[Core] Add register_model() to KVConnectorBase_V1 for CacheBlend by zbennett10 · Pull Request #37339 · vllm-project/vllm

zbennett10 · 2026-03-17T19:35:40Z

Purpose

LMCache's CacheBlend feature requires access to the loaded model weights for selective layer recomputation via VLLMModelTracker.register_model(). However, vLLM never passes the model reference to KV connectors — register_kv_caches() only receives kv_caches: dict[str, torch.Tensor]. This means CacheBlend's LMCBlenderBuilder.get_or_create() fails at runtime when it calls VLLMModelTracker.get_model() because the model was never registered.

This PR adds a new register_model(model) method to KVConnectorBase_V1 (no-op by default). This avoids changing the register_kv_caches signature, which would require updating 16+ connector implementations.

Changes:

KVConnectorBase_V1.register_model() — no-op default
ActiveKVConnector.__init__() — calls connector.register_model(model) after register_kv_caches()
get_kv_connector() — accepts optional model param, passes to ActiveKVConnector
GPUModelRunner.init_kv_caches() — passes self.model to get_kv_connector()
LMCacheConnectorV1.register_model() — calls VLLMModelTracker.register_model(ENGINE_NAME, model)
LMCacheConnectorV1Impl.register_model() — same (bundled adapter)
MultiConnector.register_model() — delegates to sub-connectors
LMCacheMPConnector.register_model() — delegates to worker adapter

All other connectors (NIXL, Mooncake, FlexKV, offloading, etc.) inherit the no-op default — zero behavior change.

Test Plan

pytest tests/v1/kv_connector/test_register_model.py -v

Tests cover:

Base class has register_model and it's a no-op
ActiveKVConnector calls register_model when model is provided
ActiveKVConnector skips register_model when model is None
get_kv_connector passes model through to ActiveKVConnector
get_kv_connector returns NoOp when no transfer group
LMCacheConnectorV1.register_model calls VLLMModelTracker
LMCacheConnectorV1.register_model handles ImportError gracefully
MultiConnector.register_model delegates to all sub-connectors

Test Result

All 9 tests pass. Ruff lint passes on all 8 changed files.

github-actions · 2026-03-17T19:37:17Z

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Code Review

This pull request effectively adds a register_model() method to the KVConnector interface, enabling features like LMCache's CacheBlend to access model weights. The changes are well-implemented, maintaining backward compatibility by providing a no-op default in the base class and correctly propagating the model reference through the call stack. The inclusion of comprehensive tests is commendable. I have one suggestion to improve consistency and reduce code duplication in the LMCacheConnectorV1 implementation.

gemini-code-assist · 2026-03-17T19:38:18Z

+    def register_model(self, model: "torch.nn.Module") -> None:
+        """Register model with LMCache's VLLMModelTracker for CacheBlend.
+
+        CacheBlend's blender needs access to model weights for selective
+        layer recomputation. This method is called automatically by vLLM
+        after model loading.
+        """
+        try:
+            from lmcache.v1.compute.models.utils import VLLMModelTracker
+
+            from vllm.distributed.kv_transfer.kv_connector.v1.\
+                lmcache_integration.utils import ENGINE_NAME
+            VLLMModelTracker.register_model(ENGINE_NAME, model)
+            logger.info("Registered model with LMCache VLLMModelTracker")
+        except ImportError:
+            logger.debug(
+                "LMCache CacheBlend model registration not available"
+            )
+        except Exception:
+            logger.warning(
+                "Failed to register model with VLLMModelTracker",
+                exc_info=True,
+            )
+


The register_model method is implemented directly in LMCacheConnectorV1, but other methods in this class (e.g., register_kv_caches) delegate to self._lmcache_engine. This is inconsistent with the class's design and leads to code duplication, as the same register_model logic is also added to LMCacheConnectorV1Impl in vllm_v1_adapter.py.

