Posts tagged AI/ML
Accelerating Large Language Models with Flash Attention on AMD GPUs
- 15 May 2024
In this blog post, we will guide you through the process of installing Flash Attention on AMD GPUs and provide benchmarks comparing its performance to standard SDPA in PyTorch. We will also measure end-to-end prefill latency for multiple Large Language Models (LLMs) in Hugging Face.
Inferencing with Mixtral 8x22B on AMD GPUs
- 01 May 2024
Mixture of Experts (MoE) has regained prominence in the AI community since the release of Mistral AI’s Mixtral 8x7B. Inspired by this development, multiple AI companies have followed suit by releasing MoE-based models, including xAI’s Grok-1, Databricks’ DBRX, and Snowflake’s Artic. The MoE architecture provides several advantages over dense models of comparable size, including faster training times, quicker inference, and enhanced performance on benchmarks. This architecture consists of two components. The first component is sparse MoE layers that replace the dense feed-forward network (FFN) layers in the typical Transformer architecture. Each MoE layer contains a specific number of experts that are typically FFNs themselves. The second component is a router network that determines which tokens are sent to which experts. Since each token is only routed to a subset of the experts, the inference latency is significantly shorter.
Training a Neural Collaborative Filtering (NCF) Recommender on an AMD GPU
- 30 April 2024
Collaborative Filtering is a type of item recommendation where new items are recommended to the user based on their past interactions. Neural Collaborative Filtering (NCF) is a recommendation system that uses neural network to model the user-item interaction function. NCF focuses on optimizing a collaborative function, which is essentially a user-item interaction model represented by a neural network and ranks the recommended items for the user.
Table Question-Answering with TaPas
- 26 April 2024
Conventionally, the question-answering task is framed as a semantic parsing task where the question is translated to a full logical form that can be executed against the table to retrieve the correct answer. However, this requires a lot of annotated data, which can be expensive to acquire.
Multimodal (Visual and Language) understanding with LLaVA-NeXT
- 26 April 2024
LLaVa (Large Language And Vision Assistant) was introduced in 2023 and became a milestone for multimodal models. It combines a pretrained vision encoder and a pretrained LLM for general purpose visual and language understanding. In January 2024, LLaVa-NeXT was released, which boasts significant enhancements, including higher input’s visual resolution and improved logical reasoning and world knowledge.
Unlocking Vision-Text Dual-Encoding: Multi-GPU Training of a CLIP-Like Model
- 24 April 2024
In this blog, we will build a vision-text dual encoder model akin to CLIP and fine-tune it with the COCO dataset on AMD GPU with ROCm. This work is inspired by the principles of CLIP and the Hugging Face example. The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their descriptions into the same embedding space, such that the text embeddings are located near the embeddings of the images they describe. The objective during training is to maximize the similarity between the embeddings of image and text pairs in the batch while minimizing the similarity of embeddings for incorrect pairs. The model achieves this by learning a multimodal embedding space. A symmetric cross entropy loss is optimized over these similarity scores.
Transforming Words into Motion: A Guide to Video Generation with AMD GPU
- 24 April 2024
This blog introduces the advancements in text-to-video generation through enhancements to the stable diffusion model and demonstrates the process of generating videos from text prompts on an AMD GPU using Alibaba’s ModelScopeT2V model.
Inferencing with AI2’s OLMo model on AMD GPU
- 17 April 2024
In this blog, we will show you how to generate text using AI2’s OLMo model on AMD GPU.
Text Summarization with FLAN-T5
- 16 April 2024
In this blog, we showcase the language model FLAN-T5 and how to fine-tune it on a summarization task with HuggingFace in an AMD GPUs + ROCm system.
Speech-to-Text on an AMD GPU with Whisper
- 16 April 2024
Whisper is an advanced automatic speech recognition (ASR) system, developed by OpenAI. It employs a straightforward encoder-decoder Transformer architecture where incoming audio is divided into 30-second segments and subsequently fed into the encoder. The decoder can be prompted with special tokens to guide the model to perform tasks such as language identification, transcription, and translation.
