Use monarch's distributed setup utility and colocate vLLM with its workers

src/forge/actors/generator.py

@@ -48,7 +48,12 @@
     load_tensor_from_dcp,
 )
 
-from forge.controller import ForgeActor, get_proc_mesh, stop_proc_mesh
+from forge.controller import (
+    ForgeActor,
+    get_proc_mesh,
+    host_mesh_from_proc,
+    stop_proc_mesh,
+)
 from forge.data_models.completion import Completion
 from forge.data_models.prompt import to_prompt
 from forge.env import TORCHSTORE_USE_RDMA
@@ -139,17 +144,22 @@ async def launch(  # pyright: ignore[reportIncompatibleMethodOverride]
             mesh_name=cls.mesh_name,
         )
 
-        # TODO - issues/144 we will want to ensure colocation with workers
-        # We're currently locating the Generator on the local host proc mesh
-        # vLLM initialization without setting env variables at proc_mesh creation
-        # level leads to issues. Once we can create multiple proc meshes on a host mesh,
-        # we can ensure host colocation
+        # First, spawn the worker processes which may or may not be
+        # on remote hosts.
+        worker_procs = await get_proc_mesh(process_config=process_config)
+
+        # Then, grab a single host from the workers...
+        host_mesh = await host_mesh_from_proc(worker_procs)
+        singleton_slice = {k: slice(0, 1) for k in host_mesh.extent.keys()}
+        host_mesh = host_mesh.slice(**singleton_slice)
+
+        # We ask the provisioner for a single process on a single host
         generator_proc_config = copy(process_config)
         generator_proc_config.procs = 1
-        generator_proc_config.hosts = None
         generator_proc_config.with_gpus = False
-        generator_proc = await get_proc_mesh(process_config=generator_proc_config)
-
+        generator_proc = await get_proc_mesh(
+            process_config=generator_proc_config, host_mesh=host_mesh
+        )
         # TODO - expand support so name can stick within kwargs
         actor_name = kwargs.pop("name", cls.__name__)
         generator = generator_proc.spawn(
@@ -159,7 +169,6 @@ async def launch(  # pyright: ignore[reportIncompatibleMethodOverride]
             **kwargs,
         )
 
-        worker_procs = await get_proc_mesh(process_config=process_config)
         vllm_config = (
             await generator.get_vllm_config.call_one()
         )  # Config should be the same across all actors

src/forge/actors/trainer.py

@@ -5,7 +5,6 @@
 # LICENSE file in the root directory of this source tree.
 
 import logging
-import math
 import os
 import shutil
 
@@ -18,7 +17,7 @@
 import torch.distributed.checkpoint as dcp
 import torchstore as ts
 
-from monarch.actor import current_rank, current_size, endpoint
+from monarch.actor import endpoint
 from torch import Tensor
 from torch.distributed.checkpoint._nested_dict import flatten_state_dict
 from torchtitan.config.job_config import (
@@ -163,19 +162,7 @@ def __post_init__(self):
         self.step = 1  # fragile contract.
         self.num_training_steps = self.training.steps
         self.gradient_accumulation_steps = 1
-        self.rank = current_rank().rank
-        self.size = math.prod(current_size().values())
-
         env = {
-            "RANK": str(self.rank),
-            "LOCAL_RANK": str(self.rank),
-            "LOCAL_WORLD_SIZE": str(self.size),
-            "GROUP_RANK": str(self.size),
-            "GROUP_WORLD_SIZE": str(self.size),
-            "ROLE_RANK": str(self.rank),
-            "ROLE_WORLD_SIZE": str(self.size),
-            "ROLE_NAME": "rank",
-            "WORLD_SIZE": str(self.size),
             "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True",
         }
         os.environ.update(env)

src/forge/controller/provisioner.py

@@ -20,8 +20,9 @@
 
 from monarch.tools import commands
 
-from forge.controller.launcher import BaseLauncher, get_launcher
+from monarch.utils import setup_env_for_distributed
 
+from forge.controller.launcher import BaseLauncher, get_launcher
 from forge.env import all_env_vars, FORGE_DISABLE_METRICS
 from forge.types import ProcessConfig, ProvisionerConfig
 
@@ -283,6 +284,13 @@ def bootstrap(env: dict[str, str]):
                 bootstrap=functools.partial(bootstrap, env=env_vars),
             )
 
+            # Set up environment variables for PyTorch distributed...
+            await setup_env_for_distributed(
+                procs,
+                master_addr=addr,
+                master_port=port,
+            )
+
             if is_remote:
                 await self.launcher.remote_setup(procs)