[BE] tensorclass method registration check

ghstack-source-id: fa5dff657d58a035a05a39dbca84e3f9795c7fee
Pull Request resolved: #1175

tensordict/base.py

@@ -7938,7 +7938,7 @@ def reduce(
         async_op=False,
         return_premature=False,
         group=None,
-    ):
+    ) -> None:
         """Reduces the tensordict across all machines.
 
         Only the process with ``rank`` dst is going to receive the final result.
@@ -9028,7 +9028,7 @@ def newfn(item_and_out):
             return out
 
     # Stream
-    def record_stream(self, stream: torch.cuda.Stream):
+    def record_stream(self, stream: torch.cuda.Stream) -> T:
         """Marks the tensordict as having been used by this stream.
 
         When the tensordict is deallocated, ensure the tensor memory is not reused for other tensors until all work
@@ -11345,7 +11345,7 @@ def copy(self):
         """
         return self.clone(recurse=False)
 
-    def to_padded_tensor(self, padding=0.0, mask_key: NestedKey | None = None):
+    def to_padded_tensor(self, padding=0.0, mask_key: NestedKey | None = None) -> T:
         """Converts all nested tensors to a padded version and adapts the batch-size accordingly.
 
         Args:
@@ -12430,7 +12430,7 @@ def split_keys(
         default: Any = NO_DEFAULT,
         strict: bool = True,
         reproduce_struct: bool = False,
-    ):
+    ) -> Tuple[T, ...]:
         """Splits the tensordict in subsets given one or more set of keys.
 
