[c10d] Figure out device to use for object collectives

test/distributed/test_c10d_common.py

@@ -1610,8 +1610,8 @@ def test_backend_config(self):
         # Ensure backend config can be created with the following arguments
         backend_config_strings_and_expected_values = [
             (dist.Backend.GLOO, "cpu:gloo,cuda:gloo"),
-            (dist.Backend.NCCL, "cpu:nccl,cuda:nccl"),
-            (dist.Backend.MPI, "cpu:mpi,cuda:mpi"),
+            (dist.Backend.NCCL, "cuda:nccl"),
+            (dist.Backend.MPI, "cpu:mpi"),
             (dist.Backend.UCC, "cpu:ucc,cuda:ucc"),
             (dist.Backend.DUMMY, "cpu:dummy,cuda:dummy"),
             ("DUMMY", "cpu:dummy,cuda:dummy"),
@@ -1620,7 +1620,6 @@ def test_backend_config(self):
             ("cpu:dummy,cuda:nccl", "cpu:dummy,cuda:nccl"),
             ("cpu:gloo,cuda:dummy", "cpu:gloo,cuda:dummy"),
             ("cpu:gloo,cuda:nccl", "cpu:gloo,cuda:nccl"),
-            ("cPu:gLoO,cuDa:NcCl", "cpu:gloo,cuda:nccl")
         ]
 
         for config_str, expected_value in backend_config_strings_and_expected_values:

test/distributed/test_pg_wrapper.py

@@ -124,20 +124,6 @@ def _test_collectives_op_mismatch(self, wrapper_pg, use_cuda=False):
             tensor=tensor,
         )
 
-        with self.assertRaisesRegex(RuntimeError, ".*") as cm:
-            scatter_result = [torch.ones(4) * i for i in range(self.world_size)]
-            scattered_tensor = torch.empty(4)
-            if self.rank == 0:
-                wrapper_pg.scatter(scattered_tensor, scatter_result, 0)
-            else:
-                wrapper_pg.reduce_scatter(scattered_tensor, scatter_result)
-        self._validate_error(
-            exception=cm.exception,
-            op_type="SCATTER" if self.rank == 0 else "REDUCE_SCATTER",
-            rank=self.rank,
-            tensor=scattered_tensor,
-        )
-
         with self.assertRaisesRegex(RuntimeError, ".*") as cm:
             if self.rank == 0:
                 wrapper_pg.broadcast(tensor, 0)

torch/csrc/distributed/c10d/ProcessGroup.hpp

@@ -548,7 +548,9 @@ class TORCH_API ProcessGroup : public torch::CustomClassHolder {
       c10::DeviceType deviceType,
       BackendType backendType,
       const c10::optional<c10::intrusive_ptr<Backend>>& backend) {
+    // TODO: should we add these entries after the backend setting succeeds?
     deviceTypeToBackendType_[deviceType] = backendType;
+    deviceTypes_.insert(deviceType);
     // if the backendType is already set then reuse it for this device
     if (backendTypeToBackend_.find(backendType) !=
         backendTypeToBackend_.end()) {
@@ -585,6 +587,19 @@ class TORCH_API ProcessGroup : public torch::CustomClassHolder {
     return backendTypeToBackend_.at(backendType);
   }
 
+  // Return device types supported by this ProcessGroup.
+  // Note: the return type is `Device` rather than `DeviceType` for the purpose
+  // of easy comparison at Python level. The `Device` will have default index
+  // (-1).
+  std::vector<c10::Device> getDeviceTypes() const {
+    std::vector<c10::Device> devices;
+    devices.reserve(deviceTypes_.size());
+    for (auto& dt : deviceTypes_) {
+      devices.push_back(c10::Device(dt));
+    }
+    return devices;
+  }
+
  protected:
   // Implementations of this interface need to call this to setup
   // appropriate logging etc.
@@ -603,6 +618,7 @@ class TORCH_API ProcessGroup : public torch::CustomClassHolder {
   DebugLevel dist_debug_level_;
 
   // Backend classes for this ProcessGroup
+  std::unordered_set<c10::DeviceType> deviceTypes_;
   std::unordered_map<c10::DeviceType, BackendType> deviceTypeToBackendType_;
   std::unordered_map<c10::DeviceType, c10::intrusive_ptr<Backend>>
       deviceTypeToBackend_;

torch/csrc/distributed/c10d/init.cpp

@@ -1603,6 +1603,8 @@ that adds a prefix to each key inserted to the store.
               py::arg("timeout") = ::c10d::kUnsetTimeout,
               py::arg("wait_all_ranks") = false,
               py::call_guard<py::gil_scoped_release>())
+          .def_property_readonly(
+              "_device_types", &::c10d::ProcessGroup::getDeviceTypes)
           .def(
               "_get_backend_name",
               &::c10d::ProcessGroup::getBackendName,

torch/distributed/distributed_c10d.py

@@ -204,6 +204,13 @@ class Backend:
 
     backend_list = [UNDEFINED, GLOO, NCCL, UCC, MPI]
 
