What Facebook is Doing for Open-Source AI. Technical details and detailed pointers to resources. Announces what they call Tensor Comprehensions.
Announcing Tensor Comprehensions
By: Nicolas Vasilache, Oleksandr Zinenko - Inria & DI ENS, Theodoros Theodoridis - ETH Zürich, Priya Goyal, Zachary DeVito, William S. Moses - MIT CSAIL, Sven Verdoolaege, Andrew Adams, Albert Cohen - Inria & DI ENS & FAIR
Today, Facebook AI Research (FAIR) is announcing the release of Tensor Comprehensions, a C++ library and mathematical language that helps bridge the gap between researchers, who communicate in terms of mathematical operations, and engineers focusing on the practical needs of running large-scale models on various hardware backends. The main differentiating feature of Tensor Comprehensions is that it represents a unique take on Just-In-Time compilation to produce the high-performance codes that the machine learning community needs, automatically and on-demand. ....
What to expect next
This release will allow researchers and programmers to write layers in a notation that is similar to the maths they use in their papers and communicate concisely the intent of their program. They will also be able to take that notation and translate it easily into a fast implementation in a matter of minutes rather than days. As the toolchain grows, we expect usability and performance to increase and benefit the whole community.
We will release PyTorch integration for Tensor Comprehensions at a later date.
We are grateful for frequent exchanges with and feedback from the frameworks teams and are looking forward to bringing this exciting new technology to your favorite ML framework.
FAIR is committed to open science and working with the machine learning community to push AI research further. Tensor Comprehensions is already a collaboration between Facebook, Inria, ETH Zurich and MIT. Our work is in the early stages and we’re excited to share it early and look forward to improving it with feedback from the community.
Get started:
Tensor Comprehensions is available under the Apache 2.0 license.
Documentation
On ArXiv
On Slack
Email: tensorcomp@fb.com
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