Published June 2023 on YouTube

Chris Lattner: Future of Programming and AI | Lex Fridman Podcast #381

1. Introduction

Chris discusses the challenges of building a universal platform for AI infrastructure that can handle the explosion of innovation in AI and the increasing number of hardware devices. He introduces his new full stack AI infrastructure called Modular and a new programming language called Mojo that combines the usability of Python with the performance of C/C++.+

2. Mojo programming language

The Future of AI Programming:Chris discusses the need for accessible and understandable machine learning infrastructure, leading to the creation of Mojo, a programming language optimized for AI. He explains the importance of programming these new compute platforms and the challenges of making them accessible to non-experts.+
Emojis in Programming:Chris Lattner discusses the use of emojis in programming, explaining that they can provide a colorful and visual way to differentiate file extensions. He notes that while some languages already support emojis, there may be resistance to fully embracing them in the coding community.+
Simplifying AI Infrastructure:Chris discusses how Modular is tackling the big problems in AI infrastructure by building Mojo, an AI-first programming language that is also fully general. The goal is to simplify the world of AI by providing one solution for tackling GPUs, TPUs, NumPy, and other Python libraries.+
Python's Strengths and Weaknesses:Chris and Lex discuss the strengths and weaknesses of Python as a programming language. They highlight its intuitive nature, condensed code, and vast ecosystem of packages, but also acknowledge its slowness as a drawback. The conversation takes a tangent on the topic of deep-rooted religious preferences in the programming community.+

3. Code indentation

Python Indentation:Chris explains why he chose to stick with Python's indentation style, arguing that it is more beautiful and less cluttered than curly braces. He also notes that automatic formatters help prevent errors due to indentation.+
Python and Mojo:Chris explains how Mojo works with Python and how it can add features that are missing from Python. Mojo is interpreted, compiled, and statically compiled, and it allows for dynamic metaprogramming to be used in a language that has to be super efficient in specific cases.+
Compile-time Metaprogramming:Chris discusses the advantages of compile-time metaprogramming and how it can be implemented in languages like Python and C++. He also talks about the challenges of implementing it and how Mojo is using a new approach to unify the programming language model with the runtime model.+

4. The power of autotuning

Machine Learning Metaprogramming:Chris and Lex discuss the use of metaprogramming in machine learning, particularly in the abstract specification of compute problems and the mapping and transformation of these problems. They also touch on the features of autotuning and adaptive compilation and how they make machine learning systems special.+
Auto Tuning Algorithms:Chris explains how Auto Tuning algorithms work and how they can help programmers build more abstracted and portable code. He also discusses how the compiler and interpreter work together to achieve ultimate performance.+
Performance and Efficiency:Chris and Lex discuss the importance of ultimate performance for enterprises and the environment. They also talk about how auto tuning can help improve the performance and efficiency of machine learning models. Chris explains how auto tuning can simplify the process of taking advantage of new features without breaking old code.+
Python Performance Boost:Chris explains how Mojo can provide a 35,000 x speed up over Python by moving to a compiler, optimizing memory usage, and taking advantage of modern hardware features. Mojo allows users to parallelize code and control memory hierarchy, resulting in powerful weapons against slowness.+

5. Typed programming languages

Python and Types:Chris explains the role of types in Python and how they can be used to optimize code and improve code completion. Mojo allows for optional typing, making it a useful tool rather than a strict requirement.+
Python Types Rebellion:Chris Lattner discusses the cultural pressure to use types in Python and how it shouldn't be a guilty pleasure. He explains the benefits of using types for performance and predictability, but also acknowledges that it's not necessary for everyone all the time.+
Python Migration Pain:Chris discusses the painful migration from Python Two to Python Three and how he wants to prevent the world from going through that again with his new language, Mojo. He also touches on the importance of tools like static analysis in identifying bugs in large code bases.+
Python Type Annotations:Chris explains the challenges of standardizing syntax for adding types to Python, including the issue of different interpretations and the fact that types are ignored according to the Python spec. He contrasts this with Mojo, where declared types are enforced by the compiler and are not just hints.+
Mojo Language:Chris explains how the Mojo Language is designed to be extensible and high performing by using the notion of structs instead of built-in types like integers and floats. This allows for the implementation of custom types without being second-class citizens.+
Early Days of Mojo:Chris talks about the early days of Mojo, the importance of numerical types in AI and how they are still building out the library. He mentions that Mojo is useful for low-level programmers right now and they are working their way up the stack. They decided to release it early so that people can get access to it and play with it and they can build it with the community.+
Open Development:Chris shares his experience with open development and community involvement, highlighting the challenges of making everyone happy and the benefits of working with the community. He discusses the active community on Mojo's discord and how they are deliberately adding features based on a logical roadmap. Chris also talks about the responsibility of doing it right and the potential impact of healing long-standing tensions between different communities.+

