Posts tagged PyTorch
Training Transformers and Hybrid models on AMD Instinct MI300X Accelerators
- 10 December 2024
This blog is contributed by Zyphra: a Palo Alto-based AI research lab and AMD Instinct Partner.
Transformer based Encoder-Decoder models for image-captioning on AMD GPUs
- 03 December 2024
Image captioning, or the GenAI-based automatic generation of concise textual descriptions of images, has immensely important real-world applications. For example, image captioning can provide visually impaired users with textual descriptions of images for improved accessibility, image captioning can add textual descriptions to products in e-commerce applications and help children map images to their textual descriptions in early childhood educational apps. Image captioning can automatically describe objects and events in security camera footage in surveillance applications and can enable robots to auto-generate textual captions for objects and events they encountered in human-robot interaction (HRI) applications, and many more applications. Image captioning is a sequence-to-sequence (seq2seq) machine learning task: a model converting a sequence from one domain (in this case, the image), to another (its textual description). In image captioning the image is partitioned into a sequence of patches. This sequence of image patches is then converted by the model to a corresponding sequence of text tokens.
SGLang: Fast Serving Framework for Large Language and Vision-Language Models on AMD Instinct GPUs
- 13 November 2024
In the rapidly evolving landscape of artificial intelligence, the ability to deploy large language models (LLMs) and vision-language models (VLMs) efficiently is crucial for real-time applications. SGLang is an open-source framework designed to meet these demands by delivering fast backend runtime, a flexible frontend language, and extensive model support for a variety of LLMs and VLMs.
Quantized 8-bit LLM training and inference using bitsandbytes on AMD GPUs
- 13 November 2024
In this blog post we will cover the bitsandbytes 8-bit representations. As you will see, the bitsandbytes 8-bit representations significantly help reduce the memory needed for fine-tuning and inferencing LLMs. There are many quantization techniques used in the field to decrease a model size, but bitsandbytes offers quantization to decrease the size of optimizer states as well. This post will help you understand the basic principles underlying the bitsandbytes 8-bit representations, explain the bitsandbytes 8-bit optimizer and LLM.int8 techniques, and show you how to implement these on AMD GPUs using ROCm.
Distributed Data Parallel Training on AMD GPU with ROCm
- 01 November 2024
With the increase in complexity and size of machine learning models, the demand for computational resources grows. Training on a single GPU can become a bottleneck for deep learning applications, especially with large datasets and models that are slow to train on a single GPU. Parallelized training addresses this challenge. Out of the various forms of parallelized training, this blog focuses on Distributed Data Parallel (DDP), a key feature in PyTorch that accelerates training across multiple GPUs and nodes.
Torchtune on AMD GPUs How-To Guide: Fine-tuning and Scaling LLMs with Multi-GPU Power
- 24 October 2024
This blog provides a thorough how-to guide on using Torchtune to fine-tune and scale large language models (LLMs) with AMD GPUs. Torchtune is a PyTorch library designed to let you easily fine-tune and experiment with LLMs. Using Torchtune’s flexibility and scalability, we show you how to fine-tune the Llama-3.1-8B model for summarization tasks using the EdinburghNLP/xsum dataset. Using LoRA(Low-Rank Adaptation), a parameter-efficient fine-tuning technique, Torchtune enables efficient training while maintaining performance across a different number of GPUs (2, 4, 6, and 8). This post also highlights how Torchtune’s distributed training capabilities allow users to scale up LLM fine-tuning on multiple GPUs to reduce training time while maintaining the quality of the trained model, demonstrating its potential and usage on modern AMD hardware using ROCm.
CTranslate2: Efficient Inference with Transformer Models on AMD GPUs
- 24 October 2024
Transformer models have revolutionized natural language processing (NLP) by delivering high-performance results in tasks like machine translation, text summarization, text generation, and speech recognition. However, deploying these models in production can be challenging due to their high computational and memory requirements. CTranslate2 addresses these challenges by providing a custom runtime that implements various optimization techniques to accelerate Transformer models during inference.
