wkcn commented on a change in pull request #12047: [MXNET-779]Add DLPack
Transformation API
URL: https://github.com/apache/incubator-mxnet/pull/12047#discussion_r209416184
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File path: python/mxnet/_ctypes/ndarray.py
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@@ -31,21 +31,24 @@
class NDArrayBase(object):
"""Base data structure for ndarray"""
- __slots__ = ["handle", "writable"]
+ __slots__ = ["handle", "writable", "dlpack"]
# pylint: disable= no-member
- def __init__(self, handle, writable=True):
+ def __init__(self, handle, writable=True, dlpack=None):
Review comment:
Thanks.
In the other case,
```python
from torch.utils import dlpack
a = torch.array([1,2,3])
pack = dlpack.to_dlpack(a)
b = mx.nd.from_dlpack(pack)
del a, pack
```
When `dlpack.to_dlpack` is called, PyTorch will allocate `ATenDLMTensor`
which increases the refcount of Torch Tensor
[code](https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/DLConvertor.cpp#L129).
After the variables `a` and `pack` are released, `ATenDLMTensor` still
exists.
I think the deleter should be called by the new NDArray `b` when the NDArray
`b` releases. Refer to [PyTorch
FromDLPack](https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/DLConvertor.cpp#L170).
However, NDArray doesn't have explicit deleter parameter.
In my PR, `from_dlpack` will copy the dlpack object.
When the old dlpack `pack` releases, it doesn't call the deleter.
The new dlpack will be a member of the new NDArray as
`NDArray(handle=handle, dlpack=dlpack_copy)`.
When the new NDArray releases, the new dlpack will be released, then call
the deleter by the new dlpack. And the deleter will release `NDArrayDLManager`
or `ATenDLMTensor`. The refcount of the old NDArray will decrease 1.
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