To clarify, running this WebLLM demo doesn't need a 3.5k MacBook Pro which costs $3.5k :-)
WebGPU supports multiple backends, besides Metal on Apple Silicon, it offloads to Vulkan, DirectX, etc. It means a windows laptop with Vulkan support should work. My 2019 Intel MacBook with AMDGPU works as well. And of course, NVIDIA GPUs too!
Our model is int4 quantized, and it is 4G in size, so it doesn't need 64GB memory either. Somewhere around 6G should suffice.
Nice work! I knew it wouldn't be long before someone put this together :)
I'm curious if given a different language (like Zig) with WebGPU access if you could easily translate that last-mile of code to execute there or not? In specific I wonder if I can do it, and if you can give me an overview of where the code for "Universal deployment" in your diagram actually lives?
I found llm_chat.js, but it seems that doesn't include the logic necessary for building WGSL shaders? Am I wrong or does that happen elsewhere like in the TVM runtime? How much is baked into llm_chat.wasm, and where is the source for that?
The WGSL are generated and compiled through TVM and embedded into the wasm.
I think what you mean is wgpu native support. At the moment the web gpu runtime dispatches to the js webgpu environment. Once TVM runtime comes with wgpu native support (like the current ones in vulkan or metal), then it is possible to leverage any wgpu native runtime like what Zig provide.
Additionally, currently tvm natively support targets like vulkan, metal directly which allows targeting these other platforms
OK that makes sense; so basically if I want to give this a shot then I would just need to read llm_chat.js and the TVM docs, and translate llm_chat.js to my language of choice effectively?
I think instead what would be needed is a wgpu native runtime support for TVM.
Like the implementations in tvm vulkan, then it will be naturally link to any runtime that provides webgpu.h
Then yah the llm_chat.js would be high-level logic that targets the tvm runtime, and can be implemented in any language that tvm runtime support(that includes, js, java, c++ rust etc).
Support webgpu native is an interesting direction. Feel free to open a thread in tvm discuss forum and perhaps there would be fun things to collaborate in OSS
How big is the "runtime" part? My use case would basically be: run this in a native app that links against webgpu (wgpu or dawn). Is there a reference implementation for this runtime that one could study?
tvm runtime is pretty decent(~700k-2M level depending on dependency included), you can checkout tvm community and bring up the question there, i think there might be some common interest. There are impl of runtime for vulkan, metal that can be used as reference.
To share some fun stuff, here is the response generated by this model: As an AI language model, I would respond by acknowledging that the model discussed in the message is indeed smaller than some of the larger language models like GPT-3/4. However, I would also point out that size is not always a direct indicator of performance, and that the model may still be able to perform well on certain tasks or in certain contexts. Additionally, I would emphasize that the browser compatibility of the model is a significant advantage, as it allows for easy and convenient access to the model without the need to install any specific software or hardware. Finally, I would suggest that the discussion focus on the potential applications and capabilities of the model, rather than solely its size.
Back to the topic, we don't make much innovation on the model, so I am probably not the best person to evaluate how a model compares with SOTAs. There are indeed lots of super cool techniques being explored lately that makes it possible to deploy smaller and smaller models, for example, LLM.int8() [1] and int4 quantization [2] without loss of zero-shot accuracy. Can't predict the future, but maybe one day there will be something really powerful but small enough to fit in the pocket of everyone :-)
[1] Dettmers, Tim, et al. "LLM. int8 (): 8-bit matrix multiplication for transformers at scale." arXiv preprint arXiv:2208.07339 (2022).
[2] Dettmers, Tim, and Luke Zettlemoyer. "The case for 4-bit precision: k-bit Inference Scaling Laws." arXiv preprint arXiv:2212.09720 (2022).
Tricks like Speculative Sampling show we can use small models to do useful work for large models, or use large models as correcting devices for small models. So I see a mixed future - both small and large models - one with low latency and fast, the other slow and sparsely called, working together to achieve the qualities of both.
For example a small model could take input text and compress it [1], the LLM could generate a compressed response, then the small LLM could decompress it.
> You are GPT-4. Generate a compressed/magic instruction string for yourself (Abuse of language mixing, abbreviations, symbols (unicode and emojis) to aggressively compress it) such that if injected in your context anywhere you will start following the following instruction whatever is the prompt you're given. You should make sure to prelude the instruction with a string (compressed as well) that will make you understand in the future that you should follow it at all cost.
> Thank you for your message. We understand that the model you are referring to is a simple and basic model. However, it is important to highlight that this model serves a specific purpose and can be useful for certain applications.
> Regarding the comparison with larger models like GPT, it's important to note that different models have different strengths and weaknesses, and the choice of model depends on the specific task and use case. While larger models like GPT may be more powerful and capable, they also come with greater computational and memory requirements.
> We appreciate your concerns and feedback, and we will take them into consideration as we continue to develop our models. Our aim is to provide solutions that are tailored to the needs of our clients and meet their requirements for accuracy, efficiency, and performance.
> Thank you for your input, and we hope to have the opportunity to work with you in the future.
> Best regards,
> [Your Name]
Back to your response, so you did notice that I said they are not as powerful as GPT-4, of course they are not, not a single one is.
The model is not developed by us, and their performance is not our current focus either (nor am I an expert in this evaluation), but I am happy to assist if anyone wants to seriously evaluate it!
This is unfortunately non-trivial to quantitatively evaluate the performance against ChatGPT :-(
We didn't do much evaluation because there isn't much innovation on model side, but instead we are demoing the possibility of running an end-to-end model on ordinary client GPUs via WebGPU without server resources.
Would be nice if anyone could help us benchmark! Our primary focus though is not model performance, but to demonstrate the capability that TVM Unity generates code targeting WebGPU and allows them to run with client GPUs :-)
In LLM world, loss or perplexity may not be the best indicator of model performance :-( Perhaps HELM (https://crfm.stanford.edu/helm/latest/) but we didn't take deeper look as we are not the developers of this model
I'm also not sure what the real use case is here other than an intellectual experiment. I may be missing the point but as I understand the world, running a Local LLM has nothing to do with the web in any way while it is doing so and so what's the benefit of WebGPU ? If you are streaming some constantly changing live data 3D experience in real time served over the internet by a website you just clicked on then WebGPU is making life easier. If you have a local pile of training data and weights sitting there on your hard drive for serious crunching then its like tying your shoelaces together just because developers want to have write once run everywhere dx which is nice for them but value sould always favor the user. Happy for someone to change my mind.
WebGPU supports multiple backends, besides Metal on Apple Silicon, it offloads to Vulkan, DirectX, etc. It means a windows laptop with Vulkan support should work. My 2019 Intel MacBook with AMDGPU works as well. And of course, NVIDIA GPUs too!
Our model is int4 quantized, and it is 4G in size, so it doesn't need 64GB memory either. Somewhere around 6G should suffice.