[aDAG] Allow custom NCCL group for aDAG

Signed-off-by: Rui Qiao <ruisearch42@gmail.com>

python/ray/dag/compiled_dag_node.py

@@ -8,7 +8,11 @@
 import traceback
 from typing import NamedTuple
 
+<<<<<<< HEAD
 from ray.experimental.channel.cached_channel import CachedChannel
+=======
+from ray.experimental.channel.gpu_communicator import GPUCommunicator
+>>>>>>> 0d83e ([aDAG] Allow custom NCCL group for aDAG)
 import ray
 from ray.exceptions import RayTaskError, RayChannelError
 from ray.util.annotations import PublicAPI
@@ -34,6 +38,7 @@
 )
 
 from ray.experimental.channel.torch_tensor_nccl_channel import (
+    _set_nccl_group,
     _init_nccl_group,
     _destroy_nccl_group,
 )
@@ -529,6 +534,7 @@ def __init__(
         enable_asyncio: bool = False,
         asyncio_max_queue_size: Optional[int] = None,
         max_buffered_results: Optional[int] = None,
+        nccl_group: Optional[GPUCommunicator] = None,
     ):
         """
         Args:
@@ -557,6 +563,9 @@ def __init__(
                 executions is beyond the DAG capacity, the new execution would
                 be blocked in the first place; therefore, this limit is only
                 enforced when it is smaller than the DAG capacity.
+            nccl_group: The NCCL group to use for this DAG. If None, the DAG
+                will create a NCCL group internally if NCCL communication is
+                needed.
 
         Returns:
             Channel: A wrapper around ray.ObjectRef.
@@ -640,6 +649,7 @@ def __init__(
         # Type hints specified by the user for DAG (intermediate) outputs.
         self._type_hints = []
 
+        self._custom_nccl_group: Optional[GPUCommunicator] = nccl_group
         # Uniquely identifies the NCCL communicator that will be used within
         # this DAG, if any.
         self._nccl_group_id: Optional[str] = None
@@ -916,7 +926,16 @@ def _preprocess(self) -> None:
         if None in nccl_actors:
             raise ValueError("Driver cannot participate in the NCCL group.")
         if nccl_actors and self._nccl_group_id is None:
-            self._nccl_group_id = _init_nccl_group(nccl_actors)
+            if self._custom_nccl_group:
+                self._nccl_group_id = _set_nccl_group(
+                    self._custom_nccl_group, nccl_actors
+                )
+            else:
+                self._nccl_group_id = _init_nccl_group(nccl_actors)
+        elif self._custom_nccl_group:
+            raise ValueError(
+                "The DAG does not use NCCL, but a custom NCCL group was provided."
+            )
 
         if direct_input:
             self._input_num_positional_args = 1
@@ -1932,13 +1951,15 @@ def build_compiled_dag_from_ray_dag(
     enable_asyncio: bool = False,
     asyncio_max_queue_size: Optional[int] = None,
     max_buffered_results: Optional[int] = None,
+    nccl_group: Optional["GPUCommunicator"] = None,
 ) -> "CompiledDAG":
     compiled_dag = CompiledDAG(
         execution_timeout,
         buffer_size_bytes,
         enable_asyncio,
         asyncio_max_queue_size,
         max_buffered_results,
+        nccl_group,
     )
 
     def _build_compiled_dag(node):

python/ray/dag/dag_node.py

@@ -1,3 +1,4 @@
+from ray.experimental.channel.gpu_communicator import GPUCommunicator
 import ray
 from ray.dag.base import DAGNodeBase
 from ray.dag.py_obj_scanner import _PyObjScanner
@@ -166,6 +167,7 @@ def experimental_compile(
         enable_asyncio: bool = False,
         _asyncio_max_queue_size: Optional[int] = None,
         _max_buffered_results: Optional[int] = None,
+        _nccl_group: Optional["GPUCommunicator"] = None,
     ) -> "ray.dag.CompiledDAG":
         """Compile an accelerated execution path for this DAG.
 
@@ -186,6 +188,11 @@ def experimental_compile(
                 executions is beyond the DAG capacity, the new execution would
                 be blocked in the first place; therefore, this limit is only
                 enforced when it is smaller than the DAG capacity.
+            _nccl_group: The NCCL group to use for this DAG. If None, the DAG
+                will create a NCCL group internally if NCCL communication is
+                needed. Providing a nccl group here allows reusing, which
+                avoids extra memory overhead when initializing a new NCCL group
+                from aDAG.
 
