basilisk

tiny LLaMA inference server (also provides an endpoint for embedding with SentenceTransformers)

setup

1. follow the original ExLlama readme, other than installing Flask and SentenceTransformers
2. create a config.json (see config_example.json)
   • shared_secret is optional; if set, the Authorization header of all requests will be checked against this
   • embedding_model enables the /basilisk/embed endpoint. select a model from this list. setting it to null disables the endpoint (it will always return 501)
3. just run basilisk.py

notes

• this doesnt take advantage of ExLlama's excellent caching stuff because it's intended for use in multiple channels at the same time
• hack: EOS is encoded as control characters ETX (\x03) and converted to its actual representations after tokenizing
  • you can use the special_convert parameter to lazily replace literal </s> with EOS

endpoints

• GET /basilisk/ping - intended to verify that the client app's config is correct, and that the basilisk server is reachable.

  simply returns the text pong.

• GET /basilisk/config - returns a JSON object containing most config keys, not including paths and secrets.

• POST /basilisk/embed - computes an embedding using SentenceTransformers.

  accepts a JSON object in the form:

  interface basiliskEmbedArguments {
      prompt: string
  }

  returns a JSON object in the form:

  interface basiliskEmbedResult {
      embedding: number[],
  
      // the model used to compute the embedding
      model: string,
      // number of dimensions in the embedding
      dimensions: number
  }

• POST /basilisk/tokenize - tokenizes a string; primarily useful for counting tokens.

  accepts a JSON object in the form:

  interface basiliskTokenizeArguments {
      prompt: string
  }

  returns a JSON object in the form:

  interface basiliskTokenizeResult {
      tokens: number[],
  
      // the string representations of the tokens
      fragments: string[]
  }

• POST /basilisk/infer - generates text from a prompt (hard-limited to 2048 tokens for now).

  accepts a JSON object in the form:

  interface basiliskInferenceArguments {
      prompt: string,
  
      // the usual sampling arguments
      temperature?: number,
      top_k?: number,
      top_p?: number,
      min_p?: number,
  
      // stopping criteria
      min_length?: number,
      max_new_tokens?: number,
      stopping_strings?: string[],
  
      // base presence(?) penalty 
      token_repetition_penalty_max?: number,
      // maintain penalty over this many tokens
      token_repetition_penalty_sustain?: number,
      // after sustain period, decay over this many tokens
      token_repetition_penalty_decay?: number,
  
      // providing an array of token sequences from previous inferences here
      // penalizes repetitive patterns utilizing the same token at the same
      // position as previous inferences
      positional_repeat_inhibit?: number[][],
      // base positional repeat penalty
      positional_repetition_penalty?: number,
  
      // convert <s> to STX and </s> to ETX?
      // you should avoid this if possible by using STX and ETX directly
      special_convert?: boolean
  }

  returns a JSON object in the form:

  interface basiliskInferenceResult {
      text: string,
      tokens: number[]
  }

ExLlama

A rewrite of the HF transformers implementation of Llama with the following goals, among others:

• Designed for use with quantized weights
• Fast and memory-efficient inference (not just attention)
• Mapping across multiple devices
• Built-in (multi) LoRA support
• Companion library of funky sampling functions

Disclaimer: This is currently a preview of a work in progress. Or maybe a proof of concept. Either way any part of it is subject to change.

Hardware/software requirements

I am developing on an RTX 4090 and an RTX 3090-Ti. Both cards support the CUDA kernel, but there might be incompatibilities with older cards. I have no way of testing that right now.

Dependencies

This list might be incomplete:

• torch tested on 2.1.0 (nightly) with cu118
• safetensors 0.3.1
• sentencepiece
• ninja
• flask (only for the web UI)

Linux/WSL prerequisites

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118

Windows prerequisites

To run on Windows (without WSL):

1. Install MSVC 2022. You can choose to install the whole Visual Studio 2022 IDE, or alternatively just the Build Tools for Visual Studio 2022 package (make sure Desktop development with C++ is ticked in the installer), it doesn't really matter which.
2. Install the appropriate version of PyTorch, choosing one of the CUDA versions. I am developing on the nightly build, but the stable version should also work.
3. Install CUDA Toolkit, (11.7 and 11.8 both seem to work, just make sure to match PyTorch's Compute Platform version).
4. For best performance, enable Hardware Accelerated GPU Scheduling.

How to

Install dependencies, clone repo and run benchmark:

pip install safetensors sentencepiece ninja

git clone https://github.com/turboderp/exllama
cd exllama

python test_benchmark_inference.py -d <path_to_model_files> -p -ppl

The CUDA extension is loaded at runtime so there's no need to install it separately. It will be compiled on the first run and cached to ~/.cache/torch_extensions/ which could take a little while. If nothing happens at first, give it a minute to compile.

Chatbot example:

python test_chatbot.py -d <path_to_model_files> -un "Jeff" -p prompt_chatbort.txt

Web UI

I made a simple web UI for it. Like the rest of the project, it's a work in progress. Don't look at the JavaScript, it was mostly written by ChatGPT and it will haunt your dreams. But it sort of works, and it's kinda fun, especially multibot mode:

To run it:

pip install flask

python webui/app.py -d <path_to_model_files>

Note that sessions are stored in ~/exllama_sessions/.