PyTorch C++ Extension on AMD GPU
- 16 April 2024
This blog demonstrates how to use the PyTorch C++ extension with an example and discusses its advantages over regular PyTorch modules. The experiments were carried out on AMD GPUs and ROCm 5.7.0 software. For more information about supported GPUs and operating systems, see System Requirements (Linux).
Programming AMD GPUs with Julia
- 16 April 2024
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.
Program Synthesis with CodeGen
- 16 April 2024
CodeGen is a family of standard transformer-based auto-regressive language models for program synthesis, which as defined by the authors as a method for generating computer programs that solve specified problems, using input-output examples or natural language descriptions.
Interacting with Contrastive Language-Image Pre-Training (CLIP) model on AMD GPU
- 16 April 2024
Contrastive Language-Image Pre-Training (CLIP) is a multimodal deep learning model that bridges vision and natural language. It was introduced in the paper “Learning Transferrable Visual Models from Natural Language Supervision” (2021) from OpenAI, and it was trained contrastively on a huge amount (400 million) of web scraped data of image-caption pairs (one of the first models to do this).
Instruction fine-tuning of StarCoder with PEFT on multiple AMD GPUs
- 16 April 2024
In this blog, we will show you how to fine-tune the StarCoder base model on AMD GPUs with an instruction-answer pair dataset so that it can follow instructions to generate code and answer questions. We will also show you how to use parameter-efficient fine-tuning (PEFT) to minimize the computation cost for the fine-tuning process.
Enhancing LLM Accessibility: A Deep Dive into QLoRA Through Fine-tuning Llama 2 on a single AMD GPU
- 15 April 2024
Building on the previous blog Fine-tune Llama 2 with LoRA blog, we delve into another Parameter Efficient Fine-Tuning (PEFT) approach known as Quantized Low Rank Adaptation (QLoRA). The focus will be on leveraging QLoRA for the fine-tuning of Llama-2 7B model using a single AMD GPU with ROCm. This task, made possible through the use of QLoRA, addresses challenges related to memory and computing limitations. The exploration aims to showcase how QLoRA can be employed to enhance accessibility to open-source large language models.
Developing Triton Kernels on AMD GPUs
- 15 April 2024
OpenAI has developed a powerful GPU focused programming language and compiler called Triton that works seamlessly with AMD GPUs. The goal of Triton is to enable AI engineers and scientists to write high-performant GPU code with minimal expertise. Triton kernels are performant because of their blocked program representation, allowing them to be compiled into highly optimized binary code. Triton also leverages Python for kernel development, making it both familiar and accessible. And the kernels can be easily compiled by simply declaring the triton.jit python decorator before the kernel.
GPU Unleashed: Training Reinforcement Learning Agents with Stable Baselines3 on an AMD GPU in Gymnasium Environment
- 11 April 2024
This blog will delve into the fundamentals of deep reinforcement learning, guiding you through a practical code example that utilizes an AMD GPU to train a Deep Q-Network (DQN) policy within the Gymnasium environment.
ResNet for image classification using AMD GPUs
- 09 April 2024
In this blog, we demonstrate training a simple ResNet model for image classification on AMD GPUs using ROCm on the CIFAR10 dataset. Training a ResNet model on AMD GPUs is simple, requiring no additional work beyond installing ROCm and appropriate PyTorch libraries.
Small language models with Phi-2
- 08 April 2024
Like many other LLMs, Phi-2 is a transformer-based model with a next-word prediction objective that is trained on billions of tokens. At 2.7 billion parameters, Phi-2 is a relatively small language model, but it achieves outstanding performance on a variety of tasks, including common sense reasoning, language understanding, math, and coding. For reference, GPT 3.5 has 175 billion parameters and the smallest version of LLaMA-2 has 7 billion parameters. According to Microsoft, Phi-2 is capable of matching or outperforming models up to 25 times larger due to more carefully curated training data and model scaling.
Using the ChatGLM-6B bilingual language model with AMD GPUs
- 04 April 2024
ChatGLM-6B is an open bilingual (Chinese-English) language model with 6.2 billion parameters. It’s optimized for Chinese conversation based on General Language Model (GLM) architecture. GLM is a pretraining framework that seeks to combine the strengths of autoencoder models (like BERT) and autoregressive models (like GPT). The GLM framework randomly blanks out continuous spans of tokens from the input text (autoencoding methodology) and trains the model to sequentially reconstruct the spans (autoregressive pretraining methodology).
