[V1][PP] Support PP for MultiprocExecutor (#14219)

Signed-off-by: jiang1.li <jiang1.li@intel.com>
Signed-off-by: jiang.li <jiang1.li@intel.com>

tests/distributed/test_pipeline_parallel.py

@@ -100,9 +100,8 @@ class PPTestSettings:
                               eager_mode=True,
                               chunked_prefill=False),
             ],
-            # only ray is supported for V1
+            distributed_backends=["mp", "mp", "ray", "ray"],
-            distributed_backends=["mp", "ray", "ray"],
+            vllm_major_versions=["0", "1", "0", "1"],
-            vllm_major_versions=["0", "0", "1"],
             task=task,
             test_options=PPTestOptions(multi_node_only=multi_node_only,
                                        load_format=load_format),
@@ -350,6 +349,11 @@ def _compare_tp(
         # Temporary. Currently when zeromq + SPMD is used, it does not properly
         # terminate because of a Ray Compiled Graph issue.
         common_args.append("--disable-frontend-multiprocessing")
+    elif distributed_backend == "mp":
+        # Both V0/V1 of multiprocessing executor support PP
+        pp_env = {
+            "VLLM_USE_V1": vllm_major_version,
+        }
     else:
         pp_env = None
 

vllm/engine/arg_utils.py

@@ -1338,11 +1338,10 @@ class EngineArgs:
                 and _warn_or_fallback("Engine in background thread")):
             return False
 
-        # PP is supported on V1 with Ray distributed executor,
-        # but off for MP distributed executor for now.
         if (self.pipeline_parallel_size > 1
-                and self.distributed_executor_backend != "ray"):
+                and self.distributed_executor_backend not in ["ray", "mp"]):
-            name = "Pipeline Parallelism without Ray distributed executor"
+            name = "Pipeline Parallelism without Ray distributed executor " \
+                    "or multiprocessing executor"
             _raise_or_fallback(feature_name=name, recommend_to_remove=False)
             return False
 

vllm/v1/executor/multiproc_executor.py

@@ -8,7 +8,7 @@ import threading
 import time
 import traceback
 import weakref
-from concurrent.futures import Future
+from concurrent.futures import Future, ThreadPoolExecutor
 from dataclasses import dataclass
 from enum import Enum, auto
 from functools import partial
@@ -53,10 +53,11 @@ class MultiprocExecutor(Executor):
 
         self.world_size = self.parallel_config.world_size
         tensor_parallel_size = self.parallel_config.tensor_parallel_size
-        assert self.world_size == tensor_parallel_size, (
+        pp_parallel_size = self.parallel_config.pipeline_parallel_size
+        assert self.world_size == tensor_parallel_size * pp_parallel_size, (
             f"world_size ({self.world_size}) must be equal to the "
-            f"tensor_parallel_size ({tensor_parallel_size}). "
+            f"tensor_parallel_size ({tensor_parallel_size}) x pipeline"
-            f"Pipeline parallelism is not yet implemented in v1")
+            f"_parallel_size ({pp_parallel_size}). ")
 
         # Set multiprocessing envs that are common to V0 and V1
         set_multiprocessing_worker_envs(self.parallel_config)
@@ -104,6 +105,17 @@ class MultiprocExecutor(Executor):
                 self._ensure_worker_termination(
                     [w.proc for w in unready_workers])
 
+        # For pipeline parallel, we use a thread pool for asynchronous
+        # execute_model.
+        self.io_thread_pool: Optional[ThreadPoolExecutor] = None
+        if self.max_concurrent_batches > 1:
+            # Note: must use only 1 IO thread to keep dequeue sequence
+            # from the response queue
+            self.io_thread_pool = ThreadPoolExecutor(
+                max_workers=1, thread_name_prefix="mp_exec_io")
+
+        self.output_rank = self._get_output_rank()
+
     def start_worker_monitor(self):
         workers = self.workers
         self_ref = weakref.ref(self)
@@ -145,7 +157,9 @@ class MultiprocExecutor(Executor):
     ) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]:
         (output, ) = self.collective_rpc("execute_model",
                                          args=(scheduler_output, ),
-                                         rank0_reply_only=True,
+                                         unique_reply_rank=self.output_rank,
+                                         non_block=self.max_concurrent_batches
+                                         > 1,
                                          timeout=EXECUTE_MODEL_TIMEOUT_S)
         return output
 
