[Core] Multiprocessing Pipeline Parallel support (vllm-project#6130)

Co-authored-by: Murali Andoorveedu <[email protected]>

.buildkite/test-pipeline.yaml

@@ -54,7 +54,7 @@ steps:
 - label: Core Test
   mirror_hardwares: [amd]
   fast_check: true
-  commands: 
+  commands:
   - pytest -v -s core
   - pytest -v -s distributed/test_parallel_state.py
 
@@ -73,7 +73,7 @@ steps:
   commands:
   - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py
-    - pytest -v -s distributed/test_pipeline_parallel.py
+    - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
   - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py
 
@@ -123,7 +123,7 @@ steps:
 
 - label: Engine Test
   mirror_hardwares: [amd]
-  commands: 
+  commands:
   - pytest -v -s engine test_sequence.py test_config.py test_logger.py
   # OOM in the CI unless we run this separately
   - pytest -v -s tokenization

tests/distributed/test_pipeline_parallel.py

@@ -1,28 +1,42 @@
+import os
+
 import pytest
 
 from ..utils import compare_two_settings
 
+VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
+
 
 @pytest.mark.parametrize(
-    "TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME", [
-        (2, 2, 0, 1, "meta-llama/Meta-Llama-3-8B"),
-        (2, 2, 1, 0, "meta-llama/Meta-Llama-3-8B"),
-        (1, 3, 0, 0, "meta-llama/Meta-Llama-3-8B"),
-        (1, 4, 0, 1, "meta-llama/Meta-Llama-3-8B"),
-        (1, 4, 1, 0, "meta-llama/Meta-Llama-3-8B"),
+    "TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME, DIST_BACKEND",
+    [
+        (2, 2, 0, 1, "meta-llama/Meta-Llama-3-8B", "ray"),
+        (2, 2, 1, 0, "meta-llama/Meta-Llama-3-8B", "ray"),
+        (1, 3, 0, 0, "meta-llama/Meta-Llama-3-8B", "ray"),
+        (1, 4, 0, 1, "meta-llama/Meta-Llama-3-8B", "ray"),
+        (1, 4, 1, 0, "meta-llama/Meta-Llama-3-8B", "ray"),
+        (2, 2, 0, 1, "meta-llama/Meta-Llama-3-8B", "mp"),
+        (2, 2, 1, 0, "meta-llama/Meta-Llama-3-8B", "mp"),
+        (1, 3, 0, 0, "meta-llama/Meta-Llama-3-8B", "mp"),
+        (1, 4, 0, 1, "meta-llama/Meta-Llama-3-8B", "mp"),
+        (1, 4, 1, 0, "meta-llama/Meta-Llama-3-8B", "mp"),
     ])
-def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME):
+def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME,
+                    DIST_BACKEND):
+    if VLLM_MULTI_NODE and DIST_BACKEND == "mp":
+        pytest.skip("Skipping multi-node pipeline parallel test for "
+                    "multiprocessing distributed backend")
 
     pp_args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
-        "bfloat16",
+        "float16",
         "--pipeline-parallel-size",
         str(PP_SIZE),
         "--tensor-parallel-size",
         str(TP_SIZE),
         "--distributed-executor-backend",
-        "ray",
+        DIST_BACKEND,
     ]
 
     # compare without pipeline parallelism

vllm/config.py

@@ -712,10 +712,6 @@ def __init__(
         self.rank = 0
 
     def _verify_args(self) -> None:
-        if (self.pipeline_parallel_size > 1
-                and self.distributed_executor_backend == "mp"):
-            raise NotImplementedError("Pipeline parallelism is not supported "
-                                      "yet with multiprocessing.")
         if self.distributed_executor_backend not in ("ray", "mp", None):
             raise ValueError(
                 "Unrecognized distributed executor backend. Supported values "

vllm/executor/executor_base.py

@@ -1,4 +1,3 @@
-import asyncio
 from abc import ABC, abstractmethod
 from typing import List, Optional, Set, Tuple
 
@@ -132,26 +131,6 @@ def __del__(self):
 
 class ExecutorAsyncBase(ExecutorBase):
 
