[Feature] RB compability with compile

torchrl/data/replay_buffers/replay_buffers.py

@@ -131,6 +131,9 @@ class ReplayBuffer:
             .. warning:: As of now, the generator has no effect on the transforms.
         shared (bool, optional): whether the buffer will be shared using multiprocessing or not.
             Defaults to ``False``.
+        compilable (bool, optional): whether the writer is compilable.
+            If ``True``, the writer cannot be shared between multiple processes.
+            Defaults to ``False``.
 
     Examples:
         >>> import torch
@@ -216,11 +219,20 @@ def __init__(
         checkpointer: "StorageCheckpointerBase" | None = None,  # noqa: F821
         generator: torch.Generator | None = None,
         shared: bool = False,
+        compilable: bool = None,
     ) -> None:
         self._storage = storage if storage is not None else ListStorage(max_size=1_000)
         self._storage.attach(self)
+        if compilable is not None:
+            self._storage._compilable = compilable
+            self._storage._len = self._storage._len
+
         self._sampler = sampler if sampler is not None else RandomSampler()
-        self._writer = writer if writer is not None else RoundRobinWriter()
+        self._writer = (
+            writer
+            if writer is not None
+            else RoundRobinWriter(compilable=bool(compilable))
+        )
         self._writer.register_storage(self._storage)
 
         self._get_collate_fn(collate_fn)
@@ -601,7 +613,9 @@ def _add(self, data):
         return index
 
     def _extend(self, data: Sequence) -> torch.Tensor:
-        with self._replay_lock, self._write_lock:
+        is_compiling = torch.compiler.is_dynamo_compiling()
+        nc = contextlib.nullcontext()
+        with self._replay_lock if not is_compiling else nc, self._write_lock if not is_compiling else nc:
             if self.dim_extend > 0:
                 data = self._transpose(data)
             index = self._writer.extend(data)
@@ -654,7 +668,7 @@ def update_priority(
 
     @pin_memory_output
     def _sample(self, batch_size: int) -> Tuple[Any, dict]:
-        with self._replay_lock:
+        with self._replay_lock if not torch.compiler.is_dynamo_compiling() else contextlib.nullcontext():
             index, info = self._sampler.sample(self._storage, batch_size)
             info["index"] = index
             data = self._storage.get(index)
@@ -1753,6 +1767,7 @@ def __init__(
         num_buffer_sampled: int | None = None,
         generator: torch.Generator | None = None,
         shared: bool = False,
+        compilable: bool = False,
         **kwargs,
     ):
 

torchrl/data/replay_buffers/storages.py

@@ -57,10 +57,15 @@ class Storage:
     _rng: torch.Generator | None = None
 
     def __init__(
-        self, max_size: int, checkpointer: StorageCheckpointerBase | None = None
+        self,
+        max_size: int,
+        checkpointer: StorageCheckpointerBase | None = None,
+        compilable: bool = False,
     ) -> None:
         self.max_size = int(max_size)
         self.checkpointer = checkpointer
+        self._compilable = compilable
+        self._attached_entities_set = set()
 
     @property
     def checkpointer(self):
@@ -80,11 +85,11 @@ def _is_full(self):
     def _attached_entities(self):
         # RBs that use a given instance of Storage should add
         # themselves to this set.
-        _attached_entities = self.__dict__.get("_attached_entities_set", None)
-        if _attached_entities is None:
-            _attached_entities = set()
-            self.__dict__["_attached_entities_set"] = _attached_entities
-        return _attached_entities
+        return getattr(self, "_attached_entities_set", None)
+
+    @torch._dynamo.assume_constant_result
+    def _attached_entities_iter(self):
+        return list(self._attached_entities)
 
     @abc.abstractmethod
     def set(self, cursor: int, data: Any, *, set_cursor: bool = True):
@@ -140,6 +145,7 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
     def _empty(self):
         ...
 
+    @torch._dynamo.disable()
     def _rand_given_ndim(self, batch_size):
         # a method to return random indices given the storage ndim
         if self.ndim == 1:
@@ -330,6 +336,9 @@ class TensorStorage(Storage):
             measuring the storage size. For instance, a storage of shape ``[3, 4]``
             has capacity ``3`` if ``ndim=1`` and ``12`` if ``ndim=2``.
             Defaults to ``1``.
+        compilable (bool, optional): whether the storage is compilable.
+            If ``True``, the writer cannot be shared between multiple processes.
+            Defaults to ``False``.
 