@@ -154,7 +168,8 @@ class MultiprocExecutor(Executor):
                        timeout: Optional[float] = None,
                        args: tuple = (),
                        kwargs: Optional[dict] = None,
-                       rank0_reply_only: bool = False) -> list[Any]:
+                       non_block: bool = False,
+                       unique_reply_rank: Optional[int] = None) -> list[Any]:
         if self.is_failed:
             raise RuntimeError("Executor failed.")
 
@@ -171,22 +186,35 @@ class MultiprocExecutor(Executor):
                 send_method = cloudpickle.dumps(
                     method, protocol=pickle.HIGHEST_PROTOCOL)
             self.rpc_broadcast_mq.enqueue(
-                (send_method, args, kwargs, rank0_reply_only))
+                (send_method, args, kwargs, unique_reply_rank))
 
-            workers = (self.workers[0], ) if rank0_reply_only else self.workers
+            workers = (self.workers[unique_reply_rank],
-            responses = [None] * len(workers)
+                       ) if unique_reply_rank is not None else self.workers
-            for w in workers:
+            responses = []
-                dequeue_timeout = None if deadline is None else (
+
-                    deadline - time.monotonic())
+            def get_response(w: WorkerProcHandle,
+                             dequeue_timeout: Optional[float] = None,
+                             cancel_event: Optional[threading.Event] = None):
                 status, result = w.worker_response_mq.dequeue(
-                    timeout=dequeue_timeout, cancel=self.shutdown_event)
+                    timeout=dequeue_timeout, cancel=cancel_event)
 
                 if status != WorkerProc.ResponseStatus.SUCCESS:
                     raise RuntimeError(
                         f"Worker failed with error '{result}', please check the"
                         " stack trace above for the root cause")
+                return result
 
-                responses[w.rank] = result
+            for w in workers:
+                dequeue_timeout = None if deadline is None else (
+                    deadline - time.monotonic())
+
+                if non_block:
+                    result = self.io_thread_pool.submit(  # type: ignore
+                        get_response, w, dequeue_timeout, self.shutdown_event)
+                else:
+                    result = get_response(w, dequeue_timeout)
+
+                responses.append(result)
 
             return responses
         except TimeoutError as e:
@@ -225,6 +253,11 @@ class MultiprocExecutor(Executor):
         if not getattr(self, 'shutting_down', False):
             self.shutting_down = True
             self.shutdown_event.set()
+
+            if self.io_thread_pool is not None:
+                self.io_thread_pool.shutdown(wait=False, cancel_futures=True)
+                self.io_thread_pool = None
+
             for w in self.workers:
                 w.worker_response_mq = None
             self._ensure_worker_termination([w.proc for w in self.workers])
@@ -235,6 +268,22 @@ class MultiprocExecutor(Executor):
         self.collective_rpc("check_health", timeout=10)
         return
 
+    @property
+    def max_concurrent_batches(self) -> int:
+        return self.parallel_config.pipeline_parallel_size
+
+    def _get_output_rank(self) -> int:
+        # Only returns ModelRunnerOutput from TP rank=0 and PP rank=-1
+        # (the first TP worker of the last PP stage).
+        # Example:
+        # Assuming TP=8, PP=4, then the world_size=32
+        # 0-7, PP rank 0
+        # 8-15, PP rank 1
+        # 16-23, PP rank 2
+        # 24-31, PP rank 3
+        # so world_size - tp_size = 32 - 8 = 24 should be PP rank = -1 (i.e. 3)
+        return self.world_size - self.parallel_config.tensor_parallel_size
+
 