Total body segmentation using MONAI Deploy on an AMD GPU
- 04 April 2024
Medical Open Network for Artificial Intelligence (MONAI) is an open-source organization that provides PyTorch implementation of state-of-the-art medical imaging models, ranging from classification and segmentation to image generation. Catering to the needs of researchers, clinicians, and fellow domain contributors, MONAI’s lifecycle provides three different end-to-end workflow tools: MONAI Core, MONAI Label, and MONAI Deploy.
Retrieval Augmented Generation (RAG) using LlamaIndex
- 04 April 2024
To run this blog, you will need the following:
Inferencing and serving with vLLM on AMD GPUs
- 04 April 2024
vLLM is a high-performance, memory-efficient serving engine for large language models (LLMs). It leverages PagedAttention and continuous batching techniques to rapidly process LLM requests. PagedAttention optimizes memory utilization by partitioning the Key-Value (KV) cache into manageable blocks. The KV cache stores previously computed keys and values, enabling the model to focus on calculating attention solely for the current token. These blocks are subsequently managed through a lookup table, akin to memory page handling in operating systems.
Image classification using Vision Transformer with AMD GPUs
- 04 April 2024
The Vision Transformer (ViT) model was first proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ViT is an attractive alternative to conventional Convolutional Neural Network (CNN) models due to its excellent scalability and adaptability in the field of computer vision. On the other hand, ViT can be more expensive compared to CNN for large input images as it has quadratic computation complexity with respect to input size.
Building semantic search with SentenceTransformers on AMD
- 04 April 2024
In this blog, we explain how to train a SentenceTransformers model on the Sentence Compression dataset to perform semantic search. We use the BERT base model (uncased) as the base transformer and apply Hugging Face PyTorch libraries.
Scale AI applications with Ray
- 01 April 2024
Most machine-learning (ML) workloads today require multiple GPUs or nodes to achieve the performance or scale required by applications. However, scaling workloads beyond single node/single GPU workloads is difficult and require some expertise in distributed processing.
Large language model inference optimizations on AMD GPUs
- 15 March 2024
Large language models (LLMs) have transformed natural language processing and comprehension, facilitating a multitude of AI applications in diverse fields. LLMs have various promising use cases, including AI assistants, chatbots, programming, gaming, learning, searching, and recommendation systems. These applications leverage the capabilities of LLMs to provide personalized and interactive experiences, which enhances user engagement.
Building a decoder transformer model on AMD GPU(s)
- 12 March 2024
In this blog, we demonstrate how to run Andrej Karpathy’s beautiful PyTorch re-implementation of GPT on single and multiple AMD GPUs on a single node using PyTorch 2.0 and ROCm. We use the works of Shakespeare to train our model, then run inference to see if our model can generate Shakespeare-like text.
Question-answering Chatbot with LangChain on an AMD GPU
- 11 March 2024
LangChain is a framework designed to harness the power of language models for building cutting-edge applications. By connecting language models to various contextual sources and providing reasoning abilities based on the given context, LangChain creates context-aware applications that can intelligently reason and respond. In this blog, we demonstrate how to use LangChain and Hugging Face to create a simple question-answering chatbot. We also demonstrate how to augment our large language model (LLM) knowledge with additional information using the Retrieval Augmented Generation (RAG) technique, then allow our bot to respond to queries based on the information contained within specified documents.
Music Generation With MusicGen on an AMD GPU
- 08 March 2024
MusicGen is an autoregressive, transformer-based model that predicts the next segment of a piece of music based on previous segments. This is a similar approach to language models predicting the next token.
Efficient image generation with Stable Diffusion models and ONNX Runtime using AMD GPUs
- 23 February 2024
In this blog, we show you how to use pre-trained Stable Diffusion models to generate images from text (text-to-image), transform existing visuals (image-to-image), and restore damaged pictures (inpainting) on AMD GPUs using ONNX Runtime.