To improve consistency and maintainability, register_model should delegate the call to self._lmcache_engine. This also makes the implementation in LMCacheConnectorV1Impl effective and avoids having the logic in two places.

def register_model(self, model: "torch.nn.Module") -> None: """Register model with LMCache's VLLMModelTracker for CacheBlend. Delegates to the underlying LMCache engine implementation. """ if hasattr(self._lmcache_engine, "register_model"): self._lmcache_engine.register_model(model)

Good catch. Updated in a26cd78: LMCacheConnectorV1.register_model now delegates to self._lmcache_engine.register_model() with a hasattr guard (same pattern as register_kv_caches). The VLLMModelTracker logic lives solely in LMCacheConnectorV1Impl. Tests updated accordingly.

mergify · 2026-03-17T22:22:17Z

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vLLM never passes the model reference to KV connectors, so LMCache's CacheBlend blender cannot access model weights for selective layer recomputation. VLLMModelTracker.register_model() is never called. Add register_model(model) to KVConnectorBase_V1 as a separate method (no-op by default). ActiveKVConnector calls it after register_kv_caches when model is available. LMCache connectors override to call VLLMModelTracker.register_model(ENGINE_NAME, model). All other connectors inherit the no-op default — zero behavior change. Signed-off-by: Zach Bennett <zach@worldflow.ai> Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

…torV1 - Remove duplicated VLLMModelTracker logic from LMCacheConnectorV1.register_model - Delegate to self._lmcache_engine.register_model() using hasattr guard (consistent with register_kv_caches pattern) - The implementation lives in LMCacheConnectorV1Impl (vllm_v1_adapter.py) - Update tests: LMCacheConnectorRegisterModel tests now verify delegation; add TestLMCacheConnectorV1ImplRegisterModel for VLLMModelTracker logic Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

orozery · 2026-03-18T05:56:35Z

@zbennett10 Can you somehow get the weights from the vllm config?
i.e. something like:

attn_layers = get_layers_from_vllm_config(vllm_config, Attention)
for layer_name, attn_module in attn_layers.items():
    for name, param in attn_module.named_parameters():
        ...

zbennett10 · 2026-03-18T14:41:43Z

@zbennett10 Can you somehow get the weights from the vllm config? i.e. something like:
attn_layers = get_layers_from_vllm_config(vllm_config, Attention)
for layer_name, attn_module in attn_layers.items():
    for name, param in attn_module.named_parameters():
        ...

@orozery Thank you for the quick feedback. With the amount of PRs going into this repo.. I don't know how you all keep up :)

get_layers_from_vllm_config is exactly the right pattern for most KV
connector use cases. We investigated this and ran into one limitation for CacheBlend specifically.

The Attention objects stored in static_forward_context (registered in
attention/attention.py) are the attention backends — they contain the attention
implementation, KV cache metadata, and quantization config, but not qkv_proj or
o_proj. LMCache's CacheBlend needs access to those projection weights for selective
layer recomputation.

The believe the access pattern in LMCache is:

 # In LMCBaseModel.__init__
 for i in range(self.num_layers):
     layer = vllm_model.model.layers[i]
     qkv, _ = layer.self_attn.qkv_proj(hidden_states)  # needs qkv_proj
     rotary = layer.self_attn.rotary_emb               # needs rotary_emb

Since qkv_proj lives in the parent self_attn block (not in the Attention backend that
gets registered in static_forward_context), we can't satisfy this via vllm_config alone.

Two options if you'd prefer to avoid threading model through:

Keep register_model(model) as-is — it's called once at init, no overhead at inference time
Store vllm_config in LMCache's connector and call get_layers_from_vllm_config lazily
on first CacheBlend use, then walk up from the Attention object to its parent
(though parent_module traversal is not straightforward in PyTorch.. )

Happy to go with whichever approach you prefer. If there's a way to reach the parent
self_attn blocks via vllm_config that I'm not seeing, we can rework this... just let me know. Thanks :)

orozery · 2026-03-18T15:22:17Z

I'm actually in favor of passing information of the front door, instead of having connectors hack their way to get the information they need :)
The things is we keep adding new functions very specific functions to the connector API.
Ideally, we would add the model to register_kv_caches and rename it appropriately.
As you said, this will break all existing connectors which is undesirable.
At a certain point we will redesign a clean connector API that will consolidate all existing APIs more cleanly, and will be able to capture future APIs neatly without breaking changes.
I think that the short-term solution is that we add new APIs via a generic as possible abstraction.
For example, instead of introducing register_model, we can do introduce something like

@dataclass
class WorkerConnectorInitializationData:
    model: "torch.nn.Module | None" = None

@dataclass
class WorkerConnectorInitlizationResponse:
   pass

def initialize_worker_connector(
   self, initialization_data: WorkerConnectorInitializationData
) -> WorkerConnectorInitlizationResponse:
   pass

This API can be later extended both in information flowing from the connector and to the connector, without breaking existing connectors.