         The method will return ``N+1`` tensordicts, where ``N`` is the number of

tensordict/tensorclass.py

@@ -119,6 +119,7 @@ def __subclasscheck__(self, subclass):
 }
 # Methods to be executed from tensordict, any ref to self means 'tensorclass'
 _METHOD_FROM_TD = [
+    "dumps",
     "load_",
     "memmap",
     "memmap_",
@@ -143,21 +144,48 @@ def __subclasscheck__(self, subclass):
     "_items_list",
     "_maybe_names",
     "_multithread_apply_flat",
+    "_multithread_apply_nest",
     "_multithread_rebuild",  # rebuild checks if self is a non tensor
     "_propagate_lock",
     "_propagate_unlock",
     "_reduce_get_metadata",
     "_values_list",
+    "bytes",
+    "cat_tensors",
     "data_ptr",
+    "depth",
     "dim",
+    "dtype",
+    "entry_class",
+    "get_item_shape",
+    "get_non_tensor",
+    "irecv",
+    "is_consolidated",
+    "is_contiguous",
+    "is_cpu",
+    "is_cuda",
     "is_empty",
+    "is_floating_point",
     "is_memmap",
+    "is_meta",
     "is_shared",
+    "isend",
     "items",
     "keys",
+    "make_memmap",
+    "make_memmap_from_tensor",
     "ndimension",
     "numel",
+    "numpy",
+    "param_count",
+    "pop",
+    "recv",
+    "reduce",
+    "saved_path",
+    "send",
     "size",
+    "sorted_keys",
+    "to_struct_array",
     "values",
     # "ndim",
 ]
@@ -212,9 +240,6 @@ def __subclasscheck__(self, subclass):
     "_map",
     "_maybe_remove_batch_dim",
     "_memmap_",
-    "_multithread_apply_flat",
-    "_multithread_apply_nest",
-    "_multithread_rebuild",
     "_permute",
     "_remove_batch_dim",
     "_repeat",
@@ -233,6 +258,8 @@ def __subclasscheck__(self, subclass):
     "addcmul",
     "addcmul_",
     "all",
+    "amax",
+    "amin",
     "any",
     "apply",
     "apply_",
@@ -243,31 +270,43 @@ def __subclasscheck__(self, subclass):
     "atan_",
     "auto_batch_size_",
     "auto_device_",
+    "bfloat16",
     "bitwise_and",
     "bool",
+    "cat",
+    "cat_from_tensordict",
     "ceil",
     "ceil_",
     "chunk",
+    "clamp",
     "clamp_max",
     "clamp_max_",
     "clamp_min",
     "clamp_min_",
     "clear",
     "clear_device_",
+    "complex128",
+    "complex32",
+    "complex64",
     "consolidate",
     "contiguous",
     "copy_",
+    "copy_at_",
     "cos",
     "cos_",
     "cosh",
     "cosh_",
     "cpu",
+    "create_nested",
     "cuda",
     "cummax",
     "cummin",
     "densify",
+    "detach",
+    "detach_",
     "div",
     "div_",
+    "double",
     "empty",
     "erf",
     "erf_",
@@ -280,20 +319,43 @@ def __subclasscheck__(self, subclass):
     "expand_as",
     "expm1",
     "expm1_",
+    "fill_",
+    "filter_empty_",
     "filter_non_tensor_data",
     "flatten",
+    "flatten_keys",
+    "float",
+    "float16",
+    "float32",
+    "float64",
     "floor",
     "floor_",
     "frac",
     "frac_",
     "from_any",
+    "from_consolidated",
     "from_dataclass",
+    "from_h5",
+    "from_modules",
     "from_namedtuple",
     "from_pytree",
+    "from_struct_array",
+    "from_tuple",
+    "fromkeys",
     "gather",
+    "gather_and_stack",
+    "half",
+    "int",
+    "int16",
+    "int32",
+    "int64",
+    "int8",
     "isfinite",
     "isnan",
+    "isneginf",
+    "isposinf",
     "isreal",
+    "lazy_stack",
     "lerp",
     "lerp_",
     "lgamma",
@@ -310,13 +372,16 @@ def __subclasscheck__(self, subclass):
     "log_",
     "logical_and",
     "logsumexp",
+    "make_memmap_from_storage",
     "map",
     "map_iter",
     "masked_fill",
     "masked_fill_",
+    "masked_select",
     "max",
     "maximum",
     "maximum_",
+    "maybe_dense_stack",
     "mean",
     "min",
     "minimum",
@@ -336,13 +401,22 @@ def __subclasscheck__(self, subclass):
     "norm",
     "permute",
     "pin_memory",
+    "pin_memory_",
+    "popitem",
     "pow",
     "pow_",
     "prod",
+    "qint32",
+    "qint8",
+    "quint4x2",
+    "quint8",
     "reciprocal",
     "reciprocal_",
+    "record_stream",
     "refine_names",
+    "rename",
     "rename_",  # TODO: must be specialized
+    "rename_key_",
     "repeat",
     "repeat_interleave",
     "replace",
@@ -351,6 +425,10 @@ def __subclasscheck__(self, subclass):
     "round",
     "round_",
     "select",
+    "separates",
+    "set_",
+    "set_non_tensor",
+    "setdefault",
     "sigmoid",
     "sigmoid_",
     "sign",
@@ -361,9 +439,13 @@ def __subclasscheck__(self, subclass):
     "sinh_",
     "softmax",
     "split",
+    "split_keys",
     "sqrt",
     "sqrt_",
     "squeeze",
+    "stack",
+    "stack_from_tensordict",
+    "stack_tensors",
     "std",
     "sub",
     "sub_",
@@ -373,13 +455,21 @@ def __subclasscheck__(self, subclass):
     "tanh",
     "tanh_",
     "to",
+    "to_h5",
     "to_module",
     "to_namedtuple",
+    "to_padded_tensor",
     "to_pytree",
     "transpose",
     "trunc",
     "trunc_",
+    "type",
+    "uint16",
+    "uint32",
+    "uint64",
+    "uint8",
     "unflatten",
+    "unflatten_keys",
     "unlock_",
     "unsqueeze",
     "var",
@@ -388,10 +478,6 @@ def __subclasscheck__(self, subclass):
     "zero_",
     "zero_grad",
 ]
-assert not any(v in _METHOD_FROM_TD for v in _FALLBACK_METHOD_FROM_TD), set(
-    _METHOD_FROM_TD
-).intersection(_FALLBACK_METHOD_FROM_TD)
-assert len(set(_FALLBACK_METHOD_FROM_TD)) == len(_FALLBACK_METHOD_FROM_TD)
 