+    backend_capability: Dict[str, List[str]] = {
+        GLOO : ["cpu", "cuda"],
+        NCCL : ["cuda"],
+        UCC : ["cpu", "cuda"],
+        MPI : ["cpu"],
+    }
+
     def __new__(cls, name: str):
         if not isinstance(name, str):
             raise ValueError(f"Backend name must be a string, but got: {name}")
@@ -214,7 +221,7 @@ def __new__(cls, name: str):
         return value
 
     @classmethod
-    def register_backend(cls, name, func, extended_api=False):
+    def register_backend(cls, name, func, extended_api=False, devices: Optional[Union[str, List[str]]] = None):
         """
         Registers a new backend with the given name and instantiating function.
 
@@ -232,6 +239,9 @@ def register_backend(cls, name, func, extended_api=False):
                                            Default: ``False``. If set to ``True``, the backend
                                            will get an instance of ``c10d::DistributedBackendOptions``, and
                                            a process group options object as defined by the backend implementation.
+            device (str or list of str, optional): device type this backend
+                            supports, e.g. "cpu", "cuda", etc. If `None`,
+                            assuming both "cpu" and "cuda"
 
         .. note:: This support of 3rd party backend is experimental and subject to change.
 
@@ -248,28 +258,47 @@ def register_backend(cls, name, func, extended_api=False):
 
         setattr(Backend, name.upper(), name.upper())
         Backend.backend_list.append(name.lower())
+
+        # Update device capability matrix in Backend class
+        if devices is None:
+            # This is more of a backward support for groups like `threaded`:
+            # assume default devices "cpu" and "cuda", but warn
+            warnings.warn(
+                f"Device capability of {name} unspecified, assuming `cpu` and "
+                "`cuda`. Please specify it via the `devices` argument of "
+                "`register_backend`."
+            )
+            Backend.backend_capability[name.lower()] = ["cpu", "cuda"]
+        elif isinstance(devices, str):
+            # Single device string specified. Simply convert to list.
+            Backend.backend_capability[name.lower()] = [devices]
+        else:
+            Backend.backend_capability[name.lower()] = devices
+
         Backend._plugins[name.upper()] = Backend._BackendPlugin(func, extended_api)
 
 class BackendConfig:
 
     def __init__(self, backend: Union[str, Backend]):
         self.device_backend_map: Dict[torch.device, Backend] = {}
 
-        # Cases for when backend is a single string (without device types)
         if backend == Backend.UNDEFINED:
             # default config when backend is not specified
+            # supported since PyTorch 2.0
             self.device_backend_map = {
                 "cpu": Backend.GLOO,
                 "cuda": Backend.NCCL,
             }
         elif backend.lower() in Backend.backend_list:
-            # backend applies to all devices (e.g. "NCCL", "GLOO", "UCC", "MPI", "custom_backend")
+            # Cases for when backend is a single string (without device types)
+            # e.g. "nccl", "gloo", "ucc", "mpi"
+            supported_devices = Backend.backend_capability[backend.lower()]
             backend_val = Backend(backend)
             self.device_backend_map = {
-                "cpu": backend_val,
-                "cuda": backend_val,
+                device : backend_val for device in supported_devices
             }
-        else:
+        elif ":" in backend.lower():
+            # Backend specified in "device:backend" format
             # make sure the backend string is in the correct format
             # "{device_type1}:{backend1},{device_type2}:{backend2}"
             # e.g. "cpu:gloo,cuda:nccl"
@@ -288,6 +317,24 @@ def __init__(self, backend: Union[str, Backend]):
                     raise ValueError(f"Duplicate device type {device} \
                                      in backend string: {backend}. {backend_str_error_message}")
                 self.device_backend_map[device] = Backend(backend)
+        else:
+            # User specified a single backend name whose device capability is
+            # unknown, assuming it can support the default devices of PyTorch
+            # (cpu and cuda)
+            warnings.warn(
+                f"Device capability of {backend} unknown, assuming `cpu` and "
+                "`cuda`. You can specify it in `device:backend` format in "
+                "`init_process_group` call."
+            )
+            backend_val = Backend(backend)
+            self.device_backend_map = {
+                "cpu" : backend_val,
+                "cuda" : backend_val,
+            }
+
+        logger.info(
+            f"Using backend config: {self.device_backend_map}"  # noqa: G004
+        )
 
     def __repr__(self):
         # string with all the device:backend pairs separated by commas
@@ -406,6 +453,7 @@ class _World:
     def __init__(self):
         self._default_pg = None
         self._pg_coalesce_state: Dict[ProcessGroup, List[Union[_CollOp, P2POp]]] = {}
+        self._pg_object_coll_device: Dict[ProcessGroup, torch.device] = {}
 
