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https://github.com/EricLBuehler/candle-vllm
Efficent platform for inference and serving local LLMs including an OpenAI compatible API server.
https://github.com/EricLBuehler/candle-vllm
Last synced: 5 days ago
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Efficent platform for inference and serving local LLMs including an OpenAI compatible API server.
- Host: GitHub
- URL: https://github.com/EricLBuehler/candle-vllm
- Owner: EricLBuehler
- License: mit
- Created: 2023-10-29T11:21:48.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-11-21T10:07:44.000Z (23 days ago)
- Last Synced: 2024-12-09T03:12:04.044Z (5 days ago)
- Language: Rust
- Size: 32.7 MB
- Stars: 272
- Watchers: 8
- Forks: 28
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://github.com/EricLBuehler/candle-vllm/actions/workflows/ci.yml)
Efficient, easy-to-use platform for inference and serving local LLMs including an OpenAI compatible API server.
## Features
- OpenAI compatible API server provided for serving LLMs.
- Highly extensible trait-based system to allow rapid implementation of new module pipelines,
- Streaming support in generation.
- Efficient management of key-value cache with PagedAttention.
- Continuous batching.
- `In-situ` quantization (and `In-situ` marlin format conversion)
- `GPTQ/Marlin` format quantization (4-bit)
## Develop Status
Currently, candle-vllm supports chat serving for the following models.
| Model ID | Model Type | Supported | Speed (A100, `BF16`) | Throughput (`BF16`, `bs=16`) | Quantized (A100, `Q4K` or `Marlin`) | Throughput (`GTPQ/Marlin`, `bs=16`) |
|--|--|--|--|--|--|--|
| #1 | **LLAMA** |✅|65 tks/s (LLaMa3.1 8B) | 553 tks/s (LLaMa3.1 8B) | 75 tks/s (LLaMa3.1 8B), **115 tks/s (LLaMa3.1 8B, Marlin)** |**968 tks/s (LLaMa3.1 8B)**|
| #2 | **Mistral** |✅|70 tks/s (7B)| 585 tks/s (7B) | 96 tks/s (7B), **115 tks/s (7B, Marlin)** |**981 tks/s (7B)**|
| #3 | **Phi (v1, v1.5, v2)** |✅|97 tks/s (2.7B, F32+BF16)|TBD|-|TBD|
| #4 | **Phi-3 （3.8B, 7B）** |✅|107 tks/s (3.8B)| 744 tks/s (3.8B)|135 tks/s (3.8B)|TBD|
| #5 | **Yi** |✅|75 tks/s (6B)| 566 tks/s (6B) | 105 tks/s (6B)|TBD|
| #6 | **StableLM** |✅|99 tks/s (3B)|TBD|-|TBD|
| #7 | BigCode/StarCode |TBD|TBD|TBD |-|TBD|
| #8 | ChatGLM |TBD|TBD|TBD |-|TBD|
| #9 | **QWen2 (1.8B, 7B)** |✅|148 tks/s (1.8B)|784 tks/s (1.8B) |-|TBD|
| #10 | **Google Gemma** |✅|130 tks/s (2B)|TBD |**73 tks/s (Gemma2-9B, Marlin)** |**587 tks/s (Gemma2-9B)**|
| #11 | Blip-large (Multimodal) |TBD|TBD|TBD |-|TBD|
| #12 | Moondream-2 (Multimodal LLM) |TBD|TBD|TBD |-|TBD|
## Demo Chat with candle-vllm (~110 tokens/s, LLaMa3.1 8B, 4-bit Marlin, on A100)
https://github.com/user-attachments/assets/66b5b90e-e2ca-4f0b-82d7-99aa9f85568c
## Usage
See [this folder](examples/) for some examples.
