Author: Bryce Mackin, Sr. Product Manager, AI Group
Visual Language Models (VLMs) are redefining data interpretation by combining visual and text insights to tackle complex, real-world applications. From predicting traffic flow in live feeds to detecting subtle changes in medical scans and tracking shopper behavior in crowded stores, VLMs drive insights across diverse fields. AMD strengthens these models with targeted optimizations such as mixed-precision training and parallel processing that enable VLMs to process and merge visual and text data proficiently. By streamlining and accelerating how VLMs handle complex, multi-modal tasks, AMD equips them to deliver faster, more precise results in industries where accuracy, efficiency, and response time are critical.
We Don’t Make the Model; We Make It Faster and More Accurate
In visual question answering, optimizations made by AMD boost the model's ability to quickly and accurately process visual details and associated questions. By accelerating each pathway, AMD enables VLMs to generate context-aware responses that are both reliable and precise. Rather than modifying model architecture, AMD improves performance using targeted techniques designed to maximize model adaptability and speed within the AMD ecosystem.
- Holistic pretraining is the process of simultaneously training a model on image and text data, building connections between the two for better accuracy and flexibility. Unlike sequential pretraining, which trains each modality separately, this approach enables the model to interpret images and language together. AMD pretraining pipeline augments this approach, allowing faster and more efficient model setup. This is particularly valuable for clients who may not have the extensive resources needed for large-scale model pretraining. The enhancements from AMD provide clients with high-quality, ready-to-deploy models, cutting costs and deployment time.
- Instruction tuning fine-tunes a model to follow specific instructions, enabling it to respond accurately to a particular prompt. This capability is valuable for targeted applications such as retail analytics, where instruction tuning can improve the model’s ability to track customer paths or identify frequently visited areas. By applying instruction tuning, AMD enhances the model's ability to handle these targeted tasks more precisely. This fine-tuning process empowers clients to focus the model's capabilities on the functions most relevant to their industry, delivering tailored insights with increased accuracy.
- In-context learning allows a model to adjust its responses based on the structure of input prompts without additional fine-tuning. This real-time flexibility is helpful for applications requiring structured responses, like identifying items in inventory based on specific categories. For instance, in inventory management, a model with in-context learning might be prompted to identify particular items in an image based on a list format (e.g., "Find fruits, vegetables, and beverages”). The model adapts its response to match the requested categories without requiring additional training, providing a fast and practical solution for structured queries. The deployment pipeline of AMD supports these capabilities, enabling models to perform reliably across a range of prompt formats.
Overcoming VLM limitations
VLMs often struggle with tasks that require interpreting multiple images in sequence or analyzing video, as they are typically designed for single-image processing. AMD addresses these limitations by optimizing VLM processing on its hardware, enabling smoother handling of sequential inputs, increasing speed and efficiency, and allowing VLMs to execute effectively in applications that require contextual understanding over time.
Multi-image Reasoning
AMD enable VLMs to better handle multi-image reasoning tasks, like tracking disease progression in medical imaging, by processing and analyzing time-series data with improved speed and responsiveness. By fine-tuning resource allocation and data handling, AMD helps VLMs process multiple images in sequence capably, making these models well-suited to tasks where understanding cumulative changes is essential.
Video Content Understanding
Another challenging area for standard VLMs is video analysis, where the model must process a continuous stream of visual data. AMD's work ensures that VLMs can handle video content more efficiently, with streamlined processing that allows for fast identification and summarization of key events. This approach is advantageous in fields like security, where extracting moments of interest from hours of video footage is time-intensive. In applications such as meeting recaps or security footage review, AMD's improvements enable VLMs to deliver quick, contextually accurate summaries, saving time and resources.
A Full-Stack Approach Makes the Difference
AMD Instinct™ GPUs provide a strong foundation for VLMs across applications, from portable devices to high-demand data centers, supporting both standard and intensive AI workloads. The open-source AMD ROCm™ software stack complements AMD GPUs, maximizing compatibility with most machine learning frameworks, including PyTorch, TensorFlow, and Hugging Face, enabling users to run models like LLaMA and Stable Diffusion seamlessly on AMD hardware.
ROCm incorporates advanced techniques such as quantization to reduce model size without sacrificing accuracy and mixed-precision training to speed up processing, cutting training time from months to days. The flexibility of ROCm allows it to scale from edge devices to large data centers, making AMD GPUs highly suitable for a wide range of performance needs. Resources like ROCm accelerate deployment and customization and with its open-source, community-driven approach, it fosters continuous innovation, creating an ecosystem that evolves with user needs and industry progress.
AMD also optimizes inference speed through both hardware and software improvements. Using mixed-precision training, AMD accelerates computations by adjusting numerical precision based on task requirements, balancing speed with accuracy. Additionally, the ROCm platform supports parallel processing on AMD GPUs, enabling efficient handling of large datasets and complex queries. These augmentations allow VLMs to perform well in time-sensitive applications like autonomous driving while also adapting to less urgent tasks, such as offline image generation.
For a deeper dive, we encourage you to explore our linked resources on Vision-Text Dual Encoding and LLaMA3.2 Vision. Unlocking Vision-Text Dual-Encoding: Multi-GPU Training of a CLIP-Like Model provides insights into how AMD optimizes dual processing paths for visual and textual data, explaining how these improvements make VLMs more adaptable and responsive. Inference with Llama 3.2 Vision LLMs on AMD GPUs Using ROCm offers a closer look at the approach of AMD to instruction tuning and multi-image reasoning, detailing how these techniques help models deliver contextually accurate responses across varied applications.
Footnote:
The information contained herein is for informational purposes only and is subject to change without notice. While every precaution has been taken in the preparation of this document, it may contain technical inaccuracies, omissions and typographical errors, and AMD is under no obligation to update or otherwise correct this information. Advanced Micro Devices, Inc. makes no representations or warranties with respect to the accuracy or completeness of the contents of this document, and assumes no liability of any kind, including the implied warranties of non-infringement, merchantability or fitness for particular purposes, with respect to the operation or use of AMD hardware, software or other products described herein. No license, including implied or arising by estoppel, to any intellectual property rights is granted by this document. Terms and limitations applicable to the purchase or use of AMD products are as set forth in a signed agreement between the parties or in AMD's Standard Terms and Conditions of Sale. GD-18
© 2024 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo, AMD Instinct, AMD ROCm, and combinations thereof are trademarks of Advanced Micro Devices, Inc. Other product names used in this publication are for identification purposes only and may be trademarks of their respective owners. Python is a trademark of the Python Software Foundation. PyTorch, the PyTorch logo and any related marks are trademarks of The Linux Foundation.
