cache_manager_v2/ is KVCacheStoredBlockData::cacheSalt (eventManager.h:88), which is event payload for consumers and is not read anywhere else in that file.
So str -> uint64 happens only in Python, and on main the two Python derivations disagree:
KVCacheManagerV2._derive_reuse_salt:sha256(salt).digest()[:8], little-endiankv_cache_hash.get_cache_salt_id:blake3(salt).digest(8), little-endian
This PR keeps the engine's bytes exactly and moves the router onto them, so no id a running worker has already published changes.
If you meant the V1 C++ hasher, then I agree there is a real defect there, but it is a different and breaking one. blockKey.cpp:367-370 mixes std::hash<std::string>{}(cacheSalt) straight into the root block hash. That is implementation-defined, so the router cannot reproduce it from Python at all and salted requests can never match a V1 worker — which is the limitation I documented on get_cache_salt_id rather than worked around. Fixing it means changing every published V1 block hash, so it wants its own PR and its own compatibility discussion. Happy to take that one if you want it.
One thing worth flagging either way: the docstring on _derive_reuse_salt on main claims the derivation "matches C++ blockKey hashing on cacheSalt". It does not, and never did — that is sha256-truncated on one side and std::hash on the other. That stale comment is a plausible origin for this exact confusion, and the PR replaces it with what the code actually does.
Separately, whenever you have a moment: this PR has had no CI at all, since /bot run needs someone on your side. I would rather have it red and fix it than merged unrun.
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The caveat I left on the follow-up commit is now cleared.
The pre-fix failure is not a near miss. Router and worker produce disjoint keys for the same LoRA request: Every block differs, so a LoRA request scores zero matches against a worker that is holding exactly its blocks. That is the behaviour reported in #18156. Method note, since this is not a clean checkout: the newest published wheel is still 1.3.0rc24 while this branch sits on rc25-era This PR still has had no CI run at all, so everything above is my own bench rather than yours. |
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Approving — the comments below are optional touch-ups, not blockers.
Verified the id chain end to end for this fix: LoRARequest.adapter_id → LoraConfig(task_id=...) (executor/base_worker.py:350) → LlmRequest.lora_task_id (llm_request.py:1611) → ReuseScope.lora_id, and the C++ BlockKeyHasher mixes loraTaskId in the same position/way as hash_v1_block_key, so the router-side ids now line up with both V1 and V2 workers. Both routing entry points (get_next_server and the coordinator's routing_key) funnel through _routing_key_sync, so the fix covers the whole path.
Two things worth addressing beyond the inline comments:
-
Removing
tensorrt_llm.inputs.utils.get_cache_salt_iddeletes an importable, non-underscored symbol. It has no in-tree callers, but anything outside the repo importing it breaks, and the survivingruntime.kv_cache_hash.get_cache_salt_idreturns a different value than the removed one did (sha256 vs blake3). Worth a release-note line rather than a silent removal. -
Description hygiene: it says
test_get_request_lora_id_extraction"covers the object and dict request shapes", but the test only exercises aCompletionRequestand aSimpleNamespace—_get_request_lora_idusesgetattronly, so a dict-shaped request would silently returnNone. Either drop the claim or add the dict case and support it (if a dict-shaped request can actually reach the router, that is a real gap; if not, the docstring/description shouldn't imply it).
Otherwise the round-trip test that drives the real KVCacheEventManager._v1_hash_from_radix_block against the router's recomputation is the right shape for this class of bug — it's the check that would have caught both divergences.
Notes on lines outside the diff:
- tensorrt_llm/serve/router_utils.py:387:
_tokenize_and_compute_block_hashes_with_salt(and_tokenize_and_compute_block_hashesabove it) have no callers anywhere intensorrt_llm/—_routing_key_syncuses the_by_algovariant. Threadinglora_task_idthrough them adds a second signature that has to be kept in sync with the live one for no benefit. Prefer deleting both dead helpers in this PR instead of extending them.
| if cache_salt_id is None: | ||
| # takes no cache_salt_id or lora_task_id, so fall back to Python whenever | ||
| # either is set (rare opt-in; never in the plain agent/chat completion path). | ||
| if cache_salt_id is None and lora_task_id is None: |
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The native fast path doesn't need to be disabled for LoRA — only for the salt. The nanobind BlockKey binding exposes a two-arg constructor BlockKey(tokens, lora_task_id=...) (cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManager.cpp:412-414, backing blockKey.h:56), and BlockKeyHasher mixes loraTaskId in exactly the position hash_v1_block_key does, so the two stay bit-exact.
