[P/D] NIXL Integration (#17751)

Signed-off-by: ApostaC <yihua98@uchicago.edu>
Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
Signed-off-by: rshaw@neuralmagic.com <robertgshaw2@gmail.com>
Signed-off-by: Robert Shaw <rshaw@neuralmagic.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Nick Hill <nhill@redhat.com>
Signed-off-by: Brent Salisbury <bsalisbu@redhat.com>
Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
Co-authored-by: ApostaC <yihua98@uchicago.edu>
Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: mgoin <mgoin64@gmail.com>
Co-authored-by: Nick Hill <nhill@redhat.com>
Co-authored-by: Tyler Michael Smith <tysmith@redhat.com>
Co-authored-by: Brent Salisbury <bsalisbu@redhat.com>

vllm/entrypoints/openai/serving_chat.py

@@ -1086,6 +1086,7 @@ class OpenAIServingChat(OpenAIServing):
             choices=choices,
             usage=usage,
             prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),
+            kv_transfer_params=final_res.kv_transfer_params,
         )
 
         return response

vllm/entrypoints/openai/serving_completion.py

@@ -482,7 +482,7 @@ class OpenAIServingCompletion(OpenAIServing):
             model=model_name,
             choices=choices,
             usage=usage,
-        )
+            kv_transfer_params=final_res_batch[0].kv_transfer_params)
 
     def _create_completion_logprobs(
         self,

vllm/envs.py

@@ -112,6 +112,8 @@ if TYPE_CHECKING:
     VLLM_XGRAMMAR_CACHE_MB: int = 0
     VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
     VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
+    VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
+    VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5557
 
 
 def get_default_cache_root():
@@ -747,6 +749,14 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # insecure method and it is needed for some reason.
     "VLLM_ALLOW_INSECURE_SERIALIZATION":
     lambda: bool(int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))),
+
+    # IP address used for NIXL handshake between remote agents.
+    "VLLM_NIXL_SIDE_CHANNEL_HOST":
+    lambda: os.getenv("VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"),
+
+    # Port used for NIXL handshake between remote agents.
+    "VLLM_NIXL_SIDE_CHANNEL_PORT":
+    lambda: int(os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5557")),
 }
 
 # end-env-vars-definition

vllm/forward_context.py

@@ -11,10 +11,6 @@ import torch.distributed as dist
 
 import vllm.envs as envs
 from vllm.config import VllmConfig
-from vllm.distributed.kv_transfer import (get_kv_transfer_group,
-                                          has_kv_transfer_group,
-                                          is_v1_kv_transfer_group)
-from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
 from vllm.logger import init_logger
 
 if TYPE_CHECKING:
@@ -106,16 +102,6 @@ def set_forward_context(attn_metadata: Any,
         attn_metadata=attn_metadata,
         dp_metadata=dp_metadata)
 
-    # KVConnector: trigger (possibly async) load before forward.
-    # Each attn layer will block until the reading is complete.
-    trigger_kv_transfer = (attn_metadata is not None
-                           and has_kv_transfer_group()
-                           and is_v1_kv_transfer_group())
-    if trigger_kv_transfer:
-        kv_connector = get_kv_transfer_group()
-        assert isinstance(kv_connector, KVConnectorBase_V1)
-        kv_connector.start_load_kv(_forward_context)
-
     try:
         yield
     finally:
@@ -152,11 +138,4 @@ def set_forward_context(attn_metadata: Any,
                                  "(batchsize, count, median_time(ms)): %s"),
                                 forward_stats)
 
