[Feature] AsyncBatchedCollector: backend params and performance optimizations

- Three-tier backend system: `backend` (global default), `env_backend`
  (env pool override), `policy_backend` (transport override), mirroring
  the device parameter pattern.
- Lock-free SlotTransport: per-env slots with no shared lock, replacing
  ThreadingTransport as the default for in-process threading.
- min_batch_size parameter for InferenceServer to accumulate requests.
- Batch drain from result queue (get_nowait after first blocking get).
- Remove redundant .copy() in ProcessorAsyncEnvPool._env_exec.

Co-authored-by: Cursor <cursoragent@cursor.com>
ghstack-source-id: d7fc567
Pull-Request: #3511
Co-authored-by: Cursor <cursoragent@cursor.com>

Author: vmoens

benchmarks/bench_collectors.py

@@ -12,7 +12,8 @@
   2. Collector (ParallelEnv x N)        -- single-process, N envs in sub-procs
   3. MultiCollector (sync, x N)         -- N sub-processes, sync delivery
   4. MultiCollector (async, x N)        -- N sub-processes, async delivery
-  5. AsyncBatchedCollector (threading)   -- AsyncEnvPool + InferenceServer
+  5. AsyncBatched (env=thread, pol=thread)  -- threading pool + threading transport
+  6. AsyncBatched (env=mp, pol=thread)      -- multiprocessing pool + threading transport
 """
 from __future__ import annotations
 
@@ -368,33 +369,33 @@ def policy_factory():
         )
     )
 
-    # 5. AsyncBatchedCollector (threading backend)
+    # 5. AsyncBatchedCollector (env=threading, policy=threading)
     results.append(
         bench(
-            f"AsyncBatchedCollector threading (x{num_envs})",
+            f"AsyncBatched env=thread pol=thread (x{num_envs})",
             lambda: AsyncBatchedCollector(
                 create_env_fn=[make_env_fn] * num_envs,
                 policy=policy_factory(),
                 frames_per_batch=frames_per_batch,
                 total_frames=-1,
                 max_batch_size=num_envs,
-                backend="threading",
+                env_backend="threading",
             ),
             target_frames=total_frames,
         )
     )
 
-    # 6. AsyncBatchedCollector (multiprocessing backend)
+    # 6. AsyncBatchedCollector (env=multiprocessing, policy=threading)
     results.append(
         bench(
-            f"AsyncBatchedCollector mp (x{num_envs})",
+            f"AsyncBatched env=mp pol=thread (x{num_envs})",
             lambda: AsyncBatchedCollector(
                 create_env_fn=[make_env_fn] * num_envs,
                 policy=policy_factory(),
                 frames_per_batch=frames_per_batch,
                 total_frames=-1,
                 max_batch_size=num_envs,
-                backend="multiprocessing",
+                env_backend="multiprocessing",
             ),
             target_frames=total_frames,
         )

examples/collectors/async_batched_collector.py

@@ -6,14 +6,21 @@
 
 Architecture:
   - An :class:`~torchrl.envs.AsyncEnvPool` runs environments in parallel
-    using the chosen backend (``"threading"`` or ``"multiprocessing"``).
+    using the chosen ``env_backend`` (``"threading"`` or ``"multiprocessing"``).
   - One lightweight coordinator thread per environment owns a slot in the pool
     and an inference client.
   - An :class:`~torchrl.modules.InferenceServer` batches incoming observations
-    and runs a single forward pass.
+    and runs a single forward pass.  The communication layer (transport) is
+    controlled by ``policy_backend`` (``"threading"``, ``"multiprocessing"``,
+    ``"ray"``, or ``"monarch"``).
   - There is no global synchronisation barrier -- fast envs keep stepping
     while slow ones wait for inference.
 
+Backend parameters:
+  - ``backend``        -- global default for both env pool and policy transport.
+  - ``env_backend``    -- override for the env pool (falls back to ``backend``).
+  - ``policy_backend`` -- override for the transport (falls back to ``backend``).
+
 The user only supplies:
   - A list of environment factories
   - A policy (or policy factory)

