Skip to content

Latest commit

 

History

History
95 lines (76 loc) · 3.99 KB

README.md

File metadata and controls

95 lines (76 loc) · 3.99 KB
Raw

ChAI: Chapel Artificial Intelligence

ChAI is a library for AI/ML in Chapel. Due to Chapel's highly parallel nature, it is well-suited for AI/ML tasks; the goal of the library is to provide a foundation for such tasks, enabling local, distributed, and GPU-enabled computations.

Please note that ChAI was developed as part of an internship summer project by Iain Moncrief, and as such is in a relatively early stage of development.

Overall Design

ChAI intends to provide a PyTorch-like API that is familiar to newcomers from other languages. To this end, it provides a number of tensor primitives, including pure-Chapel implementations of operations such as matrix-matrix multiplication and convolution (in fact, ChAI provides two tensor data types; see Static and Dynamic Tensors below). On top of this low-level API, ChAI defines a layer system, which makes it possible to compose pieces such as Conv2D, Linear, and Flatten into a feed-forward neural network.

ChAI's tensors keep track of the computational graph, making it usable for both feed-forward and back-propagation tasks; however, the feed-forward components have received more attention at this time.

Examples

The examples folder contains various sample programs written using ChAI.

Thus far, the concrete test for ChAI has been the MNIST dataset; specifically, ChAI's PyTorch interop has been used to load a pre-trained convolutional MNIST classifier and execute it on multiple locales. The MultiLocaleInference.chpl file demonstrates this.

Getting Started

To use ChAI, you need to have installed Chapel; you can follow the installation instructions on this page to do so.

Once you have Chapel installed, you can use the following command to clone ChAI:

git clone https://github.com/chapel-lang/ChAI.git

You can then compile one of the example ChAI programs using the following command:

chpl examples/ConvLayerTest.chpl -M lib
./ConvLayerTest

The above should produce the following output:

Tensor([ 0.0   1.0   2.0   3.0   4.0   5.0   6.0   7.0]
       [ 8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0]
       [16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0]
       [24.0  25.0  26.0  27.0  28.0  29.0  30.0  31.0]
       [32.0  33.0  34.0  35.0  36.0  37.0  38.0  39.0]
       [40.0  41.0  42.0  43.0  44.0  45.0  46.0  47.0]
       [48.0  49.0  50.0  51.0  52.0  53.0  54.0  55.0]
       [56.0  57.0  58.0  59.0  60.0  61.0  62.0  63.0],
       shape = (1, 8, 8),
       rank = 3)

Tensor([474.0  510.0  546.0  582.0  618.0  654.0]
       [762.0  798.0  834.0  870.0  906.0  942.0]
       [1050.0  1086.0  1122.0  1158.0  1194.0  1230.0]
       [1338.0  1374.0  1410.0  1446.0  1482.0  1518.0]
       [1626.0  1662.0  1698.0  1734.0  1770.0  1806.0]
       [1914.0  1950.0  1986.0  2022.0  2058.0  2094.0],
       shape = (1, 6, 6),
       rank = 3)

Static and Dynamic Tensors

Chapel's type system is static and relatively strict; to iterate over tensors -- thus implementing various mathematical operations -- the dimensions of the tensors need to be known at compile-time. However, this does not mesh well with the ability to dynamically load models from files on disk (since the contents of the files can be arbitrary).

To mediate between these two requirements, ChAI provides two tensor types: StaticTensor and DynamicTensor. The StaticTensor includes the rank of the tensor; this makes it possible to iterate over it and perform the "usual" operations. The DynamicTensor is a rank-erased version of DynamicTensor; it cannot be iterated over, but it can be dynamically cast back to a StaticTensor when needed. Both StaticTensor and DynamicTensor support the same operations; DynamicTensor performs a dynamic cast to StaticTensor under the hood.