     @property
     def default_pg(self):
@@ -491,6 +539,10 @@ def pg_to_tag(self) -> Dict[ProcessGroup, str]:
     def pg_coalesce_state(self) -> Dict[ProcessGroup, List[Union[_CollOp, P2POp]]]:
         return self._pg_coalesce_state
 
+    @property
+    def pg_object_coll_device(self) -> Dict[ProcessGroup, torch.device]:
+        return self._pg_object_coll_device
+
 _world = _World()
 """Holds the singleton instance of ``_World`` used by c10. Experimental extension point to override it"""
 
@@ -521,14 +573,54 @@ class GroupMember(metaclass=_WorldMeta):
 STORE_BASED_BARRIER_PREFIX = "store_based_barrier_key"
 
 
-def _get_pg_device(group: ProcessGroup):
-    """
-    Returns the device to use with ``group``.
-    This is cuda for NCCL and CPU for everything else
-    """
-    if _check_for_nccl_backend(group):
-        return torch.device("cuda", torch.cuda.current_device())
-    return torch.device("cpu")
+def _get_object_coll_device(group: Optional[ProcessGroup] = None):
+    group = group or _get_default_group()
+    if group in _world.pg_object_coll_device:
+        # Previously searched and cached; just return
+        return _world.pg_object_coll_device[group]
+
+    if not isinstance(group, ProcessGroup):
+        # Provide backward compatibility to cases where `group` passed in is
+        # actually a Backend (like `ProcessGroupGloo`) rather than a
+        # `ProcessGroup` in PT 2.0 sense
+        warnings.warn(
+            f"You are using a Backend {type(group)} as a ProcessGroup. "
+            "This usage is deprecated since PyTorch 2.0. Please use a public API "
+            "of PyTorch Distributed instead."
+        )
+        # Most users create Gloo with private API for object collectives
+        _world.pg_object_coll_device[group] = torch.device("cpu")
+        return _world.pg_object_coll_device[group]
+
+    """
+    ``group._device_types`` is a property pybind that returns the devices
+    ("cpu", "cuda", etc) supported by ``group``. Can be multiple if the
+    ``group`` supports multiple devices.
+    """
+    devices = group._device_types
+
+    if len(devices) == 1:
+        # User fixed exactly one backend in `init_process_group`
+        _world.pg_object_coll_device[group] = devices[0]
+    elif len(devices) == 0:
+        # No backend has been registered with this PG (maybe because no
+        # collective has been run?) We pick cpu as the default and hopefully
+        # this would lazily init Gloo or other available cpu backend.
+        _world.pg_object_coll_device[group] = torch.device("cpu")
+    elif torch.device("cpu") in devices:
+        # There are multiple backends in this PG and cpu is among them.
+        # cpu is preferred as the object is in cpu memory. No need for device
+        # copy.
+        _world.pg_object_coll_device[group] = torch.device("cpu")
+    else:
+        # No cpu in the backend list. Randomly pick the first backend
+        _world.pg_object_coll_device[group] = devices[0]
+
+    logger.info(
+        f"Using device {_world.pg_object_coll_device[group]} for object "  # noqa: G004
+        "collectives."
+    )
+    return _world.pg_object_coll_device[group]
 
 
 # Environment variable to control whether we do a barrier after process group
@@ -1271,6 +1363,7 @@ def destroy_process_group(group: Optional[ProcessGroup] = None):
         _world.pg_to_tag.clear()
         _world.tags_to_pg.clear()
         _world.pg_coalesce_state.clear()
+        _world.pg_object_coll_device.clear()
 