Simplifying deep learning: A guide to PyTorch Lightning
- 08 February 2024
PyTorch Lightning is a higher-level wrapper built on top of PyTorch. Its purpose is to simplify and abstract the process of training PyTorch models. It provides a structured and organized approach to machine learning (ML) tasks by abstracting away the repetitive boilerplate code, allowing you to focus more on model development and experimentation. PyTorch Lightning works out-of-the-box with AMD GPUs and ROCm.
Two-dimensional images to three-dimensional scene mapping using NeRF on an AMD GPU
- 07 February 2024
This tutorial aims to explain the fundamentals of NeRF and its implementation in PyTorch. The code used in this tutorial is inspired by Mason McGough’s colab notebook and is implemented on an AMD GPU.
Using LoRA for efficient fine-tuning: Fundamental principles
- 05 February 2024
Low-Rank Adaptation of Large Language Models (LoRA) is used to address the challenges of fine-tuning large language models (LLMs). Models like GPT and Llama, which boast billions of parameters, are typically cost-prohibitive to fine-tune for specific tasks or domains. LoRA preserves pre-trained model weights and incorporates trainable layers within each model block. This results in a significant reduction in the number of parameters that need to be fine-tuned and considerably reduces GPU memory requirements. The key benefit of LoRA is that it substantially decreases the number of trainable parameters–sometimes by a factor of up to 10,000–leading to a considerable decrease in GPU resource demands.
Fine-tune Llama 2 with LoRA: Customizing a large language model for question-answering
- 01 February 2024
In this blog, we show you how to fine-tune Llama 2 on an AMD GPU with ROCm. We use Low-Rank Adaptation of Large Language Models (LoRA) to overcome memory and computing limitations and make open-source large language models (LLMs) more accessible. We also show you how to fine-tune and upload models to Hugging Face.
Pre-training BERT using Hugging Face & TensorFlow on an AMD GPU
- 29 January 2024
This blog explains an end-to-end process for pre-training the Bidirectional Encoder Representations from Transformers (BERT) base model from scratch using Hugging Face libraries with a TensorFlow backend for English corpus text (WikiText-103-raw-v1).
Pre-training BERT using Hugging Face & PyTorch on an AMD GPU
- 26 January 2024
This blog explains an end-to-end process for pre-training the Bidirectional Encoder Representations from Transformers (BERT) base model from scratch using Hugging Face libraries with a PyTorch backend for English corpus text (WikiText-103-raw-v1).
Accelerating XGBoost with Dask using multiple AMD GPUs
- 26 January 2024
XGBoost is an optimized library for distributed gradient boosting. It has become the leading machine learning library for solving regression and classification problems. For a deeper dive into how gradient boosting works, we recommend reading Introduction to Boosted Trees.
LLM distributed supervised fine-tuning with JAX
- 25 January 2024
In this article, we review the process for fine-tuning a Bidirectional Encoder Representations from Transformers (BERT)-based large language model (LLM) using JAX for a text classification task. We explore techniques for parallelizing this fine-tuning procedure across multiple AMD GPUs, then evaluate our model’s performance on a holdout dataset. For this, we use a (BERT)-base-cased transformer model with a General Language Understanding Evaluation (GLUE) benchmark dataset on multiple AMD GPUs.
Pre-training a large language model with Megatron-DeepSpeed on multiple AMD GPUs
- 24 January 2024
In this blog, we show you how to pre-train a GPT-3 model using the Megatron-DeepSpeed framework on multiple AMD GPUs. We also demonstrate how to perform inference on the text-generation task with your pre-trained model.
Efficient image generation with Stable Diffusion models and AITemplate using AMD GPUs
- 24 January 2024
Stable Diffusion has emerged as a groundbreaking advancement in the field of image generation, empowering users to translate text descriptions into captivating visual output.
Efficient deployment of large language models with Text Generation Inference on AMD GPUs
- 24 January 2024
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs) with unparalleled efficiency. TGI is tailored for popular open-source LLMs, such as Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5. Optimizations include tensor parallelism, token streaming using Server-Sent Events (SSE), continuous batching, and optimized transformers code. It has a robust feature set that includes quantization, safetensors, watermarking (for determining if text is generated from language models), logits warper, and support for custom prompt generation and fine-tuning.
Creating a PyTorch/TensorFlow code environment on AMD GPUs
- 11 September 2023
Note: This blog was previously part of the AMD lab notes blog series.