-    def __init__(
-        self,
-        model_config: ModelConfig,
-        cache_config: CacheConfig,
-        parallel_config: ParallelConfig,
-        scheduler_config: SchedulerConfig,
-        device_config: DeviceConfig,
-        load_config: LoadConfig,
-        lora_config: Optional[LoRAConfig],
-        multimodal_config: Optional[MultiModalConfig],
-        speculative_config: Optional[SpeculativeConfig],
-        prompt_adapter_config: Optional[PromptAdapterConfig],
-    ) -> None:
-        self.pp_locks: Optional[List[asyncio.Lock]] = None
-
-        super().__init__(model_config, cache_config, parallel_config,
-                         scheduler_config, device_config, load_config,
-                         lora_config, multimodal_config, speculative_config,
-                         prompt_adapter_config)
-
     @abstractmethod
     async def execute_model_async(
             self,

vllm/executor/gpu_executor.py

@@ -12,6 +12,15 @@
 logger = init_logger(__name__)
 
 
+def create_worker(worker_module_name, worker_class_name, **kwargs):
+    wrapper = WorkerWrapperBase(
+        worker_module_name=worker_module_name,
+        worker_class_name=worker_class_name,
+    )
+    wrapper.init_worker(**kwargs)
+    return wrapper.worker
+
+
 class GPUExecutor(ExecutorBase):
 
     def _init_executor(self) -> None:
@@ -51,25 +60,30 @@ def _get_worker_kwargs(
             or (rank % self.parallel_config.tensor_parallel_size == 0),
         )
 
+    def _get_create_worker_kwargs(
+            self,
+            local_rank: int = 0,
+            rank: int = 0,
+            distributed_init_method: Optional[str] = None) -> Dict:
+        worker_kwargs = self._get_worker_kwargs(local_rank, rank,
+                                                distributed_init_method)
+        if self.speculative_config is None:
+            worker_kwargs.update(worker_module_name="vllm.worker.worker",
+                                 worker_class_name="Worker")
+        else:
+            worker_kwargs.update(
+                worker_module_name="vllm.spec_decode.spec_decode_worker",
+                worker_class_name="create_spec_worker")
+        return worker_kwargs
+
     def _create_worker(self,
                        local_rank: int = 0,
                        rank: int = 0,
                        distributed_init_method: Optional[str] = None):
-
-        if self.speculative_config is None:
-            worker_module_name = "vllm.worker.worker"
-            worker_class_name = "Worker"
-        else:
-            worker_module_name = "vllm.spec_decode.spec_decode_worker"
-            worker_class_name = "create_spec_worker"
-
-        wrapper = WorkerWrapperBase(
-            worker_module_name=worker_module_name,
-            worker_class_name=worker_class_name,
-        )
-        wrapper.init_worker(**self._get_worker_kwargs(local_rank, rank,
-                                                      distributed_init_method))
-        return wrapper.worker
+        return create_worker(**self._get_create_worker_kwargs(
+            local_rank=local_rank,
+            rank=rank,
+            distributed_init_method=distributed_init_method))
 
     def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks by invoking the

vllm/executor/multiproc_gpu_executor.py

@@ -7,12 +7,13 @@
 
 from vllm.executor.distributed_gpu_executor import (  # yapf: disable
     DistributedGPUExecutor, DistributedGPUExecutorAsync)
+from vllm.executor.gpu_executor import create_worker
 from vllm.executor.multiproc_worker_utils import (ProcessWorkerWrapper,
                                                   ResultHandler, WorkerMonitor)
 from vllm.logger import init_logger
 from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.triton_utils import maybe_set_triton_cache_manager
-from vllm.utils import (cuda_device_count_stateless,
+from vllm.utils import (_run_task_with_lock, cuda_device_count_stateless,
                         error_on_invalid_device_count_status,
                         get_distributed_init_method, get_open_port,
                         get_vllm_instance_id, make_async,
@@ -26,7 +27,8 @@ class MultiprocessingGPUExecutor(DistributedGPUExecutor):
 
     def _init_executor(self) -> None:
         # Create the parallel GPU workers.
-        world_size = self.parallel_config.tensor_parallel_size
+        world_size = self.parallel_config.world_size
+        tensor_parallel_size = self.parallel_config.tensor_parallel_size
 
         # Set CUDA_VISIBLE_DEVICES for the driver, inherited by workers
         if "CUDA_VISIBLE_DEVICES" not in os.environ:
@@ -49,8 +51,15 @@ def _init_executor(self) -> None:
         if world_size > 1:
             maybe_set_triton_cache_manager()
 
-        assert world_size <= cuda_device_count_stateless(), (
-            "please set tensor_parallel_size to less than max local gpu count")
+        cuda_device_count = cuda_device_count_stateless()
+        # Use confusing message for more common TP-only case.
+        assert tensor_parallel_size <= cuda_device_count, (
+            f"please set tensor_parallel_size ({tensor_parallel_size}) "
+            f"to less than max local gpu count ({cuda_device_count})")
+
+        assert world_size <= cuda_device_count, (
+            f"please ensure that world_size ({world_size}) "
+            f"is less than than max local gpu count ({cuda_device_count})")
 
         error_on_invalid_device_count_status()
 