Speed Up Text Generation with Speculative Sampling on AMD GPUs
- 15 October 2024
As the size of transformer models grow, so does the cost of conducting inference, impacting latency and throughput. Compression methods such as quantization and distillation, as well as hardware-aware optimizations such as Flash Attention and Triton, have been proposed to cut down the computation cost at different levels. However, these models either compromise on accuracy or require major changes to the model implementation.
Leaner LLM Inference with INT8 Quantization on AMD GPUs using PyTorch
- 03 October 2024
With the scale of large language models (LLMs) reaching hundred of billions of parameters, the ways we represent data within these enormous models dramatically impacts the resources required to train them (e.g. the number of GPUs needed for inference). In our previous blogs (JAX mixed precision training; PyTorch AMP), we already demonstrated how mixed precision training can accelerate LLMs training process. In this blog post we will push things further and show you how quantization into an even lower precision data formats can speed up inference, saving time and memory, without sacrificing the overall performance of the model. Quantization is a technique where the precision of a model’s parameters is reduced from a 32-bit floating point (FP32) or a 16-bit floating point (FP16) to an 8-bit integer (INT8). Standard models typically use 32-bit floating-point (FP32) precision. However, this higher precision is not always necessary for inference tasks. By converting model weights and activations to lower precision formats like INT8 (8-bit integer), we can achieve faster computations and lower memory usage, effectively reducing the model size by three-fourths (from 32-bit) or half (from 16-bit) with only a slight accuracy reduction, which is often outweighed by the speed gains.
Fine-tuning Llama 3 with Axolotl using ROCm on AMD GPUs
- 23 September 2024
Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling machines to understand and generate human-like language. However, these models are often trained on vast amounts of general-purpose data, which can make them less effective for specific tasks or domains. Fine-tuning involves training a pre-trained LLM on a specialized dataset to enhance its performance on specific tasks. As Andrej Karpathy analogized, this process is akin to allowing someone to practice a particular skill. Just as a person might need to practice a skill in a specific context to become proficient, an LLM needs to be fine-tuned on a specific dataset to become proficient in a particular task. For instance, an LLM can be fine-tuned for tasks such as financial forecasting, technical support, legal advising, medical diagnosis, or even instruction following. By fine-tuning an LLM, organizations can achieve better results and improve information security by limiting the exposure of sensitive data.
Image Classification with BEiT, MobileNet, and EfficientNet using ROCm on AMD GPUs
- 03 September 2024
Image classification is a key task in computer vision aiming at “understanding” an entire image. The outcome of an image classifier is a label or a category for the image as a whole, unlike object recognition where the task is to detect and classify multiple objects within an image.
Using AMD GPUs for Enhanced Time Series Forecasting with Transformers
- 19 August 2024
Time series forecasting (TSF) is a key concept in fields such as signal processing, data science, and machine learning (ML). TSF involves predicting future behavior of a system by analyzing its past temporal patterns, using historical data to forecast future data points. Classical approaches to TSF relied on a variety of statistical methods. Recently, machine learning techniques have been increasingly used for TSF, generating discussions within the community about whether these modern approaches outperform the classical statistical ones (see: Are Transformers Effective for Time Series Forecasting? and Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)).
Optimizing RoBERTa: Fine-Tuning with Mixed Precision on AMD
- 29 July 2024
In this blog we explore how to fine-tune the Robustly Optimized BERT Pretraining Approach (RoBERTa) large language model, with emphasis on PyTorch’s mixed precision capabilities. Specifically, we explore using AMD GPUs for mixed precision fine-tuning to achieve faster model training without any major impacts on accuracy.
DBRX Instruct on AMD GPUs
- 11 July 2024
In this blog, we showcase DBRX Instruct, a mixture-of-experts large language model developed by Databricks, on a ROCm-capable system with AMD GPUs.
Accelerate PyTorch Models using torch.compile on AMD GPUs with ROCm
- 11 July 2024
PyTorch 2.0 introduces torch.compile(), a tool to vastly accelerate PyTorch code and models. By converting PyTorch code into highly optimized kernels, torch.compile delivers substantial performance improvements with minimal changes to the existing codebase. This feature allows for precise optimization of individual functions, entire modules, and complex training loops, providing a versatile and powerful tool for enhancing computational efficiency.