As written, every block of every LoRA request in the routing hot path now takes the per-token Python loop — and LoRA is the main case this PR exists to fix, so it's not the rare opt-in the comment describes. Suggest:
if cache_salt_id is None:
return _NativeBlockKeyHasher.hash(_NativeBlockKey(token_ids, lora_task_id), parent)and a test asserting the native and Python paths agree for a non-None lora_task_id, so the equivalence is pinned rather than assumed.
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You were right, and I should have acted on this when you raised it. The binding takes lora_task_id, and BlockKeyHasher::hash mixes loraTaskId after the tokens with hash64Mix, the same position hash_v1_block_key uses, so only the salt needs the Python loop. Done in 991de58: with no salt, block_key_hasher now hashes BlockKey(token_ids, lora_task_id=...) natively.
One detail I added: LoRARequest.lora_int_id is not range-checked and arrives on the request (CompletionRequest.lora_request), and nanobind raises on a negative or wider-than-64-bit int where the Python path wraps it. The id is masked to 64 bits before the native call, so both paths give the same hash for every input instead of a bad adapter id turning into an exception in the routing path.
Two new tests: test_native_block_key_hasher_matches_python_v1_with_lora checks the native path against hash_v1_block_key for 0, 7, 2**32+3, 2**63, 2**64-1, 2**64+5 and -1, and test_block_key_hasher_python_fallback_only_for_cache_salt fails if a LoRA-only request ever reaches the Python loop again. Both pass against the rc29 bindings.
| """ | ||
| import hashlib | ||
|
|
||
| digest = hashlib.sha256(cache_salt.encode("utf-8")).digest() |
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import hashlib can move to module scope now. The function-local import was there because blake3 is an optional third-party dependency; hashlib is stdlib and already imported all over the process, so the deferral just costs a dict lookup per call on a per-request path.
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Stamp on behalf of runtime devs, delegating proper review to @NVIDIA/trt-llm-kv-cache-manager-devs; please ping me if you think this is not accurate
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[by Codex] @lowsfer Could you review this PR? Thanks! |
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Rebased onto main The conflict was import-only.
One thing this PR has never had: a CI run. Only the DCO check has ever reported on it, so the approvals are standing on the GPU evidence I posted by hand rather than on pipeline results. Could someone with the permission kick off @lowsfer, @NVIDIA/trt-llm-kv-cache-manager-devs, this is waiting on the KV-cache-manager review that @mikeiovine delegated. |
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@brnguyen2 you traced the id chain end to end on this one, so you are the right person to ask about mechanics rather than the code. This PR has two approvals and has still never had a CI run. So, plainly: what is the path for an externally-authored PR to get pipeline coverage here? If it needs someone internal to queue it, I am happy to wait for whoever that is rather than pinging further. I mainly want to know whether this is sitting in a queue or waiting on something from me, because from the outside those two look identical. State, unchanged since your review: rebased onto main |
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[by Codex] @lowsfer Friendly reminder: could you review this PR? Thanks! |
| code path (``_v1_hash_from_radix_block``) over a radix chain seeded with | ||
| the worker's ``ReuseScope``. | ||
| """ | ||
| from tensorrt_llm._torch.pyexecutor.kv_cache_manager_v2 import \ |
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Is the import correct? The module is under _torch.pyexecutor.kv_cache.kv_cache_manager_v2
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It was not. kv_cache_manager_v2.py moved to _torch/pyexecutor/kv_cache/, and when I rebased onto the move I kept the old path in three lazy imports: this one, and the ones in test_cache_salt_id_derivation_matches_worker_path and test_routing_key_sync_carries_lora_and_salt_to_block_hashes. The imports sit inside the test bodies, so collection still passed, but 7 test cases fail with ModuleNotFoundError when they run. I reproduced exactly that against the 1.3.0rc29 wheel. Fixed in e6eeb3c: all three now use tensorrt_llm._torch.pyexecutor.kv_cache.kv_cache_manager_v2, the path the rest of the suite uses, and all 7 run and pass (numbers in my comment below). Thanks for catching it.
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Nothing was skipped or deselected in any run. The second row is the new and re-pinned tests meeting the unfixed library. The last row isolates the import fix.
