KV-Cache multi-tier offloading async batched lookup by effi-ofer · Pull Request #44193 · vllm-project/vllm

effi-ofer · 2026-06-01T10:53:54Z

Purpose

Add async and batch lookup to multi-tier offloading. This significantly speeds up the secondary tier overall performance.

Test Plan

Run the kv_offloading/tiering tests concentrating on fs secondary tier as well as object store secondary tier (obj store PR has yet to be merged).
Run inference perf benchmark using llm-d-benchmark.

Test Result

.venv/bin/python -m pytest tests/v1/kv_offload/test_fs_tier.py

=================================================================== test session starts ===================================================================
platform linux -- Python 3.12.13, pytest-9.0.3, pluggy-1.6.0
rootdir: /workspace/vllm
configfile: pyproject.toml
plugins: anyio-4.13.0
collected 9 items

tests/v1/kv_offload/test_fs_tier.py ......... [100%]

==================================================================== warnings summary =====================================================================
:488
:488: DeprecationWarning: builtin type SwigPyPacked has no module attribute

:488
:488: DeprecationWarning: builtin type SwigPyObject has no module attribute

tests/v1/kv_offload/test_fs_tier.py: 14 warnings
/workspace/vllm/.venv/lib/python3.12/site-packages/torch/jit/_script.py:365: DeprecationWarning: torch.jit.script_method is deprecated. Please switch to torch.compile or torch.export.
warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================================================= 9 passed, 16 warnings in 4.10s ==============================================================

Essential Elements of an Effective PR Description Checklist

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

github-actions · 2026-06-01T10:54:03Z

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🚀

effi-ofer · 2026-06-01T10:54:21Z

@orozery @rshavitt @ronensc

mergify · 2026-06-03T15:28:42Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @effi-ofer.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

depthfirst-app · 2026-06-05T19:32:55Z

+            )
+            for k in keys
+        ]
+        results = self._tier._agent.query_memory(descriptors, "OBJ", "OBJ")


⚪ Severity: LOW

batch_lookup() runs on the background worker thread and calls self._tier._agent.query_memory() without synchronization, while the scheduler thread concurrently calls _agent.register_memory(), _agent.transfer(), _agent.check_xfer_state(), etc. The nixl agent is a C/C++ extension with no documented thread-safety guarantees — concurrent access may corrupt internal state, leading to memory safety violations or undefined behavior.
Helpful? Add 👍 / 👎

💡 Fix Suggestion

Suggestion: Add a threading.Lock to ObjectStoreSecondaryTierManager to serialize all access to self._agent. Specifically:

In ObjectStoreSecondaryTierManager.__init__() (around line 103), after creating self._agent, create a lock:

self._agent_lock = threading.Lock()

In ObjAsyncLookupWorker.batch_lookup() (line 81), wrap the query_memory call with the lock:

with self._tier._agent_lock: results = self._tier._agent.query_memory(descriptors, "OBJ", "OBJ")

Wrap every other self._agent call in the scheduler-thread methods (_submit_transfer, get_finished_jobs, lookup/_exists, shutdown, and _register_primary_kv) with the same self._agent_lock. This includes calls to register_memory(), prep_xfer_dlist(), transfer(), check_xfer_state(), release_xfer_handle(), release_dlist_handle(), and deregister_memory().

This mirrors the pattern used in vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py where NIXL thread-safety is explicitly addressed by limiting concurrent access.

From Claude:

The bot's concern is effectively a non-issue for the OBJ backend. Here's why: 1. queryMem doesn't touch devIdToObjKey_ — the only shared mutable state in the engine. It reads object keys directly from the descriptor's metaInfo field (the key string passed in from Python). 2. postXfer only reads devIdToObjKey_, and registerMem/deregisterMem are the only writers. These registration calls happen at startup (in __init__), long before the background thread starts issuing query_memory calls. 3. The S3 client itself (checkObjectExistsAsync) uses an asio thread pool executor internally — it's designed for concurrent async I/O. Multiple concurrent HTTP requests to S3 are the normal operating mode. 4. No shared mutable state is touched concurrently between queryMem (background thread) and postXfer/check_xfer_state (scheduler thread). They both just issue independent HTTP requests through the same S3 client, which handles concurrency internally via its executor. Bottom line: The depthfirst bot flagged this based on the general principle "shared object, no lock = danger." But in practice, queryMem and the transfer methods are effectively stateless operations dispatching independent HTTP requests through an internally-concurrent S3 client. No lock needed. You could add a one-line comment in the PR noting this for future readers, but no code change is required.

orozery

Thanks @effi-ofer !
Can you please change the PR title to be more descriptive?

