George Hotz | Programming | tinygrad updates and fast MCTS search

George Hotz | Programming | tinygrad updates and fast MCTS search | tinygrad.org/#tinybox

Welcome! It’s been a long time since I last streamed. I've been working on some updates to tinygrad. We'll explore functionality improvements, recent commits, and some debugging progress. Also, we're discussing what tinygrad is for, how it integrates with existing solutions like PyTorch, and how it’s going to replace them - not without mentioning some fierce competition in the field.

Let's delve right into the details. You can see I’m in a new place now, a shared office where I’m streaming from today. Mostly what I want to showcase is the progress made in tinygrad. Tons of commits have been added since I last streamed. There's a lot to show, like the introduction of Monte Carlo Tree Search (MCTS) for kernel optimization and a higher level of accuracy because of improved initialization.

We also went over recent adventures including a trip to Poland and Italy. Tinygrad, unlike C comma Inc., is a remote-first company that operates entirely over GitHub and Discord. Our GitHub repository is at tinygrad, and we’re making great strides toward the goal of replacing PyTorch.

For a while, I was competing with what I perceived as "idiots" in the self-driving car space. Here, our competition like PyTorch, JAX, Mojo, etc., is both fierce and respectable.

Key Updates in Tinygrad:

Headed towards replacing PyTorch.
Enhanced documentation with a focus on ‘meth’ methods.
Major improvements in initialization.
Shared office, new Place.

More significantly, there has been a distinct difference in how we approach logistics compared to earlier days. Tinygrad’s code quality and the documentation are heaps better now. Reviewing some code: for instance, a minute alteration in tensor initialization from ‘caming uniform’ to ‘tensor uniform’ yielded significant accuracy improvements.

I want to show you how Beam Search and MCTS are used to optimize the performance of tinygrad. By increasing the beam size or Monte Carlo Tree Search nodes, significant speed improvements can be made.

A major part of today's update focusses on the extensive progress made in benchmarking tinygrad’s performance. Compared to TensorFlow and PyTorch, performance has consistently improved with intelligent search algorithms.

In demonstrating its prowess, we also look at how tinygrad performs on different hardware, such as AMD vs Nvidia GPUs and latest developments around reducing driver complexity for better efficiency.

Components within the system - kernel, lower, uops, MCTS - have been optimized for faster compilation and execution. The AMD driver has also seen considerable upgrades; bypasses and optimizations that make it almost as efficient as Nvidia's hardware.

Moreover, we reflect on the importance of building robust search frameworks to truly harness the capabilities of hardware when engaged in intensive computations such as AI model training or in complex gradients.

Optimizations don't come without challenges. Wherever possible, tasks have been parallelized, drivers optimized, and techniques leveraged to ensure minimal time is spent on different computational tasks. Adjusting MCTS and Beam Search parameters, and handling kernel rollouts, have been key focal points in these developments.

Conclusion: This stream illustrates considerable strides in tinygrad's efficiency through both foundational and incremental improvements - reaffirming its potential as a PyTorch replacement while contending with kernel optimizations through search algorithms.

If you're following along, join our Discord to dive deeper into specific improvements or contribute to ongoing development.

Keywords

Keywords:

Tinygrad
Monte Carlo Tree Search (MCTS)
Kernel Optimization
Beam Search
Documentation
PyTorch Replacement
AMD Optimization
Nvidia
Driver Complexity
Parallelization
Programming Infrastructure

FAQ

FAQ:

Q1: What are the latest updates in Tinygrad? A1: Tinygrad has seen substantial progress including higher accuracy due to better initialization, enhanced documentation, notable driver improvements especially for AMD, and the introduction of MCTS for kernel optimization.

Q2: How does Tinygrad compare with PyTorch and JAX? A2: Tinygrad, when optimized using MCTS or Beam Search, shows competitive performance to PyTorch and JAX, especially on platforms like Mac and AMD hardware. However, it still needs some improvements to fully match these platforms on Nvidia hardware.

Q3: What improvements have been made to Tinygrad's initialization? A3: The initialization has been simplified from 'caming uniform' to 'tensor uniform,' resulting in higher accuracy.

Q4: How do Beam Search and MCTS enhance Tinygrad’s performance? A4: By using Beam Search and MCTS, Tinygrad can search and select the most efficient kernel execution path, significantly speeding up computational tasks.

Q5: Why was the AMD driver challenging, and what was done to optimize it? A5: AMD drivers had numerous bugs, so we bypassed AMD’s runtime and wrote our own driver to directly interact with the hardware, resolving most issues and significantly improving performance.

Q6: What is the role of documentation in tinygrad development? A6: Enhanced documentation has been crucial for making tinygrad more understandable and usable for developers, helping them contribute more effectively or utilizing Tinygrad in their projects.