[Feature] Rename _TensorDict into TensorDictBase (#316)

test/mocking_classes.py

@@ -16,7 +16,7 @@
     UnboundedContinuousTensorSpec,
     OneHotDiscreteTensorSpec,
 )
-from torchrl.data.tensordict.tensordict import _TensorDict, TensorDict
+from torchrl.data.tensordict.tensordict import TensorDictBase, TensorDict
 from torchrl.envs.common import _EnvClass
 
 spec_dict = {
@@ -110,15 +110,15 @@ def _step(self, tensordict):
         done = torch.tensor([done], dtype=torch.bool, device=self.device)
         return TensorDict({"reward": n, "done": done, "next_observation": n}, [])
 
-    def _reset(self, tensordict: _TensorDict, **kwargs) -> _TensorDict:
+    def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
         self.max_val = max(self.counter + 100, self.counter * 2)
 
         n = torch.tensor([self.counter]).to(self.device).to(torch.get_default_dtype())
         done = self.counter >= self.max_val
         done = torch.tensor([done], dtype=torch.bool, device=self.device)
         return TensorDict({"done": done, "next_observation": n}, [])
 
-    def rand_step(self, tensordict: Optional[_TensorDict] = None) -> _TensorDict:
+    def rand_step(self, tensordict: Optional[TensorDictBase] = None) -> TensorDictBase:
         return self.step(tensordict)
 
 
@@ -144,7 +144,7 @@ def _get_in_obs(self, obs):
     def _get_out_obs(self, obs):
         return obs
 
-    def _reset(self, tensordict: _TensorDict) -> _TensorDict:
+    def _reset(self, tensordict: TensorDictBase) -> TensorDictBase:
         self.counter += 1
         state = torch.zeros(self.size) + self.counter
         tensordict = tensordict.select().set(
@@ -156,8 +156,8 @@ def _reset(self, tensordict: _TensorDict) -> _TensorDict:
 
     def _step(
         self,
-        tensordict: _TensorDict,
-    ) -> _TensorDict:
+        tensordict: TensorDictBase,
+    ) -> TensorDictBase:
         tensordict = tensordict.to(self.device)
         a = tensordict.get("action")
         assert (a.sum(-1) == 1).all()
@@ -199,7 +199,7 @@ def _get_in_obs(self, obs):
     def _get_out_obs(self, obs):
         return obs
 
-    def _reset(self, tensordict: _TensorDict) -> _TensorDict:
+    def _reset(self, tensordict: TensorDictBase) -> TensorDictBase:
         self.counter += 1
         self.step_count = 0
         state = torch.zeros(self.size) + self.counter
@@ -211,8 +211,8 @@ def _reset(self, tensordict: _TensorDict) -> _TensorDict:
 
     def _step(
         self,
-        tensordict: _TensorDict,
-    ) -> _TensorDict:
+        tensordict: TensorDictBase,
+    ) -> TensorDictBase:
         self.step_count += 1
         tensordict = tensordict.to(self.device)
         a = tensordict.get("action")

test/test_distributions.py

@@ -9,7 +9,7 @@
 import torch
 from _utils_internal import get_available_devices
 from torch import nn, autograd
-from torchrl.data.tensordict.tensordict import _TensorDict
+from torchrl.data.tensordict.tensordict import TensorDictBase
 from torchrl.modules import (
     TanhNormal,
     NormalParamWrapper,
@@ -59,7 +59,7 @@ def test_delta(device, div_up, div_down):
 
 def _map_all(*tensors_or_other, device):
     for t in tensors_or_other:
-        if isinstance(t, (torch.Tensor, _TensorDict)):
+        if isinstance(t, (torch.Tensor, TensorDictBase)):
             yield t.to(device)
         else:
             yield t

test/test_rb.py

@@ -21,7 +21,7 @@
     LazyMemmapStorage,
     LazyTensorStorage,
 )
-from torchrl.data.tensordict.tensordict import assert_allclose_td, _TensorDict
+from torchrl.data.tensordict.tensordict import assert_allclose_td, TensorDictBase
 