-        # KVConnector: each attn layer triggers (possibly async) save.
-        # Ensure all those operations complete before forward() is done.
-        if trigger_kv_transfer:
-            kv_connector = get_kv_transfer_group()
-            assert isinstance(kv_connector, KVConnectorBase_V1)
-            kv_connector.wait_for_save()
-
         _forward_context = prev_context

vllm/outputs.py

@@ -4,7 +4,7 @@ import time
 from collections.abc import MutableSequence
 from collections.abc import Sequence as GenericSequence
 from dataclasses import dataclass
-from typing import Generic, Optional, Union
+from typing import Any, Generic, Optional, Union
 
 import torch
 from typing_extensions import TypeVar, deprecated
@@ -103,6 +103,7 @@ class RequestOutput:
         encoder_prompt_token_ids: The token IDs of the encoder prompt.
                                   None if decoder-only.
         num_cached_tokens: The number of tokens with prefix cache hit.
+        kv_transfer_params: The params for remote K/V transfer.
     """
 
     def __init__(
@@ -120,6 +121,7 @@ class RequestOutput:
         num_cached_tokens: Optional[int] = None,
         *,
         multi_modal_placeholders: Optional[MultiModalPlaceholderDict] = None,
+        kv_transfer_params: Optional[dict[str, Any]] = None,
     ) -> None:
         self.request_id = request_id
         self.prompt = prompt
@@ -133,11 +135,13 @@ class RequestOutput:
         self.encoder_prompt = encoder_prompt
         self.encoder_prompt_token_ids = encoder_prompt_token_ids
         self.num_cached_tokens = num_cached_tokens
+        self.kv_transfer_params = kv_transfer_params
 
     def add(self, next_output: "RequestOutput", aggregate: bool) -> None:
         """Merge subsequent RequestOutput into this one"""
 
         self.finished |= next_output.finished
+        self.kv_transfer_params = next_output.kv_transfer_params
 
         for next_completion in next_output.outputs:
             for i, completion in enumerate(self.outputs):

vllm/v1/core/kv_cache_manager.py

@@ -36,6 +36,12 @@ class KVCacheBlocks:
         """Converts the KVCacheBlocks instance to a list of block IDs."""
         return [block.block_id for block in self.blocks]
 
+    def get_unhashed_block_ids(self) -> list[int]:
+        """Get block_ids of unhashed blocks from KVCacheBlocks instance."""
+        return [
+            block.block_id for block in self.blocks if block.block_hash is None
+        ]
+
 
 class KVCacheManager:
 
@@ -116,6 +122,12 @@ class KVCacheManager:
                 - The number of computed tokens.
         """
 
+        # Request already has blocks from async load via KVConnector.
+        num_existing_blocks = len(
+            self.single_type_manager.req_to_blocks[request.request_id])
+        if num_existing_blocks > 0:
+            return KVCacheBlocks.create_empty(), request.num_computed_tokens
+
         # Prefix caching is disabled or
         # When the request requires prompt logprobs, we skip prefix caching.
         if (not self.enable_caching
@@ -173,6 +185,7 @@ class KVCacheManager:
         num_new_tokens: int,
         new_computed_blocks: Optional[KVCacheBlocks] = None,
         num_lookahead_tokens: int = 0,
+        delay_cache_blocks: bool = False,
     ) -> Optional[KVCacheBlocks]:
         """Add slots for a request with new tokens to append.
 
@@ -186,6 +199,9 @@ class KVCacheManager:
             num_lookahead_tokens: The number of speculative tokens to allocate.
                 This is used by spec decode proposers with kv-cache such 
                 as eagle.
+            delay_cache_blocks: Whether to skip caching the blocks. This is
+                used by P/D when allocating blocks used in a KV transfer
+                which will complete in a future step.
 
         Blocks layout:
         ```
@@ -255,7 +271,9 @@ class KVCacheManager:
         new_blocks = self.single_type_manager.allocate_new_blocks(
             request.request_id, num_tokens_need_slot)
 
-        if not self.enable_caching:
+        # P/D: delay caching blocks if we have to recv from
+        # remote. Update state for locally cached blocks.
+        if not self.enable_caching or delay_cache_blocks:
             return KVCacheBlocks(new_blocks)
 