6. Immutability

Immutability and Value Semantics:Chris explains the concept of value semantics and how it can help prevent bugs by making collections behave like proper values, allowing for logical copies of data to be passed around without changing the original. The implementation requires low-level language design, but when done efficiently, it does not significantly impact performance.+
Defensive Copying:Chris explains the concept of defensive copying in programming and how it's used in languages like Python and Swift. He highlights the importance of using defensive copying to prevent bugs and maintain the integrity of the code.+
Value Semantics:Chris explains the concept of value semantics and how it can define away bugs and reduce the number of copies in practice. He also discusses the challenges of implementing this concept in a performant way across different kinds of objects in Python.+
Ownership and Argument Conventions:Chris explains how Mojo takes the best ideas from different programming languages to create a system of ownership. He also discusses how argument conventions work with transfer ownership and the challenges of dealing with types that can't be copied in systems programming.+
Atomic Numbers:Chris explains the concept of atomic numbers and how they cannot be copied or moved once created. He also discusses the importance of understanding lower levels of abstraction in programming, while Lex shares his stress with concurrent programming.+
The Future of Computing:Chris discusses the trend towards specialization of hardware, the invention of the tensor by machine learning people, and how it has enabled an explosion in AI compute accelerators.+

7. Distributed deployment

Distributed Machine Learning:Chris discusses the challenges of deploying machine learning models and the tension between usability for researchers versus deployment efficiency. He explains the industry struggle and how their modular compute platform dynamically partitions models with billions of parameters and distributes their execution across multiple machines, enabling unparalleled efficiency, scale, and reliability for the largest workloads.+
Challenges of Deploying Machine Learning:Chris discusses the challenges of deploying machine learning models in production, including the complexities of language and serving, and the limitations of TensorFlow and PyTorch. He explains how Mojo can help with these challenges and addresses the bitter enemy that the industry is fighting against.+
Complexity in Machine Learning:Chris discusses the massive complexity in hardware, software stacks, and modeling in machine learning. He explains how this complexity has not been rethought and how Modular is working to approach this with a first principles approach.+
Modular Computing:Chris discusses how modular computing can help software developers build applications and solutions that scale over many years, using proven technology and algorithms from different quadrants of the industry.+
General Purpose AI Programming:Chris and Lex discuss the challenges of using point solutions for machine learning technology and the need for a general purpose programming language that can be compiled across different hardware. Chris explains that their mission is to save the world from terrible AI software and that a programming language is necessary to achieve this goal. They also touch on the importance of accelerators and exotic numerics within the stack.+
Hardware Innovation Philosophy:Chris shares his philosophy on hardware innovation, stating that there isn't one right solution and that specialization happens. He believes that hardware innovation is a good thing and is interested in unlocking that innovation.+
Exotic Computing:Chris discusses the potential of analog computing and the challenges of building a chip that uses it. He explains that the industry is trying to turn the problem of writing special kernels into a compiler problem. This would provide a more general and extensible interface for dealing with the general case.+
Compiler vs. Programmability:Chris explains how the trend of turning everything into a compiler has excluded people who are not compiler experts. He discusses how modular technology brings programmability back into the world and enables researchers and hardware innovators to extend the stack without having to hack the compiler itself.+
Heterogeneous Runtimes:Chris explains how modern devices like cell phones and laptops have become heterogeneous systems with multiple CPUs, GPUs, and dedicated hardware blocks that need to work together. He discusses how machine learning is moving towards higher levels of abstraction, giving the system more flexibility in mapping algorithms onto the system.+
Hierarchical Asynchronous Communication:Chris and Lex discuss the challenges of managing parallel computing in both small systems like smartphones and large systems like data centers. They explore the complexities of leveraging the strengths of specialized systems and the importance of uniformity and consistency in managing hierarchical asynchronous communication.+
Optimizing Code with Autotune:Chris discusses the benefits of simple and predictable systems in computer science, and how autotune can be used to optimize code. He explains how autotune can be turned into a machine learning problem, and how it can be integrated into the modular stack.+
Machine Learning Bottlenecks:Chris explains how to make machine learning code faster by focusing on the hardware and memory limitations rather than just trying to make Python faster. He discusses how bottlenecking occurs when sending data back and forth to memory, especially with large training sizes and complicated models. Chris introduces the concept of kernel fusion as a solution to these bottlenecks.+
Compiler Bag of Tricks:Chris explains the importance of a good compiler and how it can handle complexity and generalize to work on bigger problems. He also highlights the current state of machine learning tooling and how it needs to improve to be more reliable and integrated.+
Machine Learning Optimization:Chris explains how the fragmentation of machine learning systems leads to complexity and how optimization is achieved through hierarchical levels of abstraction, such as vectorization and parallelization. The details matter greatly in achieving good performance, and getting the right magic numbers and parameters can make a significant difference in performance.+

