Cohere uses Google Cloud’s new TPU v4 Pods on its quest to create larger and more powerful language models
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Over the past few years, advances in training large language models (LLMs) have moved natural language processing (NLP) from a bleeding-edge technology that few companies could access, to a powerful component of many common applications. From chatbots to content moderation to categorization, a general rule for NLP is that the larger the model, the greater the accuracy it’s able to achieve in understanding and generating language.
But in the quest to create larger and more powerful language models, scale has become a major challenge. Once a model becomes too large to fit on a single device, it requires distributed training strategies, which in turn require extensive compute resources with vast memory capacity and fast interconnects. You also need specialized algorithms to optimize the hardware and time resources.
Cohere engineers are working on solutions to this scaling challenge that have already yielded results. Cohere provides developers a platform for working with powerful LLMs without the infrastructure or deep ML expertise that such projects typically require. In a new technical paper, Scalable Training of Language Models using JAX pjit and TPUv4, engineers at Cohere demonstrate how their new FAX framework deployed on Google Cloud’s recently announced Cloud TPU v4 Pods addresses the challenges of scaling LLMs to hundreds of billions of parameters. Specifically, the report reveals breakthroughs in training efficiency that Cohere was able to achieve through tensor and data parallelism.
This framework aims to accelerate the research, development, and production of large language models with two significant improvements: scalability and rapid prototyping. Cohere will be able to improve its models by training larger ones more quickly, delivering better models to its customers faster. The framework also supports rapid prototyping of models that address specific objectives — for example, creating a generative model that powers customer-service chatbot — by experimenting and testing new ideas. The ability to switch back and forth among model types and optimize for different objectives will ultimately allow Cohere to offer models optimized for particular use cases.
The FAX framework relies heavily on the partitioned just-in-time compilation (pjit) feature of JAX, which abstracts the relationship between device and workload. This allows Cohere engineers to optimize efficiency, and performance by aligning devices and processes in the ideal configuration for the task at hand. Pjit works by compiling an arbitrary function into a single program (an XLA computation), that runs on multiple devices — even those residing on different hosts.
Cohere’s new solution also takes advantage of Google Cloud’s new TPU v4 Pods to perform tensor parallelism. which is more efficient than the earlier pipeline parallelism implementation. As the name suggests, the pipeline parallel approach uses accelerators in a linear fashion to scale a workload, like a single long assembly line. Accelerators must process each micro-batch of data before passing it along to the next one, and then run the backward pass in reverse order.
Tensor parallelism eliminates the accelerator idle time of pipeline parallelism, also known as the pipeline bubble. Tensor parallelism involves partitioning large tensors (mathematical arrays that define the relationship among multiple objects such as the words in a paragraph) across accelerators to perform computations at the same time on multiple devices. If pipeline parallelism is an ever-lengthening assembly line, tensor parallelism is a series of parallel assembly lines — one making the engine, the other the body, etc. — that simultaneously come together to form a complete car in a fraction of the time.
These computations are then collated, a process made practical thanks to Google Cloud TPU v4 VMs, which more than double the computational power. The superior performance of v4 chips has enabled Cohere to iterate on ideas and validate them 1.7X faster in computation than before.
At Cohere, we build cutting-edge natural language processing (NLP) services, including APIs for language generation, classification, and search. These tools are built on top of a set of language models that Cohere trains from scratch on Cloud TPUs using JAX. We saw a 70% improvement in training time for our largest model when moving from Cloud TPU v3 Pods to Cloud TPU v4 Pods, allowing faster iterations for our researchers and higher quality results for our customers. The exceptionally low carbon footprint of Cloud TPU v4 Pods was another key factor for us.
Aidan Gomez
CEO and co-founder, Cohere
Why Google Cloud for LLM training?
