Posted in 2024

Transformer based Encoder-Decoder models for image-captioning on AMD GPUs

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.

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SGLang: Fast Serving Framework for Large Language and Vision-Language Models on AMD Instinct GPUs

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.

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Quantized 8-bit LLM training and inference using bitsandbytes on AMD GPUs

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.

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Introducing AMD’s Next-Gen Fortran Compiler

2024 November 13 by Justin Chang, Brian Cornille, Michael Klemm, and Johanna Potyka.

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Distributed Data Parallel Training on AMD GPU with ROCm

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.

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Torchtune on AMD GPUs How-To Guide: Fine-tuning and Scaling LLMs with Multi-GPU Power

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.

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CTranslate2: Efficient Inference with Transformer Models on AMD GPUs

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.

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Inference with Llama 3.2 Vision LLMs on AMD GPUs Using ROCm

Meta’s Llama models now support multimodal capabilities, expanding their functionality beyond traditional text-only applications. The Llama 3.2 models are available in a range of sizes, including medium-sized 11B and 90B multimodal models for vision-text reasoning tasks, and lightweight 1B and 3B text-only models designed for edge and mobile devices.

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Speed Up Text Generation with Speculative Sampling on AMD GPUs

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.

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Multinode Fine-Tuning of Stable Diffusion XL on AMD GPUs with Hugging Face Accelerate and OCI’s Kubernetes Engine (OKE)

As the scale and complexity of generative AI and deep learning models grow, multinode training, basically dividing a training job across several processors, has become an essential strategy to speed up training and fine-tuning processes of large generative AI models like SDXL. By distributing the training workload across multiple GPUs on multiple nodes, multinode setups can significantly accelerate the training process. In this blog post we will show you, step-by step, how to set-up and fine-tune a Stable Diffusion XL (SDXL) model in a multinode Oracle Cloud Infrastructure’s (OCI) Kubernetes Engine (OKE) on AMD GPUs using ROCm.

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Enhancing vLLM Inference on AMD GPUs

11 October, 2024 by Clint Greene.

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Supercharging JAX with Triton Kernels on AMD GPUs

Ready to supercharge your deep learning applications on AMD GPUs? In this blog, we’ll show you how to develop a custom fused dropout activation kernel for matrices in Triton, seamlessly call it from JAX, and benchmark its performance with ROCm. This powerful combination will take your model’s performance to the next level.

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Leaner LLM Inference with INT8 Quantization on AMD GPUs using PyTorch

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.

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Fine-tuning Llama 3 with Axolotl using ROCm on AMD GPUs

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.

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Inferencing and serving with vLLM on AMD GPUs

19 September, 2024 by Clint Greene.

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Getting to Know Your GPU: A Deep Dive into AMD SMI

17 Sep, 2024 by Matt Elliott

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Introducing the AMD ROCm™ Offline Installer Creator: Simplifying Deployment for AI and HPC

Document headings start at H2, not H1 [myst.header]

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Optimize GPT Training: Enabling Mixed Precision Training in JAX using ROCm on AMD GPUs

This blog builds on the nanoGPT model we discussed in A Guide to Implementing and Training Generative Pre-trained Transformers (GPT) in JAX on AMD GPUs. Here we will show you how to incorporate mixed precision training to the JAX-implemented nanoGPT model we discussed in our previous blog.

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Image Classification with BEiT, MobileNet, and EfficientNet using ROCm on AMD GPUs

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.

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Seismic stencil codes - part 3

12 Aug, 2024 by Justin Chang and Ossian O’Reilly.

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Seismic stencil codes - part 2

12 Aug, 2024 by Justin Chang and Ossian O’Reilly.

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Seismic stencil codes - part 1

12 Aug, 2024 by Justin Chang and Ossian O’Reilly.

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Benchmarking Machine Learning using ROCm and AMD GPUs: Reproducing Our MLPerf Inference Submission

Measuring the performance of new technologies is as old as human history, and often as intriguing (consider for example that we still compare the performance of new electric vehicle motors using horsepower). In the rapidly advancing field of machine learning (ML) MLPerf was established by MLCommons on May 2nd 2018 and quickly became the golden standard of measuring the accuracy, speed, and efficiency of AI. MLPerf provides benchmarks on training, HPC and Inference performance. Companies across the industry use MLPerf submissions to evaluate the performance of various GPUs and software platforms, and make their technology adoption decisions based on these results.

