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The world of artificial intelligence, machine learning, and other emerging tech has surprised us lately. By now, you must have known about Adept AI and its groundbreaking capabilities of general intelligence through ACT-1, a transformer for actions. If not, check this article here. Today, we will take a step forward in their advancement and understand the new algorithm in the works – FlashAttention.
Watch Adept AI in action. Adept AI Demo – About Adept AI – Careers in Adept AI – Twitter Adept AI Labs
Join the Adept AI waitlist here.
What is FlashAttention?
The team at Adept AI claims that FlashAttention Algorithm will speedup attention while reducing memory usage. Where, you ask? During the transformer training. To date, the grey area in the language model intelligence was training on long sequences. Even after the deepening and widening of transformers.
FlashAttention is pretty new, and a few companies and research teams are testing it to improve training speeds. Adept is cruising to make the algorithm better to help testers and onboard new organizations.
One of the major works in the backdrop is making FlashAttention fast enough for long sequences to train the language models with longer context. Let’s see how they progress.
Imagine if we could scale up the context length of transformers to train models. We could use this to understand books, high-resolution images, web pages, multi-turn user interactions, and long-form videos. Wouldn’t it be great? For now, this is a challenging area.
Though the FlashAttention algorithm reorders the attention computation and uses classical techniques such as tiling and recomputation to speed it up significantly, reducing memory usage from quadratic to linear in sequence length, it is not optimized for very long sequences.
Here is the key advantage of FlashAttention. It can handle large data sets with ease, making it a valuable tool for data science and big data analytics.
The algorithm uses a combination of standard attention and memory-efficient exact attention to delivering precise results. Even when training data is limited.
FlashAttention is also highly adaptable. It makes it a valuable asset for automation, robotics, and other areas of computer science where real-time processing is critical.
Research (GitHub) points out that FlashAttention yields the fastest BERT training on MLPerf cloud instances.
How FlashAttention works?
When large transformers train on long sequences with modern parallelism techniques such as data parallel, pipeline parallel, and tensor parallel, the batch size can get very small. So, the number of heads is approximately 8-12.
FlashAttention has been parallelized over the batch size and some heads. Furthermore, to make use of the multiprocessors on the GPU, it has now been parallelized over the sequence length dimension.
Read more on Attention Parallelism and Forward Pass Computation here. Part-time research fellow Tri Dao explains this concept really well.
Join the Adept AI waitlist here to access the alpha release.
So, how does FlashAttention change Adept AI and language models?
FlashAttention will help machine learning (ML) models with long context to capture the history of user interactions. It makes them more personalized and effective. This advancement will enhance the idea of having a personal assistant who is intelligent. Also, has a shocking memory to help work out the tasks easily.
With ML models increasingly deployed and interacting with billions of users daily, the ability to remember past actions and user feedback is becoming crucial. It will change how we look at ML today.
As ML models evolve to incorporate multiple modalities such as text, vision, and speech, long-context modeling will become even more important. It will enable models to comprehend complex media such as books, high-resolution images, and videos.
Furthermore, the team at FlashAttention is enthusiastic about this vision and welcomes input from individuals or organizations who believe their applications could benefit from these ideas. Connect with them on Twitter.
Adept AI open source Persimmon-8B – September 7, 2023
Adept has open-sourced Persimmon-8B, a highly adept language model with an Apache license.
This model has unique features, including a large context size, superior performance compared to other 8B models, and efficient inference code.
They evaluate model quality by having it generate text responses rather than using traditional probability-based metrics.
Persimmon-8B outperforms similar models in various tasks and has specific architecture modifications.
The release includes details about the model and a fast inference code, achieving high inference speed without a separate C++ codebase.
This release is the beginning of more to come from Adept. Read More about this open source on Adept’s official website.
Fuyu-8B: A Remarkable Multimodal Model Release – October 17, 2023
In the latest announcement, an introduction has been made regarding the launch of Fuyu-8B, a compact version of a multimodal model, now accessible on HuggingFace. This release holds considerable significance due to several standout features:
Streamlined Architecture: Fuyu-8B boasts a simpler architecture and training process compared to other multimodal models. This simplicity enhances its understandability, scalability, and deployment potential.
Tailored for Digital Agents: With a primary focus on digital agents, Fuyu-8B excels in supporting a wide range of image resolutions, addressing graph and diagram queries, responding to UI-based questions, and executing fine-grained screen image localization.
Rapid Responsiveness: Notably, this model delivers responses for large images within a mere 100 milliseconds, marking an impressive feat in terms of speed.
Consistent Performance: Despite being optimized for specific use cases, Fuyu-8B performs admirably in standardized image understanding benchmarks, particularly in tasks like visual question-answering and natural image captioning.
Release and Future Prospects
This release comes with an open license (CC-BY-NC), inviting the community to leverage and build upon this model. Additionally, insights into the outcomes of Fuyu-Medium (a larger model not being released) have been shared, alongside a glimpse of capabilities exclusive to internal models.
Acknowledging this as an initial raw model release, there’s an indication that further instruction-tuning, postprocessing, or sampling strategies to control outputs might be necessary for specific use cases in the future.