orozery · 2026-06-07T07:44:28Z

+            max_results: Capacity of the lookup state cache.
+        """
+        ...
+


Let's avoid changing base.py and manager.py.
The tiering manager should not be aware of AsyncLookup.
The secondary tier wanting async lookup (e.g. fs tier) would delegate lookup, on_request_finished, and on_schedule_end to the the async lookup component.

orozery · 2026-06-07T07:45:54Z

+    return int.from_bytes(key[:8], "big")
+
+
+class AsyncLookupWorker(ABC):


Let's call this AsyncLookupManager instead.
The manager manages lookups and uses a background thread to execute them.

orozery · 2026-06-07T07:51:20Z

+
+    def __init__(
+        self,
+        tier_idx: int,


Let's drop tier_idx as it is not propagated by the tiering manager.
Instead, use tier_type: str.

orozery · 2026-06-07T07:51:41Z

+    def __init__(
+        self,
+        tier_idx: int,
+        max_results: int = 1_000_000,


Let's drop max_results to simplify things.
i.e. don't support LRU eviction.

orozery · 2026-06-07T07:56:08Z

+FOUND: int = 1  # present in this tier
+
+
+def _slot(key: OffloadKey) -> int:


I don't see a reason for this operation.
We can use OffloadKey as dict keys directly, without going through _slot.

It was saving us a bit of memory (30MB on 1M entries) but at an increased risk of collision. Removed.

orozery · 2026-06-07T08:33:11Z

+                if self._lookup_state.get(slot, _IN_FLIGHT) == _IN_FLIGHT:
+                    self._lookup_state[slot] = status
+
+    def update_cached_exists(self, keys: Collection[OffloadKey]) -> None:


Assuming we drop the LRU, let's change this function to invalidate.
It should simply remove keys from self._lookup_state.

created cleanup()

orozery · 2026-06-07T08:33:49Z

+                # Only apply if the slot is still _IN_FLIGHT.  A FOUND written
+                # by update_cached_exists() during the worker's lookup takes
+                # precedence and must not be overwritten.
+                if self._lookup_state.get(slot, _IN_FLIGHT) == _IN_FLIGHT:


Assuming we drop LRU, we can remove this if.

no longer relevant

orozery · 2026-06-07T08:34:51Z

+    def shutdown(self) -> None:
+        """Stop the worker thread."""
+        self._shutdown_event.set()
+        self._thread.join(timeout=5.0)


Can we drop the timeout?

orozery · 2026-06-07T08:35:39Z

+        while not self._shutdown_event.is_set():
+            # Block until flush() posts a batch.
+            try:
+                pending = self._lookup_queue.get(timeout=1.0)


If we change _lookup_queue to a SimpleQueue we can drop the timeout and the try-catch.
On shutdown, just use a "poisen pillow", e.g. pushing of a dummy value to _lookup_queue to unblock the worker thread and allow it to read the shutdown event.
We can actually switch to a shutdown boolean instead of an event.

That's already done - switched to SimpleQueue, removed the timeout and try-catch, and use None as the poison pill. And I removed _shutdown_event entirely since the None handles the exit cleanly - no boolean or event needed.

orozery · 2026-06-07T08:54:04Z

+    def batch_lookup(
+        self, keys: list[OffloadKey], req_context: ReqContext
+    ) -> list[bool | None]:
+        return [os.path.exists(self._tier.file_mapper.get_file_name(k)) for k in keys]


We can avoid list allocation, changing batch_lookup to return Iterable[bool | None]:

Suggested change

return [os.path.exists(self._tier.file_mapper.get_file_name(k)) for k in keys]

return (os.path.exists(self._tier.file_mapper.get_file_name(k)) for k in keys)

no longer relevant.

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

orozery

Thanks @effi-ofer !
Can you please also add a test_async_lookup.py file with unit testing?

orozery · 2026-06-08T13:18:50Z

+                    self._lookup_state[key] = LookupState(
+                        result=result, request_ids=state.request_ids
+                    )


Let's switch LookupState type from NamedTuple to @dataclass(slots=True).
Then, we can simply set state.result = result.

orozery · 2026-06-08T13:22:22Z

+
+class LookupState(NamedTuple):
+    result: bool | None
+    request_ids: set[str]


Let's add a small comment explaining what does this field hold.

orozery · 2026-06-08T13:24:02Z

+    def batch_lookup(
+        self, keys: list[OffloadKey], req_context: ReqContext
+    ) -> list[bool | None]:
+        return [os.path.exists(self._tier.file_mapper.get_file_name(k)) for k in keys]


orozery · 2026-06-08T13:28:48Z

-        """
-        Collect completed jobs from the finished-jobs queue.
-        """


revert deletion

orozery · 2026-06-08T13:29:09Z

-        Shuts down the thread pool, clearing pending tasks and waiting for
-        active threads to complete.