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No wheel contains #19154 yet, so I tested on an L4 with the rc29 wheel, its source commit plus the router half of #19154, and these six commits; the three router files are byte-identical to this branch. 112 passed with the PR, 22 of those fail against the same library without it, and reverting only the uint64 wrap gives 4 V2 @liji-nv the import you flagged is fixed and holds after the rebase. Could you take another look, and trigger |
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Review comments at @tests/unittest/disaggregated/test_router.py:
- Around line 102-104: Update test_block_hashes_wrap_out_of_range_lora_id to use
supported uint64 LoRA IDs, or explicitly normalize IDs in the V2 worker before
testing wraparound; do not imply the worker accepts out-of-range IDs without
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📒 Files selected for processing (4)
tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.pytensorrt_llm/serve/router.pytensorrt_llm/serve/router_utils.pytests/unittest/disaggregated/test_router.py
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| The worker's LoRA id is a uint64 on both the V1 ``BlockKey`` and the V2 | ||
| ``ReuseScope``, so an id outside [0, 2**64) must hash as its wrapped value | ||
| rather than make the native binding reject the request. |
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🗄️ Data Integrity & Integration | 🟡 Minor | ⚡ Quick win
🔎 Supported by static analysis
🏁 Script executed:
set -eu
printf '%s\n' '--- changed test references ---'
rg -n -F -- 'out-of-range' tests/unittest/disaggregated/test_router.py || true
rg -n -F -- '2**64' tests/unittest/disaggregated/test_router.py || true
rg -n -F -- 'lora_id' tests/unittest/disaggregated/test_router.py || true
printf '%s\n' '--- worker/cache references ---'
rg -n -F -- '_create_kv_cache' . --glob '!build/**' --glob '!dist/**' --glob '!*.pyc' || true
rg -n -F -- 'class ReuseScope' . --glob '!build/**' --glob '!dist/**' || true
rg -n -F -- 'ReuseScope(' . --glob '!build/**' --glob '!dist/**' || true
rg -n -F -- 'class LlmRequest' . --glob '!build/**' --glob '!dist/**' || true
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nl -ba tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py | sed -n '3688,3720p'
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nl -ba tensorrt_llm/_torch/pyexecutor/kv_cache/kv_cache_manager_v2.py | sed -n '6535,6575p'
printf '%s\n' '--- ReuseScope declarations and conversion ---'
nl -ba tensorrt_llm/runtime/kv_cache_manager_v2/__init__.pyi | sed -n '96,122p'
nl -ba cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManagerV2.cpp | sed -n '670,735p'
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printf '%s\n' '--- LlmRequest LoRA field ---'
nl -ba tensorrt_llm/_torch/pyexecutor/llm_request.py | sed -n '896,980p'
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printf '%s\n' '--- V1 BlockKey and LoRA conversion ---'
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find . -path '*nanobind*' -type f | head -80
printf '%s\n' '--- router binding and conversion ---'
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official nanobind documentation integer conversion Python int uint64_t negative overflow
💡 Result:
Nanobind’s docs don’t appear to spell out the `uint64_t` edge cases directly. The closest authoritative source is the **current `master` implementation**: its arithmetic caster routes unsigned 64-bit inputs through `load_u64`, and converts C++ unsigned 64-bit values back to Python with `PyLong_FromUnsignedLongLong`. ([raw.githubusercontent.com](https://raw.githubusercontent.com/wjakob/nanobind/master/include/nanobind/nb_cast.h))
So the implementation is designed to handle the full `uint64_t` range. Whether a negative Python `int` or an integer larger than `2⁶⁴−1` is rejected—and the exact exception—is determined by `load_u64`; the cited header delegates that check, so it alone doesn’t establish those details. The source is `master`, not a version-specific release. The changelog notes that since nanobind 2.14.0, implicit integer-argument conversions require Python’s `__index__` protocol. ([github.com](https://github.com/wjakob/nanobind/blob/master/docs/changelog.rst?utm_source=openai))
Citations:
- 1: https://raw.githubusercontent.com/wjakob/nanobind/master/include/nanobind/nb_cast.h
- 2: https://github.com/wjakob/nanobind/blob/master/docs/changelog.rst?utm_source=openai
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nanobind 2.9.0 nb_cast.h load_u64 uint64_t Python negative overflow
💡 Result:
In **nanobind 2.9.0**, `uint64_t` arguments are dispatched to `detail::load_u64()` by the arithmetic caster in `nb_cast.h`. That function delegates to `load_int()`, which uses Python’s unsigned-integer conversion (`PyLong_AsUnsignedLongLong` for a 64-bit value). A negative Python integer therefore **does not wrap to a `uint64_t`**: CPython reports an error, which nanobind clears, and the conversion returns `false`. ([raw.githubusercontent.com](https://raw.githubusercontent.com/wjakob/nanobind/v2.9.0/include/nanobind/nb_cast.h))
The same path rejects values above `UINT64_MAX`. With ordinary function-argument conversion, a failed cast means the overload won’t accept the argument; exact exception behavior can depend on the calling context.
