Update on "Add Opinfo entry for add_alias"

[ghstack-poisoned]

torch/_inductor/lowering.py

@@ -1478,61 +1478,6 @@ def reindexer(idx):
     return TensorBox(ir.GenericView.create(input, sizes, reindexer))
 
 
-@register_lowering(aten.diagonal, type_promotion_kind=None)
-def diagonal(input, offset: int = 0, dim1: int = 0, dim2: int = 1):
-    original_shape = input.get_size()
-    num_dims = len(original_shape)
-    dim1 = canonicalize_dim(idx=dim1, rank=num_dims)
-    dim2 = canonicalize_dim(idx=dim2, rank=num_dims)
-
-    check(
-        dim1 != dim2, lambda: f"diagonal dimensions cannot be identical {dim1}, {dim2}"
-    )
-
-    offset_negative = V.graph.sizevars.evaluate_expr(sympy.Lt(offset, 0))
-    if offset_negative:
-        diag_size = V.graph.sizevars.evaluate_max(
-            V.graph.sizevars.evaluate_min(
-                original_shape[dim1] + offset, original_shape[dim2]
-            ),
-            0,  # type: ignore[arg-type]
-        )
-    else:
-        diag_size = V.graph.sizevars.evaluate_max(
-            V.graph.sizevars.evaluate_min(
-                original_shape[dim1], original_shape[dim2] - offset
-            ),
-            0,  # type: ignore[arg-type]
-        )
-
-    base_idx = (0, 0)
-    if offset_negative:
-        base_idx = (-offset, 0)
-    else:
-        base_idx = (0, offset)
-
-    sizes = [s for i, s in enumerate(original_shape) if i not in (dim1, dim2)]
-    sizes.append(diag_size)
-
-    def reindexer(idx):
-        diag_idx = idx[-1]
-        original_idx = [0] * len(original_shape)
-        cur_dim = 0
-        for d in range(num_dims):
-            if d == dim1:
-                original_idx[d] = diag_idx + base_idx[0]
-            elif d == dim2:
-                original_idx[d] = diag_idx + base_idx[1]
-            else:
-                original_idx[d] = idx[cur_dim]
-                cur_dim += 1
-
-        assert cur_dim == len(original_shape) - 2
-        return original_idx
-
-    return TensorBox(ir.GenericView.create(input, sizes, reindexer))
-
-
 @register_lowering(aten.diagonal_copy, type_promotion_kind=None)
 def diagonal_copy(input, offset: int = 0, dim1: int = 0, dim2: int = 1):
     return clone(diagonal(input, offset, dim1, dim2))