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Delete torchtune's inplace copy definition for NF4 #1294

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merged 3 commits into from
Aug 23, 2024
Merged

Delete torchtune's inplace copy definition for NF4 #1294

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f68876e
Override ops.aten.copy_ in torchtune with inplace version
ebsmothers Aug 9, 2024
ce86906
Merge branch 'main' into redefine-inplace-copy
ebsmothers Aug 23, 2024
9ebfb42
delete inplace copy override
ebsmothers Aug 23, 2024
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35 changes: 0 additions & 35 deletions torchtune/modules/low_precision/_register_nf4_dispatch_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,6 @@

import torch
from torchao.dtypes.nf4tensor import implements as nf4_tensor_impl, to_nf4
from torchtune.modules.low_precision._utils import _get_torchao_version
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Strictly speaking this util is now no longer used anywhere in our library, but I am inclined to keep it in for possible (likely?) future usage

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Nope - get rid of it until we need it again. It's easy enough to find in git history.



@nf4_tensor_impl([torch.ops.aten.clone.default])
Expand All @@ -18,37 +17,3 @@ def clone(func, *args, **kwargs):
in precision.
"""
return to_nf4(args[0][0].get_original_weight())


should_define_inplace_copy = True
ao_version, is_nightly = _get_torchao_version()
if ao_version:
if (is_nightly and ao_version >= "2024.5.20") or (
not is_nightly and ao_version >= "0.2.0"
):
should_define_inplace_copy = False

if should_define_inplace_copy:
# TorchAO have `NF4.copy_` starting from `0.2.0`
# it's a superset of `inplace_copy` since it covers `NF4.copy_(NF4)`
@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
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