Citations:
- 1: https://raw.githubusercontent.com/wjakob/nanobind/v2.9.0/include/nanobind/nb_cast.h
Do not document out-of-range IDs as worker-supported.
test_block_hashes_wrap_out_of_range_lora_id exercises only router hashing. The V2 worker passes the raw ID to the ReuseScope nanobind constructor, whose uint64_t conversion rejects negative and above-UINT64_MAX Python integers rather than wrapping them. Replace these cases with supported uint64 values, or add explicit worker-side normalization before testing wrapped values.
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Review comment at @tests/unittest/disaggregated/test_router.py around lines 102
- 104:
Update test_block_hashes_wrap_out_of_range_lora_id to use supported uint64 LoRA
IDs, or explicitly normalize IDs in the V2 worker before testing wraparound; do
not imply the worker accepts out-of-range IDs without that normalization.
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Fair on the docstring: it implied the worker hashes the wrapped id. It doesn't. The worker's LlmRequest binding takes lora_task_id as std::optional<uint64_t> and rejects an out-of-range id itself. The router reads lora_int_id from the client request before that, so the wrap only keeps the router's own native bindings from raising mid-routing; for in-range ids it is the identity. Reworded both docstrings in da00f5a, no code change. Keeping the out-of-range cases, since that router behavior is what they pin.
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[by Claude Code] @lowsfer Could you review this PR? Thanks! |
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[by Claude Code] @lowsfer Could you review this PR? Thanks! |
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The LoRA part is correct and still needed on main: router and worker hashes agree for V1, V2 and V2_64, and both routing entry points go through _routing_key_sync. However, the branch now conflicts with main, and #20048 already replaced the cache-salt design with string salts, so the salt-derivation half should be dropped. Please rebase so it keeps only the LoRA plumbing on top of main's cache_salt: Optional[str] signatures, update the salt-dependent tests and the title, and then we can run CI.
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| def get_cache_salt_id(cache_salt: str) -> int: | |||
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#20048 supersedes this: get_cache_salt_id is deleted on main, V2 uses the raw string salt (ReuseScope(salt: str | None)), and V1 uses KVCacheEventManager._hash_cache_salt. Please drop the changes to this file and to _derive_reuse_salt. Carried over, ReuseScope(salt=<int>) raises a TypeError. Also watch for auto-merge leftovers: the get_cache_salt_id import in kv_cache_manager_v2.py and an unused import hashlib.
| # sequence_to_blockchain_keys yields; its first pair is the root itself, so | ||
| # skip it. In KvCacheManager the last token is not part of any block key. | ||
| reuse_scope = ReuseScope(salt=cache_salt_id) | ||
| reuse_scope = ReuseScope(lora_id=_to_uint64(lora_task_id), salt=cache_salt_id) |
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When resolving the conflict, keep main's _compute_block_hashes(..., cache_salt: Optional[str]) signature, add lora_task_id next to it, and use ReuseScope(lora_id=_to_uint64(lora_task_id), salt=cache_salt). main's body derives the V1 salt id from cache_salt, so keeping this branch's cache_salt_id parameter would make every V1 hash call fail with a NameError.
| def test_cache_salt_id_derivation_matches_worker_path(): | ||
| from tensorrt_llm.inputs.utils import \ | ||
| get_cache_salt_id as worker_get_cache_salt_id | ||
| def test_cache_salt_id_derivation_matches_worker_path() -> None: |
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After the rebase this test no longer applies, since get_cache_salt_id is gone and _derive_reuse_salt returns a str, so please remove it. In the round-trip tests (L684, L751), build the worker scope with a string salt and pass KVCacheEventManager._hash_cache_salt(salt) as the V1 salt id.
