[Refactor] Deprecate recurrent_mode API to use decorators/CMs instead

ghstack-source-id: 80f705e
Pull Request resolved: #2584

Author: Vincent Moens

docs/source/reference/modules.rst

@@ -373,6 +373,8 @@ algorithms, such as DQN, DDPG or Dreamer.
     OnlineDTActor
     RSSMPosterior
     RSSMPrior
+    set_recurrent_mode
+    recurrent_mode
 
 Multi-agent-specific modules
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

test/test_cost.py

@@ -47,6 +47,7 @@
     DistributionalQValueActor,
     OneHotCategorical,
     QValueActor,
+    recurrent_mode,
     SafeSequential,
     WorldModelWrapper,
 )
@@ -15507,6 +15508,29 @@ def test_set_deprecated_keys(self, adv, kwargs):
 
 
 class TestBase:
+    def test_decorators(self):
+        class MyLoss(LossModule):
+            def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
+                assert recurrent_mode()
+                assert exploration_type() is ExplorationType.DETERMINISTIC
+                return TensorDict()
+
+            def actor_loss(self, tensordict: TensorDictBase) -> TensorDictBase:
+                assert recurrent_mode()
+                assert exploration_type() is ExplorationType.DETERMINISTIC
+                return TensorDict()
+
+            def something_loss(self, tensordict: TensorDictBase) -> TensorDictBase:
+                assert recurrent_mode()
+                assert exploration_type() is ExplorationType.DETERMINISTIC
+                return TensorDict()
+
+        loss = MyLoss()
+        loss.forward(None)
+        loss.actor_loss(None)
+        loss.something_loss(None)
+        assert not recurrent_mode()
+
     @pytest.mark.parametrize("expand_dim", [None, 2])
     @pytest.mark.parametrize("compare_against", [True, False])
     @pytest.mark.skipif(not _has_functorch, reason="functorch is needed for expansion")

test/test_tensordictmodules.py

@@ -36,6 +36,7 @@
     OnlineDTActor,
     ProbabilisticActor,
     SafeModule,
+    set_recurrent_mode,
     TanhDelta,
     TanhNormal,
     ValueOperator,
@@ -729,6 +730,31 @@ def test_errs(self):
         with pytest.raises(KeyError, match="is_init"):
             lstm_module(td)
 
+    @pytest.mark.parametrize("default_val", [False, True, None])
+    def test_set_recurrent_mode(self, default_val):
+        lstm_module = LSTMModule(
+            input_size=3,
+            hidden_size=12,
+            batch_first=True,
+            in_keys=["observation", "hidden0", "hidden1"],
+            out_keys=["intermediate", ("next", "hidden0"), ("next", "hidden1")],
+            default_recurrent_mode=default_val,
+        )
+        assert lstm_module.recurrent_mode is bool(default_val)
+        with set_recurrent_mode(True):
+            assert lstm_module.recurrent_mode
+            with set_recurrent_mode(False):
+                assert not lstm_module.recurrent_mode
+                with set_recurrent_mode("recurrent"):
+                    assert lstm_module.recurrent_mode
+                    with set_recurrent_mode("sequential"):
+                        assert not lstm_module.recurrent_mode
+                    assert lstm_module.recurrent_mode
+                assert not lstm_module.recurrent_mode
+            assert lstm_module.recurrent_mode
+        assert lstm_module.recurrent_mode is bool(default_val)
+
+    @pytest.mark.filterwarnings("ignore::DeprecationWarning")
     def test_set_temporal_mode(self):
         lstm_module = LSTMModule(
             input_size=3,
@@ -754,7 +780,8 @@ def test_python_cudnn(self):
             num_layers=2,
             in_keys=["observation", "hidden0", "hidden1"],
             out_keys=["intermediate", ("next", "hidden0"), ("next", "hidden1")],
-        ).set_recurrent_mode(True)
+            default_recurrent_mode=True,
+        )
         obs = torch.rand(10, 20, 3)
 
         hidden0 = torch.rand(10, 20, 2, 12)
@@ -1109,6 +1136,31 @@ def test_errs(self):
         with pytest.raises(KeyError, match="is_init"):
             gru_module(td)
 