         Returns:
             A compiled DAG.
@@ -218,6 +225,7 @@ def experimental_compile(
             enable_asyncio,
             _asyncio_max_queue_size,
             _max_buffered_results,
+            _nccl_group,
         )
 
     def execute(

python/ray/experimental/channel/__init__.py

@@ -10,6 +10,7 @@
     SynchronousWriter,
     WriterInterface,
 )
+from ray.experimental.channel.gpu_communicator import GPUCommunicator
 from ray.experimental.channel.intra_process_channel import IntraProcessChannel
 from ray.experimental.channel.shared_memory_channel import Channel, CompositeChannel
 from ray.experimental.channel.torch_tensor_nccl_channel import TorchTensorNcclChannel
@@ -19,6 +20,7 @@
     "AwaitableBackgroundWriter",
     "CachedChannel",
     "Channel",
+    "GPUCommunicator",
     "ReaderInterface",
     "SynchronousReader",
     "SynchronousWriter",

python/ray/experimental/channel/common.py

@@ -7,7 +7,7 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 
 import ray
-from ray.experimental.channel.nccl_group import _NcclGroup
+from ray.experimental.channel.gpu_communicator import GPUCommunicator
 from ray.experimental.channel.serialization_context import _SerializationContext
 from ray.util.annotations import DeveloperAPI, PublicAPI
 
@@ -112,7 +112,7 @@ class ChannelContext:
 
     def __init__(self):
         # Used for the torch.Tensor NCCL transport.
-        self.nccl_groups: Dict[str, "_NcclGroup"] = {}
+        self.nccl_groups: Dict[str, "GPUCommunicator"] = {}
 
     @staticmethod
     def get_current() -> "ChannelContext":

python/ray/experimental/channel/conftest.py

@@ -1,11 +1,13 @@
 import asyncio
 from collections import defaultdict
+from typing import Optional, Tuple
 from unittest import mock
 
 import torch
 
 import ray
 import ray.experimental.channel as ray_channel
+from ray.experimental.channel.gpu_communicator import TorchTensorAllocator
 
 
 @ray.remote(num_cpus=0)
@@ -74,13 +76,21 @@ def send(self, tensor: torch.Tensor, peer_rank: int):
         ray.get(barrier.wait.remote(self.num_ops[barrier_key], tensor))
         self.num_ops[barrier_key] += 1
 
-    def recv(self, buf: torch.Tensor, peer_rank: int):
+    def recv(
+        self,
+        shape: Tuple[int],
+        dtype: torch.dtype,
+        peer_rank: int,
+        allocator: Optional[TorchTensorAllocator] = None,
+    ):
         # "Receive" the tensor from the barrier actor.
         barrier_key = f"barrier-{peer_rank}-{self.get_self_rank()}"
         barrier = ray.get_actor(name=barrier_key)
         received_tensor = ray.get(barrier.wait.remote(self.num_ops[barrier_key]))
+        buf = allocator(shape, dtype)
         buf[:] = received_tensor[:]
         self.num_ops[barrier_key] += 1
+        return buf
 
 
 def start_nccl_mock():