test/test_inference_server.py

@@ -22,6 +22,7 @@
     InferenceTransport,
     MPTransport,
     RayTransport,
+    SlotTransport,
     ThreadingTransport,
 )
 from torchrl.modules.inference_server._monarch import MonarchTransport
@@ -728,7 +729,7 @@ def test_basic_collection(self):
             frames_per_batch=frames_per_batch,
             total_frames=total_frames,
             max_batch_size=num_envs,
-            backend="threading",
+            env_backend="threading",
         )
         total_collected = 0
         for batch in collector:
@@ -746,7 +747,7 @@ def test_policy_factory(self):
             frames_per_batch=10,
             total_frames=20,
             max_batch_size=num_envs,
-            backend="threading",
+            env_backend="threading",
         )
         total_collected = 0
         for batch in collector:
@@ -786,7 +787,7 @@ def test_yield_completed_trajectories(self):
             total_frames=30,
             yield_completed_trajectories=True,
             max_batch_size=num_envs,
-            backend="threading",
+            env_backend="threading",
         )
         count = 0
         for batch in collector:
@@ -804,7 +805,7 @@ def test_shutdown_idempotent(self):
             policy=policy,
             frames_per_batch=10,
             total_frames=10,
-            backend="threading",
+            env_backend="threading",
         )
         # Consume one batch to start
         for _batch in collector:
@@ -820,7 +821,7 @@ def test_endless_collector(self):
             policy=policy,
             frames_per_batch=10,
             total_frames=-1,
-            backend="threading",
+            env_backend="threading",
         )
         collected = 0
         for batch in collector:
@@ -857,9 +858,126 @@ def postproc(td):
             frames_per_batch=10,
             total_frames=20,
             postproc=postproc,
-            backend="threading",
+            env_backend="threading",
         )
         for _ in collector:
             pass
         collector.shutdown()
         assert called["count"] >= 1
+
+
+# =============================================================================
+# Tests: SlotTransport
+# =============================================================================
+
+
+class TestSlotTransport:
+    def test_single_request(self):
+        transport = SlotTransport(num_slots=4)
+        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()
+            assert result["action"].shape == (2,)
+
+    def test_concurrent_actors(self):
+        """Multiple threads submit concurrently via slot clients."""
+        n_actors = 4
+        n_requests = 30
+        transport = SlotTransport(num_slots=n_actors)
+        policy = _make_policy()
+
+        results_per_actor: list[list[TensorDictBase]] = [[] for _ in range(n_actors)]
+        clients = [transport.client() for _ in range(n_actors)]
+
+        def actor_fn(actor_id):
+            for _ in range(n_requests):
+                td = TensorDict({"observation": torch.randn(4)})
+                result = clients[actor_id](td)
+                results_per_actor[actor_id].append(result)
+
+        with InferenceServer(policy, transport, max_batch_size=n_actors):
+            with concurrent.futures.ThreadPoolExecutor(max_workers=n_actors) as pool:
+                futs = [pool.submit(actor_fn, i) for i in range(n_actors)]
+                concurrent.futures.wait(futs)
+                for f in futs:
+                    f.result()
+
+        for actor_results in results_per_actor:
+            assert len(actor_results) == n_requests
+            for r in actor_results:
+                assert "action" in r.keys()
+                assert r["action"].shape == (2,)
+
+    def test_too_many_clients_raises(self):
+        """Creating more clients than slots raises RuntimeError."""
+        transport = SlotTransport(num_slots=2)
+        transport.client()
+        transport.client()
+        with pytest.raises(RuntimeError, match="slots"):
+            transport.client()
+
+    def test_submit_raises(self):
+        """Direct submit() on SlotTransport is not supported."""
+        transport = SlotTransport(num_slots=1)
+        td = TensorDict({"observation": torch.randn(4)})
+        with pytest.raises(NotImplementedError):
+            transport.submit(td)
+
+    def test_exception_propagates(self):
+        """Model exceptions propagate through SlotTransport."""
+
+        def bad_model(td):
+            raise ValueError("slot model error")
+
+        transport = SlotTransport(num_slots=1)
+        with InferenceServer(bad_model, transport, max_batch_size=4):
+            client = transport.client()
+            td = TensorDict({"observation": torch.randn(4)})
+            with pytest.raises(ValueError, match="slot model error"):
+                client(td)
+
+
+# =============================================================================
+# Tests: min_batch_size
+# =============================================================================
+
+
+class TestMinBatchSize:
+    def test_min_batch_size_accumulates(self):
+        """With min_batch_size > 1, the server waits for enough items."""
+        min_bs = 4
+        seen_sizes = []
+
+        def tracking_collate(items):
+            seen_sizes.append(len(items))
+            return lazy_stack(items)
+
+        transport = ThreadingTransport()
+        policy = _make_policy()
+        n = 8
+
+        with InferenceServer(
+            policy,
+            transport,
+            max_batch_size=16,
+            min_batch_size=min_bs,
+            collate_fn=tracking_collate,
+            timeout=1.0,
+        ):
+            client = transport.client()
+            # Submit items from threads to give the server time to accumulate
+            with concurrent.futures.ThreadPoolExecutor(max_workers=n) as pool:
+                futs = [
+                    pool.submit(
+                        lambda: client(TensorDict({"observation": torch.randn(4)}))
+                    )
+                    for _ in range(n)
+                ]
+                for f in futs:
+                    f.result(timeout=10.0)
+
+        # At least one batch should have >= min_batch_size items
+        assert any(s >= min_bs for s in seen_sizes)