ProcessGroupBabyNCCL: support multiple streams and use event on start (#91)

Author: d4l3k

torchft/process_group.py

@@ -19,17 +19,18 @@
 import logging
 import queue
 import threading
+from contextlib import contextmanager, nullcontext
 from dataclasses import dataclass
 from datetime import timedelta
 from typing import (
     TYPE_CHECKING,
     Any,
     Callable,
     Dict,
+    Generator,
     List,
     Optional,
     Tuple,
-    Type,
     TypeVar,
     Union,
     cast,
@@ -58,9 +59,9 @@
     BroadcastOptions,
     ReduceOp,
     Work,
-    _world,
 )
 from torch.futures import Future
+from torch.utils._pytree import tree_any
 
 if TYPE_CHECKING:
     from torchft.manager import Manager
@@ -586,29 +587,52 @@ def __init__(
         self._timeout = timeout
 
     def wait(self, timeout: Optional[timedelta] = None) -> bool:
+        self._pg._assert_alive()
+
         self._tx.put(("wait", self._op_id), timeout=self._timeout)
-        assert _get(self._rx, self._timeout) == self._op_id
+        op_id, event = cast(
+            Tuple[int, Optional[torch.cuda.Event]],
+            _get(self._rx, timeout or self._timeout),
+        )
+        assert op_id == self._op_id
+        if event is not None:
+            event.wait()
         return True
 
+    def synchronize(self) -> None:
+        # TODO: No one seems to use this and NCCL wait already only waits the
+        # stream and is non-blocking on the CPU side so no real need for a
+        # separate call.
+        raise NotImplementedError("not implemented")
+
     def get_future(self) -> Future[object]:
         return self._pg._get_future(self._op_id)
 
     def __del__(self) -> None:
         self._tx.put(("del", self._op_id), timeout=self._timeout)
 
 
-class _BabyWorkNCCL(_BabyWork):
-    def wait(self, timeout: Optional[timedelta] = None) -> bool:
-        self._tx.put(("synchronize", self._op_id), timeout=self._timeout)
-        # pyre-fixme[23]: unable to unpack into 2 values
-        op_id, event = _get(self._rx, self._timeout)
-        assert op_id == self._op_id
-        assert isinstance(event, torch.cuda.Event)
+def _is_any_cuda(obj: object) -> bool:
+    """
+    Returns true if any of the tensors in the object are CUDA tensors.
 
-        # Wait on Event makes the stream wait but not the CPU thread.
-        event.wait()
+    Supports lists, tuples, dicts, and tensors.
+    """
+    return tree_any(lambda obj: isinstance(obj, torch.Tensor) and obj.is_cuda, obj)
 
-        return True
+
+@dataclass
+class _OpMetadata:
+    work: Work
+    stream: Optional[torch.cuda.Stream]
+
+    @contextmanager
+    def set_stream(self) -> Generator[None, None, None]:
+        if self.stream is not None:
+            with torch.cuda.stream(self.stream):
+                yield
+        else:
+            yield
 
 
 class ProcessGroupBaby(ProcessGroup):
@@ -617,11 +641,8 @@ class ProcessGroupBaby(ProcessGroup):
     subprocess. Since it's running in a subprocess all tensors need to be in
     shared memory or will be moved to shared memory. CUDA tensors are implicitly
     share able and don't need any changes.
-
     """
 
-    WORK_CLASS: Type[_BabyWork] = _BabyWork
-
     def __init__(self, timeout: Union[float, timedelta] = 60.0) -> None:
         super().__init__(0, 1)
 
@@ -679,7 +700,14 @@ def configure(self, store_addr: str, rank: int, world_size: int) -> None:
 
         self._p = ctx.Process(
             target=self._worker,
-            args=(store_addr, rank, world_size, self._tx, self._rx, self._future_queue),
+            args=(
+                store_addr,
+                rank,
+                world_size,
+                self._tx,
+                self._rx,
+                self._future_queue,
+            ),
             daemon=True,
         )
         self._p.start()
@@ -716,23 +744,70 @@ def _worker(
                 return
             tx.put(None)
 
