Add method to broadcast and reduce distributed model parameters

skrl/models/torch/base.py

@@ -7,7 +7,7 @@
 import numpy as np
 import torch
 
-from skrl import logger
+from skrl import config, logger
 
 
 class Model(torch.nn.Module):
@@ -743,3 +743,48 @@ def update_parameters(self, model: torch.nn.Module, polyak: float = 1) -> None:
                 for parameters, model_parameters in zip(self.parameters(), model.parameters()):
                     parameters.data.mul_(1 - polyak)
                     parameters.data.add_(polyak * model_parameters.data)
+
+    def broadcast_parameters(self, rank: int = 0):
+        """Broadcast model parameters to the whole group (e.g.: across all nodes) in distributed runs
+
+        After calling this method, the distributed model will contain the broadcasted parameters from ``rank``
+
+        :param rank: Worker/process rank from which to broadcast model parameters (default: ``0``)
+        :type rank: int
+
+        Example::
+
+            # broadcast model parameter from worker/process with rank 1
+            >>> if config.torch.is_distributed:
+            ...     model.update_parameters(source_model, rank=1)
+        """
+        object_list = [self.state_dict()]
+        torch.distributed.broadcast_object_list(object_list, rank)
+        self.load_state_dict(object_list[0])
+
+    def reduce_parameters(self):
+        """Reduce model parameters across all workers/processes in the whole group (e.g.: across all nodes)
+
+        After calling this method, the distributed model parameters will be bitwise identical for all workers/processes
+
+        Example::
+
+            # reduce model parameter across all workers/processes
+            >>> if config.torch.is_distributed:
+            ...     model.reduce_parameters()
+        """
+        # batch all_reduce ops: https://github.com/entity-neural-network/incubator/pull/220
+        gradients = []
+        for parameters in self.parameters():
+            if parameters.grad is not None:
+                gradients.append(parameters.grad.view(-1))
+        gradients = torch.cat(gradients)
+
+        torch.distributed.all_reduce(gradients, op=torch.distributed.ReduceOp.SUM)
+
+        offset = 0
+        for parameters in self.parameters():
+            if parameters.grad is not None:
+                parameters.grad.data.copy_(gradients[offset:offset + parameters.numel()] \
+                                           .view_as(parameters.grad.data) / config.torch.world_size)
+                offset += parameters.numel()