         # Speculated tokens might be rejected in the future, so we does
@@ -350,3 +368,16 @@ class KVCacheManager:
             A list of KV cache events.
         """
         return self.block_pool.take_events()
+
+    def get_block_ids(self, request_id: str) -> list[int]:
+        """Get the block ids of a request."""
+        assert request_id in self.single_type_manager.req_to_blocks
+        return [
+            block.block_id
+            for block in self.single_type_manager.req_to_blocks[request_id]
+        ]
+
+    def get_num_blocks(self, request_id: str):
+        """Get the number of blocks."""
+        assert request_id in self.single_type_manager.req_to_blocks
+        return len(self.single_type_manager.req_to_blocks[request_id])

vllm/v1/core/sched/interface.py

@@ -4,6 +4,7 @@ from collections.abc import Iterable
 from typing import TYPE_CHECKING, Optional, Union
 
 if TYPE_CHECKING:
+    from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
     from vllm.v1.core.sched.output import SchedulerOutput
     from vllm.v1.engine import EngineCoreOutputs
     from vllm.v1.metrics.stats import SchedulerStats
@@ -137,3 +138,6 @@ class SchedulerInterface(ABC):
     def shutdown(self) -> None:
         """Shutdown the scheduler."""
         raise NotImplementedError
+
+    def get_kv_connector(self) -> Optional["KVConnectorBase_V1"]:
+        return None

vllm/v1/core/sched/scheduler.py

@@ -5,13 +5,15 @@ from __future__ import annotations
 import time
 from collections import defaultdict, deque
 from collections.abc import Iterable
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 from vllm.config import VllmConfig
 from vllm.distributed.kv_events import EventPublisherFactory, KVEventBatch
 from vllm.distributed.kv_transfer.kv_connector.factory import (
     KVConnectorFactory)
-from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorRole
+from vllm.distributed.kv_transfer.kv_connector.v1 import (KVConnectorBase_V1,
+                                                          KVConnectorRole,
+                                                          KVTransferParams)
 from vllm.logger import init_logger
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.v1.core.encoder_cache_manager import (EncoderCacheManager,
@@ -96,6 +98,9 @@ class Scheduler(SchedulerInterface):
         # This is flushed at the end of each scheduling step.
         self.finished_req_ids: set[str] = set()
 
+        # P/D: requests in process of recving KV transfers
+        self.finished_recving_kv_req_ids: set[str] = set()
+
         # OPTIMIZATION: Cache the CachedRequestData objects to avoid creating
         # them at each scheduling step.
         # Request id -> deque of CachedRequestData
@@ -307,6 +312,16 @@ class Scheduler(SchedulerInterface):
 
                 request = self.waiting[0]
 
+                # P/D: skip request if still waiting for remote kvs.
+                if request.status == RequestStatus.WAITING_FOR_REMOTE_KVS:
+                    is_ready = self._update_waiting_for_remote_kv(request)
+                    if is_ready:
+                        request.status = RequestStatus.WAITING
+                    else:
+                        self.waiting.popleft()
+                        skipped_waiting_requests.appendleft(request)
+                        continue
+
                 # Skip request if the structured output request is still waiting
                 # for FSM compilation.
                 if request.status == RequestStatus.WAITING_FOR_FSM:
@@ -330,49 +345,55 @@ class Scheduler(SchedulerInterface):
                     continue
 
                 # Get already-cached tokens.
-                computed_blocks, num_computed_tokens = \
+                new_computed_blocks, num_computed_tokens = \
                     self.kv_cache_manager.get_computed_blocks(
                         request)
 
                 # Get externally-cached tokens if using a KVConnector.
-                num_external_tokens = (
-                    0 if self.connector is None else
+                num_external_tokens, load_kv_async = (
+                    (0, False) if self.connector is None else
                     self.connector.get_num_new_matched_tokens(
                         request, num_computed_tokens))
 