Merge into the existing docstring instead of overriding.

orozery · 2026-06-08T13:35:05Z

+                        len(keys),
+                        exc,
+                    )
+                    hits = [False] * len(keys)


Assuming we change batch_lookup to return an Iterable:

Suggested change

hits = [False] * len(keys)

hits = (False for _ in range(keys))

orozery · 2026-06-08T13:43:08Z

+            )
+            for k in keys
+        ]
+        results = self._tier._agent.query_memory(descriptors, "OBJ", "OBJ")


From Claude:

The bot's concern is effectively a non-issue for the OBJ backend. Here's why: 1. queryMem doesn't touch devIdToObjKey_ — the only shared mutable state in the engine. It reads object keys directly from the descriptor's metaInfo field (the key string passed in from Python). 2. postXfer only reads devIdToObjKey_, and registerMem/deregisterMem are the only writers. These registration calls happen at startup (in __init__), long before the background thread starts issuing query_memory calls. 3. The S3 client itself (checkObjectExistsAsync) uses an asio thread pool executor internally — it's designed for concurrent async I/O. Multiple concurrent HTTP requests to S3 are the normal operating mode. 4. No shared mutable state is touched concurrently between queryMem (background thread) and postXfer/check_xfer_state (scheduler thread). They both just issue independent HTTP requests through the same S3 client, which handles concurrency internally via its executor. Bottom line: The depthfirst bot flagged this based on the general principle "shared object, no lock = danger." But in practice, queryMem and the transfer methods are effectively stateless operations dispatching independent HTTP requests through an internally-concurrent S3 client. No lock needed. You could add a one-line comment in the PR noting this for future readers, but no code change is required.

orozery · 2026-06-08T13:45:56Z

-        self, key: OffloadKey, req_context: ReqContext | None = None
-    ) -> bool | None:
-        return os.path.exists(self.file_mapper.get_file_name(key))
+    def lookup(self, key: OffloadKey, req_context: ReqContext) -> bool | None:


Need to verify fs tier unit tests don't pass req_context = None.

Claude claims all fs tier test calls pass _CTX (which is ReqContext(req_id="test")), never None. No issue there.
This is also true for the obj test unit.

orozery · 2026-06-08T13:47:44Z

+        request_ids.
+        """
+        keys_to_remove: list[OffloadKey] = []
+        for key, state in self._lookup_state.items():


We should keep a req_id->set(keys) dictionary to avoid iterating over all _lookup_state items here.

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

orozery · 2026-06-09T06:46:47Z

+        """
+        if self._need_to_drain:
+            self.drain_results()
+            self._need_to_drain = True  # TODO: results might not be ready yet


Switch this to False here

Set it to True in flush, unconditionally.

scheduler flush 1 sets to True
scheduler flush 2 sets to True
worker picks up the two batches, process the first.
scheduler drains results and sets need to drain to False.

orozery · 2026-06-09T06:47:36Z

+                    if hit is True:
+                        results.append((key, True))
+                    elif hit is False:
+                        results.append((key, False))
+                    # hit is None → stays in-flight (not added to results)


None is no longer possible:

Suggested change

if hit is True:

results.append((key, True))

elif hit is False:

results.append((key, False))

# hit is None → stays in-flight (not added to results)

results.append((key, hit))

orozery · 2026-06-09T06:48:43Z

+        touch the primary tier or scheduler state.
+
+        Returns a list parallel to keys: True if present, False if not
+        found, None if the tier is busy (retry later).


Update docstring, None is not possible.

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

orozery · 2026-06-09T16:12:11Z

+        while time.monotonic() < deadline:
+            if not mgr._pending_results.empty():
+                return
+            time.sleep(0.01)


Let's avoid polling and timeout:

import threading class InMemoryLookupManager(AsyncLookupManager): """Test subclass backed by an in-memory set.""" def __init__(self, existing_keys: set[OffloadKey] | None = None): super().__init__(tier_type="test") self._existing = existing_keys or set() self._results_ready = threading.Event() def batch_lookup( self, keys: list[OffloadKey], req_context: ReqContext ) -> Iterable[bool]: results = [k in self._existing for k in keys] self._results_ready.set() return results

Then replace all self._wait_for_drain(mgr) with:

mgr._results_ready.wait() mgr._results_ready.clear()

orozery · 2026-06-09T16:15:53Z

    return results


+def async_lookup(


Let's rename this to lookup_and_wait

orozery · 2026-06-09T16:17:56Z

    return results


+def async_lookup(


Same comment as test_fs_tier.py:

Rename async_lookup to lookup_and_wait

Switch to using an event instead of polling with timeout.