…ta pattern Replace the specific `register_model(model: nn.Module)` method on KVConnectorBase_V1 with a generic dataclass-based initialization API: @DataClass class WorkerConnectorInitializationData: model: torch.nn.Module | None = None @DataClass class WorkerConnectorInitializationResponse: pass def initialize_worker_connector( self, initialization_data: WorkerConnectorInitializationData, ) -> WorkerConnectorInitializationResponse This addresses reviewer feedback (orozery) on PR vllm-project#37339. The dataclass pattern is extensible without breaking existing connectors: new optional fields can be added to WorkerConnectorInitializationData in the future (e.g. vllm_config, attn_backend) without changing any connector signatures. Changes: - base.py: add dataclasses, replace register_model no-op with initialize_worker_connector returning WorkerConnectorInitializationResponse - kv_connector.py: always call initialize_worker_connector(data), removing the conditional model-is-not-None guard (each connector decides) - lmcache_connector.py: override, extract data.model, guard before forwarding to _lmcache_engine.register_model - lmcache_mp_connector.py: delegate to worker_adapter - multi_connector.py: fan out to all sub-connectors with same data object - vllm_v1_adapter.py: override, extract data.model, call VLLMModelTracker - test_register_model.py: fully updated for new API (16 tests) Signed-off-by: Zach Bennett <zach@worldflowai.com> Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

zbennett10 · 2026-03-18T16:21:08Z

I'm actually in favor of passing information of the front door, instead of having connectors hack their way to get the information they need :) The things is we keep adding new functions very specific functions to the connector API. Ideally, we would add the model to register_kv_caches and rename it appropriately. As you said, this will break all existing connectors which is undesirable. At a certain point we will redesign a clean connector API that will consolidate all existing APIs more cleanly, and will be able to capture future APIs neatly without breaking changes. I think that the short-term solution is that we add new APIs via a generic as possible abstraction. For example, instead of introducing register_model, we can do introduce something like
@dataclass
class WorkerConnectorInitializationData:
    model: "torch.nn.Module | None" = None

@dataclass
class WorkerConnectorInitlizationResponse:
   pass

def initialize_worker_connector(
   self, initialization_data: WorkerConnectorInitializationData
) -> WorkerConnectorInitlizationResponse:
   pass
This API can be later extended both in information flowing from the connector and to the connector, without breaking existing connectors.

Great feedback.. check out this new interface. I think it maps much more cleanly - let me know what you think.

zbennett10 · 2026-03-18T16:22:25Z

+    def initialize_worker_connector(
+        self,
+        initialization_data: WorkerConnectorInitializationData,
+    ) -> WorkerConnectorInitializationResponse:
+        """Register model with LMCache's VLLMModelTracker for CacheBlend.
+
+        CacheBlend's blender needs access to model weights for selective
+        layer recomputation. Called automatically by vLLM after model
+        loading.
+        """
+        model = initialization_data.model
+        if model is not None:
+            try:
+                from lmcache.v1.compute.models.utils import VLLMModelTracker
+
+                from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration.utils import (  # noqa: E501
+                    ENGINE_NAME,
+                )
+
+                VLLMModelTracker.register_model(ENGINE_NAME, model)
+                logger.info("Registered model with VLLMModelTracker")
+            except ImportError:
+                logger.debug("LMCache CacheBlend model registration not available")
+            except Exception:
+                logger.warning(
+                    "Failed to register model with VLLMModelTracker",
+                    exc_info=True,
+                )
+        return WorkerConnectorInitializationResponse()
+


@orozery Example usage

orozery · 2026-03-19T04:13:20Z

Thanks!
I take it back a bit: we can skip WorkerConnectorInitlizationResponse for now as it is empty.

@NickLucche @njhill your thoughts about this API?
I like it. This will be useful also for passing in the new cross-layers KV caches (for hybrid models).

orozery · 2026-03-19T04:28:20Z

@NickLucche @njhill your thoughts about this API? I like it. This will be useful also for passing in the new cross-layers KV caches (for hybrid models).