 # These methods require a copy of the non tensor data
 _FALLBACK_METHOD_FROM_TD_COPY = [
@@ -863,6 +949,14 @@ def __torch_function__(
         cls.device = property(_device, _device_setter)
     if not hasattr(cls, "batch_size") and "batch_size" not in expected_keys:
         cls.batch_size = property(_batch_size, _batch_size_setter)
+    if not hasattr(cls, "batch_dims") and "batch_dims" not in expected_keys:
+        cls.batch_dims = property(_batch_dims)
+    if not hasattr(cls, "requires_grad") and "requires_grad" not in expected_keys:
+        cls.requires_grad = property(_requires_grad)
+    if not hasattr(cls, "is_locked") and "is_locked" not in expected_keys:
+        cls.is_locked = property(_is_locked)
+    if not hasattr(cls, "ndim") and "ndim" not in expected_keys:
+        cls.ndim = property(_batch_dims)
     if not hasattr(cls, "shape") and "shape" not in expected_keys:
         cls.shape = property(_batch_size, _batch_size_setter)
     if not hasattr(cls, "names") and "names" not in expected_keys:
@@ -2158,6 +2252,18 @@ def _batch_size(self) -> torch.Size:
     return self._tensordict.batch_size
 
 
+def _batch_dims(self) -> torch.Size:
+    return self._tensordict.batch_dims
+
+
+def _requires_grad(self) -> torch.Size:
+    return self._tensordict.requires_grad
+
+
+def _is_locked(self) -> torch.Size:
+    return self._tensordict.is_locked
+
+
 def _batch_size_setter(self, new_size: torch.Size) -> None:  # noqa: D417
     """Set the value of batch_size.
 

test/test_tensorclass.py

@@ -99,7 +99,11 @@ def _get_methods_from_class(cls):
     methods = set()
     for name in dir(cls):
         attr = getattr(cls, name)
-        if inspect.isfunction(attr) or inspect.ismethod(attr):
+        if (
+            inspect.isfunction(attr)
+            or inspect.ismethod(attr)
+            or isinstance(attr, property)
+        ):
             methods.add(name)
 
     return methods
@@ -122,6 +126,34 @@ def test_tensorclass_stub_methods():
 
     if missing_methods:
         raise Exception(f"Missing methods in tensorclass.pyi: {missing_methods}")
+        raise Exception(
+            f"Missing methods in tensorclass.pyi: {sorted(missing_methods)}"
+        )
+
+
+def test_tensorclass_instance_methods():
+    @tensorclass
+    class X:
+        x: torch.Tensor
+
+    tensorclass_pyi_path = (
+        pathlib.Path(__file__).parent.parent / "tensordict/tensorclass.pyi"
+    )
+    tensorclass_abstract_methods = _get_methods_from_pyi(str(tensorclass_pyi_path))
+
+    tensorclass_methods = _get_methods_from_class(X)
+
+    missing_methods = (
+        tensorclass_abstract_methods - tensorclass_methods - {"data", "grad"}
+    )
+    missing_methods = [
+        method for method in missing_methods if (not method.startswith("_"))
+    ]
+
+    if missing_methods:
+        raise Exception(
+            f"Missing methods in tensorclass.pyi: {sorted(missing_methods)}"
+        )
 
 
 def _make_data(shape):
@@ -188,6 +220,12 @@ class MyClass1:
             MyClass1(torch.zeros(3, 1), "z", batch_size=[3, 1]),
             batch_size=[3, 1],
         )
+        assert x.shape == x.batch_size
+        assert x.batch_size == (3, 1)
+        assert x.ndim == 2
+        assert x.batch_dims == 2
+        assert x.numel() == 3
+
         assert not x.all()
         assert not x.any()
         assert isinstance(x.all(), bool)