process_group: added registration to support DeviceMesh and functional_collectives

torchft/process_group.py

@@ -4,26 +4,33 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from abc import ABC
 import logging
-from typing import Type, List, Optional, Callable, Tuple
-from datetime import timedelta
 import threading
+from abc import ABC
+from datetime import timedelta
+from typing import Callable, List, Optional, Tuple, Type
 
-from torch.futures import Future
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from torch._C._distributed_c10d import (
+    _register_process_group,
+    _unregister_process_group,
+)
 from torch.distributed import (
-    ProcessGroup as BaseProcessGroup,
-    Store,
-    TCPStore,
-    PrefixStore,
     BroadcastOptions,
+    DeviceMesh,
+    get_rank,
+    PrefixStore,
+    ProcessGroup as BaseProcessGroup,
     ProcessGroupGloo as BaseProcessGroupGloo,
     ProcessGroupNCCL as BaseProcessGroupNCCL,
+    Store,
+    TCPStore,
 )
-import torch.distributed as dist
-from torch.distributed.distributed_c10d import Work
-import torch
-import torch.multiprocessing as mp
+from torch.distributed.distributed_c10d import _world, Work
+
+from torch.futures import Future
 
 logger = logging.getLogger(__name__)
 
@@ -62,6 +69,11 @@ def create_store(store_addr: str) -> Store:
 
 
 class ProcessGroup(BaseProcessGroup):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._group_name = None
+
     def configure(self, store_addr: str, rank: int, world_size: int) -> None:
         raise NotImplementedError("not implemented")
 
@@ -90,6 +102,44 @@ def size(self) -> int:
     def getBackendName(self) -> str:
         raise NotImplementedError("not implemented")
 
+    def register(self, name: str) -> None:
+        """
+        Registers the process group with the global registry. This enables usage
+        with things like functional_collectives which are compilable.
+
+        This should only be called once.
+
+        Args:
+            name: name must be a unique name for this process group
+        """
+
+        self._group_name = f"{self.getBackendName()}:{name}"
+        _register_process_group(self.group_name, self)
+
+        # This is needed for DeviceMesh to work
+        # Resizable worlds don't fit well into DeviceMesh so we register a world
+        # size 1 PG.
+        _world.pg_map[self] = (None, None)
+        _world.pg_names[self] = self._group_name
+        _world.pg_to_tag[self] = self._group_name
+        _world.tags_to_pg.setdefault(self._group_name, []).append(self)
+        # these PGs can be resized so we lie about the rank mapping
+        _world.pg_group_ranks[self] = {get_rank(): 0}
+
+    @property
+    def group_name(self) -> str:
+        if self._group_name is None:
+            raise ValueError("ProcessGroup name not set")
+        return self._group_name
+
+    def unregister(self) -> None:
+        """
+        Unregisters the process group with the global registry.
+
+        Must be registered first.
+        """
+        _unregister_process_group(self.group_name)
+
 
 class ProcessGroupWrapper(ProcessGroup):
     PG_CLASS: Type[BaseProcessGroup]
@@ -458,3 +508,32 @@ class ProcessGroupBabyNCCL(ProcessGroupBaby):
 
     def getBackendName(self):
         return "torchft-baby-nccl"
+
+
+def extend_device_mesh(
+    mesh: DeviceMesh, pg: ProcessGroup, name: str = "dp", dim: int = 0
+) -> DeviceMesh:
+    """
+    This is a helper method to extend a traditional DeviceMesh with a torchft ProcessGroup for usage with DeviceMesh based APIs such as FSDPv2 with hybrid sharding.
+
+    Resizable PGs aren't natively supported by DeviceMesh so we lie to
+    DeviceMesh and say the PG is world size 1. This is fine as long as any
+    numeric scaling is handled at the PG level.
+
+    Args:
+        mesh: The DeviceMesh to extend
+        pg: The ProcessGroup to add to the mesh
+        name: The name of the new dimension
+        dim: The dimension to add the ProcessGroup to
+    """
+    groups = mesh.get_all_groups()
+    groups.insert(dim, pg)
+    mesh_dim_names = list(mesh.mesh_dim_names)
+    mesh_dim_names.insert(dim, name)
+
+    return DeviceMesh.from_group(
+        group=groups,
+        device_type=mesh.device_type,
+        mesh=mesh.mesh.unsqueeze(dim),
+        mesh_dim_names=mesh_dim_names,
+    )

torchft/process_group_test.py

@@ -6,11 +6,17 @@
 
 from unittest import TestCase, skipUnless
 from concurrent.futures import ThreadPoolExecutor
+import os
 
 import torch
 from torch.distributed import TCPStore, ReduceOp
 import torch.distributed as dist
 from torch import nn
+from torch._C._distributed_c10d import (
+    _resolve_process_group,
+)
+from torch.distributed import _functional_collectives
+from torch.distributed.device_mesh import init_device_mesh
 
 from torchft.process_group import (
     ProcessGroupBabyGloo,
@@ -19,6 +25,7 @@
     ProcessGroupNCCL,
     ProcessGroupDummy,
     ProcessGroup,
+    extend_device_mesh,
 )
 
 
@@ -140,3 +147,44 @@ def run(rank: int) -> None:
         b_work.get_future().wait()
 
         torch.testing.assert_close(at.cpu(), bt.cpu())
+
+    def test_device_mesh(self) -> None:
+        os.environ["MASTER_ADDR"] = "localhost"
+        os.environ["MASTER_PORT"] = str(0)
+        os.environ["RANK"] = str(0)
+        os.environ["WORLD_SIZE"] = str(1)
+
+        mesh_1d = init_device_mesh("cpu", mesh_shape=(1,), mesh_dim_names=("tp",))
+
+        store = TCPStore(
+            host_name="localhost", port=0, is_master=True, wait_for_workers=False
+        )
+        store_addr = f"localhost:{store.port}/prefix"
+
+        pg = ProcessGroupGloo()
+        pg.register("test_device_mesh")
+        pg.configure(store_addr, 0, 1)
+
+        mesh_2d = extend_device_mesh(mesh_1d, pg)
+        assert mesh_2d.ndim == 2
+
+    def test_functional_collectives(self) -> None:
+        store = TCPStore(
+            host_name="localhost", port=0, is_master=True, wait_for_workers=False
+        )
+        store_addr = f"localhost:{store.port}/prefix"
+
+        pg = ProcessGroupGloo()
+        pg.configure(store_addr, 0, 1)
+
+        pg.register("test_func_col")
+
+        self.assertEqual(pg.group_name, "torchft-gloo:test_func_col")
+
+        self.assertIs(_resolve_process_group(pg.group_name), pg)
+
+        try:
+            t = torch.zeros(10)
+            _functional_collectives.all_reduce(t, "sum", pg).wait()
+        finally:
+            pg.unregister()