Add boiler plate code to Tensor subclass

torchao/utils.py

@@ -512,6 +512,27 @@ def _get_tensor_impl_constructor(
     return tensor_class._LAYOUT_CONSTRUCTOR_TABLE[layout_class]
 
 
+def _get_to_kwargs(self, *args, **kwargs):
+    # `torch._C._nn._parse_to` can't handle `layout` argument
+    for arg in args:
+        if isinstance(arg, torch.layout):
+            args.remove(arg)
+    if "layout" in kwargs:
+        kwargs.pop("layout")
+    # ignoring `non_blocking` and `memory_format` args since these are not
+    # very useful for most of the tensor subclasses
+    # if in the future there are use cases that need these, we'd recommend
+    # to override `_get_to_kwargs` and return these args
+    device, dtype, _, _ = torch._C._nn._parse_to(*args, **kwargs)
+    device = self.device if device is None else device
+    dtype = self.dtype if dtype is None else dtype
+    kwargs = {
+        "device": device,
+        "dtype": dtype,
+    }
+    return kwargs
+
+
 class TorchAOBaseTensor(torch.Tensor):
     """A util tensor subclass that provides commonly used functions
        new tensor subclass can inherit it to get all the utility functions
@@ -552,26 +573,24 @@ class PlainAQTTensorImpl(...):
     __torch_function__ = classmethod(_dispatch__torch_function__)
     register_layout = classmethod(_register_layout)
     get_tensor_impl_constructor = classmethod(_get_tensor_impl_constructor)
+    _get_to_kwargs = _get_to_kwargs
+
+    def __tensor_flatten__(self):
+        raise NotImplementedError("Subclasses must implement __tensor_flatten__")
+
+    @classmethod
+    def __tensor_unflatten__(
+        cls, tensor_data_dict, tensor_attributes, outer_size, outer_stride
+    ):
+        raise NotImplementedError("Subclasses must implement __tensor_unflatten__")
+
+    def __repr__(self):
+        raise NotImplementedError("Subclasses must implement __repr__")
 
-    def _get_to_kwargs(self, *args, **kwargs):
-        # `torch._C._nn._parse_to` can't handle `layout` argument
-        for arg in args:
-            if isinstance(arg, torch.layout):
-                args.remove(arg)
-        if "layout" in kwargs:
-            kwargs.pop("layout")
-        # ignoring `non_blocking` and `memory_format` args since these are not
-        # very useful for most of the tensor subclasses
-        # if in the future there are use cases that need these, we'd recommend
-        # to override `_get_to_kwargs` and return these args
-        device, dtype, _, _ = torch._C._nn._parse_to(*args, **kwargs)
-        device = self.device if device is None else device
-        dtype = self.dtype if dtype is None else dtype
-        kwargs = {
-            "device": device,
-            "dtype": dtype,
-        }
-        return kwargs
+    def get_layout(self):
+        if not hasattr(self, "_layout"):
+            return None
+        return self._layout
 
 
 def fill_defaults(args, n, defaults_tail):