foldedtensor 0.2.1

PyTorch extension for handling deeply nested sequences of variable length

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FoldedTensor: PyTorch extension for handling deeply nested sequences of variable length

foldedtensor is a PyTorch extension that provides efficient handling of tensors containing deeply nested sequences variable sizes. It enables the flattening/unflattening (or unfolding/folding) of data dimensions based on a inner structure of sequence lengths. This library is particularly useful when working with data that can be split in different ways and enables you to avoid choosing a fixed representation.

Installation

The library can be installed with pip:

pip install foldedtensor

Features

• 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

Example

import torch
from foldedtensor import as_folded_tensor

# Creating a folded tensor from a nested list
# There are 2 samples, the first with 5 lines, the second with 1 line.
# Each line contain between 1 and 2 words.
ft = as_folded_tensor(
    [
        [[1], [], [], [], [2, 3]],
        [[4, 3]],
    ],
    data_dims=("samples", "words"),
    full_names=("samples", "lines", "words"),
    dtype=torch.long,
)
print(ft)
# FoldedTensor([[1, 2, 3],
#               [4, 3, 0]])

# Refold on the lines and words dims (flatten the samples dim)
print(ft.refold(("lines", "words")))
# FoldedTensor([[1, 0],
#               [0, 0],
#               [0, 0],
#               [0, 0],
#               [2, 3],
#               [4, 3]])

# Refold on the words dim only: flatten everything
print(ft.refold(("words",)))
# FoldedTensor([1, 2, 3, 4, 3])

# Working with PyTorch operations
embedder = torch.nn.Embedding(10, 16)
embedding = embedder(ft.refold(("words",)))
print(embedding.shape)
# torch.Size([5, 16]) # 5 words total, 16 dims

refolded_embedding = embedding.refold(("samples", "words"))
print(refolded_embedding.shape)
# torch.Size([2, 5, 16]) # 2 samples, 5 words max, 16 dims

Comparison with alternatives

Unlike other ragged or nested tensor implementations, a FoldedTensor does not enforce a specific structure on the nested data, and does not require padding all dimensions. This provides the user with greater flexibility when working with data that can be arranged in multiple ways depending on the data transformation. Moreover, the C++ optimization ensures high performance, making it ideal for handling deeply nested tensors efficiently.

Here is a comparison with other common implementations for handling nested sequences of variable length:

Feature	NestedTensor	MaskedTensor	FoldedTensor
Inner data structure	Flat	Padded	Arbitrary
Max nesting level	1	1	∞
From nested python lists	No	No	Yes
Layout conversion	To padded	No	Any
Reduction ops w/o padding	Yes	No	No