Don't call dequantize in __repr__ (#965)

Summary:
To reduce the need for `dequantize` to work when people add new things,
if people want to see the dequantized value, they can print `weight.dequantize()`
instead of relying on `__repr__`

Test Plan:
test locally:
```
from torchao import quantize_, int8_weight_only
import torch
l = torch.nn.Linear(2, 2)
quantize_(l, int8_weight_only())
print(l)
print(l.dequantize())
```

```
AffineQuantizedTensor(layout_tensor=PlainAQTLayout(data=tensor([[ 127,  -77],
        [-128,  -40]], dtype=torch.int8)... , scale=tensor([0.0007, 0.0032])... , zero_point=tensor([0, 0])... , layout_type=PlainLayoutType()), block_size=(1, 2), shape=torch.Size([2, 2]), device=cpu, dtype=torch.float32, requires_grad=False)
tensor([[ 0.0856, -0.0519],
        [-0.4070, -0.1272]])
```

Reviewers:

Subscribers:

Tasks:

Tags:

torchao/dtypes/affine_quantized_tensor.py

@@ -188,8 +188,8 @@ def __init__(
 
     def __repr__(self):
         return (
-            f"{self.__class__.__name__}(data={str(self.dequantize())}..., shape={self.shape}, block_size={self.block_size}, "
-            f"device={self.device}, dtype={self.dtype}, requires_grad={self.requires_grad}, layout_tensor={self.layout_tensor})"
+            f"{self.__class__.__name__}(layout_tensor={self.layout_tensor}, block_size={self.block_size}, "
+            f"shape={self.shape}, device={self.device}, dtype={self.dtype}, requires_grad={self.requires_grad})"
         )
 
     def _quantization_type(self):
@@ -694,7 +694,7 @@ class BlockSparseAQTLayout(PlainAQTLayout):
     scale: Optional[torch.Tensor]
     zero_point: Optional[torch.Tensor]
 
-    __slots__ = ["bsr_crow_indices", "bsr_col_indices", "bsr_values", "scale", "zero_point"] 
+    __slots__ = ["bsr_crow_indices", "bsr_col_indices", "bsr_values", "scale", "zero_point"]
 
     @staticmethod
     def __new__(  # noqa: PYI034
@@ -703,7 +703,7 @@ def __new__(  # noqa: PYI034
         bsr_crow_indices: Optional[torch.Tensor],
         bsr_col_indices: Optional[torch.Tensor],
         bsr_values: Optional[torch.Tensor],
-        scale: Optional[torch.Tensor], 
+        scale: Optional[torch.Tensor],
         zero_point: Optional[torch.Tensor],
         layout_type: LayoutType,
         requires_grad: bool = False,
@@ -727,7 +727,7 @@ def __init__(  # noqa: PYI034
         bsr_crow_indices: Optional[torch.Tensor],
         bsr_col_indices: Optional[torch.Tensor],
         bsr_values: Optional[torch.Tensor],
-        scale: Optional[torch.Tensor], 
+        scale: Optional[torch.Tensor],
         zero_point: Optional[torch.Tensor],
         layout_type: LayoutType,
         requires_grad: bool = False,
@@ -739,7 +739,7 @@ def __init__(  # noqa: PYI034
         self.zero_point = zero_point
         self.layout_type = layout_type
 
-    def __tensor_flatten__(self): 
+    def __tensor_flatten__(self):
         inner_tensors = list(
             filter(lambda x: getattr(self, x) is not None, self.__slots__)
         )
@@ -774,7 +774,7 @@ def from_plain(cls, int_data, scale, zero_point, layout_type):
             bsr_crow_indices=bsr_tensor.crow_indices(),
             bsr_col_indices=bsr_tensor.col_indices(),
             bsr_values=bsr_tensor.values(),
-            scale=scale, 
+            scale=scale,
             zero_point=zero_point,
             layout_type = layout_type,
             requires_grad=False,
@@ -820,7 +820,7 @@ def __torch_dispatch__(cls, func, types, args, kwargs):
             return args[0].bsr_values.detach()
 
         if func is aten._nnz.default:
-            return args[0].bsr_values.shape[0]  
+            return args[0].bsr_values.shape[0]
 
         raise NotImplementedError(
             f"BlockSparseAQTLayout dispatch: attempting to run {func}, this is not supported"