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fix: fix deepcopy torchtensor #1720

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Merged
JoanFM merged 1 commit into from
Jul 21, 2023
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fix: fix deepcopy torchtensor #1720

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7 changes: 7 additions & 0 deletions docarray/typing/tensor/torch_tensor.py
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Expand Up @@ -271,3 +271,10 @@ def _docarray_from_ndarray(cls: Type[T], value: np.ndarray) -> T:
def _docarray_to_ndarray(self) -> np.ndarray:
"""cast itself to a numpy array"""
return self.detach().cpu().numpy()

def new_empty(self, *args, **kwargs):

@samsja samsja Jul 21, 2023

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can we copy the full signature of the original method ?

@JoanFM JoanFM Jul 21, 2023

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if it is always the same, I do not think it makes sense, this is way more mantainable

@samsja samsja Jul 21, 2023

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But this break everything related to mypy and pycharm feature. In DocArray v2 we always repeat the full signature of the function

@JoanFM JoanFM Jul 21, 2023

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but mypy check passes

@JoanFM JoanFM Jul 21, 2023

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The method on TorchEmbedding does the same.
This is a method that noone should use
It is easier to forget to update this method than anything else.

@samsja samsja Jul 21, 2023

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okay oaky

@samsja samsja Jul 21, 2023

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mypy pass because it does not look at it. It will only check if there is type hint

"""
This method enables the deepcopy of `TorchTensor` by returning another instance of this subclass.
If this function is not implemented, the deepcopy will throw an RuntimeError from Torch.
"""
return self.__class__(*args, **kwargs)
16 changes: 16 additions & 0 deletions tests/units/typing/tensor/test_torch_tensor.py
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Expand Up @@ -187,6 +187,22 @@ class MMdoc(BaseDoc):
assert not (doc.embedding == doc_copy.embedding).all()


def test_deepcopy_tensor():
from docarray import BaseDoc

class MMdoc(BaseDoc):
embedding: TorchTensor

doc = MMdoc(embedding=torch.randn(32))
doc_copy = doc.copy(deep=True)

assert doc.embedding.data_ptr() != doc_copy.embedding.data_ptr()
assert (doc.embedding == doc_copy.embedding).all()

doc_copy.embedding = torch.randn(32)
assert not (doc.embedding == doc_copy.embedding).all()


@pytest.mark.parametrize('requires_grad', [True, False])
def test_json_serialization(requires_grad):
orig_doc = MyDoc(tens=torch.rand(10, requires_grad=requires_grad))
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