 @dataclass
 class UnreadyWorkerProcHandle:
@@ -280,12 +329,14 @@ class WorkerProc:
         all_kwargs: list[dict] = [
             {} for _ in range(vllm_config.parallel_config.world_size)
         ]
+        is_driver_worker = (
+            rank % vllm_config.parallel_config.tensor_parallel_size == 0)
         all_kwargs[rank] = {
             "vllm_config": vllm_config,
             "local_rank": local_rank,
             "rank": rank,
             "distributed_init_method": distributed_init_method,
-            "is_driver_worker": rank == 0,
+            "is_driver_worker": is_driver_worker,
         }
         wrapper.init_worker(all_kwargs)
         self.worker = wrapper
@@ -455,7 +506,7 @@ class WorkerProc:
     def worker_busy_loop(self):
         """Main busy loop for Multiprocessing Workers"""
         while True:
-            method, args, kwargs, rank0_only = self.rpc_broadcast_mq.dequeue()
+            method, args, kwargs, output_rank = self.rpc_broadcast_mq.dequeue()
 
             try:
                 if isinstance(method, str):
@@ -470,11 +521,11 @@ class WorkerProc:
                 logger.exception("WorkerProc hit an exception.")
                 # exception might not be serializable, so we convert it to
                 # string, only for logging purpose.
-                if not rank0_only or self.rank == 0:
+                if output_rank is None or self.rank == output_rank:
                     self.worker_response_mq.enqueue(
                         (WorkerProc.ResponseStatus.FAILURE, str(e)))
                 continue
 
-            if not rank0_only or self.rank == 0:
+            if output_rank is None or self.rank == output_rank:
                 self.worker_response_mq.enqueue(
                     (WorkerProc.ResponseStatus.SUCCESS, output))

vllm/v1/worker/gpu_model_runner.py

@@ -1016,7 +1016,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         self,
         scheduler_output: "SchedulerOutput",
         intermediate_tensors: Optional[IntermediateTensors] = None,
-    ) -> Union[ModelRunnerOutput, torch.Tensor]:
+    ) -> Union[ModelRunnerOutput, IntermediateTensors]:
         # Update KVConnector with the KVConnector metadata forward().
         if has_kv_transfer_group():
             get_kv_transfer_group().bind_connector_metadata(

vllm/v1/worker/gpu_worker.py

@@ -15,11 +15,12 @@ from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment,
                               set_custom_all_reduce)
 from vllm.distributed.kv_transfer import ensure_kv_transfer_initialized
-from vllm.distributed.parallel_state import get_pp_group
+from vllm.distributed.parallel_state import get_pp_group, get_tp_group
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor import set_random_seed
 from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
 from vllm.utils import GiB_bytes
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
 from vllm.v1.outputs import ModelRunnerOutput
@@ -266,7 +267,22 @@ class Worker(WorkerBase):
         self,
         scheduler_output: "SchedulerOutput",
     ) -> Optional[ModelRunnerOutput]:
-        output = self.model_runner.execute_model(scheduler_output)
+        intermediate_tensors = None
+        if not get_pp_group().is_first_rank:
+            intermediate_tensors = IntermediateTensors(
+                get_pp_group().recv_tensor_dict(
+                    all_gather_group=get_tp_group()))
+
+        output = self.model_runner.execute_model(scheduler_output,
+                                                 intermediate_tensors)
+
+        if not get_pp_group().is_last_rank:
+            assert isinstance(output, IntermediateTensors)
+            get_pp_group().send_tensor_dict(output.tensors,
+                                            all_gather_group=get_tp_group())
+            return None
+
+        assert isinstance(output, ModelRunnerOutput)
         return output if self.is_driver_worker else None
 
     def profile(self, is_start: bool = True):