                 # Total computed tokens (local + external).
                 num_computed_tokens += num_external_tokens
 
+                encoder_inputs_to_schedule = None
+                new_encoder_budget = encoder_budget
+
+                # P/D: loading remote KV, do not allocate for new work.
+                if load_kv_async:
+                    num_new_tokens = 0
                 # Number of tokens to be scheduled.
-                # We use `request.num_tokens` instead of
-                # `request.num_prompt_tokens` to consider the resumed requests,
-                # which have output tokens.
-                num_new_tokens = request.num_tokens - num_computed_tokens
-                if (0 < self.scheduler_config.long_prefill_token_threshold <
-                        num_new_tokens):
-                    num_new_tokens = (
-                        self.scheduler_config.long_prefill_token_threshold)
-                num_new_tokens = min(num_new_tokens, token_budget)
-                assert num_new_tokens > 0
-
-                # Schedule encoder inputs.
-                if request.has_encoder_inputs:
-                    (encoder_inputs_to_schedule, num_new_tokens,
-                     new_encoder_budget) = self._try_schedule_encoder_inputs(
-                         request, num_computed_tokens, num_new_tokens,
-                         encoder_budget)
-                    if num_new_tokens == 0:
-                        # The request cannot be scheduled.
-                        break
                 else:
-                    encoder_inputs_to_schedule = None
-                    new_encoder_budget = encoder_budget
+                    # We use `request.num_tokens` instead of
+                    # `request.num_prompt_tokens` to consider the resumed
+                    # requests, which have output tokens.
+                    num_new_tokens = request.num_tokens - num_computed_tokens
+                    if (0 < self.scheduler_config.long_prefill_token_threshold
+                            < num_new_tokens):
+                        num_new_tokens = (
+                            self.scheduler_config.long_prefill_token_threshold)
+                    num_new_tokens = min(num_new_tokens, token_budget)
+                    assert num_new_tokens > 0
+
+                    # Schedule encoder inputs.
+                    if request.has_encoder_inputs:
+                        (encoder_inputs_to_schedule, num_new_tokens,
+                         new_encoder_budget
+                         ) = self._try_schedule_encoder_inputs(
+                             request, num_computed_tokens, num_new_tokens,
+                             encoder_budget)
+                        if num_new_tokens == 0:
+                            # The request cannot be scheduled.
+                            break
 
                 new_blocks = self.kv_cache_manager.allocate_slots(
                     request,
                     num_new_tokens + num_external_tokens,
-                    computed_blocks,
+                    new_computed_blocks,
                     num_lookahead_tokens=self.num_lookahead_tokens,
+                    delay_cache_blocks=load_kv_async,
                 )
                 if new_blocks is None:
                     # The request cannot be scheduled.
@@ -384,10 +405,18 @@ class Scheduler(SchedulerInterface):
                 if self.connector is not None:
                     self.connector.update_state_after_alloc(
                         request,
+                        new_computed_blocks + new_blocks,
                         num_external_tokens,
                     )
 
                 self.waiting.popleft()
+                if load_kv_async:
+                    # If loading async, allocate memory and put request
+                    # into the WAITING_FOR_REMOTE_KV state.
+                    skipped_waiting_requests.appendleft(request)
+                    request.status = RequestStatus.WAITING_FOR_REMOTE_KVS
+                    continue
+
                 if request.use_structured_output:
                     structured_output_request_ids[
                         request.request_id] = req_index
@@ -407,7 +436,7 @@ class Scheduler(SchedulerInterface):
                 if self.lora_config and request.lora_request:
                     scheduled_loras.add(request.lora_request.lora_int_id)
                 req_to_new_block_ids[request.request_id] = (
-                    computed_blocks + new_blocks).get_block_ids()
+                    self.kv_cache_manager.get_block_ids(request.request_id))
                 num_scheduled_tokens[request.request_id] = num_new_tokens
                 token_budget -= num_new_tokens
                 request.status = RequestStatus.RUNNING
@@ -698,6 +727,7 @@ class Scheduler(SchedulerInterface):
             stopped = False
             new_logprobs = None
             new_token_ids = generated_token_ids
+            kv_transfer_params = None
 
             # Append generated tokens and check for stop. Note that if
             # a request is still being prefilled, we expect the model runner
@@ -709,7 +739,7 @@ class Scheduler(SchedulerInterface):
                 # This must be called before we make the EngineCoreOutput.
                 stopped = check_stop(request, self.max_model_len)
                 if stopped:
-                    self._free_request(request)
+                    kv_transfer_params = self._free_request(request)
                     del new_token_ids[num_new:]  # Trim new tokens if needed.
                     break
 