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Performing natural language processing tasks with LLMs on ROCm running on AMD GPUs

In this blog you will learn how to use ROCm, running on AMD’s Instinct GPUs, for a range of popular and useful natural language processing (NLP) tasks, using different large language models (LLMs). The blog includes a simple to follow hands-on guide that shows you how to implement LLMs for core NLP applications ranging from text generation and sentiment analysis to extractive question answering (QA), and solving a math problem.

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Using AMD GPUs for Enhanced Time Series Forecasting with Transformers

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)).

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Inferencing with Grok-1 on AMD GPUs

We demonstrate that the massive Grok-1 model from xAI can run seamlessly on the AMD MI300X GPU accelerator by leveraging the ROCm software platform.

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Optimizing RoBERTa: Fine-Tuning with Mixed Precision on AMD

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.

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Graph analytics on AMD GPUs using Gunrock

Graphs and graph analytics are related concepts that can help us understand complex data and relationships. In this context, a graph is a mathematical model that represents entities (called nodes or vertices) and their connections (called edges or links). And graph analytics is a form of data analysis that uses graph structures and algorithms to reveal insights from the data.

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Using statistical methods to reliably compare algorithm performance in large generative AI models with JAX Profiler on AMD GPUs

This blog provides a comprehensive guide on measuring and comparing the performance of various algorithms in a JAX-implemented generative AI model. Leveraging the JAX Profiler and statistical analysis, this blog demonstrates how to reliably evaluate key steps and compare algorithm performance on AMD GPUs.

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DBRX Instruct on AMD GPUs

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.

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Accelerate PyTorch Models using torch.compile on AMD GPUs with ROCm

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.

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Accelerating models on ROCm using PyTorch TunableOp

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.

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A Guide to Implementing and Training Generative Pre-trained Transformers (GPT) in JAX on AMD GPUs

2 July, 2024 by Douglas Jia.

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Mamba on AMD GPUs with ROCm

28, Jun 2024 by Sean Song, Jassani Adeem, Moskvichev Arseny.

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Deep Learning Recommendation Models on AMD GPUs

28, June 2024 by Phillip Dang.

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Fine-tuning and Testing Cutting-Edge Speech Models using ROCm on AMD GPUs

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.

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TensorFlow Profiler in practice: Optimizing TensorFlow models on AMD GPUs

TensorFlow Profiler consists of a set of tools designed to measure resource utilization and performance during the execution of TensorFlow models. It offers insights into how a model interacts with hardware resources, including execution time and memory usage. TensorFlow Profiler helps in pinpointing performance bottlenecks, allowing us to fine-tune the execution of models for improved efficiency and faster outcomes which can be crucial in scenarios where near-real-time predictions are required.

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Stone Ridge Expands Reservoir Simulation Options with AMD Instinct™ Accelerators

Stone Ridge Technology (SRT) pioneered the use of GPUs for high performance reservoir simulation (HPC) nearly a decade ago with ECHELON, its flagship software product. ECHELON, the first of its kind, engineered from the outset to harness the full potential of massively parallel GPUs, stands apart in the industry for its power, efficiency, and accuracy. Now, ECHELON has added support for AMDInstinct accelerators into its simulation engine, offering new flexibility and optionality to its clients.

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Segment Anything with AMD GPUs

4 Jun, 2024 by Sean Song.

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SmoothQuant model inference on AMD Instinct MI300X using Composable Kernel

The AMD ROCm™ Composable Kernel (CK) library provides a programming model for writing performance-critical kernels for machine learning workloads. It generates a general-purpose kernel during the compilation phase through a C++ template, enabling developers to achieve operation fusions on different data precisions.

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Unveiling performance insights with PyTorch Profiler on an AMD GPU

29 May, 2024 by Phillip Dang.

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Panoptic segmentation and instance segmentation with Detectron2 on AMD GPUs

23, May 2024 by Vara Lakshmi Bayanagari.

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Siemens taps AMD Instinct™ GPUs to expand high-performance hardware options for Simcenter STAR-CCM+

Siemens recently announced that its Simcenter STAR-CCM+ multi-physics computational fluid dynamics (CFD) software now supports AMD Instinct™ GPUs for GPU-native computation. This move addresses its users’ needs for computational efficiency, reduced simulation costs and energy usage, and greater hardware choice.

