[BugFix] Fix PP performance and PP kv connector output regression

Signed-off-by: Nick Hill <nhill@redhat.com>

Author: njhill

vllm/v1/engine/core.py

@@ -180,11 +180,13 @@ def __init__(
             logger.info("Batch queue is enabled with size %d", self.batch_queue_size)
             self.batch_queue = deque(maxlen=self.batch_queue_size)
 
+        self.ec_producer = (
+            vllm_config.ec_transfer_config is not None
+            and vllm_config.ec_transfer_config.is_ec_producer
+        )
+
         self.request_block_hasher: Callable[[Request], list[BlockHash]] | None = None
-        if (
-            self.vllm_config.cache_config.enable_prefix_caching
-            or kv_connector is not None
-        ):
+        if vllm_config.cache_config.enable_prefix_caching or kv_connector is not None:
             caching_hash_fn = get_hash_fn_by_name(
                 vllm_config.cache_config.prefix_caching_hash_algo
             )
@@ -244,7 +246,7 @@ def _initialize_kv_caches(
 
         elapsed = time.time() - start
         logger.info_once(
-            ("init engine (profile, create kv cache, warmup model) took %.2f seconds"),
+            "init engine (profile, create kv cache, warmup model) took %.2f seconds",
             elapsed,
             scope="local",
         )
@@ -310,6 +312,16 @@ def log_error_detail(self, scheduler_output: SchedulerOutput):
             )
             raise err
 
+    def _log_err_callback(self, scheduler_output: SchedulerOutput):
+        """Log error details of a future that's not expected to return a result."""
+
+        def callback(f, sched_output=scheduler_output):
+            with self.log_error_detail(sched_output):
+                result = f.result()
+                assert result is None
+
+        return callback
+
     def step(self) -> tuple[dict[int, EngineCoreOutputs], bool]:
         """Schedule, execute, and make output.
 
@@ -373,31 +385,30 @@ def step_with_batch_queue(
             exec_future = self.model_executor.execute_model(
                 scheduler_output, non_block=True
             )
-            model_executed = scheduler_output.total_num_scheduled_tokens > 0
-
-            if scheduler_output.pending_structured_output_tokens:
-                # We need to defer sampling until we have processed the model output
-                # from the prior step.
-                deferred_scheduler_output = scheduler_output
-                # Block-wait for execute to return (continues running async on the GPU).
-                with self.log_error_detail(scheduler_output):
-                    exec_result = exec_future.result()
-                    assert exec_result is None
+            if not self.ec_producer:
+                model_executed = scheduler_output.total_num_scheduled_tokens > 0
+
+            if not model_executed:
+                # No sampling required (no requests scheduled).
+                future = cast(Future[ModelRunnerOutput], exec_future)
             else:
-                # We aren't waiting for any tokens, get any grammar output immediately.
-                grammar_output = self.scheduler.get_grammar_bitmask(scheduler_output)
-                # Block-wait for execute to return (continues running async on the GPU).
-                with self.log_error_detail(scheduler_output):
-                    exec_result = exec_future.result()
-
-                if exec_result is None:
-                    # Call sample tokens.
+                exec_future.add_done_callback(self._log_err_callback(scheduler_output))
+
+                if not scheduler_output.pending_structured_output_tokens:
+                    # We aren't waiting for any tokens, get any grammar output
+                    # and sample immediately.
+                    grammar_output = self.scheduler.get_grammar_bitmask(
+                        scheduler_output
+                    )
                     future = self.model_executor.sample_tokens(
                         grammar_output, non_block=True
                     )
                 else:
-                    # No sampling required (e.g. all requests finished).
-                    future = cast(Future[ModelRunnerOutput], exec_future)
+                    # We need to defer sampling until we have processed the model output
+                    # from the prior step.
+                    deferred_scheduler_output = scheduler_output
+
+            if not deferred_scheduler_output:
                 # Add this step's future to the queue.
                 batch_queue.appendleft((future, scheduler_output))
                 if (

vllm/v1/worker/gpu_model_runner.py

@@ -7,7 +7,7 @@
 from collections import defaultdict
 from collections.abc import Iterator
 from contextlib import contextmanager
-from copy import deepcopy
+from copy import copy, deepcopy
 from functools import reduce
 from itertools import product
 from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
@@ -240,7 +240,6 @@ class ExecuteModelState(NamedTuple):
     hidden_states: torch.Tensor
     sample_hidden_states: torch.Tensor
     aux_hidden_states: list[torch.Tensor] | None
-    kv_connector_output: KVConnectorOutput | None
     ec_connector_output: ECConnectorOutput | None
 
 
@@ -549,6 +548,7 @@ def __init__(
 
         # Ephemeral state transferred between execute_model() and sample_tokens().
         self.execute_model_state: ExecuteModelState | None = None
+        self.kv_connector_output: KVConnectorOutput | None = None
 
     def reset_mm_cache(self) -> None:
         if self.mm_budget:
@@ -2674,6 +2674,7 @@ def execute_model(
                     # Return the intermediate tensors.
                     assert isinstance(hidden_states, IntermediateTensors)
                     hidden_states.kv_connector_output = kv_connector_output
+                    self.kv_connector_output = kv_connector_output
                     return hidden_states
 
                 if self.is_pooling_model:
@@ -2724,18 +2725,31 @@ def execute_model(
             hidden_states,
             sample_hidden_states,
             aux_hidden_states,
-            kv_connector_output,
             ec_connector_output,
         )
+        self.kv_connector_output = kv_connector_output
         return None
 
     @torch.inference_mode
     def sample_tokens(
         self, grammar_output: "GrammarOutput | None"
     ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
+        kv_connector_output = self.kv_connector_output
+        self.kv_connector_output = None
+
         if self.execute_model_state is None:
             # Nothing to do (PP non-final rank case), output isn't used.
-            return None  # noqa
+            if not kv_connector_output:
+                return None  # noqa
+
+            # In case of PP with kv transfer, we need to pass through the
+            # kv_connector_output
+            if kv_connector_output.is_empty():
+                return EMPTY_MODEL_RUNNER_OUTPUT
+
+            output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
+            output.kv_connector_output = kv_connector_output
+            return output
 
         # Unpack ephemeral state.
         (
@@ -2746,7 +2760,6 @@ def sample_tokens(
             hidden_states,
             sample_hidden_states,
             aux_hidden_states,
-            kv_connector_output,
             ec_connector_output,
         ) = self.execute_model_state
         # Clear ephemeral state.

vllm/v1/worker/gpu_worker.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """A GPU worker class."""
 
-import copy
 import gc
 import os
 from contextlib import AbstractContextManager, nullcontext
@@ -45,7 +44,6 @@
 from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
 from vllm.v1.outputs import (
-    EMPTY_MODEL_RUNNER_OUTPUT,
     AsyncModelRunnerOutput,
     DraftTokenIds,
     ModelRunnerOutput,
@@ -573,18 +571,7 @@ def execute_model(
             all_gather_tensors=all_gather_tensors,
         )
 
-        kv_connector_output = output.kv_connector_output
-        if not kv_connector_output:
-            return None
-
-        # In case of PP with kv transfer, we need to pass through the
-        # kv_connector_output
-        if kv_connector_output.is_empty():
-            return EMPTY_MODEL_RUNNER_OUTPUT
-
-        output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
-        output.kv_connector_output = kv_connector_output
-        return output
+        return None
 
     def take_draft_token_ids(self) -> DraftTokenIds | None:
         return self.model_runner.take_draft_token_ids()