@@ -739,7 +769,8 @@ class Scheduler(SchedulerInterface):
 
             # Get prompt logprobs for this request.
             prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
-            if new_token_ids:
+            if new_token_ids or kv_transfer_params:
+
                 # Add EngineCoreOutput for this Request.
                 outputs.append(
                     EngineCoreOutput(
@@ -749,7 +780,10 @@ class Scheduler(SchedulerInterface):
                         new_logprobs=new_logprobs,
                         new_prompt_logprobs_tensors=prompt_logprobs_tensors,
                         stop_reason=request.stop_reason,
-                        events=request.take_events()))
+                        events=request.take_events(),
+                        kv_transfer_params=kv_transfer_params,
+                    ))
+
             else:
                 # Invariant: EngineCore returns no partial prefill outputs.
                 assert not prompt_logprobs_tensors
@@ -757,6 +791,9 @@ class Scheduler(SchedulerInterface):
             if not stopped:
                 new_running.append(request)
 
+        # P/D: update state for finished KV Transfers.
+        self._update_from_kv_xfer_finished(model_runner_output)
+
         # Return the cached request data to the queue so they can be reused.
         for req_data in scheduler_output.scheduled_cached_reqs:
             # NOTE(rob): since we free stopped reqs above, adding stopped reqs
@@ -811,15 +848,27 @@ class Scheduler(SchedulerInterface):
             request.status = finished_status
             self._free_request(request)
 
-    def _free_request(self, request: Request) -> None:
+    def _free_request(self, request: Request) -> Optional[dict[str, Any]]:
+
         assert request.is_finished()
-        self.kv_cache_manager.free(request)
-        self.kv_cache_manager.free_block_hashes(request)
+
+        delay_free_blocks, kv_xfer_params = self._connector_finished(request)
         self.encoder_cache_manager.free(request)
         self._cached_reqs_data.pop(request.request_id, None)
-        del self.requests[request.request_id]
         self.finished_req_ids.add(request.request_id)
 
+        if not delay_free_blocks:
+            self._free_blocks(request)
+
+        return kv_xfer_params
+
+    def _free_blocks(self, request: Request):
+        assert request.is_finished()
+        assert request.request_id not in self._cached_reqs_data
+        self.kv_cache_manager.free(request)
+        self.kv_cache_manager.free_block_hashes(request)
+        del self.requests[request.request_id]
+
     def get_num_unfinished_requests(self) -> int:
         return len(self.waiting) + len(self.running)
 