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AMD Collaboration with the University of Michigan offers High Performance Open-Source Solutions to the Bioinformatics Community

Long read DNA sequencing technology is revolutionizing genetic diagnostics and precision medicine by helping us discover structural variants and assemble whole genomes. It also helps us study evolutionary relationships. Lower sequencing costs and high-throughput portable long read sequencers are revolutionizing precision medicine today. Long read sequencers from the top manufacturers including Oxford Nanopore (ONT) and PacBio, can produce reads that are much longer than previous generations of sequencers. However, long reads vary in length and are significantly more error prone than short reads. Sequence alignment (on CPUs) is one of the main bottlenecks in long read processing workflows.

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Accelerating Large Language Models with Flash Attention on AMD GPUs

15, May 2024 by Clint Greene.

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Reading AMD GPU ISA

For an application developer it is often helpful to read the Instruction Set Architecture (ISA) for the GPU architecture that is used to perform its computations. Understanding the instructions of the pertinent code regions of interest can help in debugging and achieving performance optimization of the application.

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AMD in Action: Unveiling the Power of Application Tracing and Profiling

Rocprof is a robust tool designed to analyze and optimize the performance of HIP programs on AMD ROCm platforms, helping developers pinpoint and resolve performance bottlenecks. Rocprof provides a variety of profiling data, including performance counters, hardware traces, and runtime API/activity traces.

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Step-by-Step Guide to Use OpenLLM on AMD GPUs

OpenLLM is an open-source platform designed to facilitate the deployment and utilization of large language models (LLMs), supporting a wide range of models for diverse applications, whether in cloud environments or on-premises. In this tutorial, we will guide you through the process of starting an LLM server using OpenLLM, enabling interaction with the server from your local machine, with special emphasis on leveraging the capabilities of AMD GPUs.

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Inferencing with Mixtral 8x22B on AMD GPUs

1, May 2024 by Clint Greene.

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Training a Neural Collaborative Filtering (NCF) Recommender on an AMD GPU

30, Apr 2024 by Vara Lakshmi Bayanagari.

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Table Question-Answering with TaPas

26 Apr, 2024 by Phillip Dang.

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Multimodal (Visual and Language) understanding with LLaVA-NeXT

26, Apr 2024 by Phillip Dang.

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Application portability with HIP

Many scientific applications run on AMD-equipped computing platforms and supercomputers, including Frontier, the first Exascale system in the world. These applications, coming from a myriad of science domains, were ported to run on AMD GPUs using the Heterogeneous-compute Interface for Portability (HIP) abstraction layer. HIP enables these High-Performance Computing (HPC) facilities to transition their CUDA codes to run and take advantage of the latest AMD GPUs. The effort involved in porting these scientific applications varies from a few hours to a few weeks and largely depends on the complexity of the original source code. Figure 1 shows several examples of applications that have been ported and the corresponding porting effort.

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Unlocking Vision-Text Dual-Encoding: Multi-GPU Training of a CLIP-Like Model

24 Apr, 2024 by Sean Song.

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Transforming Words into Motion: A Guide to Video Generation with AMD GPU

24 Apr, 2024 by Douglas Jia.

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C++17 parallel algorithms and HIPSTDPAR

The C++17 standard added the concept of parallel algorithms to the pre-existing C++ Standard Library. The parallel version of algorithms like std::transform maintain the same signature as the regular serial version, except for the addition of an extra parameter specifying the execution policy to use. This flexibility allows users that are already using the C++ Standard Library algorithms to take advantage of multi-core architectures by just introducing minimal changes to their code.

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Inferencing with AI2’s OLMo model on AMD GPU

17 Apr, 2024 by Douglas Jia.

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Text Summarization with FLAN-T5

16, Apr 2024 by Phillip Dang.

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Speech-to-Text on an AMD GPU with Whisper

16 Apr, 2024 by Clint Greene.

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PyTorch C++ Extension on AMD GPU

16, Apr 2024 by Vara Lakshmi Bayanagari.

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Programming AMD GPUs with Julia

Julia is a high-level, general-purpose dynamic programming language that automatically compiles to efficient native code via LLVM, and supports multiple platforms. With LLVM, comes the support for programming GPUs, including AMD GPUs.

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Program Synthesis with CodeGen

16, Apr 2024 by Phillip Dang.

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Interacting with Contrastive Language-Image Pre-Training (CLIP) model on AMD GPU

16, Apr 2024 by Sean Song.