 
 collate_fn_dict = {
@@ -128,7 +128,7 @@ def test_add(self, rbtype, storage, size, prefetch):
         data = self._get_datum(rbtype)
         rb.add(data)
         s = rb._storage[0]
-        if isinstance(s, _TensorDict):
+        if isinstance(s, TensorDictBase):
             assert (s == data.select(*s.keys())).all()
         else:
             assert (s == data).all()
@@ -142,12 +142,12 @@ def test_extend(self, rbtype, storage, size, prefetch):
         for d in data[-length:]:
             found_similar = False
             for b in rb._storage:
-                if isinstance(b, _TensorDict):
+                if isinstance(b, TensorDictBase):
                     b = b.exclude("index").select(*set(d.keys()).intersection(b.keys()))
                     d = d.select(*set(d.keys()).intersection(b.keys()))
 
                 value = b == d
-                if isinstance(value, (torch.Tensor, _TensorDict)):
+                if isinstance(value, (torch.Tensor, TensorDictBase)):
                     value = value.all()
                 if value:
                     found_similar = True
@@ -160,18 +160,18 @@ def test_sample(self, rbtype, storage, size, prefetch):
         data = self._get_data(rbtype, size=5)
         rb.extend(data)
         new_data = rb.sample(3)
-        if not isinstance(new_data, (torch.Tensor, _TensorDict)):
+        if not isinstance(new_data, (torch.Tensor, TensorDictBase)):
             new_data = new_data[0]
 
         for d in new_data:
             found_similar = False
             for b in data:
-                if isinstance(b, _TensorDict):
+                if isinstance(b, TensorDictBase):
                     b = b.exclude("index").select(*set(d.keys()).intersection(b.keys()))
                     d = d.select(*set(d.keys()).intersection(b.keys()))
 
                 value = b == d
-                if isinstance(value, (torch.Tensor, _TensorDict)):
+                if isinstance(value, (torch.Tensor, TensorDictBase)):
                     value = value.all()
                 if value:
                     found_similar = True

test/test_tensor_spec.py

@@ -18,7 +18,7 @@
     UnboundedContinuousTensorSpec,
     OneHotDiscreteTensorSpec,
 )
-from torchrl.data.tensordict.tensordict import TensorDict, _TensorDict
+from torchrl.data.tensordict.tensordict import TensorDict, TensorDictBase
 
 
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.float64, None])
@@ -376,7 +376,7 @@ def test_nested_composite_spec(self, is_complete, device, dtype):
         ts = self._composite_spec(is_complete, device, dtype)
         ts["nested_cp"] = self._composite_spec(is_complete, device, dtype)
         td = ts.rand()
-        assert isinstance(td["nested_cp"], _TensorDict)
+        assert isinstance(td["nested_cp"], TensorDictBase)
         keys = list(td.keys())
         for key in keys:
             if key != "nested_cp":

test/test_tensordict.py

@@ -18,7 +18,7 @@
     LazyStackedTensorDict,
     stack as stack_td,
     pad,
-    _TensorDict,
+    TensorDictBase,
 )
 from torchrl.data.tensordict.utils import _getitem_batch_size, convert_ellipsis_to_idx
 
@@ -833,7 +833,7 @@ def test_masking_set(self, td_name, device, from_list):
         def zeros_like(item, n, d):
             if isinstance(item, (MemmapTensor, torch.Tensor)):
                 return torch.zeros(n, *item.shape[d:], dtype=item.dtype, device=device)
-            elif isinstance(item, _TensorDict):
+            elif isinstance(item, TensorDictBase):
                 batch_size = item.batch_size
                 batch_size = [n, *batch_size[d:]]
                 out = TensorDict(
@@ -1344,7 +1344,7 @@ def test_flatten_keys(self, td_name, device, inplace, separator):
 
         td_flatten = td.flatten_keys(inplace=inplace, separator=separator)
         for key, value in td_flatten.items():
-            assert not isinstance(value, _TensorDict)
+            assert not isinstance(value, TensorDictBase)
         assert (
             separator.join(["nested_tensordict", "nested_nested_tensordict", "a"])
             in td_flatten.keys()