8. Mojo vs CPython

Python on Mojo:Chris discusses how Mojo is a superset of Python and how it can run Python code as if it's Mojo code. He explains that Mojo is not done yet, but already people are seeing speed ups by moving small pieces of Python code over without changing it. The goal is to be a full superset of Python and be at least as fast, if not faster, than Python.+
Python Integration Layer:Chris explains how they built an integration layer that allows them to import arbitrary existing packages using Python, which provides full compatibility with all the Python objects and C packages. This gives an incremental migration path to Mojo while still being fully compatible with the vast Python ecosystem.+
Python Performance Mojo:Chris explains the benefits of using Mojo for Python performance instead of redesigning CPython, which is already being improved by other teams. Mojo provides a one-world solution that allows for untyped, dynamic code with the ability to add types for performance benefits. By moving important packages to Mojo, users can gain massive performance benefits and other advantages.+
Python to Mojo Conversion:Chris and Lex discuss the process of converting Python code to Mojo, including the possibility of automating the process and the benefits of using Mojo. They also touch on the potential for future tools to improve code and modernize it.+
Python's Superset Challenge:Chris and Lex discuss the challenge of making a language a superset of another language, using Python as an example. They touch on the importance of dynamic metaprogramming features and how Python's design decisions have allowed for elegant scaling. They also compare Python's design to that of C and C++ and the challenges of building a superset on top of a subset.+
Challenges of Building a C++ Parser:Chris Lattner discusses the challenges of building a C++ parser and the trade-off between building a theoretically beautiful software and building a practical software that people will actually use. He shares his experience with Clang, a C++ parser, and how he had to implement long tail compatibility and weird features to make it work with existing code bases.+

9. Guido van Rossum

Python's Future:Chris discusses his conversations with Guido, the creator of Python, about the future of the language and how Mojo fits into the Python community. They talk about the importance of not fragmenting the community and the potential for Mojo to help Python overcome its reputation for being slow.+
Language Evolution:Chris discusses the evolution of programming languages and the challenge of avoiding community fragmentation. He shares lessons learned from Swift and how they were able to integrate with the Objective C runtime. The trade-off between doing the hard thing that enables scale versus doing the theoretically pure and ideal thing is also discussed.+
Integrating Python with Mojo:Chris discusses how the integration of Python and Mojo can be achieved by creating Python interfaces to Mojo TPUs, similar to the approach taken with Swift. This will help the community adopt technologies more progressively.+

10. Mojo vs PyTorch vs TensorFlow

Chris discusses the problem of AI fragmentation and how the modular engine can help solve it. He explains that the engine is not meant to replace PyTorch and TensorFlow, but rather provide better performance and tooling. Mojo, a programming language, can help solve the C++ Python feud and is in service of solving big AI problems.+

