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.
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.
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.
We are excited to share a brief preview of AMD’s
Next-Gen Fortran Compiler,
our new open source Fortran complier supporting OpenMP offloading. AMD’s
Next-Gen Fortran Compiler
is a downstream flavor of LLVM Flang, optimized for AMD GPUs.
Our Next-Gen Fortran Compiler
enables OpenMP offloading and offers a direct interface to ROCm and HIP.
In this blog post you will:
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.
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.
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.
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.
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.
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.
In this blog, we’ll demonstrate the latest performance enhancements in vLLM inference on AMD Instinct accelerators using ROCm. In a nutshell, vLLM optimizes GPU memory utilization, allowing more efficient handling of large language models (LLMs) within existing hardware constraints, maximizing throughput and minimizing latency. We start the blog by briefly explaining how causal language models like Llama 3 and ChatGPT generate text, motivating the need to enhance throughput and reduce latency. If you’re new to vLLM, we also recommend reading our introduction to Inferencing and serving with vLLM on AMD GPUs.
ROCm 6.2 introduces support for the following vLLM features which we will use in this blog post.
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.
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.
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.
In the rapidly evolving field of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools for understanding and generating human-like text. However, deploying these models efficiently at scale presents significant challenges. This is where vLLM comes into play. vLLM is an innovative open-source library designed to optimize the serving of LLMs using advanced techniques. Central to vLLM is PagedAttention, a novel algorithm that enhances the efficiency of the model’s attention mechanism by managing it as virtual memory. This approach optimizes GPU memory utilization, facilitating the processing of longer sequences and enabling more efficient handling of large models within existing hardware constraints. Additionally, vLLM incorporates continuous batching to maximize throughput and minimize latency. By leveraging these cutting-edge techniques, vLLM significantly improves the performance and scalability of LLM deployment, allowing organizations to harness the power of state-of-the-art AI models more effectively and economically.
For system administrators and power users working with AMD hardware, performance optimization and efficient monitoring of resources is paramount. The AMD System Management Interface command-line tool, amd-smi, addresses these needs.
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.
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.
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.
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)).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
executionpolicy 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.
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.
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:
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.
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.