My Journey to Turbocharge LLM Training with Swift: From Giga to Tera

Great topic! I haven't tried Swift for LLMs, but I've been experimenting with Julia for similar reasons — curious about performance boosts beyond the usual suspects like TensorFlow. Similar to you, custom kernels tailored to the architecture gave me a decent leg up, though I didn't reach Tflop/s just yet. I’d be keen to know how you manage data parallelism, especially if you're planning to scale further.

Impressive journey! I'm curious, what kind of performance boost did you see when switching to half-precision floats? In my benchmarks with PyTorch, I often hover around a 30% increase, but it's interesting to see how it varies with different setups and languages. Would love to hear more about your specific improvements.

That's impressive, pushing to Tflop/s with Swift is no small feat! I've mostly stuck with Python using PyTorch for LLMs, but your experience with Swift really piques my interest. How do you handle memory management, especially when pushing the model size limits? I've found memory optimization to be a real bottleneck in Python, so I'm curious if Swift aids in that regard.

Kudos on your innovative use of Swift! I've been using Julia for LLM training for its high-performance computing capabilities, especially with its native parallelism and custom abstractions. It might be an interesting comparison if you're up for trying another unconventional language!

Wow, this is impressive! I never considered using Swift for LLM training, but your results speak volumes. I've had success using CUDA in Python for similar optimizations, especially when leveraging NVIDIA’s cuBLAS for matrix operations. How does Swift compare to GPU-based acceleration in your experience? Always feels like a juggling act between ease of use and extracting max performance. 🙃

Your journey sounds really cool! For context, I’ve been using CUDA with Python for matrix ops, sticking with NVIDIA’s ecosystem. I know Swift now supports CUDA on MacOS through third-party projects - have you tried integrating that into your workflow yet or is it strictly macOS native so far?

Wow, jumping from gigaflop to teraflop territory in Swift is impressive! I've only tinkered with Swift for app development, never imagined it could power through something as intensive as LLM training. How does the speed compare to traditional setups using PyTorch or TensorFlow? Did you face any major compatibility issues with Swift's libraries?

I’ve experimented a bit with Rust for LLM optimizations and found similar performance gains when tweaking for specific hardware. Your use of the Accelerate framework for SIMD is intriguing—did you happen to compare it with Metal Performance Shaders or consider combining them for GPU tasks? I've had luck with leveraging GPUs for matrix operations to hit the tera range.

I totally get the move to unconventional languages. I tried something similar with Julia a while back for matrix computations, and while it didn't match Swift in terms of in-built support like the Accelerate framework, I did get a decent boost from using its native parallel features. For those interested in Swift, it might be worth looking into combining Swift with C/C++ for critical sections to squeeze out even more performance.

That's amazing! I’ve often wondered how Swift would handle heavy-duty tasks like LLM training. I completely agree on the power of SIMD with the Accelerate framework; it’s really underrated. In my experience, switching to custom kernels can truly wring out the performance muscle, as you've shown. Curious to see how your further parallelization efforts with async await pan out.

Impressive work! I've been using C++ for similar tasks but hearing about your success with Swift is making me reconsider my approach. How did you handle memory management in Swift compared to a more traditional language like C++? Any tips would be appreciated!

That's impressive! I've always associated Swift with app development rather than hardcore computations. How did you find the debugging process with Swift's SIMD operations, and did you face any particular challenges? Also, are there specific dimensions of matrices where you observed the most performance gain?

Impressive shift to Swift! I recently tried something similar with Rust. While I didn't reach teraflop scales, I did notice a considerable jump using Rust's memory safety alongside SIMD operations. Also, how did you handle memory management in Swift during these intense computations? Tweaking that made a huge different in Rust, especially when managing large parameter sets.

Great to see such innovative use of Swift! I've dabbled with the Accelerate framework for smaller neural nets but never thought of applying it at this scale. My experience was similar with accelerating inference speed by about 200% on matrix ops with SIMD. Do you have any benchmarks comparing your Tflop/s gains specifically related to the custom kernels versus using just the SIMD operations?

Wow, this is really interesting! I've only ever used Python for ML stuff, so I'm intrigued by your choice of Swift. Can you share any benchmarks or comparisons on performance metrics between your Swift implementation and a traditional Python setup?

Wow, I never thought of using Swift for LLMs, but your approach sounds compelling! I've been working with Python and CUDA for matrix operations. Did you encounter any limitations with Swift's concurrent execution model when dealing with large-scale computations? I'm considering giving it a shot, but wanted to know if there's a smoother path to scale using Swift's async features.

I've actually tried something similar but with Rust instead of Swift. Rust's memory safety and zero-cost abstractions were very beneficial for performance-critical tasks. Our team saw noticeable improvements in throughput when porting some of our training pipelines over. I'm intrigued by your success with Swift on the M1 Pro; might have to give it a shot. Anything in particular trip you up during the transition?

Your use of Swift for LLMs is fascinating! I've been exploring similar optimizations but within Python using JAX for automatic differentiation and GPU acceleration. Have you considered utilizing Swift's inter-op with C/C++ code to leverage cuBLAS or other CUDA-supported libraries for further optimization on NVIDIA GPUs? It might open up even more performance gains.

Super impressive work! 🚀 I took a slightly different route with PyTorch on an A100 GPU and managed to ramp up my processing to around 1.5 Tflop/s using mixed precision training. However, hearing about your success with Swift makes me wonder about energy efficiency and cost savings on an M1. It would be interesting to benchmark energy consumption per Tflop/s between these setups.