-            work = {}
+            streams: Dict[str, torch.cuda.Stream] = {}
+            work: Dict[int, _OpMetadata] = {}
             next_op_id: int = 0
 
             while True:
                 op = rx.get()
                 cmd = op[0]
                 if cmd == "func":
-                    func_name, args, kwargs = op[1:]
-                    args = _PickleSafeOptions.unsafe_args(args)
-                    fn = getattr(pg, func_name)
-                    work[next_op_id] = fn(*args, **kwargs)
+                    func_name, args, kwargs, stream_device, stream_id, event = op[1:]
+
+                    # To avoid potential deadlocks we need to preserve the
+                    # stream/synchronization behavior of the parent process.
+                    # We allocate one Stream per stream_id to make sure that we
+                    # don't accidentally introduce cross stream synchronization
+                    # points.
+                    if stream_id is not None:
+                        stream_key = f"{stream_device}/{stream_id}"
+                        if stream_key not in streams:
+                            streams[stream_key] = torch.cuda.Stream(
+                                device=stream_device
+                            )
+                        stream = streams[stream_key]
+                    else:
+                        stream = None
+
+                    with (
+                        torch.cuda.stream(stream)
+                        if stream is not None
+                        else nullcontext()
+                    ):
+                        # Make the stream wait on the cuda event to make sure we
+                        # don't start the operation until the tensor is ready.
+                        if event is not None:
+                            event.wait()
+
+                        args = _PickleSafeOptions.unsafe_args(args)
+                        fn = getattr(pg, func_name)
+                        work[next_op_id] = _OpMetadata(
+                            work=fn(*args, **kwargs),
+                            stream=stream,
+                        )
                     tx.put(next_op_id)
                     next_op_id += 1
                 elif cmd == "wait":
                     op_id: int = op[1]
-                    work[op_id].wait()
-                    tx.put(op_id)
+
+                    metadata = work[op_id]
+
+                    with metadata.set_stream():
+                        # With WorkNCCL this makes the stream wait not the CPU when
+                        # no timeout is passed.
+                        metadata.work.wait()
+
+                        # Register event on the stream that we can pass to the main
+                        # process.
+                        event = (
+                            torch.cuda.current_stream().record_event(
+                                torch.cuda.Event(interprocess=True)
+                            )
+                            if metadata.stream is not None
+                            else None
+                        )
+
+                    tx.put((op_id, event))
                 elif cmd == "del":
                     op_id: int = op[1]
                     del work[op_id]
@@ -746,23 +821,8 @@ def callback(fut: Future[object]) -> None:
                         except Exception as e:
                             future_queue.put((op_id, _FUTURE_EXCEPTION, e))
 
-                    work[op_id].get_future().add_done_callback(callback)
+                    work[op_id].work.get_future().add_done_callback(callback)
                     tx.put(op_id)
-                elif cmd == "synchronize":
-                    # CUDA only, use events instead of waiting on CPU
-                    op_id = op[1]
-
-                    # With WorkNCCL this makes the stream wait not the CPU when
-                    # no timeout is passed.
-                    work[op_id].wait()
-
-                    # Register event on the stream that we can pass to the main
-                    # process.
-                    event = torch.cuda.Event(interprocess=True)
-                    event.record()
-
-                    del work[op_id]
-                    tx.put((op_id, event))
                 elif cmd == "num_active_work":
                     tx.put(len(work))
                 else:
@@ -771,6 +831,7 @@ def callback(fut: Future[object]) -> None:
         except Exception as e:
             logger.exception("worker errored")
             tx.put(e)
+            raise
 
     def _future_handler(self, future_queue: mp.Queue) -> None:
         try:
@@ -792,6 +853,8 @@ def _future_handler(self, future_queue: mp.Queue) -> None:
             logger.exception(f"got unexpected error in future handler: {e}")
 
     def _get_future(self, op_id: int) -> Future[object]:
+        self._assert_alive()
+
         with self._futures_lock:
             fut = Future()  # pyre-fixme[29]: is not a function
             self._futures[op_id] = fut
@@ -804,22 +867,52 @@ def _get_future(self, op_id: int) -> Future[object]:
         return fut
 
     def _run_func(self, func: str, *args: object, **kwargs: object) -> Work:
+        self._assert_alive()
+
         rx = self._rx
         tx = self._tx
         assert rx is not None
         assert tx is not None
 
