[Feature] Local and Remote WeightUpdaters

docs/source/reference/collectors.rst

@@ -117,6 +117,76 @@ try to limit the cases where a deepcopy will be executed. The following chart sh
 
    Policy copy decision tree in Collectors.
 
+Weight Synchronization in Distributed Environments
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+In distributed and multiprocessed environments, ensuring that all instances of a policy are synchronized with the
+latest trained weights is crucial for consistent performance. The API introduces a flexible and extensible
+mechanism for updating policy weights across different devices and processes, accommodating various deployment scenarios.
+
+Local and Remote Weight Updaters
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The weight synchronization process is facilitated by two main components: :class:`~torchrl.collectors.LocalWeightUpdaterBase`
+and :class:`~torchrl.collectors.RemoteWeightUpdaterBase`. These base classes provide a structured interface for
+implementing custom weight update logic, allowing users to tailor the synchronization process to their specific needs.
+
+- :class:`~torchrl.collectors.LocalWeightUpdaterBase`: This component is responsible for updating the policy weights on
+  the local inference worker. It is particularly useful when the training and inference occur on the same machine but on
+  different devices. Users can extend this class to define how weights are fetched from a server and applied locally.
+  It is also the extension point for collectors where the workers need to ask for weight updates (in contrast with
+  situations where the server decides when to update the worker policies).
+- :class:`~torchrl.collectors.RemoteWeightUpdaterBase`: This component handles the distribution of policy weights to
+  remote inference workers. It is essential in distributed systems where multiple workers need to be kept in sync with
+  the central policy. Users can extend this class to implement custom logic for synchronizing weights across a network of
+  devices or processes.
+
+Extending the Updater Classes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To accommodate diverse use cases, the API allows users to extend the updater classes with custom implementations.
+This flexibility is particularly beneficial in scenarios involving complex network architectures or specialized hardware
+setups. By implementing the abstract methods in these base classes, users can define how weights are retrieved,
+transformed, and applied, ensuring seamless integration with their existing infrastructure.
+
+Default Implementations
+~~~~~~~~~~~~~~~~~~~~~~~
+
+For common scenarios, the API provides default implementations of these updaters, such as
+:class:`~torchrl.collectors.VanillaLocalWeightUpdater`, :class:`~torchrl.collectors.MultiProcessedRemoteWeightUpdate`,
+:class:`~torchrl.collectors.RayRemoteWeightUpdater`, :class:`~torchrl.collectors.RPCRemoteWeightUpdater`, and
+:class:`~torchrl.collectors.DistributedRemoteWeightUpdater`.
+These implementations cover a range of typical deployment configurations, from single-device setups to large-scale
+distributed systems.
+
+Practical Considerations
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+When designing a system that leverages this API, consider the following:
+
+- Network Latency: In distributed environments, network latency can impact the speed of weight updates. Ensure that your
+  implementation accounts for potential delays and optimizes data transfer where possible.
+- Consistency: Ensure that all workers receive the updated weights in a timely manner to maintain consistency across
+  the system. This is particularly important in reinforcement learning scenarios where stale weights can lead to
+  suboptimal policy performance.
+- Scalability: As your system grows, the weight synchronization mechanism should scale efficiently. Consider the
+  overhead of broadcasting weights to a large number of workers and optimize the process to minimize bottlenecks.
+
+By leveraging the API, users can achieve robust and efficient weight synchronization across a variety of deployment
+scenarios, ensuring that their policies remain up-to-date and performant.
+
+.. currentmodule:: torchrl.collectors
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    LocalWeightUpdaterBase
+    RemoteWeightUpdaterBase
+    VanillaLocalWeightUpdater
+    MultiProcessedRemoteWeightUpdate
+    RayRemoteWeightUpdater
+    DistributedRemoteWeightUpdater
+    RPCRemoteWeightUpdater
 
 Collectors and replay buffers interoperability
 ----------------------------------------------

test/test_distributed.py

@@ -390,25 +390,29 @@ def _test_distributed_collector_updatepolicy(
             update_interval=update_interval,
             **cls.distributed_kwargs(),
         )
-        total = 0
-        first_batch = None
-        last_batch = None
-        for i, data in enumerate(collector):
-            total += data.numel()
-            assert data.numel() == frames_per_batch
-            if i == 0:
-                first_batch = data
-                policy.weight.data += 1
-            elif total == total_frames - frames_per_batch:
-                last_batch = data
-        assert (first_batch["action"] == 1).all(), first_batch["action"]
-        if update_interval == 1:
-            assert (last_batch["action"] == 2).all(), last_batch["action"]
-        else:
-            assert (last_batch["action"] == 1).all(), last_batch["action"]
-        collector.shutdown()
-        assert total == total_frames
-        queue.put("passed")
+        try:
+
+            total = 0
+            first_batch = None
+            last_batch = None
+            for i, data in enumerate(collector):
+                total += data.numel()
+                assert data.numel() == frames_per_batch
+                if i == 0:
+                    first_batch = data
+                    policy.weight.data += 1
+                elif total == total_frames - frames_per_batch:
+                    last_batch = data
+            assert (first_batch["action"] == 1).all(), first_batch["action"]
+            if update_interval == 1:
+                assert (last_batch["action"] == 2).all(), last_batch["action"]
+            else:
+                assert (last_batch["action"] == 1).all(), last_batch["action"]
+            assert total == total_frames
+            queue.put("passed")
+        finally:
+            collector.shutdown()
+            queue.put("not passed")
 
     @pytest.mark.parametrize(
         "collector_class",
@@ -490,12 +494,14 @@ def test_distributed_collector_sync(self, sync, frames_per_batch=200):
             sync=sync,
             **self.distributed_kwargs(),
         )
-        total = 0
-        for data in collector:
-            total += data.numel()
-            assert data.numel() == frames_per_batch
-        collector.shutdown()
-        assert total == 200
+        try:
+            total = 0
+            for data in collector:
+                total += data.numel()
+                assert data.numel() == frames_per_batch
+            assert total == 200
+        finally:
+            collector.shutdown()
 
     @pytest.mark.parametrize(
         "collector_class",
@@ -517,12 +523,14 @@ def test_distributed_collector_class(self, collector_class):
             frames_per_batch=frames_per_batch,
             **self.distributed_kwargs(),
         )
-        total = 0
-        for data in collector:
-            total += data.numel()
-            assert data.numel() == frames_per_batch
-        collector.shutdown()
-        assert total == 200
+        try:
+            total = 0
+            for data in collector:
+                total += data.numel()
+                assert data.numel() == frames_per_batch
+            assert total == 200
+        finally:
+            collector.shutdown()
 
     @pytest.mark.parametrize(
         "collector_class",

torchrl/collectors/__init__.py

@@ -12,9 +12,21 @@
     MultiSyncDataCollector,
     SyncDataCollector,
 )
+from .weight_update import (
+    LocalWeightUpdaterBase,
+    MultiProcessedRemoteWeightUpdate,
+    RayRemoteWeightUpdater,
+    RemoteWeightUpdaterBase,
+    VanillaLocalWeightUpdater,
+)
 
 __all__ = [
     "RandomPolicy",
+    "LocalWeightUpdaterBase",
+    "RemoteWeightUpdaterBase",
+    "VanillaLocalWeightUpdater",
+    "RayRemoteWeightUpdater",
+    "MultiProcessedRemoteWeightUpdate",
     "aSyncDataCollector",
     "DataCollectorBase",
     "MultiaSyncDataCollector",