11. Swift programming language

Swift for TensorFlow:Chris discusses his involvement in the Swift for TensorFlow project and the challenges of integrating Swift with machine learning while also pushing the boundaries of programming models. He explains how the project influenced the industry and led to innovations such as the graph program abstraction in PyTorch 2.+
Lessons Learned:Chris and Lex discuss the lessons learned from developing Swift for TensorFlow, including the importance of meeting the world where it is and the benefits of using Python for machine learning. They also touch on the advantages of Swift's efficiency and its compatibility with eager mode.+

12. Julia programming language

Programming Language Evolution:Chris discusses the evolution of programming languages with Lex and how new algorithms in machine learning and artificial intelligence could lead to the emergence of a new programming language. They also compare Julia and Python, with Chris explaining why Mojo is different from Julia and why Python is popular due to its package ecosystem and low startup time.+
Python's Popularity:Chris and Lex discuss the reasons behind Python's popularity, including its low compile time, simple object representation, and integration with notebooks. They also mention how Python has become the main language of teaching software engineering in schools and how it owns the machine learning space.+

13. Switching programming languages

The Future of Mojo:Chris discusses the potential of Mojo as a programming language for machine learning and its adoption by the community. He emphasizes the importance of solving problems and low adoption cost for tech to be successful. He also acknowledges the power and excitement of other programming languages and their different trade-offs.+
The Middle Path:Chris discusses how the middle path of progressively learning new things and adopting them where it makes sense is the more likely path for most people in programming. He explains how Mojo allows programmers to start with what they already know and discover new ideas that make their code simpler, more beautiful, and faster.+
Future of Computing:Chris discusses the future of computing and how the demand for AI has caused an explosion of hardware. He believes that computing will not get simpler and that a universal platform is needed to help people scale and grow without having to rewrite their code every time a new device comes out.+
Advantages of Adopting Mojo:Chris explains the benefits of adopting Mojo, including increased performance, portability to different devices, and improved safety through the use of real types. He also discusses how adopting new technology can lead to redesigning and improving existing systems.+

14. Mojo playground

Mojo Playground Launch:Chris discusses the launch of Mojo Playground, a new technology that provides free compute and allows users to play with it in a limited, controlled way. The technology is currently running on cloud VMs, but the team is working on making it available for download locally. Chris also reflects on the lessons learned from the launch of Swift in 2014.+
Building Swift Right:Chris shares his experience launching Swift at Apple and the technical debt that came with it. He emphasizes the importance of iterating through the development process and building the best product, even if it takes longer.+

15. Jeremy Howard

Mojo's Launch:Chris discusses the launch of Mojo and the overwhelming response they received from the public. People are excited about ownership and taking a step beyond Rust, as well as the ability to make code faster. Jeremy Howard is credited with pushing for this kind of thing for years.+
Machine Learning Efficiency:Chris and Jeremy discuss the importance of efficiency in machine learning and how it can make the field more accessible to a wider audience. They also touch on the role of performance in AI research and how it relates to the size of data sets.+
Python Packaging Problems:Chris and Lex discuss the difficulties of Python packaging, particularly when it comes to managing dependencies and versioning. They touch on the challenges of packaging C code and how Mojo aims to solve these issues.+
Python Package Discovery:Chris and Lex discuss the challenges of package discovery in the Python ecosystem and explore potential solutions, including the use of specialized interfaces and reducing the amount of C in the ecosystem to make it more scalable.+

16. Function overloading

Python Function Overloading:Chris explains why Python does not have function overloading, citing the limitations of the dictionary within a Python object and the dynamic nature of the language. He also discusses the challenges of implementing overloading in a type language.+
Python Functions:Chris and Lex discuss the challenges of retrofitting typing systems into existing Python code, the benefits of modeling different behavior in type languages, and the trade-offs between def and FN in Python function definitions.+
The Power of Strictness:Chris and Lex discuss the benefits and drawbacks of using strict programming languages like FN in comparison to more flexible languages like Python. They also touch on the importance of testing and how different challenges can be solved through different languages.+

