[jira] [Updated] (ARROW-4452) [Python] Serializing sparse torch tensors
[
https://issues.apache.org/jira/browse/ARROW-4452?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Antoine Pitrou updated ARROW-4452:
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Component/s: Python
> [Python] Serializing sparse torch tensors
> -
>
> Key: ARROW-4452
> URL: https://issues.apache.org/jira/browse/ARROW-4452
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
>Reporter: Philipp Moritz
>Priority: Major
> Labels: pull-request-available
> Time Spent: 40m
> Remaining Estimate: 0h
>
> Using the pytorch serialization handler on sparse Tensors:
> {code:java}
> import torch
> i = torch.LongTensor([[0, 2], [1, 0], [1, 2]])
> v = torch.FloatTensor([3, 4, 5 ])
> tensor = torch.sparse.FloatTensor(i.t(), v, torch.Size([2,3]))
> pyarrow.serialization.register_torch_serialization_handlers(pyarrow.serialization._default_serialization_context)
> s = pyarrow.serialize(tensor,
> context=pyarrow.serialization._default_serialization_context) {code}
> Produces this result:
> {code:java}
> TypeError: can't convert sparse tensor to numpy. Use Tensor.to_dense() to
> convert to a dense tensor first.{code}
> We should provide a way to serialize sparse torch tensors, especially now
> that we are getting support for sparse Tensors.
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[jira] [Updated] (ARROW-4452) [Python] Serializing sparse torch tensors
[
https://issues.apache.org/jira/browse/ARROW-4452?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
ASF GitHub Bot updated ARROW-4452:
--
Labels: pull-request-available (was: )
> [Python] Serializing sparse torch tensors
> -
>
> Key: ARROW-4452
> URL: https://issues.apache.org/jira/browse/ARROW-4452
> Project: Apache Arrow
> Issue Type: Improvement
>Reporter: Philipp Moritz
>Priority: Major
> Labels: pull-request-available
>
> Using the pytorch serialization handler on sparse Tensors:
> {code:java}
> import torch
> i = torch.LongTensor([[0, 2], [1, 0], [1, 2]])
> v = torch.FloatTensor([3, 4, 5 ])
> tensor = torch.sparse.FloatTensor(i.t(), v, torch.Size([2,3]))
> pyarrow.serialization.register_torch_serialization_handlers(pyarrow.serialization._default_serialization_context)
> s = pyarrow.serialize(tensor,
> context=pyarrow.serialization._default_serialization_context) {code}
> Produces this result:
> {code:java}
> TypeError: can't convert sparse tensor to numpy. Use Tensor.to_dense() to
> convert to a dense tensor first.{code}
> We should provide a way to serialize sparse torch tensors, especially now
> that we are getting support for sparse Tensors.
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[jira] [Updated] (ARROW-4452) [Python] Serializing sparse torch tensors
[
https://issues.apache.org/jira/browse/ARROW-4452?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Philipp Moritz updated ARROW-4452:
--
Description:
Using the pytorch serialization handler on sparse Tensors:
{code:java}
import torch
i = torch.LongTensor([[0, 2], [1, 0], [1, 2]])
v = torch.FloatTensor([3, 4, 5 ])
tensor = torch.sparse.FloatTensor(i.t(), v, torch.Size([2,3]))
pyarrow.serialization.register_torch_serialization_handlers(pyarrow.serialization._default_serialization_context)
s = pyarrow.serialize(tensor,
context=pyarrow.serialization._default_serialization_context) {code}
Produces this result:
{code:java}
TypeError: can't convert sparse tensor to numpy. Use Tensor.to_dense() to
convert to a dense tensor first.{code}
We should provide a way to serialize sparse torch tensors, especially now that
we are getting support for sparse Tensors.
was:
Using the pytorch serialization handler on sparse Tensors:
{code:java}
import torch
i = torch.LongTensor([[0, 2], [1, 0], [1, 2]])
v = torch.FloatTensor([3, 4, 5 ])
tensor = torch.sparse.FloatTensor(i.t(), v, torch.Size([2,3]))
register_torch_serialization_handlers(pyarrow.serialization._default_serialization_context)
s = pyarrow.serialize(tensor,
context=pyarrow.serialization._default_serialization_context) {code}
Produces this result:
{code:java}
TypeError: can't convert sparse tensor to numpy. Use Tensor.to_dense() to
convert to a dense tensor first.{code}
We should provide a way to serialize sparse torch tensors, especially now that
we are getting support for sparse Tensors.
> [Python] Serializing sparse torch tensors
> -
>
> Key: ARROW-4452
> URL: https://issues.apache.org/jira/browse/ARROW-4452
> Project: Apache Arrow
> Issue Type: Improvement
>Reporter: Philipp Moritz
>Priority: Major
>
> Using the pytorch serialization handler on sparse Tensors:
> {code:java}
> import torch
> i = torch.LongTensor([[0, 2], [1, 0], [1, 2]])
> v = torch.FloatTensor([3, 4, 5 ])
> tensor = torch.sparse.FloatTensor(i.t(), v, torch.Size([2,3]))
> pyarrow.serialization.register_torch_serialization_handlers(pyarrow.serialization._default_serialization_context)
> s = pyarrow.serialize(tensor,
> context=pyarrow.serialization._default_serialization_context) {code}
> Produces this result:
> {code:java}
> TypeError: can't convert sparse tensor to numpy. Use Tensor.to_dense() to
> convert to a dense tensor first.{code}
> We should provide a way to serialize sparse torch tensors, especially now
> that we are getting support for sparse Tensors.
--
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