[Feature] no_cuda_sync arg in collectors

ghstack-source-id: a2a30a5ca9be16fb82804baa9aab987c16745abe
Pull Request resolved: #2727

test/test_collector.py

@@ -10,6 +10,8 @@
 import os
 
 import sys
+from typing import Optional
+from unittest.mock import patch
 
 import numpy as np
 import pytest
@@ -469,6 +471,80 @@ def test_output_device(self, main_device, storing_device):
             break
         assert data.device == storing_device
 
+    class CudaPolicy(TensorDictSequential):
+        def __init__(self, n_obs):
+            module = torch.nn.Linear(n_obs, n_obs, device="cuda")
+            module.weight.data.copy_(torch.eye(n_obs))
+            module.bias.data.fill_(0)
+            m0 = TensorDictModule(module, in_keys=["observation"], out_keys=["hidden"])
+            m1 = TensorDictModule(
+                lambda a: a + 1, in_keys=["hidden"], out_keys=["action"]
+            )
+            super().__init__(m0, m1)
+
+    class GoesThroughEnv(EnvBase):
+        def __init__(self, n_obs, device):
+            self.observation_spec = Composite(observation=Unbounded(n_obs))
+            self.action_spec = Unbounded(n_obs)
+            self.reward_spec = Unbounded(1)
+            self.full_done_specs = Composite(done=Unbounded(1, dtype=torch.bool))
+            super().__init__(device=device)
+
+        def _step(
+            self,
+            tensordict: TensorDictBase,
+        ) -> TensorDictBase:
+            a = tensordict["action"]
+            if self.device is not None:
+                assert a.device == self.device
+            out = tensordict.empty()
+            out["observation"] = tensordict["observation"] + (
+                a - tensordict["observation"]
+            )
+            out["reward"] = torch.zeros((1,), device=self.device)
+            out["done"] = torch.zeros((1,), device=self.device, dtype=torch.bool)
+            return out
+
+        def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
+            return self.full_done_specs.zeros().update(self.observation_spec.zeros())
+
+        def _set_seed(self, seed: Optional[int]):
+            return seed
+
+    @pytest.mark.skipif(not torch.cuda.is_available(), reason="no cuda device")
+    @pytest.mark.parametrize("env_device", ["cuda:0", "cpu"])
+    @pytest.mark.parametrize("storing_device", [None, "cuda:0", "cpu"])
+    def test_no_synchronize(self, env_device, storing_device):
+        """Tests that no_cuda_sync avoids any call to torch.cuda.synchronize() and that the data is not corrupted."""
+        collector = SyncDataCollector(
+            create_env_fn=functools.partial(
+                self.GoesThroughEnv, n_obs=1000, device=None
+            ),
+            policy=self.CudaPolicy(n_obs=1000),
+            frames_per_batch=100,
+            total_frames=1000,
+            env_device=env_device,
+            storing_device=storing_device,
+            policy_device="cuda:0",
+            # no_cuda_sync=True,
+        )
+        assert collector.env.device == torch.device(env_device)
+        i = 0
+        with patch("torch.cuda.synchronize") as mock_synchronize:
+            for d in collector:
+                for _d in d.unbind(0):
+                    u = _d["observation"].unique()
+                    assert u.numel() == 1, i
+                    assert u == i, i
+                    i += 1
+                    u = _d["next", "observation"].unique()
+                    assert u.numel() == 1, i
+                    assert u == i, i
+                mock_synchronize.assert_not_called()
+                assert (
+                    not mock_synchronize.called
+                ), "torch.cuda.synchronize should not be called"
+
 
 # @pytest.mark.skipif(
 #     IS_WINDOWS and PYTHON_3_10,

torchrl/collectors/collectors.py

@@ -440,6 +440,11 @@ class SyncDataCollector(DataCollectorBase):
         cudagraph_policy (bool or Dict[str, Any], optional): if ``True``, the policy will be wrapped
             in :class:`~tensordict.nn.CudaGraphModule` with default kwargs.
             If a dictionary of kwargs is passed, it will be used to wrap the policy.
+        no_cuda_sync (bool): if ``True``, explicit CUDA synchronizations calls will be bypassed.
+            For environments running directly on CUDA (`IsaacLab <https://github.com/isaac-sim/IsaacLab/>`_
+            or `ManiSkills <https://github.com/haosulab/ManiSkill/>`_) cuda synchronization may cause unexpected
+            crashes.
+            Defaults to ``False``.
 
