[misc] Rename Ray ADAG to Compiled Graph (#13928)

tests/basic_correctness/test_basic_correctness.py

@@ -117,7 +117,7 @@ def test_models_distributed(
         pytest.skip(f"Skip test for {test_suite}")
 
     if model == "meta-llama/Llama-3.2-1B-Instruct" and distributed_executor_backend == "ray" and attention_backend == "" and test_suite == "L4":  # noqa
-        # test ray adag
+        # test Ray Compiled Graph
         os.environ['VLLM_USE_RAY_SPMD_WORKER'] = "1"
         os.environ['VLLM_USE_RAY_COMPILED_DAG'] = "1"
 

tests/basic_correctness/test_chunked_prefill.py

@@ -93,7 +93,7 @@ def test_models_distributed(
 
     if (model == "meta-llama/Llama-3.2-1B-Instruct"
             and distributed_executor_backend == "ray"):
-        # test ray adag
+        # test Ray Compiled Graph
         os.environ['VLLM_USE_RAY_SPMD_WORKER'] = "1"
         os.environ['VLLM_USE_RAY_COMPILED_DAG'] = "1"
 

tests/distributed/test_pipeline_parallel.py

@@ -324,8 +324,8 @@ def _compare_tp(
     specific_case = tp_size == 2 and pp_size == 2 and chunked_prefill
     if distributed_backend == "ray" and (vllm_major_version == "1"
                                          or specific_case):
-        # For V1, test Ray ADAG for all the tests
-        # For V0, test Ray ADAG for a subset of the tests
+        # For V1, test Ray Compiled Graph for all the tests
+        # For V0, test Ray Compiled Graph for a subset of the tests
         pp_env = {
             "VLLM_USE_V1": vllm_major_version,
             "VLLM_USE_RAY_COMPILED_DAG": "1",
@@ -333,7 +333,7 @@ def _compare_tp(
             "VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL": "1",
         }
         # Temporary. Currently when zeromq + SPMD is used, it does not properly
-        # terminate because of aDAG issue.
+        # terminate because of a Ray Compiled Graph issue.
         common_args.append("--disable-frontend-multiprocessing")
     else:
         pp_env = None
@@ -367,8 +367,9 @@ def _compare_tp(
         if pp_env is None:
             raise
         else:
-            # Ray ADAG tests are flaky, so we don't want to fail the test
-            logger.exception("Ray ADAG tests failed")
+            # Ray Compiled Graph tests are flaky,
+            # so we don't want to fail the test
+            logger.exception("Ray Compiled Graph tests failed")
 
 
 @pytest.mark.parametrize(

vllm/envs.py

@@ -371,21 +371,22 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     "VLLM_USE_RAY_SPMD_WORKER":
     lambda: bool(int(os.getenv("VLLM_USE_RAY_SPMD_WORKER", "0"))),
 
-    # If the env var is set, it uses the Ray's compiled DAG API
-    # which optimizes the control plane overhead.
+    # If the env var is set, it uses the Ray's Compiled Graph
+    # (previously known as ADAG) API which optimizes the
+    # control plane overhead.
     # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
     "VLLM_USE_RAY_COMPILED_DAG":
     lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG", "0"))),
 
     # If the env var is set, it uses NCCL for communication in
-    # Ray's compiled DAG. This flag is ignored if
+    # Ray's Compiled Graph. This flag is ignored if
     # VLLM_USE_RAY_COMPILED_DAG is not set.
     "VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL":
     lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL", "1"))
                  ),
 
     # If the env var is set, it enables GPU communication overlap
-    # (experimental feature) in Ray's compiled DAG. This flag is ignored if
+    # (experimental feature) in Ray's Compiled Graph. This flag is ignored if
     # VLLM_USE_RAY_COMPILED_DAG is not set.
     "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM":
     lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))

vllm/executor/ray_distributed_executor.py

@@ -491,7 +491,7 @@ class RayDistributedExecutor(DistributedExecutorBase):
         async_run_remote_workers_only to complete."""
         ray.get(parallel_worker_tasks)
 
-    def _check_ray_adag_installation(self):
+    def _check_ray_cgraph_installation(self):
         import pkg_resources
         from packaging import version
 
@@ -503,10 +503,10 @@ class RayDistributedExecutor(DistributedExecutorBase):
                              f"required, but found {current_version}")
 
         import importlib.util
-        adag_spec = importlib.util.find_spec(
+        cgraph_spec = importlib.util.find_spec(
             "ray.experimental.compiled_dag_ref")
-        if adag_spec is None:
-            raise ValueError("Ray accelerated DAG is not installed. "
+        if cgraph_spec is None:
+            raise ValueError("Ray Compiled Graph is not installed. "
                              "Run `pip install ray[adag]` to install it.")
 
         cupy_spec = importlib.util.find_spec("cupy")
@@ -518,7 +518,7 @@ class RayDistributedExecutor(DistributedExecutorBase):
 
     def _compiled_ray_dag(self, enable_asyncio: bool):
         assert self.parallel_config.use_ray
-        self._check_ray_adag_installation()
+        self._check_ray_cgraph_installation()
         from ray.dag import InputNode, MultiOutputNode
         from ray.experimental.channel.torch_tensor_type import TorchTensorType
 

vllm/executor/ray_utils.py

@@ -83,9 +83,9 @@ try:
 
             execute_model_req = self.input_decoder.decode(serialized_req)
 
-            # TODO(swang): This is needed right now because Ray aDAG executes
-            # on a background thread, so we need to reset torch's current
-            # device.
+            # TODO(swang): This is needed right now because Ray Compiled Graph
+            # executes on a background thread, so we need to reset torch's
+            # current device.
             import torch
             if not self.compiled_dag_cuda_device_set:
                 torch.cuda.set_device(self.worker.device)
@@ -119,7 +119,7 @@ try:
                                           "IntermediateTensors"]],
         ) -> Union["ModelRunnerOutput", Tuple["SchedulerOutput",
                                               "IntermediateTensors"]]:
-            # this method is used to compile ray CG,
+            # This method is used by Ray Compiled Graph to execute the model,
             # and it needs a special logic of self.setup_device_if_necessary()
             self.setup_device_if_necessary()
             assert self.worker is not None, "Worker is not initialized"