As part of a multiyear technology partnership, Cohere leverages Google Cloud’s advanced AI and ML infrastructure to power its platform. Cohere develops and deploys its products on Cloud TPUs, Google Cloud’s custom-designed machine learning chips that are optimized for large-scale ML. Cohere’s recently announced their new model improvements and scalability by training an LLM using FAX on Google Cloud TPUs, and this model has demonstrated that transitioning from TPU v3 to TPU v4 has so far enabled them to achieve a total speedup of 1.7x. In addition to a significant performance boost, TPUs provide an excellent user experience with the new TPU VM architecture. Importantly, Google Cloud ensures that Cohere’s state-of-the-art ML training is achieved with the highest standards of sustainability, powered by 90% carbon-free energy in the world’s largest publicly available ML hub.
By adopting Cloud TPUs, Cohere is making LLM training faster, more economical, and more agile. This helps them provide larger and more accurate LLMs to developers, and put NLP technology in the hands of developers and businesses of all sizes.
To learn more about these LLM training advances, you can read the full paper, Scalable Training of Language Models using JAX pjit and TPUv4. To learn more about Cohere’s best practices and AI principles, you can check this article co-authored with Open AI and AI 21 Labs.
Hike: Processing Analytics Queries 20X Faster with Google Cloud Platform
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After a seamless migration to Google Cloud Platform with CloudCover and Google Cloud Professional Services, Hike has reduced its costs by 20% and processed analytics queries 20 times faster than with its previous cloud provider. The business is also using AI and machine learning to enhance the experience provided by a new sticker-based messaging app, Hike Sticker Chat.
India is a market of opportunity for businesses that provide messaging apps to consumers. With more than 1.3 billion people, the country is the second most populous in the world. However, global messaging app providers face a robust market challenge from Hike, a home-grown internet and technology startup. Launched in 2012, Hike provides innovative products such as Hike Messenger and more recently the AI- and machine-learning-enabled Hike Sticker Chat, a service that enables young people in the country to express themselves through digital stickers.
The business says it understands the people of India and communication like no one else, while its mission is to reduce individuals’ dependency on the keyboard. To do this, Hike is building one of the largest repositories of AI and machine-learning-enabled stickers for Hike Sticker Chat. This messaging platform is, according to Hike, the only product of its type that enables conversations through stickers covering more than 40 languages and local dialects.
Google Cloud Results
- Processes analytics queries 20X faster than previously
- Doubles compute throughput
- Uses Google Cloud Machine Learning Engine managed, distributed capabilities to train complex models on TensorFlow that provide delightful local sticker recommendations through Hike Sticker Chat
Founded by Kavin Bharti Mittal, the Delhi-based venture is backed by SoftBank, Tencent, Tiger Global, Foxconn, and Bharti. To date, Hike has raised $261 million in funding. In August 2016, Hike raised its Series D round of funding, led by Tencent and Foxconn, at a valuation of $1.4 billion. The business is one of the fastest Indian startups to achieve Unicorn status, doing so in less than four years.
Hike started operations on a multinational cloud service. However, as user numbers and usage grew, the business began exploring options to improve performance and stability, reduce costs, and cut administration loads. In particular, Hike wanted to reduce latency between cloud data centers.
Focus on product development
“We aimed to move away from a technology stack with single points of failure to a horizontally scaled, highly reliable, distributed systems and managed services environment that enabled us to focus on product development rather than operations,” says Aditya Gupta, Director, Engineering, Hike.
Hike then began exploring the opportunities presented by Google Cloud Platform. The business held a number of executive-level meetings with Google to understand the capabilities, roadmap, and track record of the cloud service. It then decided to proceed with a proof of concept with Google Cloud Premier Partner CloudCover.
The proof of concept revealed that when Cloud Load Balancing was operating, latency between the Google Cloud data center in Taiwan and Delhi, India, was less than the latency between the incumbent cloud provider’s data center and Delhi. Further, compute throughput was up to two times greater on Compute Engine than on the equivalent service, while Hike could complete more then 1 million connections on Compute Engine – up from 500,000 connections on the incumbent service.
Migrate to GCP
The success of the exercise prompted Hike to migrate its messaging app to Google Cloud Platform. “We chose Google Cloud Platform because of its very broad set of services and features,” explains Gupta. “In addition, Google’s innovation mindset and the richness of the partnership would allow us to be onboarded quickly to machine learning services such as Cloud Machine Learning Engine.”
