PyTorch is one of the leading frameworks for deep learning. Its core data structure is Tensor, a multi-dimensional array implementation with many advanced features like auto-differentiation. PyTorch is a massive codebase (approx. a million lines of C++, Python and CUDA code), and having a method for iterating over tensors in a very efficient manner that is independent of data type, dimension, striding and hardware is a critical feature that can lead to a very massive simplification of the codebase and make distributed development much faster and smoother. The TensorIterator C++ class within PyTorch is a complex yet useful class that is used for iterating over the elements of a tensor over any dimension and implicitly parallelizing various operations in a device independent manner.
It does this through a C++ API that is independent of type and device of the tensor, freeing the programmer of having to worry about the datatype or device when writing iteration logic for PyTorch tensors. For those coming from the NumPy universe, NpyIter is a close cousin of TensorIterator.
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