Print args/kwargs types for utils and examples (#963)

Summary:
printing arg types are useful for understanding how the op should be implemented

Test Plan:
python tutorials/developer_api_guide/print_op_and_shapes.py

Reviewers:

Subscribers:

Tasks:

Tags:

test/test_utils.py

@@ -1,6 +1,9 @@
 import unittest
 from unittest.mock import patch
+
+import torch
 from torchao.utils import torch_version_at_least
+from torchao.utils import TorchAOBaseTensor
 
 class TestTorchVersionAtLeast(unittest.TestCase):
     def test_torch_version_at_least(self):
@@ -20,7 +23,24 @@ def test_torch_version_at_least(self):
                 result = torch_version_at_least(compare_version)
 
                 self.assertEqual(result, expected_result, f"Failed for torch.__version__={torch_version}, comparing with {compare_version}")
-                print(f"{torch_version}: {result}")
+
+
+class TestTorchAOBaseTensor(unittest.TestCase):
+
+    def test_print_arg_types(self):
+        class MyTensor(TorchAOBaseTensor):
+            def __new__(cls, data):
+                shape = data.shape
+                return torch.Tensor._make_wrapper_subclass(cls, shape)  # type: ignore[attr-defined]
+
+            def __init__(self, data):
+                self.data = data
+
+
+        l = torch.nn.Linear(10, 10)
+        with self.assertRaisesRegex(NotImplementedError, "arg_types"):
+            l.weight = torch.nn.Parameter(MyTensor(l.weight))
+
 
 if __name__ == '__main__':
     unittest.main()

torchao/utils.py

@@ -388,7 +388,9 @@ class MyTensor(torch.Tensor):
        func in cls._ATEN_OP_OR_TORCH_FN_TABLE:
         return cls._ATEN_OP_OR_TORCH_FN_TABLE[func](func, types, args, kwargs)
 
-    raise NotImplementedError(f"{cls.__name__} dispatch: attempting to run unimplemented operator/function: {func}")
+    arg_types = tuple(type(arg) for arg in args)
+    kwarg_types = {k: type(arg) for k, arg in kwargs}
+    raise NotImplementedError(f"{cls.__name__} dispatch: attempting to run unimplemented operator/function: {func=}, {types=}, {arg_types=}, {kwarg_types=}")
 
 def _register_layout_cls(cls: Callable, layout_type_class: Callable):
     """Helper function for layout registrations, this is used to implement

tutorials/developer_api_guide/print_op_and_shapes.py

@@ -1,5 +1,6 @@
 import torch
 
+PRINT_ARGS = False
 linear_shapes = []
 from torch.overrides import TorchFunctionMode
 class TorchFunctionLoggingMode(TorchFunctionMode):
@@ -16,11 +17,28 @@ def __torch_function__(cls, func, types, args=(), kwargs=None):
             M, K = flattened_input_tensor.shape[0], flattened_input_tensor.shape[1]
             assert K == weight_tensor.shape[1]
             N = weight_tensor.shape[0]
-            print(f"TORCH_FUNC={str(func)} (M, K, N):", M, K, N)
+            print(f"TORCH_FUNC {func=} (M, K, N):", M, K, N)
             linear_shapes.append((M, K, N))
         else:
             arg_shape = args[0].shape if len(args) > 0 and isinstance(args[0], torch.Tensor) else None
-            print(f"TORCH_FUNC={str(func)} args[0] shape:", arg_shape)
+            if PRINT_ARGS:
+                print(f"TORCH_FUNC {func=}, {types=}, {args=}, {kwargs=}, args[0] shape: {arg_shape}")
+            else:
+                print(f"TORCH_FUNC {func=}, {types=}, args[0] shape: {arg_shape}")
+        return func(*args, **kwargs)
+
+
+from torch.utils._python_dispatch import TorchDispatchMode
+class TorchDispatchLoggingMode(TorchDispatchMode):
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+        arg_shape = args[0].shape if len(args) > 0 and isinstance(args[0], torch.Tensor) else None
+        if PRINT_ARGS:
+            print(f"ATEN_FUNC {func=}, {types=}, {args=}, {kwargs=}, args[0] shape: {arg_shape}")
+        else:
+            print(f"ATEN_FUNC {func=}, {types=}, args[0] shape: {arg_shape}")
+
         return func(*args, **kwargs)
 
 # NOTE: Modify this with your own model
@@ -33,3 +51,7 @@ def __torch_function__(cls, func, types, args=(), kwargs=None):
 
 print()
 print("all linear shapes (M, K, N):", linear_shapes)
+
+# check all aten ops that's called in the model
+# with TorchDispatchLoggingMode():
+#     m(*example_inputs)