WebGPU support

[sluijs]

Is WebGPU support on the roadmap as an alternative GPU-accelerated backend? This would be especially useful for inference on the web or for non-CUDA environments.

[LaurentMazare]

WebGPU is certainly on our radar, we already have some wasm based demos for llama2.c and whisper that you can try in a web browser. When using wasm, candle should leverage your cpu simd instructions, but having WebGPU on top of this would bring it to a far better level.

[xenova]

And if/when candle adds WebGPU support, I'll add it as a backend to Transformers.js! 🚀 Really exciting times! 🔥

[emmatyping]

Hi! I'd be interested in working on this. I've spent some time thinking about a rough plan after reading through the code:

1. Move candle-kernels to candle-cuda or candle-cuda-kernels (the name can be bikeshed'd in the PR)
2. Make a candle-wgsl(-kernels) crate, with kernels implementing the ops needed. Can maybe re-use some implementations based on https://github.com/webonnx/wonnx
3. Add a new backend implementation using wgpu-rs to execute the kernels
4. Add tests, info on how to run things
5. Maybe add flash attention kernels -- might be a lot of work so probably worth its own follow-up issue.

Some questions:

• Is wrapping with wgpu-rs acceptable? I see that the cuda wrappers use cudarc, and wgpu-rs seems to be the closest equivalent for wgsl shaders for compute. It would also be an easy way to bridge things on native to Vulkan and Metal as well (would take more work though, e.g. integrating naga).
• Do you want all kernels in one crate? In the above I suggest splitting them into their own backend-specific crates, but I guess that could be abstracted into the candle-kernels crate itself if you prefer.
• I will probably focus on the web for now, because I think it is the most promising, but I also will probably follow up with testing Vulkan, as I think that will make inference much more portable.

[Narsil]

Sounds like a very reasonable plan.

I think we can start working without tying too much to candle, maybe other projects could be interested on having webgpu support (that's why having cudarc is great it can be used by other projects, not necessarily candle, and why we keep pushing changes upstream as much as possible, like the NCCL support).

wgpu-rs : Last time I tried Vulkan, and doing compute shaders, the performance was abysmal. And it makes sense, it's not really designed for ML. In general I would go for the most performant solutions from the start, not have backends just for the sake of it. AMD already has libraries intended for ML: https://www.amd.com/en/graphics/servers-solutions-rocm we could link to that directly if it makes more sense.

For Metal I would have the same opinion, we should try and make metal usable outside of this crate and be mere users of it.

For any new backend, it is very important to create a way for USERS to create their own kernel/op. It's impossible to keep up with all the innovation imho so the most important thing is to allow users of candle to use any op they want, without having to wait for us to implement it.

[emmatyping]

re wgpu-rs, I certainly agree that native backends are the best, I only bring up Vulkan/Metal as bonuses. I was suggesting wgpu-rs because it is the major WebGPU library for Rust, it looks like Burn uses it. So I think it is the best library for the job, I just wanted to see if adding the dependency was acceptable. The alternative would be to write a bunch of bindings via web-sys around webgpu APIs.

For any new backend, it is very important to create a way for USERS to create their own kernel/op.

Certainly! I mostly was discussing the crate rename/split focused on candle-provided kernels. For user written kernels, would it not be best to simply add wgpu_fwd to the Op{N} traits that the user may implement? Are there other details I should be aware of?

[Narsil]

Certainly! I mostly was discussing the crate rename/split focused on candle-provided kernels. For user written kernels, would it not be best to simply add wgpu_fwd to the Op{N} traits that the user may implement? Are there other details I should be aware of?

Basically yes. Tensor is Send+Sync, therefore Op needs to be Send+Sync (because it's kept for gradients). That could end up being a limitation: https://github.com/huggingface/candle/blob/main/candle-examples/examples/llama_multiprocess/model.rs#L33-L38

I think it is the best library for the job

What other libraries or alternatives are there ?
Looking at this: https://www.reddit.com/r/rust/comments/159cbto/announcing_burnwgpu_new_deep_learning/jtf80xq/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button I have the feeling like it's not the correct way. We need only webGPU, not those 10 other things.
In any case this is not in our short term roadmap.