Taking that even further, we can add kv_caches (from register_kv_caches) to this API as well, so that connectors can get rid of register_kv_caches.
When we see we have enough adoption to the new API we can consider removing register_kv_caches, as well as register_cross_layers_kv_caches.

@zbennett10 BTW your current implementation only applies this new API to v2 model runner. I think we want also to add it to v1 (gpu_model_runner.py).

zbennett10 · 2026-03-19T13:44:57Z

@NickLucche @njhill your thoughts about this API? I like it. This will be useful also for passing in the new cross-layers KV caches (for hybrid models).

Taking that even further, we can add kv_caches (from register_kv_caches) to this API as well, so that connectors can get rid of register_kv_caches. When we see we have enough adoption to the new API we can consider removing register_kv_caches, as well as register_cross_layers_kv_caches.

@zbennett10 BTW your current implementation only applies this new API to v2 model runner. I think we want also to add it to v1 (gpu_model_runner.py).

Updated - thanks

mergify · 2026-03-19T14:38:36Z

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zbennett10 · 2026-03-19T15:35:52Z

@orozery
Quick backstory on why this matters: We're building https://github.com/WorldFlowAI/semblend — an open-source semantic KV cache reuse provider for LLM inference. This repo is meant to be a reference implementation of a semantic caching provider that works with multiple inference engines and also houses the official implementation based on our in-progress "SemBlend" research paper. When two prompts share the same document content but have different instructions (cross-instruction variants), exact-prefix caching misses completely. SemBlend finds these semantic matches and reuses the donor's KV tensors, achieving 2-12x TTFT speedup on long contexts.

We're working across the inference ecosystem to define a clean interface for this:

LMCache: SemanticLookupProvider ([Core] feat: add SemanticLookupProvider for approximate KV cache matching LMCache/LMCache#2803) + PostLoadHook ([Core] feat: add PostLoadHook for KV cache transformations after retrieval LMCache/LMCache#2804)
SGLang: SemanticPrefixProvider ([Core] feat(mem_cache): add SemanticPrefixProvider hook to RadixCache sgl-project/sglang#20806)
NVIDIA Dynamo KVBM: SemanticCacheLookupProvider (feat(kv-router): Python-first semantic KV cache provider interface ai-dynamo/dynamo#7520)

This vLLM PR is a foundational piece — CacheBlend's selective layer recomputation (which SemBlend uses to maintain output quality after KV injection) needs model weights. I think that the initialize_worker_connector pattern you suggested is exactly right for this and will generalize well as connectors need more initialization context.

We're also collaborating with the NVIDIA cuVS team on GPU-accelerated vector search for semantic matching at fleet scale - they have agreed to help us co-author the "SemBlend" paper we are working on. Happy to share more details if helpful! This is just the beginning of semantic kv-cache reuse... :)

zbennett10 · 2026-03-23T14:11:47Z

@orozery Don't mean to pester you - need anything else from me with this one? Thanks :D

orozery · 2026-03-23T16:44:03Z

@orozery Don't mean to pester you - need anything else from me with this one? Thanks :D

This LGTM (the connector API change).
I have not looked into the lmcache-side implementation, probably @ApostaC @KuntaiDu should have a look.
Still looking to see what others think about the API change.

zbennett10 · 2026-03-23T16:48:59Z

@orozery Don't mean to pester you - need anything else from me with this one? Thanks :D

This LGTM (the connector API change). I have not looked into the lmcache-side implementation, probably @ApostaC @KuntaiDu should have a look. Still looking to see what others think about the API change.

I think the LMCache PRs still need a lot of review/design. Which I expect :)

NickLucche

Thanks for your work @zbennett10 .
I think this new API can be arranged.
However I am not super comfortable adding changes to the ModelRunnerv2 side as I am not 100% sure the direction we want to go there is to copy paste all changes on both sides.

Would it be an issue to scope this PR for v1 only?