@@ -863,3 +912,70 @@ class Scheduler(SchedulerInterface):
     def shutdown(self) -> None:
         if self.kv_event_publisher:
             self.kv_event_publisher.shutdown()
+
+    ########################################################################
+    # P/D Related Methods
+    ########################################################################
+
+    def get_kv_connector(self) -> Optional[KVConnectorBase_V1]:
+        return self.connector
+
+    def _connector_finished(
+            self, request: Request) -> tuple[bool, Optional[KVTransferParams]]:
+        """Invoke the KV connector request_finished() method if applicable."""
+        if self.connector is None:
+            return False, None
+        block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
+        return self.connector.request_finished(request, block_ids)
+
+    def _update_waiting_for_remote_kv(self, request: Request) -> bool:
+        """
+        P/D: check if the request_id is finished_recving.
+
+        The finished_recving_kv_req_ids list is populated
+        on the previous steps()'s update_from_output based
+        on the worker side connector.
+
+        When the kv transfer is ready, we cache the blocks
+        and the request state will be moved back to WAITING from
+        WAITING_FOR_REMOTE_KV.
+        """
+        if request.request_id not in self.finished_recving_kv_req_ids:
+            return False
+
+        # Now that the blocks are ready, actually cache them.
+        block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
+        num_computed_tokens = len(block_ids) * self.block_size
+        if num_computed_tokens == request.num_tokens:
+            num_computed_tokens -= 1
+        self.kv_cache_manager.single_type_manager.cache_blocks(
+            request,
+            self.kv_cache_manager.req_to_block_hashes[request.request_id],
+            num_computed_tokens,
+        )
+
+        # Update the request state for scheduling.
+        request.num_computed_tokens = num_computed_tokens
+
+        # Return that we are ready.
+        self.finished_recving_kv_req_ids.remove(request.request_id)
+        return True
+
+    def _update_from_kv_xfer_finished(self,
+                                      model_runner_output: ModelRunnerOutput):
+        """
+        P/D: update the scheduler state based on the output.
+
+        The Worker side connectors add finished_recving and
+        finished_sending reqs to the output.
+        * if finished_sending: free the blocks
+        # if finished_recving: add to state so we can
+            scheduler the request during the next step.
+        """
+        # P/D: update recv and send status from last step.
+        for req_id in (model_runner_output.finished_recving or ()):
+            logger.debug("Finished recving KV transfer for request %s", req_id)
+            self.finished_recving_kv_req_ids.add(req_id)
+        for req_id in (model_runner_output.finished_sending or ()):
+            logger.debug("Finished sending KV transfer for request %s", req_id)
+            self._free_blocks(self.requests[req_id])

vllm/v1/engine/__init__.py

@@ -105,6 +105,7 @@ class EngineCoreOutput(
     finish_reason: Optional[FinishReason] = None
     stop_reason: Union[int, str, None] = None
     events: Optional[list[EngineCoreEvent]] = None
+    kv_transfer_params: Optional[dict[str, Any]] = None
 
     @property
     def finished(self) -> bool:

vllm/v1/engine/core.py

@@ -182,6 +182,15 @@ class EngineCore:
             # Start grammar compilation asynchronously
             self.structured_output_manager.grammar_init(req)
 
+        if req.raw_kv_transfer_params is not None:
+            if (kv_connector := self.scheduler.get_kv_connector()):
+                # Parse raw KV transfer params via connector.
+                kv_connector.set_kv_transfer_params(req)
+            else:
+                logger.warning(
+                    "Got KVTransferParams, but no KVConnector found. "
+                    "Disabling KVTransfer for this request.")
+
         self.scheduler.add_request(req)
 
     def abort_requests(self, request_ids: list[str]):

vllm/v1/engine/output_processor.py

@@ -3,7 +3,7 @@
 import asyncio
 from collections.abc import Iterable
 from dataclasses import dataclass
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import RequestOutputKind
@@ -146,6 +146,7 @@ class RequestState:
         new_token_ids: list[int],
         finish_reason: Optional[FinishReason],
         stop_reason: Union[int, str, None],
+        kv_transfer_params: Optional[dict[str, Any]] = None,
     ) -> Optional[RequestOutput]:
 
         finished = finish_reason is not None
@@ -167,13 +168,15 @@ class RequestState:
             if not outputs:
                 return None
 
-        return self._new_request_output(request_id, outputs, finished)
+        return self._new_request_output(request_id, outputs, finished,
+                                        kv_transfer_params)
 
     def _new_request_output(
         self,
         request_id: str,
         outputs: list[CompletionOutput],
         finished: bool,
+        kv_transfer_params: Optional[dict[str, Any]] = None,
     ) -> RequestOutput:
 
         if self.output_kind == RequestOutputKind.DELTA:
@@ -189,6 +192,7 @@ class RequestState:
             prompt_logprobs=prompt_logprobs,
             outputs=outputs,
             finished=finished,
+            kv_transfer_params=kv_transfer_params,
         )
 
     def _new_completion_output(
@@ -335,6 +339,7 @@ class OutputProcessor:
             new_token_ids = engine_core_output.new_token_ids
             finish_reason = engine_core_output.finish_reason
             stop_reason = engine_core_output.stop_reason
+            kv_transfer_params = engine_core_output.kv_transfer_params
 
             req_state.is_prefilling = False
 
@@ -350,7 +355,8 @@ class OutputProcessor:
 
