6/x mx cleanup: make NVFP4Tensor use base implements

Summary:

Refactors NVFP4Tensor to use the `implements` function from
`AOBaseTensor`.

Test Plan:

```
pytest test/prototype/mx_formats/ -s -x
```

Reviewers:

Subscribers:

Tasks:

Tags:
ghstack-source-id: 8370a1e
ghstack-comment-id: 3572568961
Pull-Request: #3387

Author: vkuzo

torchao/prototype/mx_formats/nvfp4_tensor.py

@@ -7,7 +7,7 @@
 import math
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Dict, Optional
+from typing import Optional
 
 import torch
 from torch.utils._python_dispatch import return_and_correct_aliasing
@@ -39,8 +39,6 @@
 
 aten = torch.ops.aten
 
-NVFP4_OPS_TABLE: Dict[Any, Any] = {}
-
 
 class NVFP4MMConfig(Enum):
     DYNAMIC = "dynamic"
@@ -55,18 +53,6 @@ class QuantizeTensorToNVFP4Kwargs(QuantizeTensorKwargs):
     use_dynamic_per_tensor_scale: bool = False
 
 
-# TODO(future PR): move over to TorchAOBaseTensor's dispatch
-def implements(aten_ops):
-    """Register aten ops to the NVFP4 op table"""
-
-    def decorator(func):
-        for op in aten_ops:
-            NVFP4_OPS_TABLE[op] = func
-        return func
-
-    return decorator
-
-
 class NVFP4Tensor(TorchAOBaseTensor):
     """NVIDIA FP4 (NVFP4) Tensor subclass.
 
@@ -141,14 +127,6 @@ def __repr__(self):
     def _quantization_type(self):
         return f"{self._is_swizzled_scales=}, {self.use_triton_kernel=}, {self.act_quant_kwargs=}"
 
-    @classmethod
-    def __torch_dispatch__(cls, func, types, args, kwargs=None):
-        # Use NVFP4-specific ops table
-        if func in NVFP4_OPS_TABLE:
-            return NVFP4_OPS_TABLE[func](func, types, args, kwargs)
-
-        raise NotImplementedError(f"{func} not implemented for NVFP4Tensor")
-
     @staticmethod
     def to_nvfp4(
         data_hp: torch.Tensor,
@@ -308,13 +286,10 @@ def _same_metadata(cls, self: "NVFP4Tensor", src: "NVFP4Tensor") -> bool:
         )
 
 
-@implements([aten.detach.default, aten.alias.default])
-def nvfp4_detach_alias(func, types, args, kwargs):
-    return return_and_correct_aliasing(
-        func, args, kwargs, args[0]._apply_fn_to_data(func)
-    )
+implements = NVFP4Tensor.implements
 
 
+# TODO(future PR): move this to AOBaseTensor (will require debugging/fixing CI)
 @implements([aten._to_copy.default])
 def nvfp4_to_copy(func, types, args, kwargs):
     """Autocast + device movement"""
@@ -354,33 +329,6 @@ def nvfp4_to_copy(func, types, args, kwargs):
     return tensor
 
 
-@implements([aten.copy_.default])
-def nvfp4_copy_(func, types, args, kwargs):
-    self = args[0]
-    src = args[1]
-    if NVFP4Tensor._same_metadata(self, src):
-        self_tensors = self.__tensor_flatten__()[0]
-        for tensor_name in self_tensors:
-            getattr(self, tensor_name).copy_(getattr(src, tensor_name))
-        return self
-    raise ValueError(
-        f"Not supported args for copy_ due to metadata mismatch: {self}, {src}"
-    )
-
-
-@implements([aten.clone.default])
-def nvfp4_clone(func, types, args, kwargs):
-    self = args[0]
-    memory_format = kwargs.get("memory_format", None)
-
-    if memory_format is not None:
-        clone_fn = lambda x: x.clone(memory_format=memory_format)
-    else:
-        clone_fn = lambda x: x.clone()
-
-    return self._apply_fn_to_data(clone_fn)
-
-
 @implements([aten.slice.Tensor])
 def nvfp4_slice(func, types, args, kwargs):
     x, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1])