NickLucche · 2026-03-24T10:52:18Z

+            from vllm.distributed.kv_transfer.kv_connector.v1.base import (
+                WorkerConnectorInitializationData,
+            )
+
+            kv_transfer_group.initialize_worker_connector(
+                WorkerConnectorInitializationData(model=self.model)
+            )
+


could you move this into the KVConnector mixin?
Ideally I'd like to have everything that is behind a has_kv_transfer_group() check in there..but we can keep the scope narrow for this PR

torch is imported at runtime, not under TYPE_CHECKING, so the string annotation is unnecessary. Signed-off-by: Zach Bennett <zach@worldflowai.com> Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

zbennett10 · 2026-03-25T18:45:14Z

Thanks for your work @zbennett10 . I think this new API can be arranged. However I am not super comfortable adding changes to the ModelRunnerv2 side as I am not 100% sure the direction we want to go there is to copy paste all changes on both sides.

Would it be an issue to scope this PR for v1 only?

That's fair. Handled! @NickLucche

NickLucche

LGTM

orozery

@zbennett10 Let's revert the changes to model runner v2 (v1/worker/gpu) and restore the changes to v1 (gpu_model_runner.py).
The import for WorkerConnectorInitializationData should be at the top together with the other imports.

Also, we got no response from the LMCache maintainers on this.
I don't want us to make any changes to their connector without their approval.
So either reach out to them, or let's move the LMCache changes to a follow-up PR.

zbennett10 · 2026-03-30T13:56:49Z

@ApostaC @chunxiaozheng @ziruiliu — This PR adds an initialize_worker_connector() hook to KVConnectorBase_V1 that passes the loaded model to connectors after model loading. This enables CacheBlend's selective layer recomputation via VLLMModelTracker.

The LMCache connector changes are:

LMCacheConnectorV1: delegates model to engine's register_model() via hasattr guard (consistent with register_kv_caches pattern)
LMCacheConnectorV1Impl: registers model with VLLMModelTracker when CacheBlend is available, with graceful fallback
LMCacheMPConnector: delegates to worker adapter

Could you review the LMCache-specific changes? The approach uses the same guard patterns already established in the codebase.

- Revert v2 model runner changes (v1/worker/gpu/), restore hook to v1 gpu_model_runner.py as requested by reviewer - Drop WorkerConnectorInitializationResponse; return None instead - Move tests from standalone test_register_model.py into test_multi_connector.py - Ensure all imports are at top level Signed-off-by: Zach Bennett <zach@worldflowai.com> Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

Add LMCache-specific tests for initialize_worker_connector to test_multi_connector.py: delegation to engine, guard clauses, VLLMModelTracker integration, and graceful import error handling. Signed-off-by: Zach Bennett <zach@worldflowai.com> Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

…nnector LMCache initialize_worker_connector tests belong alongside other LMCacheConnectorV1 tests, not in the MultiConnector test file. Signed-off-by: Zach Bennett <zach@worldflowai.com> Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

zbennett10 requested review from ApostaC, NickLucche, WoosukKwon, njhill and orozery as code owners March 17, 2026 19:35

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zbennett10 added 3 commits March 17, 2026 19:46

style: fix E501 lint errors in register_model files

86d9f44

Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

zbennett10 force-pushed the semblend/register-model-hook branch from e1d79f1 to 86d9f44 Compare March 18, 2026 02:46

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zbennett10 force-pushed the semblend/register-model-hook branch from ecf9a82 to be4243c Compare March 19, 2026 13:59

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Etelis mentioned this pull request Mar 23, 2026

Canonical KV Cache Allocation for HMA Models #37885

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zbennett10 mentioned this pull request Mar 23, 2026

[Core] feat: add SemanticLookupProvider for approximate KV cache matching LMCache/LMCache#2803

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NickLucche reviewed Mar 24, 2026

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fix: remove unnecessary string quote from type annotation

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torch is imported at runtime, not under TYPE_CHECKING, so the string annotation is unnecessary. Signed-off-by: Zach Bennett <zach@worldflowai.com> Signed-off-by: Zachary Bennett <bennett.zachary@outlook.com>

zbennett10 force-pushed the semblend/register-model-hook branch from f624633 to 531cfa2 Compare March 25, 2026 18:44

NickLucche approved these changes Mar 30, 2026

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Merge branch 'main' into semblend/register-model-hook

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zbennett10 added 2 commits March 30, 2026 08:58

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vLLM PR Review for LMCacheConnector Changes LMCache/LMCache#2909

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orozery reviewed Mar 30, 2026

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[Feat][KVConnector] Add bind_gpu_block_pool() to KVConnectorBase_V1 #39654

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