17. Error vs Exception

Zero Cost Exceptions:Chris explains the concept of zero cost exception handling in programming languages like C++ and how it affects the performance of the code. He also talks about how newer languages like Swift, Rust, Go, and Mojo treat throwing an error the same as returning a variant, making it as fast as returning something interesting.+
API Design Challenges:Chris discusses the challenges of designing APIs that utilize exceptions and the impact it has on the development of libraries. He also highlights the importance of having standards in large scale package ecosystems like Python. The implementation approach of mojo allows it to scale down to little embedded systems and run on GPUs, which would not be possible with standard zero cost exceptions.+
Nested Functions:Chris explains how nested functions work and the advantages of closures. The conversation touches on how compilers like Mojo handle try blocks and how with clauses work in Python.+

18. Mojo roadmap

Mojo's Roadmap:Chris discusses Mojo's roadmap with Lex, covering topics such as Apple support, keyword arguments, traits, and classes. He also mentions the implementation of lambda syntax, whole module import support, and global variables.+
Building an AI Stack:Chris and Lex discuss the challenges of building an AI stack, including working with hardware and writing high-performance kernels. They also delve into the complexities of defining and destroying values in programming languages like Python and C++.+
Memory Management:Chris Lattner explains how Mojo's approach to memory management improves memory use, predictability, and tail calls in programming languages. The compiler destroys unused variables as soon as possible, resulting in better ownership tracking and fewer low-level issues.+
Asynchronous Programming:Chris explains how Python's Async Await feature allows for more expressive Asynchronous programming, especially when working with networking and other blocking tasks. He also mentions that Mojo, their new language, has a high-performance compute runtime that enables non-blocking I/O, allowing full utilization of the machine.+
Python Syntactic Sugar:Chris and Lex discuss the use of syntactic sugar in Python and how it can be distracting from building core abstractions. They also touch on the importance of working with the broader Python community and implementing table stakes features like lifetime and traits.+

19. Building a company

Building an Amazing Culture:Chris Lattner shares his experience in building teams and how they are building an amazing culture at Modular. They focus on hiring the best people and creating a unique culture to ensure they can scale.+
Building Amazing Products:Chris shares his insights on how to build amazing products by having an inclusive culture and a clear vision that focuses on solving customer problems. He emphasizes the importance of understanding the core problems and giving the team space to build the right technology.+
Remote Work Bonding:Chris and Lex discuss the pros and cons of remote work, including the challenges of team bonding and brainstorming. They explore the limitations of virtual communication and the importance of in-person interactions for building trust and fostering creativity.+
Building Well-Balanced Teams:Chris emphasizes the importance of building well-balanced teams with people of different perspectives and experiences. He believes that having a team with diverse backgrounds and specialties leads to better collaboration and a happier work environment.+
Building a Liberating Company:Chris discusses the liberation of building a company from scratch and how working on tech that directly solves people's problems is exciting and makes decision-making easier. He also talks about the challenges of justifying expenses in other worlds and the freedom of being able to decide to give someone a T-shirt just because he likes them.+

20. ChatGPT

Large Language Models and Programming:Chris and Lex discuss the impact of Large Language Models on programming, including their ability to generate code and automate mechanical tasks. While LLMs can be helpful in certain instances, Chris emphasizes the importance of working with people and understanding the problem when building something beyond basic code.+
LLMs and Programming Languages:Chris discusses the potential of LLMs in learning programming languages and how they can help reduce rote tasks and increase productivity. He also talks about the possibility of LLMs being used in compilers but notes that it may be expensive. Additionally, he mentions the importance of expressing intent to the machine and the potential for LLMs to provide the spec while a different kind of net implements the code.+

21. Danger of AI

Chris L. discusses the potential impact of AI deployment, including job displacement and the threat of Skynet. Despite the rapid progress of AI, he believes that humans adapt slowly and the diffusion of technology takes time to roll out. He also suggests that AI could unlock human potential and improve our lives.+

22. Future of programming

Chris envisions a future where AI is integrated into programming languages and normal tool chains, making it accessible to more people. He believes that if complexity is driven down, more personalized experiences and practical applications of AI techniques will emerge. However, he is concerned that the current concentration of AI capability with big companies will limit participation and use of AI techniques to only a few people in the world.+

23. Advice for young people

Chris advises aspiring programmers to work on something they're excited about and to learn by building. He also suggests being open to exploring less popular areas of specialization and not just following the crowd.+