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

orozery

Thanks @effi-ofer !

Signed-off-by: Effi Ofer <effi.ofer@gmail.com> Co-authored-by: Or Ozeri <oro@il.ibm.com>

Signed-off-by: Effi Ofer <effi.ofer@gmail.com> Co-authored-by: Or Ozeri <oro@il.ibm.com> Signed-off-by: divineearthly <divineearthly@gmail.com>

Signed-off-by: Effi Ofer <effi.ofer@gmail.com> Co-authored-by: Or Ozeri <oro@il.ibm.com>

effi-ofer requested review from ApostaC and orozery as code owners June 1, 2026 10:53

mergify Bot added the v1 label Jun 1, 2026

mergify Bot added the needs-rebase label Jun 3, 2026

effi-ofer added 7 commits June 5, 2026 21:51

async lookup

b666388

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

avoid evicting _IN_FLIGHT

871cdee

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

no need to delete from the cache

fbf2f85

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

busy spin

5367403

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

handle False

a29a18a

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

remove end_of_step

0818c27

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

get_finished_jobs testing

114b205

Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

effi-ofer force-pushed the lookup branch from 50e1223 to 114b205 Compare June 5, 2026 18:54

obj store

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Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

mergify Bot removed the needs-rebase label Jun 5, 2026

depthfirst-app Bot reviewed Jun 5, 2026

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Comment thread vllm/v1/kv_offload/tiering/async_lookup.py Outdated

small fixes

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Signed-off-by: Effi Ofer <effi.ofer@gmail.com>

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orozery requested changes Jun 7, 2026

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effi-ofer changed the title ~~Lookup~~ KV-Cache multi-tier offloading async batched lookup Jun 7, 2026

effi-ofer added 6 commits June 7, 2026 22:54

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testing

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testing

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testing

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cleanup instead of lru

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orozery requested changes Jun 8, 2026

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cleanup

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effi-ofer added 5 commits June 8, 2026 22:06

_need_to_drain

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testing

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testing

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testing

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testing

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missing tests

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orozery added the ready ONLY add when PR is ready to merge/full CI is needed label Jun 9, 2026

effi-ofer added 2 commits June 9, 2026 12:37

tests

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tests

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tests

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orozery approved these changes Jun 10, 2026

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Merge branch 'main' into lookup

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orozery enabled auto-merge (squash) June 10, 2026 08:58

orozery and others added 2 commits June 10, 2026 13:55

Merge branch 'main' into lookup

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Merge branch 'main' into lookup

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orozery merged commit af65e08 into vllm-project:main Jun 10, 2026
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KV-Cache multi-tier offloading async batched lookup (vllm-project#44193)

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Signed-off-by: Effi Ofer <effi.ofer@gmail.com> Co-authored-by: Or Ozeri <oro@il.ibm.com>

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KV-Cache multi-tier offloading async batched lookup (vllm-project#44193)

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saikat-royc mentioned this pull request Jun 15, 2026

llmd fsconnector metadata cache llm-d/llm-d-kv-cache#621

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KV-Cache multi-tier offloading async batched lookup (vllm-project#44193)

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KV-Cache multi-tier offloading async batched lookup (vllm-project#44193)

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hwx707 mentioned this pull request Jun 29, 2026

[Core] Add scheduler-orchestrated worker transfers for multi-node KV cache tiering #46556

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KV-Cache multi-tier offloading async batched lookup (vllm-project#44193)

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KV-Cache multi-tier offloading async batched lookup (vllm-project#44193)

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KV-Cache multi-tier offloading async batched lookup (vllm-project#44193)

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Signed-off-by: Effi Ofer <effi.ofer@gmail.com> Co-authored-by: Or Ozeri <oro@il.ibm.com>

		return int.from_bytes(key[:8], "big")


		class AsyncLookupWorker(ABC):

		FOUND: int = 1 # present in this tier


		def _slot(key: OffloadKey) -> int:

	return [os.path.exists(self._tier.file_mapper.get_file_name(k)) for k in keys]
	return (os.path.exists(self._tier.file_mapper.get_file_name(k)) for k in keys)

		Shuts down the thread pool, clearing pending tasks and waiting for
		active threads to complete.

	hits = [False] * len(keys)
	hits = (False for _ in range(keys))

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Purpose

Test Plan

Test Result

.venv/bin/python -m pytest tests/v1/kv_offload/test_fs_tier.py

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effi-ofer commented Jun 1, 2026 •

edited by github-actions Bot

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