This is an inference engine for the language model of RWKV implemented in pure WebGPU.
cargo run --release --example gen to generate 100 tokens and measure the time cost.cargo run --release --example chat to chat with the model.cargo run --release --example chat -- --model /path/to/model.Or you can download the pre-compiled binaries from the release page and run
bash
$ chat --model /path/to/model
To use in your own rust project, simply add web-rwkv = "0.2" as a dependency in your Cargo.toml.
Check examples on how to create the environment, the tokenizer and how to run the model.
Since version v0.2.4, the engine supports batched inference, i.e., inference of a batch of prompts (with different length) in parallel.
This is achieved by a modified WKV kernel.
When building the model, the user specifies token_chunk_size (default: 32, but for powerful GPUs this could be much higher), which is the maximum number of tokens the engine could process in one run call.
After creating the model, the user creates a ModelState with max_batch specified.
This means that there are max_batch slots that could consume the inputs in parallel.
Before calling run(), the user fills each slot with some tokens as prompt.
If a slot is empty, no inference will be run for it.
After calling run(), some (but may not be all) input tokens are consumed, and logits appears in their corresponding returned slots if the inference of that slot is finished during this run.
Since there are only token_chunk_size tokens are processed during each run() call, there may be none of logits appearing in the results.
You must download the model and put in assets/models before running if you are building from source.
You can now download the coverted models here.
You may download the official RWKV World series models from HuggingFace, and convert them via the provided convert_safetensors.py.
An already-converted 0.4B model can be found under assets/models.