[Feature] Auto-batching inference server: weight sync integration

docs/source/reference/modules_inference_server.rst

@@ -9,10 +9,86 @@ The inference server provides auto-batching model serving for RL actors.
 Multiple actors submit individual TensorDicts; the server transparently
 batches them, runs a single model forward pass, and routes results back.
 
+Core API
+--------
+
 .. autosummary::
     :toctree: generated/
     :template: rl_template_noinherit.rst
 
     InferenceServer
     InferenceClient
     InferenceTransport
+
+Transport Backends
+------------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template_noinherit.rst
+
+    ThreadingTransport
+    MPTransport
+    RayTransport
+    MonarchTransport
+
+Usage
+-----
+
+The simplest setup uses :class:`ThreadingTransport` for actors that are
+threads in the same process:
+
+.. code-block:: python
+
+    from tensordict.nn import TensorDictModule
+    from torchrl.modules.inference_server import (
+        InferenceServer,
+        ThreadingTransport,
+    )
+    import torch.nn as nn
+    import concurrent.futures
+
+    policy = TensorDictModule(
+        nn.Sequential(nn.Linear(8, 64), nn.ReLU(), nn.Linear(64, 4)),
+        in_keys=["observation"],
+        out_keys=["action"],
+    )
+
+    transport = ThreadingTransport()
+    server = InferenceServer(policy, transport, max_batch_size=32)
+    server.start()
+    client = transport.client()
+
+    # actor threads call client(td) -- batched automatically
+    with concurrent.futures.ThreadPoolExecutor(16) as pool:
+        ...
+
+    server.shutdown()
+
+Weight Synchronisation
+^^^^^^^^^^^^^^^^^^^^^^
+
+The server integrates with :class:`~torchrl.weight_update.WeightSyncScheme`
+to receive updated model weights from a trainer between inference batches:
+
+.. code-block:: python
+
+    from torchrl.weight_update import SharedMemWeightSyncScheme
+
+    weight_sync = SharedMemWeightSyncScheme()
+    # Initialise on the trainer (sender) side first
+    weight_sync.init_on_sender(model=training_model, ...)
+
+    server = InferenceServer(
+        model=inference_model,
+        transport=ThreadingTransport(),
+        weight_sync=weight_sync,
+    )
+    server.start()
+
+    # Training loop
+    for batch in dataloader:
+        loss = loss_fn(training_model(batch))
+        loss.backward()
+        optimizer.step()
+        weight_sync.send(model=training_model)  # pushed to server

test/test_inference_server.py

@@ -6,6 +6,7 @@
 
 import concurrent.futures
 import threading
+import time
 
 import pytest
 import torch
@@ -557,3 +558,122 @@ def test_import_without_monarch(self):
     def test_instantiation_without_monarch_raises(self):
         with pytest.raises(ImportError, match="Monarch is required"):
             MonarchTransport()
+
+
+# =============================================================================
+# Tests: WeightSyncScheme integration (Commit 6)
+# =============================================================================
+
+
+class _SimpleWeightSync:
+    """Minimal mock that mimics the WeightSyncScheme receiver interface.
+
+    Stores a queue of weight TensorDicts. ``receive(timeout=...)`` pops
+    the next one and applies it to the model via
+    ``TensorDict.from_module / to_module``.
+    """
+
+    def __init__(self):
+        self._queue: list[TensorDictBase] = []
+        self._model = None
+        self.initialized_on_receiver = False
+        self.synchronized_on_receiver = False
+
+    def init_on_receiver(self, *, model_id, model=None, worker_idx=0, **kwargs):
+        self._model = model
+        self.initialized_on_receiver = True
+
+    def connect(self, *, worker_idx=0):
+        self.synchronized_on_receiver = True
+
+    def receive(self, timeout=None):
+        if self._queue:
+            weights = self._queue.pop(0)
+            weights.to_module(self._model)
+            return weights
+        return None
+
+    def push(self, weights: TensorDictBase):
+        """Test helper: enqueue weights for the server to pick up."""
+        self._queue.append(weights)
+
+
+class TestWeightSyncIntegration:
+    def test_weight_sync_init_called(self):
+        """Server calls init_on_receiver and connect at startup."""
+        transport = ThreadingTransport()
+        policy = _make_policy()
+        ws = _SimpleWeightSync()
+
+        with InferenceServer(policy, transport, weight_sync=ws):
+            # Give the worker thread a moment to start
+            time.sleep(0.1)
+            assert ws.initialized_on_receiver
+            assert ws.synchronized_on_receiver
+
+    def test_weight_update_applied(self):
+        """Weights pushed via weight_sync are applied to the model."""
+        transport = ThreadingTransport()
+        policy = _make_policy()
+        ws = _SimpleWeightSync()
+
+        with InferenceServer(
+            policy, transport, max_batch_size=4, weight_sync=ws
+        ) as server:
+            client = transport.client()
+
+            # Get initial prediction
+            td = TensorDict({"observation": torch.ones(4)})
+            client(td)
+
+            # Mutate the model weights externally and push via weight_sync
+            new_weights = TensorDict.from_module(policy)
+            for key in new_weights.keys(True, True):
+                new_weights[key] = torch.zeros_like(new_weights[key])
+            ws.push(new_weights)
+
+            # Give the server loop a chance to apply the update
+            time.sleep(0.2)
+
+            # Now inference should reflect zero weights
+            result_after = client(td)
+            # With zero weights the linear output should be zero (bias=0 too)
+            assert torch.allclose(result_after["action"], torch.zeros(2), atol=1e-6)
+
+    def test_inference_continues_after_weight_update(self):
+        """The server keeps serving after a weight update."""
+        transport = ThreadingTransport()
+        policy = _make_policy()
+        ws = _SimpleWeightSync()
+
+        with InferenceServer(policy, transport, max_batch_size=4, weight_sync=ws):
+            client = transport.client()
+
+            # Initial requests
+            for _ in range(5):
+                td = TensorDict({"observation": torch.randn(4)})
+                result = client(td)
+                assert "action" in result.keys()
+
+            # Push weight update
+            new_weights = TensorDict.from_module(policy)
+            ws.push(new_weights)
+
+            time.sleep(0.1)
+
+            # Continue making requests
+            for _ in range(5):
+                td = TensorDict({"observation": torch.randn(4)})
+                result = client(td)
+                assert "action" in result.keys()
+                assert result["action"].shape == (2,)
+
+    def test_no_weight_sync(self):
+        """Server works fine when weight_sync is None."""
+        transport = ThreadingTransport()
+        policy = _make_policy()
+        with InferenceServer(policy, transport, max_batch_size=4):
+            client = transport.client()
+            td = TensorDict({"observation": torch.randn(4)})
+            result = client(td)
+            assert "action" in result.keys()

