[Ray] Integration compiled DAG off by default (#2471)

vllm/engine/llm_engine.py

@@ -2,6 +2,7 @@ import copy
 from collections import defaultdict
 import os
 import time
+import pickle
 from typing import (TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple,
                     Union)
 
@@ -30,6 +31,11 @@ if TYPE_CHECKING:
 logger = init_logger(__name__)
 _LOCAL_LOGGING_INTERVAL_SEC = 5
 
+# If the env var is set, it uses the Ray's compiled DAG API
+# which optimizes the control plane overhead.
+# Run VLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
+USE_RAY_COMPILED_DAG = bool(os.getenv("VLLM_USE_RAY_COMPILED_DAG", 0))
+
 
 class LLMEngine:
     """An LLM engine that receives requests and generates texts.
@@ -124,6 +130,10 @@ class LLMEngine:
             self.stat_logger = StatLogger(
                 local_interval=_LOCAL_LOGGING_INTERVAL_SEC)
 
+        self.forward_dag = None
+        if USE_RAY_COMPILED_DAG:
+            self.forward_dag = self._compiled_ray_dag()
+
     def get_tokenizer_for_seq(self, sequence: Sequence):
         return self.tokenizer.get_lora_tokenizer(sequence.lora_request)
 
@@ -806,7 +816,8 @@ class LLMEngine:
                     "blocks_to_swap_in": scheduler_outputs.blocks_to_swap_in,
                     "blocks_to_swap_out": scheduler_outputs.blocks_to_swap_out,
                     "blocks_to_copy": scheduler_outputs.blocks_to_copy,
-                })
+                },
+                use_ray_compiled_dag=USE_RAY_COMPILED_DAG)
 
             # Only the driver worker returns the sampling results.
             output = all_outputs[0]
@@ -966,6 +977,7 @@ class LLMEngine:
         driver_args: Optional[List[Any]] = None,
         driver_kwargs: Optional[Dict[str, Any]] = None,
         max_concurrent_workers: Optional[int] = None,
+        use_ray_compiled_dag: bool = False,
         **kwargs,
     ) -> Any:
         """Runs the given method on all workers."""
@@ -974,11 +986,16 @@ class LLMEngine:
             raise NotImplementedError(
                 "max_concurrent_workers is not supported yet.")
 
-        # Start the ray workers first.
-        ray_worker_outputs = [
-            worker.execute_method.remote(method, *args, **kwargs)
-            for worker in self.workers
-        ]
+        if use_ray_compiled_dag:
+            # Right now, compiled DAG can only accept a single
+            # input. TODO(sang): Fix it.
+            output_channels = self.forward_dag.execute(1)
+        else:
+            # Start the ray workers first.
+            ray_worker_outputs = [
+                worker.execute_method.remote(method, *args, **kwargs)
+                for worker in self.workers
+            ]
 
         if driver_args is None:
             driver_args = args
@@ -991,6 +1008,37 @@ class LLMEngine:
 
         # Get the results of the ray workers.
         if self.workers:
-            ray_worker_outputs = ray.get(ray_worker_outputs)
+            if use_ray_compiled_dag:
+                try:
+                    ray_worker_outputs = [
+                        pickle.loads(chan.begin_read())
+                        for chan in output_channels
+                    ]
+                finally:
+                    # Has to call end_read in order to reuse the DAG.
+                    for chan in output_channels:
+                        chan.end_read()
+            else:
+                ray_worker_outputs = ray.get(ray_worker_outputs)
 
         return [driver_worker_output] + ray_worker_outputs
+
+    def _compiled_ray_dag(self):
+        import pkg_resources
+        required_version = "2.9"
+        current_version = pkg_resources.get_distribution("ray").version
+        if current_version < required_version:
+            raise ValueError(f"Ray version {required_version} or greater is "
+                             f"required, but found {current_version}")
+
+        from ray.dag import MultiOutputNode, InputNode
+        assert self.parallel_config.worker_use_ray
+
+        # Right now, compiled DAG requires at least 1 arg. We send
+        # a dummy value for now. It will be fixed soon.
+        with InputNode() as input_data:
+            forward_dag = MultiOutputNode([
+                worker.execute_model_compiled_dag_remote.bind(input_data)
+                for worker in self.workers
+            ])
+        return forward_dag.experimental_compile()

vllm/engine/ray_utils.py

+import pickle
+
 from typing import Optional, List, Tuple, TYPE_CHECKING
 
 from vllm.config import ParallelConfig
@@ -18,6 +20,11 @@ try:
                 from transformers.dynamic_module_utils import init_hf_modules
                 init_hf_modules()
             self.worker = None
+            # Since the compiled DAG runs a main execution
+            # in a different thread that calls cuda.set_device.
+            # The flag indicates is set_device is called on
+            # that thread.
+            self.compiled_dag_cuda_device_set = False
 
         def init_worker(self, worker_init_fn):
             self.worker = worker_init_fn()
@@ -40,6 +47,17 @@ try:
         def set_cuda_visible_devices(self, device_ids) -> None:
             set_cuda_visible_devices(device_ids)
 
+        def execute_model_compiled_dag_remote(self, ignored):
+            """Used only when compiled DAG is enabled."""
+            import torch
+            if not self.compiled_dag_cuda_device_set:
+                torch.cuda.set_device(self.worker.device)
+                self.compiled_dag_cuda_device_set = True
+
+            output = self.worker.execute_model()
+            output = pickle.dumps(output)
+            return output
+
 except ImportError as e:
     logger.warning(f"Failed to import Ray with {e!r}. "
                    "For distributed inference, please install Ray with "