Accelerating models on ROCm using PyTorch TunableOp
- 03 July 2024
In this blog, we will show how to leverage PyTorch TunableOp to accelerate models using ROCm on AMD GPUs. We will discuss the basics of General Matrix Multiplications (GEMMs), show an example of tuning a single GEMM, and finally, demonstrate real-world performance gains on an LLM (gemma) using TunableOp.
A Guide to Implementing and Training Generative Pre-trained Transformers (GPT) in JAX on AMD GPUs
- 02 July 2024
2 July, 2024 by Douglas Jia.
Mamba on AMD GPUs with ROCm
- 28 June 2024
28, Jun 2024 by Sean Song, Jassani Adeem, Moskvichev Arseny.
Fine-tuning and Testing Cutting-Edge Speech Models using ROCm on AMD GPUs
- 27 June 2024
AI Voice agents, or voice bots, are designed to communicate with people using a spoken language. Voice bots are commonly deployed in customer service and personal assistant applications, and have the potential to enter and revolutionize almost every aspect of people’s interaction with technology that can benefit from the use of voice. Automatic Speech Recognition (ASR), the technology that processes human speech into text, is essential for the creation of AI Voice agents. In this blog post we will provide you with a hands-on introduction to the deployment of three machine learning ASR models, using ROCm on AMD GPUs.
Unveiling performance insights with PyTorch Profiler on an AMD GPU
- 29 May 2024
29 May, 2024 by Phillip Dang.
Panoptic segmentation and instance segmentation with Detectron2 on AMD GPUs
- 23 May 2024
23, May 2024 by Vara Lakshmi Bayanagari.
Accelerating Large Language Models with Flash Attention on AMD GPUs
- 15 May 2024
15, May 2024 by Clint Greene.
Multimodal (Visual and Language) understanding with LLaVA-NeXT
- 26 April 2024
26, Apr 2024 by Phillip Dang.
Unlocking Vision-Text Dual-Encoding: Multi-GPU Training of a CLIP-Like Model
- 24 April 2024
24 Apr, 2024 by Sean Song.
Transforming Words into Motion: A Guide to Video Generation with AMD GPU
- 24 April 2024
24 Apr, 2024 by Douglas Jia.
Instruction fine-tuning of StarCoder with PEFT on multiple AMD GPUs
- 16 April 2024
16 Apr, 2024 by Douglas Jia.
GPU Unleashed: Training Reinforcement Learning Agents with Stable Baselines3 on an AMD GPU in Gymnasium Environment
- 11 April 2024
11 Apr, 2024 by Douglas Jia.
Using the ChatGLM-6B bilingual language model with AMD GPUs
- 04 April 2024
4, Apr 2024 by Phillip Dang.
Total body segmentation using MONAI Deploy on an AMD GPU
- 04 April 2024
4, Apr 2024 by Vara Lakshmi Bayanagari.
Automatic mixed precision in PyTorch using AMD GPUs
- 29 March 2024
As models increase in size, the time and memory needed to train them–and consequently, the cost–also increases. Therefore, any measures we take to reduce training time and memory usage can be highly beneficial. This is where Automatic Mixed Precision (AMP) comes in.
Efficient image generation with Stable Diffusion models and ONNX Runtime using AMD GPUs
- 23 February 2024
23 Feb, 2024 by Douglas Jia.
Simplifying deep learning: A guide to PyTorch Lightning
- 08 February 2024
8, Feb 2024 by Phillip Dang.
Two-dimensional images to three-dimensional scene mapping using NeRF on an AMD GPU
- 07 February 2024
7, Feb 2024 by Vara Lakshmi Bayanagari.
Using LoRA for efficient fine-tuning: Fundamental principles
- 05 February 2024
5, Feb 2024 by Sean Song.
Pre-training BERT using Hugging Face & PyTorch on an AMD GPU
- 26 January 2024
26, Jan 2024 by Vara Lakshmi Bayanagari.
Pre-training a large language model with Megatron-DeepSpeed on multiple AMD GPUs
- 24 January 2024
24 Jan, 2024 by Douglas Jia.
Creating a PyTorch/TensorFlow code environment on AMD GPUs
- 11 September 2023
Goal: The machine learning ecosystem is quickly exploding and we aim to make porting to AMD GPUs simple with this series of machine learning blogposts.