+        is_cuda = _is_any_cuda(args)
+
+        stream_device = torch.cuda.current_stream().device if is_cuda else None
+        stream_id = torch.cuda.current_stream().stream_id if is_cuda else None
+        event = (
+            torch.cuda.current_stream().record_event(
+                torch.cuda.Event(interprocess=True)
+            )
+            if is_cuda
+            else None
+        )
+
         tx.put(
-            ("func", func, _PickleSafeOptions.safe_args(args), kwargs),
+            (
+                "func",
+                func,
+                _PickleSafeOptions.safe_args(args),
+                kwargs,
+                stream_device,
+                stream_id,
+                event,
+            ),
             timeout=self._timeout,
         )
 
         op_id = _get(rx, self._timeout)
         assert isinstance(op_id, int), f"invalid return {op_id}"
 
-        return self.WORK_CLASS(
-            pg=self, tx=tx, rx=rx, op_id=op_id, timeout=self._timeout
-        )
+        return _BabyWork(pg=self, tx=tx, rx=rx, op_id=op_id, timeout=self._timeout)
+
+    def _assert_alive(self) -> None:
+        """
+        Assert that the process group is alive. This is used to ensure that
+        operations are not performed on a dead process group and any errors are surfaced.
+        """
+        p = self._p
+        assert p is not None
+        if not p.is_alive():
+            raise RuntimeError(f"child process {p.pid=} is dead {p.exitcode=}")
 
     def allreduce(
         self,
@@ -952,8 +1045,6 @@ class ProcessGroupBabyNCCL(ProcessGroupBaby):
     tensors may leak in the current PyTorch implementation. TODO fix
     """
 
-    WORK_CLASS = _BabyWorkNCCL
-
     @classmethod
     def _create_pg(cls, store: Store, rank: int, world_size: int) -> BaseProcessGroup:
         # pyre-fixme[16]: no attribute ProcessGroupNCCL

torchft/process_group_test.py

@@ -266,6 +266,31 @@ def test_baby_gloo_apis(self) -> None:
 
         self.assertEqual(a.num_active_work(), 0)
 
+    # pyre-fixme[56]: Pyre was not able to infer the type of argument
+    @skipUnless(torch.cuda.is_available(), "needs CUDA")
+    def test_baby_nccl_apis(self) -> None:
+        # set to 1 if more than >=2 gpus
+        device_id = 1 % torch.cuda.device_count()
+        torch.cuda.set_device(device_id)
+
+        store = TCPStore(
+            host_name="localhost", port=0, is_master=True, wait_for_workers=False
+        )
+
+        store_addr = f"localhost:{store.port}/prefix"
+
+        a = ProcessGroupBabyNCCL(timeout=timedelta(seconds=10))
+        a.configure(store_addr, 0, 1)
+
+        _test_pg(a, torch.randn((2, 3), device="cuda"))
+
+        torch.cuda.synchronize()
+
+        # force collection to ensure no BabyWork objects remain
+        gc.collect()
+
+        self.assertEqual(a.num_active_work(), 0)
+
     def test_dummy(self) -> None:
         pg = ProcessGroupDummy(0, 1)
         m = nn.Linear(3, 4)
@@ -282,12 +307,15 @@ def test_baby_nccl_2gpu(self) -> None:
         store_addr: str = f"localhost:{store.port}/prefix"
 
         def run(rank: int) -> Tuple[torch.Tensor, Work]:
-            a = ProcessGroupBabyNCCL()
+            a = ProcessGroupBabyNCCL(
+                timeout=timedelta(seconds=10.0),
+            )
             a.configure(store_addr, rank, 2)
-
             self.assertEqual(a.size(), 2)
 
-            at = torch.tensor([rank + 1], device=f"cuda:{rank}")
+            # We test using set_device to ensure stream device is correct.
+            torch.cuda.set_device(rank)
+            at = torch.tensor([rank + 1], device="cuda")
 
             a_work = a.allreduce([at], ReduceOp.SUM)
             return at, a_work