[Feature] Transform Module - ModuleTransform and Ray Service Refactor

docs/source/reference/envs.rst

@@ -1130,6 +1130,7 @@ to be able to create this other composition:
     InitTracker
     KLRewardTransform
     LineariseRewards
+    ModuleTransform
     MultiAction
     NoopResetEnv
     ObservationNorm

test/test_transforms.py

@@ -34,7 +34,7 @@
     TensorDictBase,
     unravel_key,
 )
-from tensordict.nn import TensorDictSequential, WrapModule
+from tensordict.nn import TensorDictModule, TensorDictSequential, WrapModule
 from tensordict.utils import _unravel_key_to_tuple, assert_allclose_td
 from torch import multiprocessing as mp, nn, Tensor
 from torchrl._utils import _replace_last, prod, set_auto_unwrap_transformed_env
@@ -122,8 +122,9 @@
 from torchrl.envs.libs.dm_control import _has_dm_control
 from torchrl.envs.libs.gym import _has_gym, GymEnv, set_gym_backend
 from torchrl.envs.libs.unity_mlagents import _has_unity_mlagents
-from torchrl.envs.transforms import VecNorm
+from torchrl.envs.transforms import ModuleTransform, VecNorm
 from torchrl.envs.transforms.llm import KLRewardTransform
+from torchrl.envs.transforms.module import RayModuleTransform
 from torchrl.envs.transforms.r3m import _R3MNet
 from torchrl.envs.transforms.transforms import (
     _has_tv,
@@ -198,6 +199,8 @@
         StateLessCountingEnv,
     )
 
+_has_ray = importlib.util.find_spec("ray") is not None
+
 IS_WIN = platform == "win32"
 if IS_WIN:
     mp_ctx = "spawn"
@@ -14888,6 +14891,130 @@ def test_transform_inverse(self):
         return
 
 
+class TestModuleTransform(TransformBase):
+    @property
+    def _module_factory_samespec(self):
+        return partial(
+            TensorDictModule,
+            nn.LazyLinear(7),
+            in_keys=["observation"],
+            out_keys=["observation"],
+        )
+
+    @property
+    def _module_factory_samespec_inverse(self):
+        return partial(
+            TensorDictModule, nn.LazyLinear(7), in_keys=["action"], out_keys=["action"]
+        )
+
+    def _single_env_maker(self):
+        base_env = ContinuousActionVecMockEnv()
+        t = ModuleTransform(module_factory=self._module_factory_samespec)
+        return base_env.append_transform(t)
+
+    def test_single_trans_env_check(self):
+        env = self._single_env_maker()
+        env.check_env_specs()
+
+    def test_serial_trans_env_check(self):
+        env = SerialEnv(2, self._single_env_maker)
+        try:
+            env.check_env_specs()
+        finally:
+            env.close(raise_if_closed=False)
+
+    def test_parallel_trans_env_check(self, maybe_fork_ParallelEnv):
+        env = maybe_fork_ParallelEnv(2, self._single_env_maker)
+        try:
+            env.check_env_specs()
+        finally:
+            env.close(raise_if_closed=False)
+
+    def test_trans_serial_env_check(self):
+        env = SerialEnv(2, ContinuousActionVecMockEnv)
+        try:
+            env = env.append_transform(
+                ModuleTransform(module_factory=self._module_factory_samespec)
+            )
+            env.check_env_specs()
+        finally:
+            env.close(raise_if_closed=False)
+
+    def test_trans_parallel_env_check(self, maybe_fork_ParallelEnv):
+        env = maybe_fork_ParallelEnv(2, ContinuousActionVecMockEnv)
+        try:
+            env = env.append_transform(
+                ModuleTransform(module_factory=self._module_factory_samespec)
+            )
+            env.check_env_specs()
+        finally:
+            env.close(raise_if_closed=False)
+
+    def test_transform_no_env(self):
+        t = ModuleTransform(module_factory=self._module_factory_samespec)
+        td = t(TensorDict(observation=torch.randn(2, 3), batch_size=[2]))
+        assert td["observation"].shape == (2, 7)
+
+    def test_transform_compose(self):
+        t = Compose(ModuleTransform(module_factory=self._module_factory_samespec))
+        td = t(TensorDict(observation=torch.randn(2, 3), batch_size=[2]))
+        assert td["observation"].shape == (2, 7)
+
+    def test_transform_env(self):
+        # TODO: We should give users the opportunity to modify the specs
+        env = self._single_env_maker()
+        env.check_env_specs()
+
+    def test_transform_model(self):
+        t = nn.Sequential(
+            Compose(ModuleTransform(module_factory=self._module_factory_samespec))
+        )
+        td = t(TensorDict(observation=torch.randn(2, 3), batch_size=[2]))
+        assert td["observation"].shape == (2, 7)
+
+    def test_transform_rb(self):
+        t = ModuleTransform(module_factory=self._module_factory_samespec)
+        rb = ReplayBuffer(transform=t)
+        rb.extend(TensorDict(observation=torch.randn(2, 3), batch_size=[2]))
+        assert rb._storage._storage[0]["observation"].shape == (3,)
+        s = rb.sample(2)
+        assert s["observation"].shape == (2, 7)
+
+        rb = ReplayBuffer()
+        rb.append_transform(t, invert=True)
+        rb.extend(TensorDict(observation=torch.randn(2, 3), batch_size=[2]))
+        assert rb._storage._storage[0]["observation"].shape == (7,)
+        s = rb.sample(2)
+        assert s["observation"].shape == (2, 7)
+
+    def test_transform_inverse(self):
+        t = ModuleTransform(
+            module_factory=self._module_factory_samespec_inverse, inverse=True
+        )
+        env = ContinuousActionVecMockEnv().append_transform(t)
+        env.check_env_specs()
+
+    @pytest.mark.skipif(not _has_ray, reason="ray required")
+    def test_ray_extension(self):
+        import ray
+
+        # Check if ray is initialized
+        ray_init = ray.is_initialized
+        try:
+            t = ModuleTransform(
+                module_factory=self._module_factory_samespec,
+                use_ray_service=True,
+                actor_name="my_transform",
+            )
+            env = ContinuousActionVecMockEnv().append_transform(t)
+            assert isinstance(t, RayModuleTransform)
+            env.check_env_specs()
+            assert ray.get_actor("my_transform") is not None
+        finally:
+            if not ray_init:
+                ray.stop()
+
+
 if __name__ == "__main__":
     args, unknown = argparse.ArgumentParser().parse_known_args()
     pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)

