Deprecate LightningDistributed and keep logic in ddp/ddpSpawn directly (#9691)

* Deprecate LightningDistributed and keep logic in ddp/ddpSpawn directly

* Deprecate LightningDistributed and keep logic in ddp/ddpSpawn directly

* Deprecate LightningDistributed and keep logic in ddp/ddpSpawn directly

* Deprecate LightningDistributed and keep logic in ddp/ddpSpawn directly

* Apply suggestions from code review

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* Apply suggestions from code review

Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>

* Deprecate LightningDistributed and keep logic in ddp/ddpSpawn directly

* Update pytorch_lightning/distributed/dist.py

Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>

* Deprecate LightningDistributed and keep logic in ddp/ddpSpawn directly

* Apply suggestions from code review

Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>

* Apply suggestions from code review

* Apply suggestions from code review

* Deprecate LightningDistributed and keep logic in ddp/ddpSpawn directly

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>

CHANGELOG.md

@@ -274,6 +274,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated passing `progress_bar_refresh_rate` to the `Trainer` constructor in favor of adding the `ProgressBar` callback with `refresh_rate` directly to the list of callbacks, or passing `enable_progress_bar=False` to disable the progress bar ([#9616](https://github.com/PyTorchLightning/pytorch-lightning/pull/9616))
 
 
+- Deprecate `LightningDistributed` and move the broadcast logic to `DDPPlugin` and `DDPSpawnPlugin` directly ([#9691](https://github.com/PyTorchLightning/pytorch-lightning/pull/9691))
+
+
 ### Removed
 
 - Removed deprecated `metrics` ([#8586](https://github.com/PyTorchLightning/pytorch-lightning/pull/8586/))
@@ -402,6 +405,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `trainer.accumulate_grad_batches` to be an int on init. Default value for it is now `None` inside Trainer ([#9652](https://github.com/PyTorchLightning/pytorch-lightning/pull/9652))
 
 
+- Fixed `broadcast` in `DDPPlugin` and ``DDPSpawnPlugin` to respect the `src` input  ([#9691](https://github.com/PyTorchLightning/pytorch-lightning/pull/9691))
+
+
 ## [1.4.8] - 2021-09-22
 
 - Fixed error reporting in DDP process reconciliation when processes are launched by an external agent ([#9389](https://github.com/PyTorchLightning/pytorch-lightning/pull/9389))

pytorch_lightning/distributed/dist.py

@@ -14,11 +14,22 @@
 from typing import Any
 
 from pytorch_lightning.overrides.torch_distributed import broadcast_object_list
+from pytorch_lightning.utilities import rank_zero_deprecation
 from pytorch_lightning.utilities.distributed import group as _group
 
 
 class LightningDistributed:
+    """
+    .. deprecated:: v1.5
+        This class is deprecated in v1.5 and will be removed in v1.7.
+        The broadcast logic will be moved to the :class:`DDPPlugin` and :class`DDPSpawnPlugin` classes.
+    """
+
     def __init__(self, rank=None, device=None):
+        rank_zero_deprecation(
+            "LightningDistributed is deprecated in v1.5 and will be removed in v1.7."
+            "Broadcast logic is implemented directly in the :class:`TrainingTypePlugin` implementations."
+        )
         self.rank = rank
         self.device = device
 

pytorch_lightning/plugins/training_type/ddp.py

@@ -31,9 +31,9 @@ from torch.nn.parallel.distributed import DistributedDataParallel
 
 import pytorch_lightning as pl
 from pytorch_lightning.core.optimizer import LightningOptimizer
-from pytorch_lightning.distributed import LightningDistributed
 from pytorch_lightning.overrides import LightningDistributedModule
 from pytorch_lightning.overrides.distributed import prepare_for_backward
+from pytorch_lightning.overrides.torch_distributed import broadcast_object_list
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
 from pytorch_lightning.plugins.training_type.parallel import ParallelPlugin
@@ -48,13 +48,9 @@ from pytorch_lightning.utilities import (
     rank_zero_deprecation,
     rank_zero_warn,
 )
-from pytorch_lightning.utilities.distributed import (
-    distributed_available,
-    init_ddp_connection,
-    rank_zero_only,
-    ReduceOp,
-    sync_ddp_if_available,
-)
+from pytorch_lightning.utilities.distributed import distributed_available
+from pytorch_lightning.utilities.distributed import group as _group
+from pytorch_lightning.utilities.distributed import init_ddp_connection, rank_zero_only, ReduceOp, sync_ddp_if_available
 from pytorch_lightning.utilities.exceptions import DeadlockDetectedException, MisconfigurationException
 from pytorch_lightning.utilities.seed import reset_seed
 from pytorch_lightning.utilities.types import STEP_OUTPUT
@@ -116,7 +112,6 @@ class DDPPlugin(ParallelPlugin):
                 " Notice that it will be overriden by the trainer setting."
             )
         self._sync_batchnorm = sync_batchnorm or False
-        self.dist = LightningDistributed()
         self.num_processes = len(self.parallel_devices) if self.parallel_devices is not None else 0
         self._ddp_kwargs = kwargs
         self._task_idx = None
@@ -269,10 +264,6 @@ class DDPPlugin(ParallelPlugin):
         # where to store ip_table
         init_ddp_connection(self.cluster_environment, self.torch_distributed_backend)
 