     Examples:
         >>> data = TensorDict({
@@ -389,6 +398,7 @@ def __init__(
         *,
         device: torch.device = "cpu",
         ndim: int = 1,
+        compilable: bool = False,
     ):
         if not ((storage is None) ^ (max_size is None)):
             if storage is None:
@@ -404,7 +414,7 @@ def __init__(
             else:
                 max_size = tree_flatten(storage)[0][0].shape[0]
         self.ndim = ndim
-        super().__init__(max_size)
+        super().__init__(max_size, compilable=compilable)
         self.initialized = storage is not None
         if self.initialized:
             self._len = max_size
@@ -423,16 +433,23 @@ def __init__(
     @property
     def _len(self):
         _len_value = self.__dict__.get("_len_value", None)
+        if not self._compilable or not isinstance(self._len_value, int):
+            if _len_value is None:
+                _len_value = self._len_value = mp.Value("i", 0)
+            return _len_value.value
         if _len_value is None:
-            _len_value = self._len_value = mp.Value("i", 0)
-        return _len_value.value
+            _len_value = self._len_value = 0
+        return _len_value
 
     @_len.setter
     def _len(self, value):
         _len_value = self.__dict__.get("_len_value", None)
-        if _len_value is None:
-            _len_value = self._len_value = mp.Value("i", 0)
-        _len_value.value = value
+        if not self._compilable:
+            if _len_value is None:
+                _len_value = self._len_value = mp.Value("i", 0)
+            _len_value.value = value
+        else:
+            self._len_value = value
 
     @property
     def _total_shape(self):
@@ -1184,9 +1201,9 @@ def _rng(self, value):
         for storage in self._storages:
             storage._rng = value
 
-    @property
-    def _attached_entities(self):
-        return set()
+    # @property
+    # def _attached_entities(self):
+    #     return set()
 
     def extend(self, value):
         raise RuntimeError

torchrl/data/replay_buffers/writers.py

@@ -40,8 +40,9 @@ class Writer(ABC):
     _storage: Storage
     _rng: torch.Generator | None = None
 
-    def __init__(self) -> None:
+    def __init__(self, compilable: bool = False) -> None:
         self._storage = None
+        self._compilable = compilable
 
     def register_storage(self, storage: Storage) -> None:
         self._storage = storage
@@ -138,10 +139,17 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
 
 
 class RoundRobinWriter(Writer):
-    """A RoundRobin Writer class for composable replay buffers."""
+    """A RoundRobin Writer class for composable replay buffers.
 
-    def __init__(self, **kw) -> None:
-        super().__init__(**kw)
+    Args:
+        compilable (bool, optional): whether the writer is compilable.
+            If ``True``, the writer cannot be shared between multiple processes.
+            Defaults to ``False``.
+
+    """
+
+    def __init__(self, compilable: bool = False) -> None:
+        super().__init__(compilable=compilable)
         self._cursor = 0
 
     def dumps(self, path):
@@ -197,7 +205,7 @@ def extend(self, data: Sequence) -> torch.Tensor:
         # Other than that, a "flat" (1d) index is ok to write the data
         self._storage.set(index, data)
         index = self._replicate_index(index)
-        for ent in self._storage._attached_entities:
+        for ent in self._storage._attached_entities_iter():
             ent.mark_update(index)
         return index
 
@@ -213,30 +221,44 @@ def _empty(self):
     @property
     def _cursor(self):
         _cursor_value = self.__dict__.get("_cursor_value", None)
+        if not self._compilable or not isinstance(_cursor_value, int):
+            if _cursor_value is None:
+                _cursor_value = self._cursor_value = mp.Value("i", 0)
+            return _cursor_value.value
         if _cursor_value is None:
-            _cursor_value = self._cursor_value = mp.Value("i", 0)
-        return _cursor_value.value
+            _cursor_value = self._cursor_value = 0
+        return _cursor_value
 
     @_cursor.setter
     def _cursor(self, value):
-        _cursor_value = self.__dict__.get("_cursor_value", None)
-        if _cursor_value is None:
-            _cursor_value = self._cursor_value = mp.Value("i", 0)
-        _cursor_value.value = value
+        if not self._compilable:
+            _cursor_value = self.__dict__.get("_cursor_value", None)
+            if _cursor_value is None:
+                _cursor_value = self._cursor_value = mp.Value("i", 0)
+            _cursor_value.value = value
+        else:
+            self._cursor_value = value
 
     @property
     def _write_count(self):
         _write_count = self.__dict__.get("_write_count_value", None)
+        if not self._compilable or not isinstance(_write_count, int):
+            if _write_count is None:
+                _write_count = self._write_count_value = mp.Value("i", 0)
+            return _write_count.value
         if _write_count is None:
-            _write_count = self._write_count_value = mp.Value("i", 0)
-        return _write_count.value
+            _write_count = self._write_count_value = 0
+        return _write_count
 
     @_write_count.setter
     def _write_count(self, value):
-        _write_count = self.__dict__.get("_write_count_value", None)
-        if _write_count is None:
-            _write_count = self._write_count_value = mp.Value("i", 0)
-        _write_count.value = value
+        if not self._compilable:
+            _write_count = self.__dict__.get("_write_count_value", None)
+            if _write_count is None:
+                _write_count = self._write_count_value = mp.Value("i", 0)
+            _write_count.value = value
+        else:
+            self._write_count_value = value
 
     def __getstate__(self):
         state = super().__getstate__()
@@ -248,7 +270,7 @@ def __getstate__(self):
 
     def __setstate__(self, state):
         cursor = state.pop("cursor__context", None)
-        if cursor is not None:
+        if not state["_compilable"] and cursor is not None:
             _cursor_value = mp.Value("i", cursor)
             state["_cursor_value"] = _cursor_value
         self.__dict__.update(state)