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changelog.md

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Changelog

v0.3.5

  • Support hashing the folded_tensor.length field (via a UserList), which is convenient for caching
  • Improve error messaging when refolding with missing dims

v0.3.4

  • Fix a data_dims access issue
  • Marginally improve the speed of handling FoldedTensors in standard torch operations
  • Use default torch types (e.g. torch.float32 or torch.torch64)

v0.3.3

  • Handle empty inputs (e.g. as_folded_tensor([[[], []], [[]]])) by returning an empty tensor
  • Correctly bubble errors when converting inputs with varying deepness (e.g. as_folded_tensor([1, [2, 3]]))

v0.3.2

  • Allow to use as_folded_tensor with no args, as a simple padding function

v0.3.1

  • Enable sharing FoldedTensor instances in a multiprocessing + cuda context by autocloning the indexer before fork-pickling an instance
  • Distribute arm64 wheels for macOS

v0.3.0

  • Allow dims after last foldable dim during list conversion (e.g. embeddings)

v0.2.2

  • Github release :octocat:
  • Fix backpropagation when refolding

v0.2.1

  • Improve performance by computing the new "padded to flattened" indexer only (and not the previous one) when refolding

v0.2.0

  • Remove C++ torch dependency in favor of Numpy due to lack of torch ABI backward/forward compatibility, making the pre-built wheels unusable in most cases
  • Require dtype to be specified when creating a FoldedTensor from a nested list

v0.1.0

Inception ! 🎉

  • Support for arbitrary numbers of nested dimensions
  • No computational overhead when dealing with already padded tensors
  • Dynamic re-padding (or refolding) of data based on stored inner lengths
  • Automatic mask generation and updating whenever the tensor is refolded
  • C++ optimized code for fast data loading from Python lists and refolding
  • Flexibility in data representation, making it easy to switch between different layouts when needed