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[by Claude Code] @lowsfer Could you review this PR? Thanks! |
…shes The worker keys KV cache reuse by the request's LoRA task id: V2 seeds the radix root with ReuseScope(lora_id=...), and the V1 event hash mixes the same id into every block key. The router recomputed block hashes without it, so a LoRA request never matched the blocks its worker had published, and two adapters sending the same prompt collided on one prefix in the router's view. Read the adapter id from the request's lora_request and thread it through _routing_key_sync into both hash paths. LoRA-only requests stay on the native V1 hasher, whose BlockKey carries the task id. Signed-off-by: Shifat Islam Santo <shifatislamsanto764@gmail.com>
…e hash bindings The native BlockKey and ReuseScope bindings take the LoRA id as a uint64, so an adapter id outside [0, 2**64) would raise in the router's V1 native and V2 hash paths, while the Python hash_v1_block_key already reduces it modulo 2**64. Share that reduction through _to_uint64 on both native paths. The worker rejects such an id itself; the wrap only keeps routing from raising, and is the identity for in-range ids. Signed-off-by: Shifat Islam Santo <shifatislamsanto764@gmail.com>
…worker's event hashes
Pin the router's block hashes against the hashes the worker publishes, over {no salt, string salt} x {no LoRA, LoRA} and all three hash algos. The V2 side chains sequence_to_blockchain_keys over the worker's ReuseScope; the V1 side chains KVCacheEventManager._hash_block_key with the scope's LoRA id and KVCacheEventManager._hash_cache_salt of its salt, the same inputs EventManager::v1HashFromBlock uses. A second test drives _routing_key_sync itself, so dropping the LoRA id from that call site fails even though _compute_block_hashes is correct. Also cover native vs Python V1 parity with LoRA ids, the uint64 wrap on every algo, and that only a cache salt leaves the native V1 hasher.
Signed-off-by: Shifat Islam Santo <shifatislamsanto764@gmail.com>
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oneKn8
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Thanks for the review. I rebased onto main ( On an A40 against the |
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@liji-nv the import-path issue from your Sep 29 review was fixed in the Sep 30 rebase. The test file now uses main's existing @bo-nv the Oct 30 rebase covers your Oct 23 points. The head ( |
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[by Claude Code] @lowsfer Could you review this PR? Thanks! |
Description
Fixes #18156.
The KV-cache-aware router scores workers by recomputing a request's block hashes and matching them against the hashes workers publish in KV cache events.
KvCacheAwareServerState.matched_tokenswalks that list and breaks on the first miss, so a first-block mismatch makesmatch_count0 for the whole request: cache-aware routing silently degrades to load balancing, with no error and no log line.The router dropped the request's LoRA id. The worker keys reuse by it: V2 seeds the radix root with
ReuseScope(lora_id=...), and the V1 event hash (EventManager::hashV1BlockKey) mixes it into every block key. Nothing undertensorrt_llm/serve/passed it, so every LoRA request scored zero matches, and because all adapters hashed identically, the router's own prefix accounting collided adapters with one another.This change adds
_get_request_lora_idnext to_get_request_cache_saltand threads the adapter id through_routing_key_sync, the_tokenize_and_compute_block_hashes*helpers,_compute_block_hashes,block_key_hasherand the V2ReuseScope, keeping main'scache_salt: Optional[str]signatures. The nativeBlockKeybinding carries the LoRA id, so LoRA requests stay on the nativeBlockKeyHasherfast path; only a salt still falls back to the Python hasher. The nativeBlockKeyandReuseScopetake the id as a uint64, so the router reduces it modulo 2**64, ashash_v1_block_keyalready does, instead of raising mid-routing. The worker rejects an out-of-range id itself.The salt-derivation half of the earlier revision is dropped: #20048 replaced that design with string salts.
Test Coverage
tests/unittest/disaggregated/test_router.py:test_router_block_hashes_round_trip_worker_event_hashes: {no salt, string salt} x {no LoRA, LoRA}, all three hash algorithms. The V2 side chainssequence_to_blockchain_keysover the worker'sReuseScope; the V1 side chainsKVCacheEventManager._hash_block_keywith the scope's LoRA id andKVCacheEventManager._hash_cache_salt(salt), the same inputsEventManager::v1HashFromBlockuses.test_routing_key_sync_carries_lora_and_salt_to_block_hashes: drives_routing_key_syncitself for a LoRA request, with and without a salt, so dropping the id at that call site fails.test_get_request_lora_id_extraction: absent and present adapter ids.test_native_block_key_hasher_matches_python_v1_with_lora: native vs Python V1 parity for LoRA ids across the uint64 boundary.test_block_hashes_wrap_out_of_range_lora_id: all three algorithms reduce an out-of-range id instead of raising.test_block_key_hasher_python_fallback_only_for_cache_salt: a LoRA-only request stays on the native hasher.Run on an NVIDIA A40 against the
tensorrt_llm==1.4.0rc2.post1wheel (source commit43f8f70da, which includes #20048) with these commits applied over it, using the repo's conftest:Each count also includes
test_gpt_oss_router_tokens_match_chat_harmony_server_input, which needsLLM_MODELS_ROOTand fails identically in every run.test_coordinator_worker,test_disagg_openai_client,test_openai_disagg_serviceandtest_kv_cache_event_managergive the same counts with and without this change.pre-commit run --files <changed files>passes.PR Checklist