use torchao copy_

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torchtune/utils/_register_nf4_dispatch_ops.py

@@ -17,26 +17,3 @@ def clone(func, *args, **kwargs):
     in precision.
     """
     return to_nf4(args[0][0].get_original_weight())
-
-
-@nf4_tensor_impl([torch.ops.aten.copy_.default])
-def inplace_copy(func, *args, **kwargs):
-    """
-    Performs an inplace copy of an incoming tensor into the tensor
-    being copied into. The inplace tensor is given by args[0][1] and the
-    tensor being copied into is given by args[0][0]. The copy is performed
-    by copying over all attributes. This method would have to be updated
-    if additional attributes are added to NF4Tensor.
-    """
-    dest_tensor = args[0][0]  # tensor we are inplace copying into
-    ref_tensor = to_nf4(
-        args[0][1].to(dest_tensor.device)
-    )  # TODO check if nf4 tensor takes in device arg
-    dest_tensor.block_size = ref_tensor.block_size
-    dest_tensor.n_blocks = ref_tensor.n_blocks
-    dest_tensor.scaler_block_size = ref_tensor.scaler_block_size
-    dest_tensor.quantized_scalers = ref_tensor.quantized_scalers
-    dest_tensor.quantization_factor = ref_tensor.quantization_factor
-    dest_tensor.scaler_mean = ref_tensor.scaler_mean
-    dest_tensor.quantized_data = ref_tensor.quantized_data
-    dest_tensor.nf4 = ref_tensor.nf4