The business called on Google Cloud Professional Services (Technical Account Management) to help ensure a seamless lift-and-shift migration over two months. Google Cloud Professional Services initially undertook a technical infrastructure kickoff to establish a foundation for architecture requirements such as identity and access management and security.
Google Cloud Professional Services team delivers smooth migration
Google Cloud Professional Services worked closely with Hike to map out and deliver the Google Cloud Platform architecture that would deliver the greatest value to the business. The Professional Services team also worked with Hike to resolve product and support queries quickly; provided project background for product and support teams; and organized project meetings and early adopter program access.
In addition, Professional Services team members worked on site at least once a week, coordinated external support during critical migration periods, and coordinated teams in five countries for a single, 17-hour migration marathon. Over 60 days, the business migrated 7,000 processor cores, running virtual machine instances used for messaging infrastructure and analytics, to Google Cloud Platform.
Throughout the exercise, Google Cloud Professional Services worked with CloudCover to educate the customers’ technology teams to achieve proficiency with Google Cloud Platform. The teams soon built up skills and knowledge of best practices and began applying them to the Google Cloud Platform environment.
The Hike Google Cloud Platform architecture comprises virtual machine instances running in Compute Engine; Cloud Storage for unified object storage; networking; a BigQuery analytics data warehouse; Cloud Dataflow to transform and enrich data; Cloud Load Balancing to distribute workloads to maximize efficiency; and Cloud Dataproc to run Hadoop clusters.
Hike is also stepping up its AI & machine learning capabilities. It uses Google Cloud Machine Learning Engine managed, distributed computing capabilities to train complex models on TensorFlow. This powers key use cases such as delightful local sticker recommendations on Hike Sticker Chat. Hike is also investing heavily on AI and machine learning research.
Hike has achieved a range of benefits from its Google Cloud Platform deployment. As well as reduced latency, improved compute throughput, and increased connection handling, Google Cloud Platform managed services have enabled the business to reduce the time and effort required to administer core infrastructure, with the saved resources allocated to improving its messaging product.
“Managed services are beginning to reduce our operational overheads,” says Gupta. “For example, managed instance groups and Cloud Load Balancing are reducing our instance count and costs, thereby reducing involvement from DevOps and developer teams.”
Google Cloud Platform 20% cheaper
Gupta and his team have calculated that running for three years on Google Cloud Platform will cost, including the cost of migration, 20 percent less than on its previous platform. BigQuery is processing queries 20 times faster than a similar service offered by the previous provider, while storing 125TB of data and streaming 1.5TB of data daily. Furthermore, Hike’s analytics pipeline costs 80 percent less than in its previous environment.
“Google Cloud Platform has played an important role in enabling us to continue to innovate and realize our mission of reducing dependency on the keyboard,” says Gupta.
Next-Level Search: Discover the Game-Changing Capabilities of Enterprise Search on Gen App Builder
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In our conversations with customers, few generative AI use cases have driven as much enthusiasm as generative search. Leaders at enterprises know the limits of traditional enterprise search, with queries producing a list of links based on pattern matching, and significant manual investigation required to find the more relevant answers. In generative AI, these leaders see an opportunity to leverage their data more effectively and deeply, including applying it to conversational apps that can answer complex questions, produce accurate summaries that synthesize many sources, and help people get the information they need, faster.
Enterprise Search on Generative AI App Builder (Gen App Builder) lets organizations create custom chatbots and semantic search applications in as little as a few minutes, with minimal coding needed to get started and enterprise-grade management and security built in. Customers can combine their internal data with the power of Google’s search technologies and generative foundation models, delivering relevant, personalized search experiences for enterprise applications or consumer-facing websites.
To date, most approaches to combining generative AI and search technology have been inadequate for the scale and reliability needed for enterprise use. For example, building search by breaking long documents into chunks and feeding each segment into an AI assistant typically isn’t scalable and doesn’t effectively provide insights across multiple sources. Likewise, many solutions are limited in the data types they can handle, prone to errors, and susceptible to data leakage. Bespoke, do-it-yourself approaches are generally no easier, with production-grade solutions often requiring complex tasks like integrating embeddings data with foundation models and significant use case data testing. Even when organizations make these efforts, the resulting solutions still tend to lack feature completeness and reliability, with significant investments of time and resources required to achieve high-quality results.
