AITENSORSET

AITENSORSET is a software library and data-structure concept for managing collections of tensors as a single logical object. It introduces the TensorSet, an abstraction that stores a finite sequence of tensors T1, T2, ..., Tn, which may differ in shape and dtype. TensorSets are designed to enable operations that act across the set as well as on individual tensors, supporting workflows that treat a collection of tensors as a unit.

Core features include representation, indexing, and set-wide transformations. TensorSet supports per-element map and reduce operations, concatenation and stacking along the set or tensor dimensions, padding and reshaping to accommodate irregular shapes, and indexing via integer positions, slices, or boolean masks. It also provides lazy evaluation to compose computation graphs, enabling fusion of operations for performance. Backends include CPU and GPU execution with interoperability with existing tensor libraries. Memory management includes zero-copy slices and optional padding strategies to handle variable sizes.

API and interoperability are central to the design. The library provides a Python interface with a TensorSet class and associated utilities. Typical operations are tensor_set.map(func), tensor_set.batch(dim), tensor_set.to_device(device), and tensor_set.to_padded_tensor(mode='auto'). Interoperability tools enable conversion to standard tensors and vice versa, allowing integration with PyTorch, TensorFlow, or NumPy pipelines.

Use cases include multi-modal and multi-task learning, variable-length sequence processing, and research into set-based neural networks where data is naturally represented as collections of tensors. Limitations include potential overhead for small collections, and the abstraction may be overkill when tensors are strictly homogeneous and uniform. The project is intended for research and prototyping rather than production systems.

See also: tensor, neural network, data structure, PyTorch, TensorFlow.