-        # set the ranks and devices
-        self.dist.rank = self.global_rank
-        self.dist.device = self.root_device
-
     def _check_can_spawn_children(self):
         if self.local_rank != 0:
             raise RuntimeError(
@@ -403,7 +394,11 @@ class DDPPlugin(ParallelPlugin):
             torch.distributed.barrier()
 
     def broadcast(self, obj: object, src: int = 0) -> object:
-        return self.dist.broadcast(obj)
+        obj = [obj]
+        if self.global_rank != src:
+            obj = [None]
+        broadcast_object_list(obj, src, group=_group.WORLD)
+        return obj[0]
 
     def pre_backward(self, closure_loss: torch.Tensor) -> None:
         """Run before precision plugin executes backward."""

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -24,9 +24,9 @@ import torch.multiprocessing as mp
 from torch.nn.parallel.distributed import DistributedDataParallel
 
 import pytorch_lightning as pl
-from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning.overrides import LightningDistributedModule
 from pytorch_lightning.overrides.distributed import prepare_for_backward
+from pytorch_lightning.overrides.torch_distributed import broadcast_object_list
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
 from pytorch_lightning.plugins.training_type.parallel import ParallelPlugin
@@ -40,13 +40,9 @@ from pytorch_lightning.utilities import (
 from pytorch_lightning.utilities.apply_func import apply_to_collection
 from pytorch_lightning.utilities.cloud_io import atomic_save
 from pytorch_lightning.utilities.cloud_io import load as pl_load
-from pytorch_lightning.utilities.distributed import (
-    distributed_available,
-    init_ddp_connection,
-    rank_zero_only,
-    ReduceOp,
-    sync_ddp_if_available,
-)
+from pytorch_lightning.utilities.distributed import distributed_available
+from pytorch_lightning.utilities.distributed import group as _group
+from pytorch_lightning.utilities.distributed import init_ddp_connection, rank_zero_only, ReduceOp, sync_ddp_if_available
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.seed import reset_seed
 from pytorch_lightning.utilities.types import STEP_OUTPUT
@@ -93,7 +89,6 @@ class DDPSpawnPlugin(ParallelPlugin):
             )
         self._sync_batchnorm = sync_batchnorm or False
         self._ddp_kwargs = kwargs
-        self.dist = LightningDistributed()
         self.num_processes = len(parallel_devices) if parallel_devices is not None else 0
         self.mp_queue = None
         self._ddp_comm_state = ddp_comm_state
@@ -193,10 +188,6 @@ class DDPSpawnPlugin(ParallelPlugin):
         #   ... need to double check that it is the correct place
         # self.trainer.call_setup_hook(self.model)
 
-        # set the ranks and devices
-        self.dist.rank = self.global_rank
-        self.dist.device = self.root_device
-
         # move the model to the correct device
         self.model_to_device()
 
@@ -324,7 +315,11 @@ class DDPSpawnPlugin(ParallelPlugin):
     def broadcast(self, obj: object, src: int = 0) -> object:
         if not distributed_available():
             return obj
-        return self.dist.broadcast(obj)
+        obj = [obj]
+        if self.global_rank != src:
+            obj = [None]
+        broadcast_object_list(obj, src, group=_group.WORLD)
+        return obj[0]
 
     def model_to_device(self):
         if self.root_device.type == "cuda":

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -342,9 +342,6 @@ class DeepSpeedPlugin(DDPPlugin):
 
         self._init_deepspeed_distributed()
 
-        # set the ranks and devices
-        self.dist.rank = self.global_rank
-        self.dist.device = self.root_device
         if not self._config_initialized:
             self._format_config()
             self._config_initialized = True

setup.cfg

@@ -46,6 +46,7 @@ omit =
     pytorch_lightning/cluster_environments/*.py
     pytorch_lightning/utilities/distributed.py
     pytorch_lightning/tuner/auto_gpu_select.py
+    pytorch_lightning/distributed/dist.py
 
 
 [flake8]

tests/deprecated_api/test_remove_1-7.py

@@ -243,3 +243,10 @@ def test_v1_7_0_lightning_logger_base_close(tmpdir):
     ):
         logger = LoggerCollection([logger])
         logger.close()
+
+
+def test_v1_7_0_deprecate_lightning_distributed(tmpdir):
+    with pytest.deprecated_call(match="LightningDistributed is deprecated in v1.5 and will be removed in v1.7."):
+        from pytorch_lightning.distributed.dist import LightningDistributed
+
+        _ = LightningDistributed()