These challenges demonstrate that to effectively implement generative search, organizations typically need more than access to powerful foundation models. They also likely need the ability to ground model outputs in specific data, so that outputs are more relevant and less likely to include mistakes or “hallucinations.” They generally need safeguards that protect their data, how it is accessed, and how it is used. And they generally need the process to be high-performant and scalable out of the box, making the functionality easy to use even if the organization lacks data science and machine learning expertise.
Let’s look at how Enterprise Search on Gen App Builder helps customers bypass these scale and reliability challenges, so they can start leveraging generative search quickly.
The intersection of generative AI and enterprise data
Enterprise Search on Gen App Builder lets developers create search engines that help ground outputs in specific data sources for accuracy and relevance, can handle multimodal data such as images, and include controls over how answer summaries are generated. Multi-turn conversations are supported so that users can ask follow up questions as they peruse outputs, and customers have control over their data—including the ability to support HIPAA compliance for healthcare use cases. All of this is available as a fully managed service, so developers can focus on building rather than cloud complexity.
Gen App Builder’s out-of-box capabilities can remove the need for data chunking, generating embeddings, or managing indexes, hiding complexity behind a straightforward interface that lets developers build apps in minutes with little or no coding and no prior machine learning experience. With the ability to ingest large volumes of documents and support for both unstructured and structured data, apps built with Gen App Builder help customers solve the long-standing headache of finding relevant information across the organization, turning tasks that used to take hours into quick searches or conversational explorations with an app.
These capabilities are underpinned by both Google’s foundation models and a variety of Google Search technologies, including:
- Semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user’s query input.
- Google’s understanding of how users search for information.
- Google’s expertise in understanding relevance, which considers factors such as content popularity and user personalization of content when determining the order in which search results are displayed.
For more advanced use cases, Gen App Builder can be easily integrated with Vertex AI for in-depth foundation model tuning, enabling flexible input and output search formats.
As always with our AI products, Gen App Builder has been evaluated for alignment with our AI Principles, which is reflected by the product’s many safeguards against bias, toxic content, and unhelpful outputs. Whether for prototypes built in minutes or apps with many custom components, Enterprise Search on Gen App Builder offers a robust suite of user-friendly tools for customers across industries and levels of expertise.
How customers are innovating with Enterprise Search
After gaining access to Enterprise Search on Gen App Builder via our trusted tester program, a number of customers are already leveraging the product for novel use cases.
Priceline is harnessing Gen App Builder and Vertex AI for a range of projects, including internal search engines for employees and a new chatbot to assist customers as they make travel plans. Slated to be available across both desktop and mobile experiences, Priceline’s chatbot will help customers find the right information faster via always-on, personalized experiences, including answering nuanced questions like, “What are the best 4-star hotel options in midtown Manhattan within walking distance to Central Park?” and “Can you help me extend my hotel reservation for an additional night?”
“Priceline is charting a course to transform the novelty of generative AI into lasting value for our customers and our business. We believe it’s not just about having the latest technology; it’s also about practically targeting innovation to the right challenges and opportunities,” said Marty Brodbeck, Chief Technology Officer, Priceline. “With Google Cloud as our AI innovation partner, we’re doubling down on our commitment to delivering the fastest, most seamless and informative booking experience for our customers, from personalized planning and travel inspiration to customer service.”
Vodafone is experimenting with Enterprise Search and foundation models on Gen App Builder to build a tool that can rapidly and securely query documents, search, and understand specific commercial terms and conditions. Vodafone Voice and Roaming Services has over 10,000 contracts with other telecommunications companies worldwide, in a variety of formats such as PDFs, images, and complex tables. Searching this document repository is often a time consuming process for employees.