Model Structure and Objectives
Adept, in its pursuit of developing a general intelligent copilot for knowledge workers, emphasizes the crucial role of image understanding in comprehending user context and executing actions on their behalf. The model, Fuyu-8B, is engineered to possess the capability of comprehending both images and text, sans the complexities found in existing multimodal models.
The architectural makeup of Fuyu-8B, depicted in a diagram, circumvents the need for a specialized image encoder. By directly projecting image patches into the first layer of the transformer, this design supports arbitrary image resolutions and significantly simplifies training and inference procedures.
Performance Evaluation
In assessing the impact of Fuyu-8B’s architectural changes, standard image-understanding datasets such as VQAv2, OKVQA, COCO Captions, and AI2D were employed to benchmark Fuyu models against existing standards.
Results suggest that the Fuyu models demonstrate noteworthy proficiency in natural image understanding, outperforming some existing models while using significantly fewer parameters.
Scrutinizing Image-Understanding Benchmarks
In the process of engaging with benchmarks, the scrutiny uncovered certain limitations and issues, particularly in question-answering and captioning datasets. The benchmarks tend to have flaws in their scoring mechanisms, specific response formats, and even erroneous annotations, influencing model evaluations.
Impressive Capabilities of Fuyu Models
Fuyu models exhibit impressive capabilities in comprehending charts, documents, and scientific diagrams, which could potentially aid knowledge workers in various scenarios.
Sneak Peek into New Capabilities
Internal models based on Fuyu showcase additional capabilities concerning OCR on high-resolution images, fine-grained text localization, and UI-related queries. These capabilities are built upon the Fuyu model class and offer a glimpse into what’s to come.
For citations and further information, refer to the blog post: Introducing our Multimodal Models (Year: October 2023)
Adept Experiments – November 9, 2023
Today, an announcement has been made regarding the rollout of Adept Experiments, introducing some of the innovative capabilities for enterprise applications on the path toward achieving comprehensive general intelligence. Each experiment, self-contained and demonstrating specific functionalities, offers an opportunity to explore the underlying technology and provide valuable feedback.
The primary focus of this introduction is on a web-based workflow builder within the first experiment named “Workflows.” This experiment showcases the understanding that an AI collaborator’s greatest value lies in assisting with personalized software workflows tailored to individual users, their roles, or their organizations.
For example, tasks like managing leads within a CRM system can significantly vary in execution across different companies.
At this initial stage, the goal is to demonstrate Adept’s fundamental capabilities: quickly learning complex or repetitive tasks from users and reliably executing them. This capability is highlighted in the “Workflows” experiment.
Request Access to Adept Experiments
Let’s consider a few points:
- Given that this is an experimental phase, it needs guidance to perform desired tasks.
- Functionality might vary across different sites, although enterprise clients have experienced over 95% reliability through tailored workflows.
Despite these limitations, there’s excitement surrounding the potential for engaging and effective workflows.
To provide insight into the possibilities, here are examples that Adept is exploring with users:
- Delegating repetitive knowledge-based tasks to Adept
- Transforming unstructured data into structured information
- Facilitating seamless transitions between various software tools
- Enabling the completion of tasks that users might not be familiar with
- The “Workflows” function is powered by ACT-2, a model fine-tuned from the Fuyu family, optimized for understanding user interfaces, comprehending data typically handled by knowledge workers, and executing actions. Additionally, language-specific models are used for tasks involving text composition.
Key highlights of the “Workflows” functionality include:
- Adept’s ability to perceive the screen and act on the computer in a manner akin to human interaction
- Adaptability to diverse user scenarios, allowing for quick integration and learning of new workflows
- Representation of tasks through a series of actions, like “click” and “type”
- Plans to evolve the system to understand instructions at higher levels of abstraction in the future
- User involvement is ensured through step-by-step action and guidance for necessary information.
The future roadmap involves continuous innovation, anticipating significant advancements in AI agents similar to the evolution seen in language and image foundation models.
Upcoming research will focus on enhanced planning capabilities, improved visual reasoning, enterprise context integration, and learning from demonstrations.
Adept is already making an impact on the day-to-day work of initial enterprise clients. Collaborations with additional partners are planned for 2024. Organizations interested in becoming early enterprise customers are encouraged to access hello@adept.ai.
To commence exploration, users can request access to Adept Experiments. It’s an exciting journey to embark upon!
Appreciation is extended to those who contributed to making this venture possible, including NVIDIA, Microsoft, Oracle, WEKA, and Plasmo, as well as investors and advisors.
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Conclusion
Adept AI’s FlashAttention algorithm is poised to revolutionize how machine learning can help across many industries.
With training data optimization for deep learning neural networks, FlashAttention aims to provide real-time results with remarkable speed and accuracy.
Moreover, this breakthrough innovation is already drawing the attention of industry leaders like Google and OpenAI. Who knows, they may be eager to get hands with this technology. Let’s see how this turns out.
By 2025, it is clear that the internet will mostly rely on machine learning to manage and interpret big data. With its speed, accuracy, and adaptability, Adept AI’s FlashAttention algorithm will play a vital role in unlocking the full potential of this technology.
With real-time results and optimized training data, FlashAttention will transform industries, data science, and computer science.
Robotics, big data analytics, or any other application that requires fast and accurate data processing, FlashAttention may be the answer. It is a game-changer in world of deep learning.
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