             # 4) Create and handle RequestOutput objects.
             if request_output := req_state.make_request_output(
-                    new_token_ids, finish_reason, stop_reason):
+                    new_token_ids, finish_reason, stop_reason,
+                    kv_transfer_params):
                 if req_state.queue is not None:
                     # AsyncLLM: put into queue for handling by generate().
                     req_state.queue.put(request_output)

vllm/v1/outputs.py

@@ -100,12 +100,16 @@ class ModelRunnerOutput:
     # [prompt_len]
     prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]]
 
-
-EMPTY_MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
-    req_ids=[],
-    req_id_to_index={},
-    sampled_token_ids=[],
-    spec_token_ids=None,
-    logprobs=None,
-    prompt_logprobs_dict={},
-)
+    # [req_ids]
+    finished_sending: Optional[set[str]] = None
+    finished_recving: Optional[set[str]] = None
+
+
+EMPTY_MODEL_RUNNER_OUTPUT = ModelRunnerOutput(req_ids=[],
+                                              req_id_to_index={},
+                                              sampled_token_ids=[],
+                                              spec_token_ids=None,
+                                              logprobs=None,
+                                              prompt_logprobs_dict={},
+                                              finished_sending=None,
+                                              finished_recving=None)

vllm/v1/request.py

 # SPDX-License-Identifier: Apache-2.0
 
 import enum
-from typing import TYPE_CHECKING, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional, Union
 
+from vllm.distributed.kv_transfer.kv_connector.v1 import KVTransferParams
 from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
 from vllm.sampling_params import SamplingParams
 from vllm.utils import is_list_of
@@ -61,6 +62,15 @@ class Request:
         self.num_encoder_inputs = len(self.mm_inputs)
         self.has_encoder_inputs = self.num_encoder_inputs > 0
 
+        # P/D: KV transfer parameters (raw and parsed).
+        raw_params = (None if sampling_params.extra_args is None
+                      else sampling_params.extra_args.get(
+                          "kv_transfer_params", None))
+        self.raw_kv_transfer_params: Optional[dict[str, Any]] = raw_params
+        # Each connector parses the raw dictionary and sets this
+        # attr the first time that the request is processed.
+        self.kv_transfer_params: Optional[KVTransferParams] = None
+
         # Sanity check
         assert len(self.mm_inputs) == len(self.mm_positions)
         if self.mm_hashes:
@@ -150,6 +160,7 @@ class RequestStatus(enum.IntEnum):
     """Status of a request."""
     WAITING = enum.auto()
     WAITING_FOR_FSM = enum.auto()
+    WAITING_FOR_REMOTE_KVS = enum.auto()
     RUNNING = enum.auto()
     PREEMPTED = enum.auto()
     # Note: anything after PREEMPTED will be considered

vllm/v1/worker/gpu_model_runner.py

 # SPDX-License-Identifier: Apache-2.0
 
+import copy
 import gc
 import time
 import weakref
@@ -17,8 +18,9 @@ from vllm.config import (CompilationLevel, VllmConfig,
                          get_layers_from_vllm_config)
 from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                           has_kv_transfer_group)
+from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
 from vllm.distributed.parallel_state import get_pp_group, graph_capture
-from vllm.forward_context import set_forward_context
+from vllm.forward_context import get_forward_context, set_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.model_loader import get_model
@@ -1065,15 +1067,14 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         scheduler_output: "SchedulerOutput",
         intermediate_tensors: Optional[IntermediateTensors] = None,
     ) -> Union[ModelRunnerOutput, IntermediateTensors]:
-        # Update KVConnector with the KVConnector metadata forward().
-        if has_kv_transfer_group():
-            get_kv_transfer_group().bind_connector_metadata(
-                scheduler_output.kv_connector_metadata)
 
         self._update_states(scheduler_output)
         if not scheduler_output.total_num_scheduled_tokens:
-            # Return empty ModelRunnerOutput if there's no work to do.
-            return EMPTY_MODEL_RUNNER_OUTPUT
+            if not has_kv_transfer_group():
+                # Return empty ModelRunnerOutput if there's no work to do.
+                return EMPTY_MODEL_RUNNER_OUTPUT
+
+            return self.kv_connector_no_forward(scheduler_output)
 