“Every day we introduce and manage new services like 5G to a roaming footprint comprising more than 700 operators in 210 countries to ensure both Vodafone and our partners’ business customers, holidaymakers, and Internet of Things devices stay connected when abroad. With Enterprise Search on Gen App Builder, we are building an intelligent assistant to securely and quickly search contracts.” said Sherif Bakir, CEO of Vodafone Voice and Roaming Services. “With generative AI, we’re accelerating otherwise protracted processes, increasing productivity and operational efficiency.”
Software startup Trender.ai, which creates customer monitoring and intelligence solutions for B2B relationships, is using Gen App Builder to build a product that can synthesize information from social media, public sources, and CRM data so that users can form more productive, personal relationships with prospects and customers.
“With Enterprise Search on Gen App Builder, we have been able to do things in the last month that we had projected to take 12-18 months on our prior roadmap,” said Betsy Bilhorn, co-founder at Trender.ai. “We had expected it would take at least 12 months to build and train models from an individual’s public social, web, and other data, and to then be able to ask questions like ‘What things are most important to this person?’ or ‘When and how is the best way to make an introduction to this prospect that they’ll respond to?’ Last year, we thought getting to this vision was a bit of a moonshot for a startup our size — but now we were able to achieve this in under a month.”
Stop searching for solutions — start discovering insights
We’re excited to see how customers use Enterprise Search on Gen App Builder to leverage data in powerful ways, discover new insights, and create useful, personal, and efficient experiences.
Today, we’re pleased to help our customers accomplish these goals, with the general availability of Enterprise Search on Gen App Builder for customers on the allowlist (i.e., approved for access). Please contact your Google Cloud sales team for access and pricing details. We are also launching two new features within Enterprise Search on Gen App Builder that are available today in our preview offering: multi-turn search, which supports asking follow-up questions, and content recommendations to find semantically relevant content. Access to the preview offering is available via Google Cloud’s trusted tester program. Read more about Enterprise Search on Gen App Builder and sign up for access on our webpage.
We’re also bringing generative AI features of Enterprise Search to our existing solutions like Contact Center AI and Document AI. As an example, starting this month, customers can preview generative AI-powered search in Document AI Warehouse. To keep up with our latest generative AI news, don’t miss The Prompt or our generative AI primer for executives on Transform with Google Cloud.
Global Communication Made Easy with Translation Hub
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Hello! ¡Hola! 你好! नमस्ते! Bonjour! Being greeted in your own language can instantly put a smile on your face, and organizations around the world recognize that messages become more inclusive and powerful when translated into many languages.
As the Head of Product Management for Translation AI at Google Cloud, I am always looking to make our user’s lives easier when it comes to language tools—like Google Cloud’s Document Translation API, which has been used to translate over 2.5 billion documents across Google Translate and our Cloud customers.
But during my time working on translation products, I have seen enterprise users making compromises, like sacrificing cost and speed of translation for quality, or paying much more for high-quality translations and making peace with the slow turnaround times and high costs. That’s why we are excited to see momentum building behind Translation Hub, which launched earlier this year. Built to address organizations’ localization and translation challenges and delight users with an incredibly intuitive and administratively simple offering, Translation Hub makes it easy to translate content at scale—users just need to select the files to be translated, choose the output languages, and then they’re off and running.
In this article, we’ll take a closer look at Translation Hub’s powerful features, and the ways it is helping customers do more with their content.
Enterprise translation, reimagined: fast, lean, and intuitive
Translation Hub is a fully-managed, self-serve translation offering, powered by Google AI and built for the enterprise. With Translation Hub, businesses can instantaneously translate content into 135 languages with a single click, via an intuitive interface that integrates human reviews (i.e., a “human in the loop”) where required.
Translation Hub is designed to be both easy to manage and use. Each organization’s Translation Hub administrator uses the Google Cloud console to onboard and manage business users, typically by adding their email, which triggers an invite to the business user. Once a business user is added, they can sign in and start requesting translations and post-editing reviews.
With advanced features like “Glossary” and “Custom Translation Models (AutoML Translation),” Translation Hub makes it easy for enterprises to control the terminology and inject domain-specific context to meet their unique needs. Furthermore, Translation Hub ensures that organizations own their Translation Memory, for use in future translations or to leverage as a rich, human-validated source of training data for custom translation models (e.g., AutoML Translation). For example, when a user edits a phrase or segment through Translation Hub, this is immediately captured and used in the next translation for all future users who initiate translation where the same content or segment appears.