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Instruction fine-tuning of StarCoder with PEFT on multiple AMD GPUs

16 Apr, 2024 by Douglas Jia.

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Affinity part 2 - System topology and controlling affinity

In Part 1 of the Affinity blog series, we looked at the importance of setting affinity for High Performance Computing (HPC) workloads. In this blog post, our goals are the following:

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Affinity part 1 - Affinity, placement, and order

Modern hardware architectures are increasingly complex with multiple sockets, many cores in each Central Processing Unit (CPU), Graphical Processing Units (GPUs), memory controllers, Network Interface Cards (NICs), etc. Peripherals such as GPUs or memory controllers will often be local to a CPU socket. Such designs present interesting challenges in optimizing memory access times, data transfer times, etc. Depending on how the system is built, hardware components are connected, and the workload being run, it may be advantageous to use the resources of the system in a specific way. In this article, we will discuss the role of affinity, placement, and order in improving performance for High Performance Computing (HPC) workloads. A short case study is also presented to familiarize you with performance considerations on a node in the Frontier supercomputer. In a follow-up article, we also aim to equip you with the tools you need to understand your system’s hardware topology and set up affinity for your application accordingly.

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Enhancing LLM Accessibility: A Deep Dive into QLoRA Through Fine-tuning Llama Model on a single AMD GPU

15, Apr 2024 by Sean Song.

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Enhancing LLM Accessibility: A Deep Dive into QLoRA Through Fine-tuning Llama 2 on a single AMD GPU

15, Apr 2024 by Sean Song.

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Developing Triton Kernels on AMD GPUs

15 Apr, 2024 by Clint Greene.

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GPU Unleashed: Training Reinforcement Learning Agents with Stable Baselines3 on an AMD GPU in Gymnasium Environment

11 Apr, 2024 by Douglas Jia.

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ResNet for image classification using AMD GPUs

9 Apr, 2024 by Logan Grado.

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Small language models with Phi-2

8, Apr 2024 by Phillip Dang.

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Using the ChatGLM-6B bilingual language model with AMD GPUs

4, Apr 2024 by Phillip Dang.

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Total body segmentation using MONAI Deploy on an AMD GPU

4, Apr 2024 by Vara Lakshmi Bayanagari.

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Retrieval Augmented Generation (RAG) using LlamaIndex

4, Apr 2024 by Clint Greene.

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Image classification using Vision Transformer with AMD GPUs

4 Apr, 2024 by Eliot Li.

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Building semantic search with SentenceTransformers on AMD

4 Apr, 2024 by Fabricio Flores.

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Scale AI applications with Ray

1, Apr 2024 by Vicky Tsang<vicktsan>, {hoverxref}Logan Grado, {hoverxref}Eliot Li.

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Automatic mixed precision in PyTorch using AMD GPUs

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.

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Large language model inference optimizations on AMD GPUs

15, Mar 2024 by Seungrok Jung.

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Building a decoder transformer model on AMD GPU(s)

12, Mar 2024 by Phillip Dang.

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Question-answering Chatbot with LangChain on an AMD GPU

11, Mar 2024 by Phillip Dang.

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Music Generation With MusicGen on an AMD GPU

8, Mar 2024 by Phillip Dang.

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Efficient image generation with Stable Diffusion models and ONNX Runtime using AMD GPUs

23 Feb, 2024 by Douglas Jia.

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Simplifying deep learning: A guide to PyTorch Lightning

8, Feb 2024 by Phillip Dang.

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Two-dimensional images to three-dimensional scene mapping using NeRF on an AMD GPU

7, Feb 2024 by Vara Lakshmi Bayanagari.

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Using LoRA for efficient fine-tuning: Fundamental principles

5, Feb 2024 by Sean Song.

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Fine-tune Llama model with LoRA: Customizing a large language model for question-answering

1, Feb 2024 by Sean Song.

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Fine-tune Llama 2 with LoRA: Customizing a large language model for question-answering

1, Feb 2024 by Sean Song.

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Pre-training BERT using Hugging Face & TensorFlow on an AMD GPU

29, Jan 2024 by Vara Lakshmi Bayanagari.

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Pre-training BERT using Hugging Face & PyTorch on an AMD GPU

26, Jan 2024 by Vara Lakshmi Bayanagari.

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Accelerating XGBoost with Dask using multiple AMD GPUs

26 Jan, 2024 by Clint Greene.

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LLM distributed supervised fine-tuning with JAX

25 Jan, 2024 by Douglas Jia.

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Pre-training a large language model with Megatron-DeepSpeed on multiple AMD GPUs

24 Jan, 2024 by Douglas Jia.

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Efficient image generation with Stable Diffusion models and AITemplate using AMD GPUs

24 Jan, 2024 by Douglas Jia.

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Efficient deployment of large language models with Text Generation Inference on AMD GPUs

24 Jan, 2024 by Douglas Jia.

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