### Step 1: Run Candle-VLLM service (assume llama2-7b model weights downloaded)
```
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
sudo apt install libssl-dev
sudo apt install pkg-config
git clone [email protected]:EricLBuehler/candle-vllm.git
cd candle-vllm
cargo run --release -- --port 2000 --weight-path /home/llama2_7b/ llama
```
You may also run specific model using huggingface model-id, e.g.,
```
cargo run --release -- --port 2000 --model-id meta-llama/Llama-2-7b-chat-hf llama
```
Run latest LLaMa3.1 using local weights
```
cargo run --release -- --port 2000 --weight-path /home/Meta-Llama-3.1-8B-Instruct/ llama3 --temperature 0. --penalty 1.0
```
__Refer to Marlin quantization below for running quantized GPTQ models.__
### Step 2:
#### Option 1: Chat with ChatUI (recommended)
Install ChatUI and its dependencies:
```
git clone [email protected]:guoqingbao/candle-vllm-demo.git
cd candle-vllm-demo
apt install npm #install npm if needed
npm install n -g #update node js if needed
n stable #update node js if needed
npm i -g pnpm #install pnpm manager
pnpm install #install ChatUI dependencies
```
Launching the ChatUI:
```
pnpm run dev # run the ChatUI
```
#### Trouble shooting for Nodejs error
`ENOSPC: System limit for number of file watchers reached`
```
echo fs.inotify.max_user_watches=524288 | sudo tee -a /etc/sysctl.conf && sudo sysctl -p
```
#### Option 2: Chat completion request with HTTP post
``` shell
curl -X POST "http://127.0.0.1:2000/v1/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "llama7b",
"messages": [
{"role": "user", "content": "Explain how to best learn Rust."}
],
"temperature": 0.7,
"max_tokens": 128,
"stop": {"Single":""}
}'
```
Sample response:
```
{"id":"cmpl-53092967-c9cf-40e0-ae26-d7ac786d59e8","choices":[{"message":{"content":" Learning any programming language requires a combination of theory, practice, and dedication. Here are some steps and resources to help you learn Rust effectively:\n\n1. Start with the basics:\n\t* Understand the syntax and basic structure of Rust programs.\n\t* Learn about variables, data types, loops, and control structures.\n\t* Familiarize yourself with Rust's ownership system and borrowing mechanism.\n2. Read the Rust book:\n\t* The Rust book is an official resource that provides a comprehensive introduction to the language.\n\t* It covers topics such","role":"[INST]"},"finish_reason":"length","index":0,"logprobs":null}],"created":1718784498,"model":"llama7b","object":"chat.completion","usage":{"completion_tokens":129,"prompt_tokens":29,"total_tokens":158}}
```
#### Option 3: Chat completion with with openai package
In your terminal, install the `openai` Python package by running `pip install openai`. I use version `1.3.5`.
Then, create a new Python file and write the following code:
```python
import openai
openai.api_key = "EMPTY"
openai.base_url = "http://localhost:2000/v1/"
completion = openai.chat.completions.create(
model="llama",
messages=[
{
"role": "user",
"content": "Explain how to best learn Rust.",
},
],
max_tokens = 64,
)
print(completion.choices[0].message.content)
```
After the `candle-vllm` service is running, run the Python script and enjoy efficient inference with an OpenAI compatible API server!
## Batched requests
Install openai API first
```
python3 -m pip install openai
```
Run the benchmark test
``` shell
python3 examples/benchmark.py --batch 16 --max_tokens 1024
```
Refer to `examples/benchmark.py`
``` python
async def benchmark():
model = "mistral7b"
max_tokens = 1024
# 16 requests
prompts = ["Explain how to best learn Rust.",
"Please talk about deep learning in 100 words.",
"Do you know the capital city of China? Talk the details of you known.",
"Who is the best female actor in the world? Explain why.",
"How to dealing with depression?",
"How to make money in short time?",
"What is the future trend of large language model?",
"The famous tech companies in the world.",
"Explain how to best learn Rust.",
"Please talk about deep learning in 100 words.",
"Do you know the capital city of China? Talk the details of you known.",
"Who is the best female actor in the world? Explain why.",
"How to dealing with depression?",
"How to make money in short time?",
"What is the future trend of large language model?",
"The famous tech companies in the world."]
# send 16 chat requests at the same time
tasks: List[asyncio.Task] = []
for i in range(len(prompts)):
tasks.append(
asyncio.create_task(
chat_completion(model, max_tokens, prompts[i]))
)
# obtain the corresponding stream object for each request
outputs: List[Stream[ChatCompletionChunk]] = await asyncio.gather(*tasks)
# tasks for streaming chat responses
tasks_stream: List[asyncio.Task] = []
for i in range(len(outputs)):
tasks_stream.append(
asyncio.create_task(
stream_response(i, outputs[i]))
)
# gathering the response texts
outputs: List[(int, str)] = await asyncio.gather(*tasks_stream)
# print the results, you may find chat completion statistics in the backend server (i.e., candle-vllm)
for idx, output in outputs:
print("\n\n Response {}: \n\n {}".format(idx, output))
asyncio.run(benchmark())
```
## GPTQ/Marlin 4-bit quantization
Candle-vllm now supports GPTQ (Marlin kernel), you may supply the `quant` (marlin) parameter if you have `Marlin` format quantized weights, such as:
```
cargo run --release -- --port 2000 --dtype f16 --weight-path /home/Meta-Llama-3.1-8B-Instruct-GPTQ-INT4-Marlin/ llama3 --quant marlin --temperature 0. --penalty 1.