@@ -60,21 +69,35 @@ def _init_executor(self) -> None:
         distributed_init_method = get_distributed_init_method(
             "127.0.0.1", get_open_port())
 
+        self.workers: List[ProcessWorkerWrapper] = []
+        # This is the list of workers that are rank 0 of each TP group EXCEPT
+        # global rank 0. These are the workers that will broadcast to the
+        # rest of the workers.
+        self.tp_driver_workers: List[ProcessWorkerWrapper] = []
+        # This is the list of workers that are not drivers and not the first
+        # worker in a TP group. These are the workers that will be
+        # broadcasted to.
+        self.non_driver_workers: List[ProcessWorkerWrapper] = []
+
         if world_size == 1:
-            self.workers = []
             self.worker_monitor = None
         else:
             result_handler = ResultHandler()
-            self.workers = [
-                ProcessWorkerWrapper(
+            for rank in range(1, world_size):
+                worker = ProcessWorkerWrapper(
                     result_handler,
                     partial(
-                        self._create_worker,
-                        rank=rank,
-                        local_rank=rank,
-                        distributed_init_method=distributed_init_method,
-                    )) for rank in range(1, world_size)
-            ]
+                        create_worker,
+                        **self._get_create_worker_kwargs(
+                            rank=rank,
+                            local_rank=rank,
+                            distributed_init_method=distributed_init_method,
+                        )))
+                self.workers.append(worker)
+                if rank % tensor_parallel_size == 0:
+                    self.tp_driver_workers.append(worker)
+                else:
+                    self.non_driver_workers.append(worker)
 
             self.worker_monitor = WorkerMonitor(self.workers, result_handler)
             result_handler.start()
@@ -136,16 +159,19 @@ def _run_workers(
             raise NotImplementedError(
                 "max_concurrent_workers is not supported yet.")
 
-        # Start the workers first.
+        if async_run_tensor_parallel_workers_only:
+            # Run only non-driver workers and just return futures.
+            return [
+                worker.execute_method(method, *args, **kwargs)
+                for worker in self.non_driver_workers
+            ]
+
+        # Start all remote workers first.
         worker_outputs = [
             worker.execute_method(method, *args, **kwargs)
             for worker in self.workers
         ]
 
-        if async_run_tensor_parallel_workers_only:
-            # Just return futures
-            return worker_outputs
-
         driver_worker_method = getattr(self.driver_worker, method)
         driver_worker_output = driver_worker_method(*args, **kwargs)
 
@@ -172,16 +198,45 @@ class MultiprocessingGPUExecutorAsync(MultiprocessingGPUExecutor,
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.driver_exec_model = make_async(self.driver_worker.execute_model)
+        self.pp_locks: Optional[List[asyncio.Lock]] = None
 
     async def _driver_execute_model_async(
         self,
         execute_model_req: Optional[ExecuteModelRequest] = None
     ) -> List[SamplerOutput]:
-        return await self.driver_exec_model(execute_model_req)
+        if not self.tp_driver_workers:
+            return await self.driver_exec_model(execute_model_req)
+
+        if self.pp_locks is None:
+            # This locks each pipeline parallel stage so multiple virtual
+            # engines can't execute on the same stage at the same time
+            # We create the locks here to avoid creating them in the constructor
+            # which uses a different asyncio loop.
+            self.pp_locks = [
+                asyncio.Lock()
+                for _ in range(self.parallel_config.pipeline_parallel_size)
+            ]
+
+        tasks = [
+            asyncio.create_task(
+                _run_task_with_lock(self.driver_exec_model, self.pp_locks[0],
+                                    execute_model_req))
+        ]
+        for pp_rank, driver_worker in enumerate(self.tp_driver_workers,
+                                                start=1):
+            tasks.append(
+                asyncio.create_task(
+                    _run_task_with_lock(driver_worker.execute_method_async,
+                                        self.pp_locks[pp_rank],
+                                        "execute_model", execute_model_req)))
+        results = await asyncio.gather(*tasks)
+
+        # Only the last PP stage has the final results.
+        return results[-1]
 
     async def _start_worker_execution_loop(self):
         coros = [
             worker.execute_method_async("start_worker_execution_loop")
-            for worker in self.workers
+            for worker in self.non_driver_workers
         ]
         return await asyncio.gather(*coros)