[emmatyping]

Basically yes. Tensor is Send+Sync

Good news there, WebAssembly doesn't have OS-style threads! The webworkers-based "threads" might require things to be Send/Sync, but I will have to look closer at that.

What other libraries or alternatives are there ?

Honestly, I didn't find any that seemed currently maintained or more than toys.

We need only webGPU, not those 10 other things.

Yeah, its possible that wgpu isn't the right project, it is pretty large, but the other part is those other features are optional, so I don't know how much it hurts to include it.

In any case this is not in our short term roadmap.

Fair enough!

[helloburke]

is now support webgpu ?

[guyoung]

candle webassembly Is there any plan to support WebGPU？

[minghuaw]

One general comment.

Move candle-kernels to candle-cuda or candle-cuda-kernels (the name can be bikeshed'd in the PR)

I feel like writing those compute shaders in glsl might be a better option. I have done some rough testing on different gpgpu performance and vulkan with glsl seems to be able to keep up with cuda while wgpu with wgsl reaches bottleneck pretty early with the same optimization tricks. On top of that, webgpu supports glsl as well, so we could have not only a webgpu backend but a vulkan one as well (I guess for folks who still want to run it natively by don't have the luxury of a nvidia GPU but an intel/AMD GPU)

[santiagomed]

@LaurentMazare are there any plans to start implementing a WebGPU backend? I see Ratchet has successfully implemented WebGPU inference and would love to see this in Candle soon as well. I would love to help with this implementation if possible too, but there's a lot of learning on my part to be done before I do so, so ideally would love to chat more since I'd have a lot of questions.

[cgisky1980]

https://github.com/cryscan/web-rwkv
Here is a RWKV LLM inference based on WGPU Vulkan。

[KimHenrikOtte]

Maybe this fork will help if WebGpu support is requested:
As part of a university project this semester, I developed a candle fork with a WGPU backend. (https://github.com/KimHenrikOtte/candle/tree/wgpu_cleanup)
I created implementations for most of the internal operations. Currently only sorting and a few operations in candle-nn are not implemented.

In the end, I managed to create an image in the browser using a newly created wuerstchen wasm example. (I don't think this would be possible otherwise as there is a 4GB ram limit and the model is too big to be held by webassembly).

I used wgpu as the backend. So the implementations should also work native with Vulkan, Metal or DirectX.
All kernels are implemented by myself.
I tried my best to make the shaders more or less performant. But for some shaders (e.g. matmul) it is quite hard to get the best performance in all scenarios.

Feature Support Table

Feature	Support Status	Notes
Data Types
f32	✅ Supported
u32	✅ Supported
u8	⚠️ Only Output of Cmp	*Only f32, I32 and U32 are available in a webGpu shader
i64	⚠️ Supported Native
f64	⚠️ Supported Native
f16	⚠️ Only in Quantized Matrices
bf16	❌ Not Supported
Operations		All operations support non-contiguous arrays
Unary Operations	✅ Supported
Binary Operations	✅ Supported
MatMul	✅ Supported
Reduce Operations	✅ Supported	Sum, Min, Max, (ArgMax, ArgMin works only if continues Dimensions are reduced)
Conv2d	✅ Supported
Conv2dTranspose	✅ Supported
Conv1d	✅ Supported
Conv1dTranspose	✅ Supported
Index Select	✅ Supported
Where_cond	✅ Supported
Pool2dMax	✅ Supported
Pool2dAvg	✅ Supported
Upsample	✅ Supported
Gather	✅ Supported
Scatter_add	✅ Supported
Index_add	✅ Supported
Quantized Matrices	✅ Supported
Not Implemented
ArgSort	❌ Not Implemented

[sluijs]

@LaurentMazare any chance that the proposed backend/webgpu kernels by @KimHenrikOtte can become integrated at some point?