Results so far

New implementation

Model	Size	grpsz	act	Seq. len.	VRAM	Prompt	Best	Worst	Ppl
Llama	7B	128	no	2,048 t	5,194 MB	10,460 t/s	160 t/s	133 t/s	6.45
Llama	13B	128	no	2,048 t	9,127 MB	5,831 t/s	97 t/s	83 t/s	5.60
Llama	30B	128	no	2,048 t	20,795 MB	2,481 t/s	46 t/s	39 t/s	4.60
Llama	30B	128	yes	2,048 t	20,795 MB	2,343 t/s	44 t/s	37 t/s	4.55
Llama	30B	32	yes	1,550 t 1	21,486 MB	2,308 t/s	40 t/s	36 t/s	4.52
Koala	13B	128	yes	2,048 t	9,127 MB	5,529 t/s	86 t/s	79 t/s	6.73
WizardLM	30B	-	no 2	2,048 t	20,199 MB	2,313 t/s	44 t/s	39 t/s	5.75

¹ Can not achieve full sequence length without OoM (yet)
² Not quite sure if this is act-order or not. Weights have no group index, at least

All tests done on stock RTX 4090 / 12900K, running with a desktop environment, with a few other apps also using VRAM.

"Prompt" speed is inference over the sequence length listed minus 128 tokens. "Worst" is the average speed for the last 128 tokens of the full context (worst case) and "Best" lists the speed for the first 128 tokens in an empty sequence (best case.)

VRAM usage is as reported by PyTorch and does not include PyTorch's own overhead (CUDA kernels, internal buffers etc.) This is somewhat unpredictable anyway. Best bet is to just optimize VRAM usage by the model, probably aiming for 20 GB on a 24 GB GPU to ensure there is room for a desktop environment and all of Torch's internals.

Perplexity is measured only to verify that the models are working. The dataset used is a particular, small sample from WikiText, so scores are not necessarily comparable to other Llama benchmarks.

Dual GPU results

Since many seem to be interested in running 65B models, I can confirm that this works with two 24 GB GPUs. The following benchmarks are from a 4090 + 3090-Ti with -gs 17.2,24:

Model	Size	groupsize	act	Seq. len.	VRAM	Prompt	Best	Worst	Ppl
Llama	65B	128	yes	2,048 t	39,804 MB	990 t/s	20 t/s	18 t/s	4.20
Llama	65B	32	yes	2,048 t	43,424 MB	976 t/s	17 t/s	16 t/s	4.11

Testing long sequences

The following tests were all done on 30B/65B, 4bit 128g with various settings, just to test the max sequence length and get a sense of what can be achieved with different or multiple GPUs right now. Llama goes incoherent generating past 2048 tokens anyway, but with some fine-tuning, who knows? Note that these tests were run a while ago and the speeds are no longer current.

	Size	Seq. len.	VRAM	Long seq.	Ind.
4090/24GB	30B	2,516 t	22,145 MB	1140 t/s	28 t/s
4090/24GB + 3070Ti/8GB	30B	3,932 t	22,055 MB + 7,377 MB	840 t/s	22 t/s
A6000/48GB (headless)	30B	9,032 t	46,863 MB	645 t/s	12 t/s
A100/80GB (headless)	65B	9,520 t	79,009 MB	650 t/s	9 t/s

Todo

Moved the todo list here.

Compatibility

I downloaded a whole bunch of GPTQ models to test compatibility. Here is the list of models confirmed to be working right now.

Recent updates

2023-05-22: Added option to auto-split layers across multiple GPUs based on VRAM allocation.

2023-05-22: Added option to dequantize layers at load-time which should speed up inference, but it turns out Torch's fp16 matmul is actually slower than the quantized matmul. Maybe bandwidth is the only bottleneck right now? Need to experiment some more.

2023-05-24: Downloaded a bunch of models from HF and set up a test script. Should be a good sampling of the most popular finetunes right now. I'll add more to the list as I come across them. They all seem to be working.

2023-05-24: Added fused rotary embeddings and some minor optimizations. 13% faster on 7B, 9% on 13B. Small improvement on larger models. Added best-case scores to benchmark results and some clarification. For easier comparisons to other implementations, or whatever.

2023-05-27: Better memory management in CUDA. Introduced auto switch between Torch's SDP backend and regular matmul attention with some tweaks. Finished CUDA MLP. All in all about 10% faster with these updates.

2023-05-29: Web UI is almost up and running. Having to learn JavaScript, and it turns out I hate JavaScript. But ChatGPT is an incredible resource for learning new languages, I gotta say, so it's not as painful as it could have been. Anyway, in the process of working with the UI I discovered I've been measuring prompt speed incorrectly. Either Torch or CUDA or the GPU driver does some sort of caching or self-calibration or lazy initialization during the first pass through the model, so subsequent passes are actually way faster than what I've been recording. Doesn't do much for individual tokens, but benchmarks updated anyway. Closing in on 10k tokens/second for 7B. (!)

2023-06-02: Web UI is now in a fairly working state. Expect it to be a little scuffed in places. There will be a rewrite at some point to make the client-side code less seizure-inducing. It has multibot mode, chat rewind and editing features, sessions, and more. I'm going to build it out with support for instruct prompting and such, in time.

2024-06-04: Refactored a whole bunch to move more of the work into the extension, setting up for more tuning options to come soon and eventually auto tuning. Also optimized a little, for about a 5% speedup.

2024-06-06: Some minor optimizations. Also it should now compile the extension more easily and run more seamlessly on Windows.