+    @pytest.mark.parametrize("default_val", [False, True, None])
+    def test_set_recurrent_mode(self, default_val):
+        gru_module = GRUModule(
+            input_size=3,
+            hidden_size=12,
+            batch_first=True,
+            in_keys=["observation", "hidden"],
+            out_keys=["intermediate", ("next", "hidden")],
+            default_recurrent_mode=default_val,
+        )
+        assert gru_module.recurrent_mode is bool(default_val)
+        with set_recurrent_mode(True):
+            assert gru_module.recurrent_mode
+            with set_recurrent_mode(False):
+                assert not gru_module.recurrent_mode
+                with set_recurrent_mode("recurrent"):
+                    assert gru_module.recurrent_mode
+                    with set_recurrent_mode("sequential"):
+                        assert not gru_module.recurrent_mode
+                    assert gru_module.recurrent_mode
+                assert not gru_module.recurrent_mode
+            assert gru_module.recurrent_mode
+        assert gru_module.recurrent_mode is bool(default_val)
+
+    @pytest.mark.filterwarnings("ignore::DeprecationWarning")
     def test_set_temporal_mode(self):
         gru_module = GRUModule(
             input_size=3,

test/test_transforms.py

@@ -10885,7 +10885,8 @@ def _make_gru_module(self, input_size=4, hidden_size=4, device="cpu"):
             in_keys=["observation", "rhs", "is_init"],
             out_keys=["output", ("next", "rhs")],
             device=device,
-        ).set_recurrent_mode(True)
+            default_recurrent_mode=True,
+        )
 
     def _make_lstm_module(self, input_size=4, hidden_size=4, device="cpu"):
         return LSTMModule(
@@ -10895,7 +10896,8 @@ def _make_lstm_module(self, input_size=4, hidden_size=4, device="cpu"):
             in_keys=["observation", "rhs_h", "rhs_c", "is_init"],
             out_keys=["output", ("next", "rhs_h"), ("next", "rhs_c")],
             device=device,
-        ).set_recurrent_mode(True)
+            default_recurrent_mode=True,
+        )
 
     def _make_batch(self, batch_size: int = 2, sequence_length: int = 5):
         observation = torch.randn(batch_size, sequence_length + 1, 4)

torchrl/_utils.py

@@ -15,9 +15,11 @@
 import os
 import pickle
 import sys
+import threading
 import time
 import traceback
 import warnings
+from contextlib import nullcontext
 from copy import copy
 from distutils.util import strtobool
 from functools import wraps
@@ -32,6 +34,11 @@
 from tensordict.utils import NestedKey
 from torch import multiprocessing as mp
 
+try:
+    from torch.compiler import is_compiling
+except ImportError:
+    from torch._dynamo import is_compiling
+
 LOGGING_LEVEL = os.environ.get("RL_LOGGING_LEVEL", "INFO")
 logger = logging.getLogger("torchrl")
 logger.setLevel(getattr(logging, LOGGING_LEVEL))
@@ -827,3 +834,19 @@ def _make_ordinal_device(device: torch.device):
     if device.type == "mps" and device.index is None:
         return torch.device("mps", index=0)
     return device
+
+
+class _ContextManager:
+    def __init__(self):
+        self._mode: Any | None = None
+        self._lock = threading.Lock()
+
+    def get_mode(self) -> Any | None:
+        cm = self._lock if not is_compiling() else nullcontext()
+        with cm:
+            return self._mode
+
+    def set_mode(self, type: Any | None) -> None:
+        cm = self._lock if not is_compiling() else nullcontext()
+        with cm:
+            self._mode = type

torchrl/envs/transforms/transforms.py

@@ -7411,7 +7411,8 @@ class BurnInTransform(Transform):
         ...     hidden_size=10,
         ...     in_keys=["observation", "hidden"],
         ...     out_keys=["intermediate", ("next", "hidden")],
-        ... ).set_recurrent_mode(True)
+        ...     default_recurrent_mode=True,
+        ... )
         >>> burn_in_transform = BurnInTransform(
         ...     modules=[gru_module],
         ...     burn_in=5,

torchrl/modules/__init__.py

@@ -80,10 +80,12 @@
     QValueActor,
     QValueHook,
     QValueModule,
+    recurrent_mode,
     SafeModule,
     SafeProbabilisticModule,
     SafeProbabilisticTensorDictSequential,
     SafeSequential,
+    set_recurrent_mode,
     TanhModule,
     ValueOperator,
     VmapModule,

torchrl/modules/tensordict_module/__init__.py

@@ -34,6 +34,15 @@
     SafeProbabilisticModule,
     SafeProbabilisticTensorDictSequential,
 )
-from .rnn import GRU, GRUCell, GRUModule, LSTM, LSTMCell, LSTMModule
+from .rnn import (
+    GRU,
+    GRUCell,
+    GRUModule,
+    LSTM,
+    LSTMCell,
+    LSTMModule,
+    recurrent_mode,
+    set_recurrent_mode,
+)
 from .sequence import SafeSequential
 from .world_models import WorldModelWrapper