torchrl/modules/inference_server/_server.py

@@ -42,6 +42,9 @@ class InferenceServer:
             :class:`~torchrl.weight_update.WeightSyncScheme` used to receive
             updated model weights from a trainer. When set, the server polls
             for new weights between inference batches.
+        weight_sync_model_id (str, optional): the model identifier used when
+            initialising the weight sync scheme on the receiver side.
+            Default: ``"policy"``.
 
     Example:
         >>> from tensordict.nn import TensorDictModule
@@ -71,6 +74,7 @@ def __init__(
         collate_fn: Callable | None = None,
         device: torch.device | str | None = None,
         weight_sync=None,
+        weight_sync_model_id: str = "policy",
     ):
         self.model = model
         self.transport = transport
@@ -79,9 +83,12 @@ def __init__(
         self.collate_fn = collate_fn if collate_fn is not None else lazy_stack
         self.device = torch.device(device) if device is not None else None
         self.weight_sync = weight_sync
+        self._weight_sync_model_id = weight_sync_model_id
 
         self._shutdown_event = threading.Event()
         self._worker: threading.Thread | None = None
+        # Protects model access during weight updates
+        self._model_lock = threading.Lock()
 
     # -- lifecycle ------------------------------------------------------------
 
@@ -119,9 +126,36 @@ def is_alive(self) -> bool:
 
     # -- background loop ------------------------------------------------------
 
+    def _init_weight_sync(self) -> None:
+        """Initialise the weight sync scheme on the receiver (server) side."""
+        ws = self.weight_sync
+        if ws is None:
+            return
+        if not ws.initialized_on_receiver:
+            ws.init_on_receiver(
+                model_id=self._weight_sync_model_id,
+                model=self.model,
+                worker_idx=0,
+            )
+        if not ws.synchronized_on_receiver:
+            ws.connect(worker_idx=0)
+
+    def _poll_weight_update(self) -> None:
+        """Non-blocking check for fresh weights from the trainer."""
+        ws = self.weight_sync
+        if ws is None:
+            return
+        with self._model_lock:
+            ws.receive(timeout=0.0)
+
     @torch.no_grad()
     def _run(self) -> None:
+        self._init_weight_sync()
+
         while not self._shutdown_event.is_set():
+            # Poll for weight updates between batches (non-blocking)
+            self._poll_weight_update()
+
             self.transport.wait_for_work(timeout=self.timeout)
 
             items, callbacks = self.transport.drain(self.max_batch_size)
@@ -133,7 +167,8 @@ def _run(self) -> None:
                 batch = batch.to(self.device)
 
             try:
-                results = self.model(batch).unbind(0)
+                with self._model_lock:
+                    results = self.model(batch).unbind(0)
                 if len(results) != len(callbacks):
                     raise RuntimeError(
                         f"Model returned {len(results)} results for a "