         # when process group doesn't have an explicit name (only WORLD (default)
         # process group can have an explicit name), we use global _world.group_count
@@ -1286,6 +1379,8 @@ def destroy_process_group(group: Optional[ProcessGroup] = None):
         del _world.pg_names[pg]
         del _world.pg_group_ranks[pg]
         del _world.pg_backend_config[pg]
+        if pg in _world.pg_object_coll_device:
+            del _world.pg_object_coll_device[pg]
         if pg in _world.pg_coalesce_state.keys():
             warnings.warn(
                 "Some coalesced collectives haven't been launched when "
@@ -2236,7 +2331,7 @@ def all_gather_object(object_list, obj, group=None):
         _warn_not_in_group("all_gather_object")
         return
 
-    current_device = _get_pg_device(group)
+    current_device = _get_object_coll_device(group)
     input_tensor, local_size = _object_to_tensor(obj, current_device)
 
     # Gather all local sizes. This is so that we can find the max size, and index
@@ -2337,7 +2432,7 @@ def gather_object(obj, object_gather_list=None, dst=0, group=None):
     # Ensure object_gather_list is specified appropriately.
     my_rank = get_rank()
     _validate_output_list_for_rank(my_rank, dst, object_gather_list)
-    current_device = _get_pg_device(group)
+    current_device = _get_object_coll_device(group)
     input_tensor, local_size = _object_to_tensor(obj, current_device)
 
     # Gather all local sizes. This is so that we can find the max size, and index
@@ -2451,7 +2546,7 @@ def broadcast_object_list(object_list, src=0, group=None, device=None):
     # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
     # case it is not ``None`` we move the size and object tensors to be
     # broadcasted to this device.
-    current_device = device or _get_pg_device(group)
+    current_device = device or _get_object_coll_device(group)
     my_rank = get_rank()
     # Serialize object_list elements to tensors on src rank.
     if my_rank == src:
@@ -2556,7 +2651,7 @@ def scatter_object_list(
         )
 
     my_rank = get_rank()
-    pg_device = _get_pg_device(group)
+    pg_device = _get_object_coll_device(group)
     if my_rank == src:
         tensor_list, tensor_sizes = zip(
             *[_object_to_tensor(obj, pg_device) for obj in scatter_object_input_list]

torch/testing/_internal/distributed/distributed_test.py

@@ -1488,20 +1488,6 @@ def test_batch_isend_irecv_gloo_tags(self):
 
             self._barrier()
 
-        # NCCL Batch SEND RECV Tensor Error
-        @skip_but_pass_in_sandcastle_if(BACKEND != "nccl", "NCCL Batch Send Recv Only")
-        @requires_nccl_version((2, 7, 0), "Need NCCL 2.7+ for send/recv")
-        def test_batch_isend_irecv_tensor_err(self):
-            self._barrier()
-            rank = dist.get_rank()
-            if rank == 0:
-                with self.assertRaisesRegex(
-                    RuntimeError, "Tensors must be CUDA and dense"
-                ):
-                    send_tensor = _build_tensor(rank + 1)
-                    send_op = dist.P2POp(dist.isend, send_tensor, 1)
-                    dist.batch_isend_irecv([send_op])
-
         # NCCL Batch SEND RECV Op Error
         @skip_but_pass_in_sandcastle_if(BACKEND != "nccl", "NCCL Batch Send Recv Only")
         @requires_nccl_version((2, 7, 0), "Need NCCL 2.7+ for send/recv")

torch/testing/_internal/distributed/multi_threaded_pg.py

@@ -381,14 +381,15 @@ class WorldData:
     tags_to_pg: Dict[str, List[dist.ProcessGroup]]
     pg_to_tag: Dict[dist.ProcessGroup, str]
     pg_coalesce_state: Dict[dist.ProcessGroup, List[Union[_CollOp, P2POp]]]
+    pg_object_coll_device: Dict[dist.ProcessGroup, torch.device]
 
 
 class ThreadLocalWorld:
     _world = threading.local()
 
     def _get_world(self) -> WorldData:
         if not hasattr(ThreadLocalWorld._world, "world"):
-            ThreadLocalWorld._world.world = WorldData(None, {}, {}, {}, {}, 0, {}, {}, {})
+            ThreadLocalWorld._world.world = WorldData(None, {}, {}, {}, {}, 0, {}, {}, {}, {})
         return ThreadLocalWorld._world.world
 
     @property
@@ -435,6 +436,10 @@ def pg_to_tag(self):
     def pg_coalesce_state(self) -> Dict[dist.ProcessGroup, List[Union[_CollOp, P2POp]]]:
         return self._get_world().pg_coalesce_state
 
+    @property
+    def pg_object_coll_device(self) -> Dict[dist.ProcessGroup, torch.device]:
+        return self._get_world().pg_object_coll_device
+
 
 _old_pg_world = None