move Tensor.__getitem__ to mixin [PR] (#16689)

tinygrad/mixin/__init__.py

@@ -45,7 +45,45 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     val = val.reshape((1,)*len(new_shape)).expand(new_shape)
     return val.clone(device=device) if buffer else val
 
-  def __getitem__(self, indices) -> Self: return self._getitem(indices)
+  def __getitem__(self, indices) -> Self:
+    """
+    Retrieves a sub-tensor using indexing.
+
+    Supported Index Types: `int | slice | Tensor | None | list | tuple | Ellipsis`
+
+    Examples:
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor.arange(12).reshape(3, 4)
+    print(t.numpy())
+    ```
+
+    - Int Indexing: Select an element or sub-tensor using integers for each dimension.
+      ```python exec="true" source="above" session="tensor" result="python"
+      print(t[1, 2].numpy())
+      ```
+
+    - Slice Indexing: Select a range of elements using slice notation (`start:end:stride`).
+      ```python exec="true" source="above" session="tensor" result="python"
+      print(t[0:2, ::2].numpy())
+      ```
+
+    - Tensor Indexing: Use another tensor as indices for advanced indexing. Using `tuple` or `list` here also works.
+      ```python exec="true" source="above" session="tensor" result="python"
+      print(t[Tensor([2, 0, 1]), Tensor([1, 2, 3])].numpy())
+      ```
+
+    - `None` Indexing: Add a new dimension to the tensor.
+      ```python exec="true" source="above" session="tensor" result="python"
+      print(t[:, None].shape)
+      ```
+
+    NOTE: Out-of-bounds indexing results in a value of `0`.
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor([1, 2, 3])
+    print(t[Tensor([4, 3, 2])].numpy())
+    ```
+    """
+    return self._getitem(indices)
 
   def _getitem(self, indices, v=None) -> Self:
     from tinygrad.uop.ops import UOp

tinygrad/tensor.py

@@ -817,46 +817,6 @@ class Tensor(RandMixin):
   def _mop(self, op:Ops, arg) -> Tensor: return self._apply_uop(UOp._mop, extra_args=(op,), arg=arg)
   def _rop(self, op:Ops, axis:tuple[int, ...]) -> Tensor: return self._apply_uop(UOp._rop, op=op, axis=axis)
 
-  def __getitem__(self, indices) -> Tensor:
-    """
-    Retrieves a sub-tensor using indexing.
-
-    Supported Index Types: `int | slice | Tensor | None | list | tuple | Ellipsis`
-
-    Examples:
-    ```python exec="true" source="above" session="tensor" result="python"
-    t = Tensor.arange(12).reshape(3, 4)
-    print(t.numpy())
-    ```
-
-    - Int Indexing: Select an element or sub-tensor using integers for each dimension.
-      ```python exec="true" source="above" session="tensor" result="python"
-      print(t[1, 2].numpy())
-      ```
-
-    - Slice Indexing: Select a range of elements using slice notation (`start:end:stride`).
-      ```python exec="true" source="above" session="tensor" result="python"
-      print(t[0:2, ::2].numpy())
-      ```
-
-    - Tensor Indexing: Use another tensor as indices for advanced indexing. Using `tuple` or `list` here also works.
-      ```python exec="true" source="above" session="tensor" result="python"
-      print(t[Tensor([2, 0, 1]), Tensor([1, 2, 3])].numpy())
-      ```
-
-    - `None` Indexing: Add a new dimension to the tensor.
-      ```python exec="true" source="above" session="tensor" result="python"
-      print(t[:, None].shape)
-      ```
-
-    NOTE: Out-of-bounds indexing results in a value of `0`.
-    ```python exec="true" source="above" session="tensor" result="python"
-    t = Tensor([1, 2, 3])
-    print(t[Tensor([4, 3, 2])].numpy())
-    ```
-    """
-    return super().__getitem__(indices)
-
   def __setitem__(self, indices, v:Tensor|PyConst|list|tuple) -> None:
     if isinstance(v, Tensor) and v.dtype != self.dtype: raise RuntimeError(f"setitem dtype mismatch: {self.dtype=} != {v.dtype=}")
     # raise if mutation would diverge from eager (allow only pure views of a realized buffer; exclude +=/-= RHS via v_uop/v_bw)