torchrl/envs/llm/transforms/dataloading.py

@@ -15,14 +15,14 @@
 
 from torchrl.data.tensor_specs import Composite, DEVICE_TYPING, TensorSpec
 from torchrl.envs.common import EnvBase
+from torchrl.envs.transforms import TensorDictPrimer, Transform
 
 # Import ray service components
-from torchrl.envs.llm.transforms.ray_service import (
+from torchrl.envs.transforms.ray_service import (
     _map_input_output_device,
     _RayServiceMetaClass,
     RayTransform,
 )
-from torchrl.envs.transforms.transforms import TensorDictPrimer, Transform
 from torchrl.envs.utils import make_composite_from_td
 
 T = TypeVar("T")
@@ -259,7 +259,7 @@ def primers(self):
     @primers.setter
     def primers(self, value: TensorSpec):
         """Set primers property."""
-        self._ray.get(self._actor.set_attr.remote("primers", value))
+        self._ray.get(self._actor._set_attr.remote("primers", value))
 
     # TensorDictPrimer methods
     def init(self, tensordict: TensorDictBase | None):
@@ -857,7 +857,3 @@ def _update_primers_batch_size(self, parent_batch_size):
     def __repr__(self) -> str:
         class_name = self.__class__.__name__
         return f"{class_name}(primers={self.primers}, dataloader={self.dataloader})"
-
-    def set_attr(self, name, value):
-        """Set attribute on the remote actor or locally."""
-        setattr(self, name, value)

torchrl/envs/llm/transforms/kl.py

@@ -18,7 +18,7 @@
 from torchrl.data import Composite, Unbounded
 from torchrl.data.tensor_specs import DEVICE_TYPING
 from torchrl.envs import EnvBase, Transform
-from torchrl.envs.llm.transforms.ray_service import _RayServiceMetaClass, RayTransform
+from torchrl.envs.transforms.ray_service import _RayServiceMetaClass, RayTransform
 from torchrl.envs.transforms.transforms import Compose
 from torchrl.envs.transforms.utils import _set_missing_tolerance
 from torchrl.modules.llm.policies.common import LLMWrapperBase

torchrl/envs/llm/transforms/tools.py

@@ -17,7 +17,7 @@
 import torch
 
 from tensordict import lazy_stack, TensorDictBase
-from torchrl import torchrl_logger
+from torchrl._utils import logger as torchrl_logger
 from torchrl.data.llm import History
 
 from torchrl.envs import Transform

torchrl/envs/transforms/__init__.py

@@ -5,9 +5,10 @@
 
 from .gym_transforms import EndOfLifeTransform
 from .llm import KLRewardTransform
+from .module import ModuleTransform
 from .r3m import R3MTransform
+from .ray_service import RayTransform
 from .rb_transforms import MultiStepTransform
-
 from .transforms import (
     ActionDiscretizer,
     ActionMask,
@@ -85,9 +86,9 @@
     "CatFrames",
     "CatTensors",
     "CenterCrop",
-    "ConditionalPolicySwitch",
     "ClipTransform",
     "Compose",
+    "ConditionalPolicySwitch",
     "ConditionalSkip",
     "Crop",
     "DTypeCastTransform",
@@ -104,6 +105,7 @@
     "InitTracker",
     "KLRewardTransform",
     "LineariseRewards",
+    "ModuleTransform",
     "MultiAction",
     "MultiStepTransform",
     "NoopResetEnv",
@@ -113,6 +115,7 @@
     "PinMemoryTransform",
     "R3MTransform",
     "RandomCropTensorDict",
+    "RayTransform",
     "RemoveEmptySpecs",
     "RenameTransform",
     "Resize",