python/ray/experimental/channel/gpu_communicator.py

@@ -0,0 +1,95 @@
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Callable, Optional, Tuple
+
+import ray
+from ray.util.annotations import DeveloperAPI
+
+if TYPE_CHECKING:
+    import torch
+
+
+# Signature for a torch.Tensor allocator is:
+# (shape: Tuple[int], dtype: torch.dtype) -> torch.Tensor.
+TorchTensorAllocator = Callable[[Tuple[int], "torch.dtype"], "torch.Tensor"]
+
+
+@DeveloperAPI
+class GPUCommunicator(ABC):
+    """
+    Communicator for a group of aDAG actors on Nvidia GPU.
+
+    The aDAG execution leverages this internally to support communication
+    between actors in the group.
+    """
+
+    def register(self, group_id: str):
+        """
+        Register the group in the Ray channel context.
+
+        This should be called once remotely on each actor
+        in the group before any other methods can be called,
+        with the same `group_id`.
+        """
+        from ray.experimental.channel.common import ChannelContext
+
+        ctx = ChannelContext.get_current()
+        ctx.nccl_groups[group_id] = self
+
+    @abstractmethod
+    def get_rank(self, actor: ray.actor.ActorHandle) -> int:
+        """
+        Return the given actor's rank in the group.
+
+        Args:
+            actor: The actor handle to look up.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_self_rank(self) -> Optional[int]:
+        """
+        Return this actor's rank.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def send(self, value: "torch.Tensor", peer_rank: int) -> None:
+        """
+        Send a torch.Tensor to a peer.
+
+        This returns when the send kernel has been queued, but the kernel may
+        not have completed. Therefore, the caller should ensure that there are
+        no concurrent writes to the sent `value` until the send has finished.
+        That is, either all writes should be submitted on the current stream
+        (self._cuda_stream) or, if on a different stream, that stream should
+        synchronize with the current stream.
+
+        Args:
+            value: The torch.Tensor to send. It should already be on this
+                actor's default device.
+            peer_rank: The rank of the actor to send to.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def recv(
+        self,
+        shape: Tuple[int],
+        dtype: "torch.dtype",
+        peer_rank: int,
+        allocator: Optional[TorchTensorAllocator] = None,
+    ) -> "torch.Tensor":
+        """
+        Receive a torch.Tensor from a peer and synchronize.
+
+        After this call returns, the receive buffer is safe to read from from
+        any stream. An RayChannelError will be raised if an error occurred (e.g.,
+        remote actor died), and the buffer is not safe to read.
+
+        Args:
+            shape: The shape of the tensor to receive.
+            dtype: The dtype of the tensor to receive.
+            peer_rank: The rank of the actor to receive from.
+            allocator: A function to allocate the tensor to receive into.
+        """
+        raise NotImplementedError

python/ray/experimental/channel/nccl_group.py

@@ -1,9 +1,13 @@
 import logging
 from types import ModuleType
-from typing import TYPE_CHECKING, List, Optional
+from typing import TYPE_CHECKING, List, Optional, Tuple
 
 import ray
 from ray.exceptions import RayChannelError
+from ray.experimental.channel.gpu_communicator import (
+    GPUCommunicator,
+    TorchTensorAllocator,
+)
 
 if TYPE_CHECKING:
     import cupy as cp
@@ -16,9 +20,10 @@
 logger = logging.getLogger(__name__)
 
 
-class _NcclGroup:
+class _NcclGroup(GPUCommunicator):
     """
-    Represents an actor's NCCL communicator.
+    Represents an actor's NCCL communicator. This is the default NCCL communicator
+    to be used in aDAG if a custom communicator is not provided.
 
     This class is not thread-safe.
     """
@@ -123,7 +128,7 @@ def get_self_rank(self) -> Optional[int]:
         """
         return self._rank
 
-    def send(self, value: "torch.Tensor", peer_rank: int):
+    def send(self, value: "torch.Tensor", peer_rank: int) -> None:
         """
         Send a torch.Tensor to a peer.
 
@@ -151,7 +156,13 @@ def send(self, value: "torch.Tensor", peer_rank: int):
             self._cuda_stream.ptr,
         )
 
-    def recv(self, buf: "torch.Tensor", peer_rank: int):
+    def recv(
+        self,
+        shape: Tuple[int],
+        dtype: "torch.dtype",
+        peer_rank: int,
+        allocator=Optional[TorchTensorAllocator],
+    ) -> "torch.Tensor":
         """
         Receive a torch.Tensor from a peer and synchronize the current stream.
 
@@ -165,6 +176,8 @@ def recv(self, buf: "torch.Tensor", peer_rank: int):
         """
         if self._closed:
             raise RayChannelError("NCCL group has been destroyed.")
+        assert allocator is not None, "NCCL group requires a tensor allocator"
+        buf = allocator(shape, dtype)
         self._comm.recv(
             self.nccl_util.get_tensor_ptr(buf),
             buf.numel(),
@@ -180,8 +193,9 @@ def recv(self, buf: "torch.Tensor", peer_rank: int):
         self._cuda_stream.synchronize()
         if self._closed:
             raise RayChannelError("NCCL group has been destroyed.")
+        return buf
 
-    def destroy(self):
+    def destroy(self) -> None:
         """
         Destroy the NCCL group.
         """