```
You may also use `AutoGPTQ` to transform a model to marlin format by loading the (quantized) model, supplying the `use_marlin=True` in `AutoGPTQ` and resaving it with "save_pretrained".
**Note:** only 4-bit GPTQ (marlin format) quantization supported at the moment, and the input data type should be `f16` (--dtype f16) or `bf16` (--dtype bf16). You need rename the transformed marlin weight to "model.safetensors" and copy the "tokenizer.json" from the source model folder.
## In-situ quantization (or in-situ marlin conversion)
Candle-vllm now supports in-situ quantization, allowing the transformation of default weights (F32/F16/BF16) or `4-bit GPTQ` weights into any GGML format (or `marlin format`) during model loading. This feature helps conserve GPU memory (or speedup inference performance through marlin kernel), making it more efficient for consumer-grade GPUs (e.g., RTX 4090). To use this feature, simply supply the quant parameter when running candle-vllm.
For unquantized models:
```
cargo run --release -- --port 2000 --weight-path /home/Meta-Llama-3.1-8B-Instruct/ llama3 --quant q4k
```
For quantized 4-bit GPTQ model:
```
cargo run --release -- --port 2000 --weight-path /home/mistral_7b-int4/ mistral --quant marlin
```
Options for `quant` parameters: ["q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "q2k", "q3k","q4k","q5k","q6k", "marlin"]
**Please note**:
1) It may takes few minutes to load F32/F16/BF16 models into quantized;
2) Marlin format in-situ conversion only support 4-bit GPTQ (with `sym=True`, `groupsize=128` or -1, `desc_act=False`).
## Usage Help
For general configuration help, run `cargo run -- --help`.
For model-specific help, run `cargo run -- --port 2000 --help`
For local model weights, run `cargo run --release -- --port 2000 --weight-path /home/llama2_7b/ llama`, change the path when needed.
`MODEL_TYPE` = ["llama", "llama3", "mistral", "phi2", "phi3", "qwen2", "gemma", "yi", "stable-lm"]
`WEIGHT_FILE_PATH` = Corresponding weight path for the given model type
```
cargo run --release -- --port 2000 --weight-path
```
or
`MODEL_ID` = Huggingface model id
```
cargo run --release -- --port 2000 --model-id
```
For kvcache configuration, set `kvcache_mem_cpu` and `kvcache_mem_gpu`, default 4GB CPU memory and 4GB GPU memory for kvcache.
For chat history settings, set `record_conversation` to `true` to let candle-vllm remember chat history. By `default`, candle-vllm `does not` record chat history; instead, the client sends both the messages and the contextual history to candle-vllm. If record_conversation is set to `true`, the client sends only new chat messages to candle-vllm, and candle-vllm is responsible for recording the previous chat messages. However, this approach requires per-session chat recording, which is not yet implemented, so the default approach `record_conversation=false` is recommended.
For chat streaming, the `stream` flag in chat request need to be set to `True`.
You may supply `penalty` and `temperature` to the model to **prevent potential repetitions**, for example:
```
cargo run --release -- --port 2000 --weight-path /home/mistral_7b/ mistral --repeat-last-n 64 --penalty 1.1 --temperature 0.7
```
`--max-gen-tokens` parameter is used to control the maximum output tokens per chat response. The value will be set to 1/5 of max_sequence_len by default.
For `consumer GPUs`, it is suggested to run the models under GGML formats (or Marlin format), e.g.,
```
cargo run --release -- --port 2000 --weight-path /home/Meta-Llama-3.1-8B-Instruct/ llama3 --quant q4k
```
where `quant` is one of ["q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "q2k", "q3k","q4k","q5k","q6k", "marlin"].
## Report issue
Installing `candle-vllm` is as simple as the following steps. If you have any problems, please create an
[issue](https://github.com/EricLBuehler/candle-lora/issues).
## Contributing
The following features are planned to be implemented, but contributions are especially welcome:
- Sampling methods:
- Beam search ([huggingface/candle#1319](https://github.com/huggingface/candle/issues/1319))
- More pipelines (from `candle-transformers`)
## Resources
- Python implementation: [`vllm-project`](https://github.com/vllm-project/vllm)
- [`vllm` paper](https://arxiv.org/abs/2309.06180)