PyTorch Conference 2024

We introduce dmx-compressor, d-Matrix's open-source LLM compression toolkit that is modular, robust, efficient, and user-friendly. It utilizes symbolic tracing and fx.Transformer for network compression while keeping the model a first-class citizen in PyTorch for the user, despite prevalent graph dynamism in LLMs. It achieves this by maintaining both the original nn.Module and a just-in-time (JIT) traced and transformed fx.GraphModule representation behind the scenes, in conjunction with an abstraction that cleanly decouples network compression from the original model graph definition. This design allows the FXIR to dynamically adapt to diverse forward call signatures and flow-control arguments throughout quantization-aware training and post-training quantization written in plain PyTorch, yielding a compressed FXIR fully compatible with application-level APIs like the Hugging Face pipeline. We also provide a graph visualizer based on fx.Interpreter for ease of debugging. We believe this project shall empower the community to build efficient LLMs for deployment on custom hardware accelerators and contribute to the PyTorch ecosystem.

LLMs are known to be compute heavy and consume lots of resources (almost all resources on phones), including memory and power. A natural thought is to leverage the AI hardware accelerators, for example, Apple Neural Engine (ANE) on Apple devices and HTP on Qualcomm SoCs, to make it run fast and efficiently. Only by optimizing the model latency, memory consumption and power usage to a certain level will users be interested in installing the models on their devices. In this session, we’d like to introduce how we leverage these AI accelerators within the PyTorch ecosystem to achieve the state-of-art performance for llama3 on device, via ExecuTorch and the partnership with Apple and Qualcomm. Hardware companies usually have their own AI accelerators. Likely they have different characteristics, one may support a list of different operators than others, and one may only support static shapes (like HTP). However, transformers-based optimization can be generic. We’ll discuss in more detail how we apply the generic optimization as well as the backend specific optimization. The techniques we applied here are not just for LLMs, but can be applied to other transformer-based models.

Description: This proposal aims to provide a comprehensive introduction to the Chinese PyTorch community, we hope to inspire more users to join and contribute, fostering a vibrant and inclusive environment for PyTorch enthusiasts in China. Chinese PyTorch Homepage Introduction to the official Chinese version of the PyTorch website, highlighting its features. Navigation tips and key sections, such as documentation, tutorials, and community events. Improve the connection of users from China with PyTorch Community. Localized Tutorials and Documentation The 2.x version not have Translated version, it hard to catch up with latest features of PyTorch if the beginner not good at English. We translated official documents and tutorials, covering everything from basic PyTorch concepts to advanced applications. Interactive tutorials No interactive tutorials(Like Google Colab) for Chinese students or beginners before, they have to setup environment before start with PyTorch, which might be hard for beginners. And now, an online notebook & tutorials are available to practice or tuning steps for beginners.

This talk explores the journey of enabling LLMs in the PyTorch ecosystem, as well as how the teams behind AOT Inductor, ExecuTorch, and torchao collaborated to create torchchat, a showcase of PyTorch’s ability to run LLM inference everywhere.

Torchchat demonstrates the ubiquity, simplicity, and quality of PyTorch’s LLM support through performant, reproducible implementations for not only Python environments, but on desktop, server, and on-device as-well.

All of our work is open source and available on GitHub.

In the last 2 years we've optimized performance of PyTorch on Arm processors. The optimizations have included changes to ATen, C10, MKLDNN operators, GEMM backend, and Torch inductor. In many cases instead of writing our own kernel we integrated the Arm compute library, used fastmath kernels with format types like bf16, implemented operator caching, selected optimal backend based on the input context etc. Through these optimizations we improved performance by over 2x. In this presentation first we will talk about how we went across this process, what those optimizations are, performance numbers for AWS Graviton3 processors for around 75 models, and CI/CD workflow details. Next, we will walk through a sample PyTorch application showing basic usage, how to tune runtime and the resulting speed up. At the end of the presentation attendees will learn about PyTorch performance optimizations on Arm processors, how to use them, and the areas where they can collaborate to further improve PyTorch for aarch64 CPUs.

PyTorch supports two kinds of datasets: Iterable datasets and indexable "map-style" datasets. Iterable datasets can be more flexible and potentially faster than their indexable cousins. They are also much harder to use correctly, and can easily lead to silently wrong results. This talk is a quick and fun intro to some of the traps that Iterable datasets lay out for you, with some tips to help you avoid them.

In an era where artificial intelligence reigns supreme, the statistics are both perplexing and thought-provoking – only a mere 13% of large language models manage to transcend the realms of research and enter the practical world of production. Who bears the responsibility when these models err, spewing out biased or discriminatory outputs? It's time to demystify the complex landscape of machine learning ethics and carve a path towards a brighter, more accountable future! In this talk, firstly, we will navigate the profound impacts of large language models across diverse domains, from the lifesaving advances in medicine to safeguarding our nations through enhanced security protocols. Secondly, as we marvel at data-driven decisions laid by these models, we will confront the darker shadows cast by – the looming spectre of bias in the data. Finally, we will delve deep into the art of building interpretable models and navigating the maze of ethical considerations. Through a live demonstration in PyTorch, we will witness how to craft unbiased, transparent, and explainable models.