One of my favorite features is that Translation Hub preserves the layout and format of documents, saving both time and money. And while Translation Hub works with Google Drive (you can ingest and export documents to Drive) and is compatible with all our Google Workspace content sources (e.g., Slides, Docs etc.), it also supports many of the most commonly used formats, like documents and presentations created in Microsoft Office, and scanned and native PDFs.
Organizations need to be able to share the output of AI-powered translation with localization teams or agencies, for review. They need to save time by leveraging glossaries or customer machine learning (ML) models. They need these capabilities in one cohesive platform—and they need all of this to be user-friendly for all kinds of business users. It’s the difference between being able to automate translations in a few scenarios and being able to translate at scale. Google Cloud’s mission is to accelerate every organization’s ability to digitally transform, and with Translation Hub, our customers can leverage the best of AI for their content translations without sacrificing speed, quality or cost.
All of Google’s Translation Innovations within a single product
Translation Hub uses Google Cloud’s Translation API, AutoML Translation, and innovations in Neural Machine Translation Quality Prediction and Translation Memory—all technologies we’ve put to good use inside Google. For example, our localization teams have realized more than $80 million in cost avoidance and savings since 2021, using Google Cloud AutoML Translation and localization expertise.
“In just three months of using Translation Hub and AutoML translation models, we saw our translated page count go up by 700% and translation cost reduced by 90%,” said Murali Nathan, digital innovation and employee experience lead, at materials science company Avery Dennison. “Beyond numbers, Google’s enterprise translation technology is driving a feeling of inclusion among our employees. Every Avery Dennison employee has access to on-demand, general purpose, and company-specific translations. English language fluency is no longer a barrier, and our employees are beginning to broadly express themselves right in their native language.”
To get started with Translation Hub, visit our solution page, and for a deep dive, check out this Google Next ‘22 session, including more details about Avery Dennison’s use case.
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Today’s business leaders face more challenges than ever, in a world that is evolving rapidly, that is hyper-connected, and that is data-driven.
As the pace of change intensifies, marketers have come under immense pressure to demonstrate value and to lead their organizations through this new landscape.
This is particularly pronounced in Asia, home to many of the world’s fastest-growing economies. Innovations in e-commerce, online payments, and communications are driving new business models and shifts in consumer behavior both here and around the globe.
Now, more than ever, strong consumer insights are essential in order to succeed. We are surrounded by signals that can help decode how behavior will change in the coming years.
Marketers who invest in detecting and developing these signals will stay ahead of the curve. In today’s world, opportunity and growth lie at the intersection of data and foresight.
This thought-leadership study showcases newly-breaking trends, examples of what companies are doing now, stories of how emergent leaders are pushing the boundaries, and what the future could hold.
The study covers:
- Cutting-edge trends including Shoppingmas, Retailarity, Leisuressence, Part-time Preneurs, and Mindful Impact
- The implications for CMOs and businesses
- How to use data and machine learning to prepare
Download Now: A Peek Into Your Consumer’s Future
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Public cloud, big data, and AI technologies offer competitive advantages and cost savings for capital markets firms ready to make the transition. This paper discusses the three phases capital markets firms go through in transitioning to public cloud, and the workloads, benefits, and cultural changes that characterize the three phases:
Infrastructure Optimizers: The first step on the public cloud journey, where firms focus on migrating specific workloads to save costs.
Cautious Strategists: Firms build on the success of their first public cloud migrations, and begin to change the way they develop technology to increase cost savings and start taking advantage of capabilities only available on public cloud.
Transformative Innovators: Firms shift to a fully public cloud-enabled mentality, and fully leverage the flexibility and agility of the public cloud to build industry-changing solutions and attract top IT talent.
Additionally, we reveal the five things that capital markets innovators who have advanced to the transformation phase do well in their adoption of cloud, big data, and AI technologies across the front, middle, and back office functions.
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