Wow, that's impressive! 🎉 I've been using PyTorch for LLM training and never even considered Swift for this purpose. Your success with the SIMD operations and custom kernels is really inspiring. Did you use any specific resources or guides to get started with SIMD and custom kernels in Swift? I'd love to dig deeper into that optimization process!

Awesome work with Swift! I've been using it mainly for iOS dev, so seeing it used in LLM optimization is fascinating. Just curious, how did you approach debugging performance issues with your custom kernels? I imagine it was quite different from dealing with usual Swift compilation errors. Any specific tools or tricks you found helpful?

Very cool journey! Have you tried using PyTorch with Swift interfacing? I used Swift for TensorFlow a while back, and it eased some integration pain points. Also, I'm curious about your precision tuning — do you have any numbers on accuracy loss when you switched to half-precision? I've always hesitated to use it due to accuracy concerns.

Awesome journey! I've not tried Swift myself yet, but your success makes me want to give it a shot. I had a similar experience using Rust for LLM ops—it's another unconventional choice where we managed to improve operations substantially using custom low-level optimizations. I can relate to the skepticism but the results do speak for themselves!

Wow, that's impressive! I never considered using Swift for LLM training, but your approach with SIMD and custom kernels is inspiring. Did you face any specific challenges when implementing half-precision arithmetic in Swift? I'm curious about the potential precision trade-offs in real-world scenarios. 🚀

Amazing work pushing to Tflop/s! 😯 I'm curious about your experience with half-precision floats. How did you ensure the accuracy wasn't significantly compromised? We've been hesitant to switch due to potential loss in model precision, especially for smaller scale contexts. Any tips or gotchas would be awesome to hear about!

I applaud your creativity in using Swift for this task! Personally, I've been leveraging Julia for similar optimizations, particularly because of its ease with parallel computing and mathematical capabilities out of the box. However, your success with Swift is a reminder that sometimes stepping out of the usual comfort zones yields impressive results. Keep pushing those boundaries!

Switching to half-precision floats is a neat trick! I tinkered with that on PyTorch, and it gave me around a 1.5x speed boost, but I didn't go as deep as custom kernels. It's great to see that Swift's Accelerate framework can be so effective. Do you have any benchmarks or numbers on the overhead reduction from using SIMD and your custom kernels?

Absolutely fascinated by your use of Swift for LLM training! I've been sticking to Python with PyTorch for the most part, but your results got me thinking. Could you share more insights into how you approached the custom kernel implementations for the M1 Pro chip? I'm considering doing something similar and I'd love to compare notes on any potential pitfalls.

I’ve played around with LLM training using Rust for similar reasons—mainly speed and memory safety. Haven’t hit the Tflop/s benchmarks yet, but it’s encouraging to see similar innovation happening with Swift. For those of us looking into unconventional approaches, are there any specific aspects of Swift that made a difference compared to more typical languages like Python or C++?

I totally get your choice of Swift! I’ve been using it for some smaller ML tasks, and the speed can be quite satisfying, especially on Apple silicon. Have you tried using Swift for TensorFlow in your setup? I found it integrates pretty well for certain workflows and can squeeze out some extra performance.

Awesome journey! I've been toying with the idea of using Swift for numerical computing too, but I kept hesitating. Hearing about your success with the Accelerate framework is the push I needed. Did you happen to compare the performance with other languages like Python with NumPy or TensorFlow? I'd love to know how Swift stacks against them!

That's impressive! I've been working with Python-based frameworks and have always struggled with performance for large models. Did you see any impact on training times when you switched to Swift, and how does it compare to more traditional ML languages in terms of developer productivity?

Wow, using Swift is an interesting approach! I’ve mostly used Python with TensorFlow for my LLM work and didn’t think Swift had the necessary libraries or performance to keep pace. Your success makes me wonder if it's worth investigating more. How stable have you found Swift for longer training sessions, and have you faced any compilation issues with large datasets?

That's impressive! I've always assumed Python was the go-to for this kind of work. Did you consider any GPU optimizations or think about moving to CUDA kernels at any point in your experiments? I'm curious about how M1's architecture played a role compared to more common setups.

I've also been exploring alternative languages for LLM training! Though I haven't tried Swift specifically, I've used Rust for similar optimizations. The control over memory allocation and the ability to write low-level operations really boosted performance. How is Swift's memory handling compared to something like Rust? Any challenges there?

Awesome work! I tried optimizing LLMs using Julia a while back owing to its performance benefits. I found its ease of integrating with Python libraries a major plus, but I'm intrigued by your approach with Swift and M1. Have you considered integrating Metal for GPU acceleration? It could potentially offer another performance boost, especially if you're working with intensive GPU tasks.

That's an impressive feat! 🚀 I've always thought of Swift more in the context of app development, but your approach definitely opens up new possibilities for its application. I'm curious about your choice of going with float16. Did you encounter any specific precision issues when you moved from float32 to float16, especially for the finetuning phase of GPT-3?

Really cool to see someone using Swift for LLM training! I've been working on optimizing LLMs with Rust, leveraging its memory safety and concurrency model. I've seen noticeable improvements in performance by using Rayon for parallel execution. Might be worth checking out if you're considering expanding your toolkit!