         # Prepare the decoder inputs.
         attn_metadata, logits_indices, spec_decode_metadata = (
@@ -1150,17 +1151,23 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         with set_forward_context(attn_metadata,
                                  self.vllm_config,
                                  num_tokens=num_input_tokens):
-            output = self.model(
+            self.maybe_setup_kv_connector(scheduler_output)
+
+            model_output = self.model(
                 input_ids=input_ids,
                 positions=positions,
                 intermediate_tensors=intermediate_tensors,
                 inputs_embeds=inputs_embeds,
             )
 
+            self.maybe_wait_for_kv_save()
+            finished_sending, finished_recving = (
+                self.get_finished_kv_transfers(scheduler_output))
+
         if self.use_aux_hidden_state_outputs:
-            hidden_states, aux_hidden_states = output
+            hidden_states, aux_hidden_states = model_output
         else:
-            hidden_states = output
+            hidden_states = model_output
 
         if not get_pp_group().is_last_rank:
             # For mid-pipeline stages, return the hidden states.
@@ -1341,8 +1348,56 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             spec_token_ids=spec_token_ids,
             logprobs=logprobs_lists,
             prompt_logprobs_dict=prompt_logprobs_dict,
+            finished_sending=finished_sending,
+            finished_recving=finished_recving,
         )
 
+    def kv_connector_no_forward(
+            self, scheduler_output: "SchedulerOutput") -> ModelRunnerOutput:
+        # KV send/recv even if no work to do.
+        with set_forward_context(None, self.vllm_config):
+            self.maybe_setup_kv_connector(scheduler_output)
+            finished_sending, finished_recving = (
+                self.get_finished_kv_transfers(scheduler_output))
+
+        if not finished_sending and not finished_recving:
+            return EMPTY_MODEL_RUNNER_OUTPUT
+
+        output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
+        output.finished_sending = finished_sending
+        output.finished_recving = finished_recving
+        return output
+
+    @staticmethod
+    def maybe_setup_kv_connector(scheduler_output: "SchedulerOutput"):
+        # Update KVConnector with the KVConnector metadata forward().
+        if has_kv_transfer_group():
+            kv_connector = get_kv_transfer_group()
+            assert isinstance(kv_connector, KVConnectorBase_V1)
+            assert scheduler_output.kv_connector_metadata is not None
+            kv_connector.bind_connector_metadata(
+                scheduler_output.kv_connector_metadata)
+
+            # Background KV cache transfers happen here.
+            # These transfers are designed to be async and the requests
+            # involved may be disjoint from the running requests.
+            # Do this here to save a collective_rpc.
+            kv_connector.start_load_kv(get_forward_context())
+
+    @staticmethod
+    def maybe_wait_for_kv_save() -> None:
+        if has_kv_transfer_group():
+            get_kv_transfer_group().wait_for_save()
+
+    @staticmethod
+    def get_finished_kv_transfers(
+        scheduler_output: "SchedulerOutput",
+    ) -> tuple[Optional[set[str]], Optional[set[str]]]:
+        if has_kv_transfer_group():
+            return get_kv_transfer_group().get_finished(
+                scheduler_output.finished_req_ids)
+        return None, None
+
     def generate_draft_token_ids(
         self,
         sampled_token_ids: list[list[int]],
@@ -1813,6 +1868,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             self.vllm_config.compilation_config.static_forward_context,
             self.kv_caches)
 
+        if has_kv_transfer_group():
+            get_kv_transfer_group().register_kv_caches(kv_caches)
+
         self.attn_metadata_builder = self.attn_backend.get_builder_cls()(
             weakref.proxy(self),
             kv_cache_config.kv_cache_groups[0].kv_cache_spec,