     Examples:
         >>> from torchrl.envs.libs.gym import GymEnv
@@ -532,6 +537,7 @@ def __init__(
         trust_policy: bool = None,
         compile_policy: bool | Dict[str, Any] | None = None,
         cudagraph_policy: bool | Dict[str, Any] | None = None,
+        no_cuda_sync: bool = False,
         **kwargs,
     ):
         from torchrl.envs.batched_envs import BatchedEnvBase
@@ -625,6 +631,7 @@ def __init__(
         else:
             self._sync_policy = _do_nothing
         self.device = device
+        self.no_cuda_sync = no_cuda_sync
         # Check if we need to cast things from device to device
         # If the policy has a None device and the env too, no need to cast (we don't know
         # and assume the user knows what she's doing).
@@ -1010,12 +1017,16 @@ def iterator(self) -> Iterator[TensorDictBase]:
         Yields: TensorDictBase objects containing (chunks of) trajectories
 
         """
-        if self.storing_device and self.storing_device.type == "cuda":
+        if (
+            not self.no_cuda_sync
+            and self.storing_device
+            and self.storing_device.type == "cuda"
+        ):
             stream = torch.cuda.Stream(self.storing_device, priority=-1)
             event = stream.record_event()
             streams = [stream]
             events = [event]
-        elif self.storing_device is None:
+        elif not self.no_cuda_sync and self.storing_device is None:
             streams = []
             events = []
             # this way of checking cuda is robust to lazy stacks with mismatching shapes
@@ -1167,9 +1178,11 @@ def rollout(self) -> TensorDictBase:
                     if self._cast_to_policy_device:
                         if self.policy_device is not None:
                             policy_input = self._shuttle.to(
-                                self.policy_device, non_blocking=True
+                                self.policy_device,
+                                non_blocking=not self.no_cuda_sync,
                             )
-                            self._sync_policy()
+                            if not self.no_cuda_sync:
+                                self._sync_policy()
                         elif self.policy_device is None:
                             # we know the tensordict has a device otherwise we would not be here
                             # we can pass this, clear_device_ must have been called earlier
@@ -1191,8 +1204,11 @@ def rollout(self) -> TensorDictBase:
 
                 if self._cast_to_env_device:
                     if self.env_device is not None:
-                        env_input = self._shuttle.to(self.env_device, non_blocking=True)
-                        self._sync_env()
+                        env_input = self._shuttle.to(
+                            self.env_device, non_blocking=not self.no_cuda_sync
+                        )
+                        if not self.no_cuda_sync:
+                            self._sync_env()
                     elif self.env_device is None:
                         # we know the tensordict has a device otherwise we would not be here
                         # we can pass this, clear_device_ must have been called earlier
@@ -1217,9 +1233,12 @@ def rollout(self) -> TensorDictBase:
                 else:
                     if self.storing_device is not None:
                         tensordicts.append(
-                            self._shuttle.to(self.storing_device, non_blocking=True)
+                            self._shuttle.to(
+                                self.storing_device, non_blocking=not self.no_cuda_sync
+                            )
                         )
-                        self._sync_storage()
+                        if not self.no_cuda_sync:
+                            self._sync_storage()
                     else:
                         tensordicts.append(self._shuttle)
 
@@ -1558,6 +1577,11 @@ class _MultiDataCollector(DataCollectorBase):
         cudagraph_policy (bool or Dict[str, Any], optional): if ``True``, the policy will be wrapped
             in :class:`~tensordict.nn.CudaGraphModule` with default kwargs.
             If a dictionary of kwargs is passed, it will be used to wrap the policy.
+        no_cuda_sync (bool): if ``True``, explicit CUDA synchronizations calls will be bypassed.
+            For environments running directly on CUDA (`IsaacLab <https://github.com/isaac-sim/IsaacLab/>`_
+            or `ManiSkills <https://github.com/haosulab/ManiSkill/>`_) cuda synchronization may cause unexpected
+            crashes.
+            Defaults to ``False``.
 
     """
 
@@ -1597,6 +1621,7 @@ def __init__(
         trust_policy: bool = None,
         compile_policy: bool | Dict[str, Any] | None = None,
         cudagraph_policy: bool | Dict[str, Any] | None = None,
+        no_cuda_sync: bool = False,
     ):
         self.closed = True
         self.num_workers = len(create_env_fn)
@@ -1636,6 +1661,7 @@ def __init__(
         self.env_device = env_devices
 
         del storing_device, env_device, policy_device, device
+        self.no_cuda_sync = no_cuda_sync
 
         self._use_buffers = use_buffers
         self.replay_buffer = replay_buffer
@@ -1909,6 +1935,7 @@ def _run_processes(self) -> None:
                     "cudagraph_policy": self.cudagraphed_policy_kwargs
                     if self.cudagraphed_policy
                     else False,
+                    "no_cuda_sync": self.no_cuda_sync,
                 }
                 proc = _ProcessNoWarn(
                     target=_main_async_collector,
@@ -2914,6 +2941,7 @@ def _main_async_collector(
     trust_policy: bool = False,
     compile_policy: bool = False,
     cudagraph_policy: bool = False,
+    no_cuda_sync: bool = False,
 ) -> None:
     pipe_parent.close()
     # init variables that will be cleared when closing
@@ -2943,6 +2971,7 @@ def _main_async_collector(
         trust_policy=trust_policy,
         compile_policy=compile_policy,
         cudagraph_policy=cudagraph_policy,
+        no_cuda_sync=no_cuda_sync,
     )
     use_buffers = inner_collector._use_buffers
     if verbose: