423 lines
21 KiB
Markdown
423 lines
21 KiB
Markdown
# candle
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[](https://discord.gg/hugging-face-879548962464493619)
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[](https://crates.io/crates/candle-core)
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[](https://docs.rs/candle-core)
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Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support)
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and ease of use. Try our online demos:
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[whisper](https://huggingface.co/spaces/lmz/candle-whisper),
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[LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2),
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[T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm),
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[yolo](https://huggingface.co/spaces/lmz/candle-yolo),
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[Segment
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Anything](https://huggingface.co/spaces/radames/candle-segment-anything-wasm).
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## Get started
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Make sure that you have [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) correctly installed as described in [**Installation**](https://huggingface.github.io/candle/guide/installation.html).
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Let's see how to run a simple matrix multiplication.
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Write the following to your `myapp/src/main.rs` file:
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```rust
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use candle_core::{Device, Tensor};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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let device = Device::Cpu;
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let a = Tensor::randn(0f32, 1., (2, 3), &device)?;
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let b = Tensor::randn(0f32, 1., (3, 4), &device)?;
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let c = a.matmul(&b)?;
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println!("{c}");
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Ok(())
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}
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```
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`cargo run` should display a tensor of shape `Tensor[[2, 4], f32]`.
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Having installed `candle` with Cuda support, simply define the `device` to be on GPU:
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```diff
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- let device = Device::Cpu;
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+ let device = Device::new_cuda(0)?;
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```
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For more advanced examples, please have a look at the following section.
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## Check out our examples
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These online demos run entirely in your browser:
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- [yolo](https://huggingface.co/spaces/lmz/candle-yolo): pose estimation and
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object recognition.
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- [whisper](https://huggingface.co/spaces/lmz/candle-whisper): speech recognition.
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- [LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2): text generation.
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- [T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm): text generation.
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- [Phi-1.5, and Phi-2](https://huggingface.co/spaces/radames/Candle-Phi-1.5-Wasm): text generation.
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- [Segment Anything Model](https://huggingface.co/spaces/radames/candle-segment-anything-wasm): Image segmentation.
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- [BLIP](https://huggingface.co/spaces/radames/Candle-BLIP-Image-Captioning): image captioning.
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We also provide a some command line based examples using state of the art models:
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- [LLaMA v1, v2, and v3](./candle-examples/examples/llama/): general LLM, includes
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the SOLAR-10.7B variant.
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- [Falcon](./candle-examples/examples/falcon/): general LLM.
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- [Codegeex4](./candle-examples/examples/codegeex4-9b/): Code completion,code interpreter,web search,fuction calling,repository-level
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- [GLM4](./candle-examples/examples/glm4/): Open Multilingual Multimodal Chat LMs by THUDM
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- [Gemma v1 and v2](./candle-examples/examples/gemma/): 2b and 7b+/9b general LLMs from Google Deepmind.
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- [RecurrentGemma](./candle-examples/examples/recurrent-gemma/): 2b and 7b
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Griffin based models from Google that mix attention with a RNN like state.
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- [Phi-1, Phi-1.5, Phi-2, and Phi-3](./candle-examples/examples/phi/): 1.3b,
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2.7b, and 3.8b general LLMs with performance on par with 7b models.
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- [StableLM-3B-4E1T](./candle-examples/examples/stable-lm/): a 3b general LLM
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pre-trained on 1T tokens of English and code datasets. Also supports
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StableLM-2, a 1.6b LLM trained on 2T tokens, as well as the code variants.
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- [Mamba](./candle-examples/examples/mamba/): an inference only
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implementation of the Mamba state space model.
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- [Mistral7b-v0.1](./candle-examples/examples/mistral/): a 7b general LLM with
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better performance than all publicly available 13b models as of 2023-09-28.
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- [Mixtral8x7b-v0.1](./candle-examples/examples/mixtral/): a sparse mixture of
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experts 8x7b general LLM with better performance than a Llama 2 70B model with
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much faster inference.
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- [StarCoder](./candle-examples/examples/bigcode/) and
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[StarCoder2](./candle-examples/examples/starcoder2/): LLM specialized to code generation.
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- [Qwen1.5](./candle-examples/examples/qwen/): Bilingual (English/Chinese) LLMs.
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- [RWKV v5 and v6](./candle-examples/examples/rwkv/): An RNN with transformer level LLM
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performance.
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- [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
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- [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
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(English/Chinese) general LLMs with 6b and 34b parameters.
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- [Quantized LLaMA](./candle-examples/examples/quantized/): quantized version of
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the LLaMA model using the same quantization techniques as
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[llama.cpp](https://github.com/ggerganov/llama.cpp).
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<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/quantized/assets/aoc.gif" width="600">
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- [Stable Diffusion](./candle-examples/examples/stable-diffusion/): text to
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image generative model, support for the 1.5, 2.1, SDXL 1.0 and Turbo versions.
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<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg" width="200">
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- [Wuerstchen](./candle-examples/examples/wuerstchen/): another text to
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image generative model.
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<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/wuerstchen/assets/cat.jpg" width="200">
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- [yolo-v3](./candle-examples/examples/yolo-v3/) and
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[yolo-v8](./candle-examples/examples/yolo-v8/): object detection and pose
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estimation models.
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<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.od.jpg" width="200"><img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.pose.jpg" width="200">
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- [segment-anything](./candle-examples/examples/segment-anything/): image
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segmentation model with prompt.
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<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/sam_merged.jpg" width="200">
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- [SegFormer](./candle-examples/examples/segformer/): transformer based semantic segmentation model.
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- [Whisper](./candle-examples/examples/whisper/): speech recognition model.
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- [EnCodec](./candle-examples/examples/encodec/): high-quality audio compression
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model using residual vector quantization.
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- [MetaVoice](./candle-examples/examples/metavoice/): foundational model for
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text-to-speech.
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- [Parler-TTS](./candle-examples/examples/parler-tts/): large text-to-speech
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model.
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- [T5](./candle-examples/examples/t5), [Bert](./candle-examples/examples/bert/),
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[JinaBert](./candle-examples/examples/jina-bert/) : useful for sentence embeddings.
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- [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
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using self-supervision (can be used for imagenet classification, depth
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evaluation, segmentation).
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- [VGG](./candle-examples/examples/vgg/),
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[RepVGG](./candle-examples/examples/repvgg): computer vision models.
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- [BLIP](./candle-examples/examples/blip/): image to text model, can be used to
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generate captions for an image.
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- [CLIP](./candle-examples/examples/clip/): multi-model vision and language
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model.
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- [TrOCR](./candle-examples/examples/trocr/): a transformer OCR model, with
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dedicated submodels for hand-writing and printed recognition.
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- [Marian-MT](./candle-examples/examples/marian-mt/): neural machine translation
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model, generates the translated text from the input text.
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- [Moondream](./candle-examples/examples/moondream/): tiny computer-vision model
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that can answer real-world questions about images.
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Run them using commands like:
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```
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cargo run --example quantized --release
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```
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In order to use **CUDA** add `--features cuda` to the example command line. If
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you have cuDNN installed, use `--features cudnn` for even more speedups.
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There are also some wasm examples for whisper and
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[llama2.c](https://github.com/karpathy/llama2.c). You can either build them with
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`trunk` or try them online:
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[whisper](https://huggingface.co/spaces/lmz/candle-whisper),
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[llama2](https://huggingface.co/spaces/lmz/candle-llama2),
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[T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm),
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[Phi-1.5, and Phi-2](https://huggingface.co/spaces/radames/Candle-Phi-1.5-Wasm),
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[Segment Anything Model](https://huggingface.co/spaces/radames/candle-segment-anything-wasm).
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For LLaMA2, run the following command to retrieve the weight files and start a
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test server:
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```bash
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cd candle-wasm-examples/llama2-c
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wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/model.bin
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wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/tokenizer.json
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trunk serve --release --port 8081
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```
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And then head over to
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[http://localhost:8081/](http://localhost:8081/).
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<!--- ANCHOR: useful_libraries --->
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## Useful External Resources
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- [`candle-tutorial`](https://github.com/ToluClassics/candle-tutorial): A
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very detailed tutorial showing how to convert a PyTorch model to Candle.
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- [`candle-lora`](https://github.com/EricLBuehler/candle-lora): Efficient and
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ergonomic LoRA implementation for Candle. `candle-lora` has
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out-of-the-box LoRA support for many models from Candle, which can be found
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[here](https://github.com/EricLBuehler/candle-lora/tree/master/candle-lora-transformers/examples).
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- [`optimisers`](https://github.com/KGrewal1/optimisers): A collection of optimisers
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including SGD with momentum, AdaGrad, AdaDelta, AdaMax, NAdam, RAdam, and RMSprop.
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- [`candle-vllm`](https://github.com/EricLBuehler/candle-vllm): Efficient platform for inference and
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serving local LLMs including an OpenAI compatible API server.
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- [`candle-ext`](https://github.com/mokeyish/candle-ext): An extension library to Candle that provides PyTorch functions not currently available in Candle.
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- [`candle-coursera-ml`](https://github.com/vishpat/candle-coursera-ml): Implementation of ML algorithms from Coursera's [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction) course.
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- [`kalosm`](https://github.com/floneum/floneum/tree/master/interfaces/kalosm): A multi-modal meta-framework in Rust for interfacing with local pre-trained models with support for controlled generation, custom samplers, in-memory vector databases, audio transcription, and more.
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- [`candle-sampling`](https://github.com/EricLBuehler/candle-sampling): Sampling techniques for Candle.
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- [`gpt-from-scratch-rs`](https://github.com/jeroenvlek/gpt-from-scratch-rs): A port of Andrej Karpathy's _Let's build GPT_ tutorial on YouTube showcasing the Candle API on a toy problem.
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- [`candle-einops`](https://github.com/tomsanbear/candle-einops): A pure rust implementation of the python [einops](https://github.com/arogozhnikov/einops) library.
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If you have an addition to this list, please submit a pull request.
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<!--- ANCHOR_END: useful_libraries --->
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<!--- ANCHOR: features --->
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## Features
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- Simple syntax, looks and feels like PyTorch.
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- Model training.
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- Embed user-defined ops/kernels, such as [flash-attention v2](https://github.com/huggingface/candle/blob/89ba005962495f2bfbda286e185e9c3c7f5300a3/candle-flash-attn/src/lib.rs#L152).
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- Backends.
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- Optimized CPU backend with optional MKL support for x86 and Accelerate for macs.
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- CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
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- WASM support, run your models in a browser.
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- Included models.
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- Language Models.
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- LLaMA v1, v2, and v3 with variants such as SOLAR-10.7B.
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- Falcon.
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- StarCoder, StarCoder2.
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- Phi 1, 1.5, 2, and 3.
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- Mamba, Minimal Mamba
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- Gemma v1 2b and 7b+, v2 2b and 9b.
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- Mistral 7b v0.1.
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- Mixtral 8x7b v0.1.
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- StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.
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- Replit-code-v1.5-3B.
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- Bert.
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- Yi-6B and Yi-34B.
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- Qwen1.5, Qwen1.5 MoE.
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- RWKV v5 and v6.
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- Quantized LLMs.
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- Llama 7b, 13b, 70b, as well as the chat and code variants.
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- Mistral 7b, and 7b instruct.
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- Mixtral 8x7b.
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- Zephyr 7b a and b (Mistral-7b based).
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- OpenChat 3.5 (Mistral-7b based).
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- Text to text.
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- T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
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- Marian MT (Machine Translation).
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- Text to image.
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- Stable Diffusion v1.5, v2.1, XL v1.0.
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- Wurstchen v2.
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- Image to text.
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- BLIP.
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- TrOCR.
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- Audio.
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- Whisper, multi-lingual speech-to-text.
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- EnCodec, audio compression model.
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- MetaVoice-1B, text-to-speech model.
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- Parler-TTS, text-to-speech model.
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- Computer Vision Models.
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- DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT,
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ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4, Hiera, FastViT.
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- yolo-v3, yolo-v8.
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- Segment-Anything Model (SAM).
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- SegFormer.
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- File formats: load models from safetensors, npz, ggml, or PyTorch files.
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- Serverless (on CPU), small and fast deployments.
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- Quantization support using the llama.cpp quantized types.
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<!--- ANCHOR_END: features --->
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## How to use
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<!--- ANCHOR: cheatsheet --->
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Cheatsheet:
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| | Using PyTorch | Using Candle |
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|------------|------------------------------------------|------------------------------------------------------------------|
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| Creation | `torch.Tensor([[1, 2], [3, 4]])` | `Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)?` |
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| Creation | `torch.zeros((2, 2))` | `Tensor::zeros((2, 2), DType::F32, &Device::Cpu)?` |
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| Indexing | `tensor[:, :4]` | `tensor.i((.., ..4))?` |
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| Operations | `tensor.view((2, 2))` | `tensor.reshape((2, 2))?` |
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| Operations | `a.matmul(b)` | `a.matmul(&b)?` |
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| Arithmetic | `a + b` | `&a + &b` |
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| Device | `tensor.to(device="cuda")` | `tensor.to_device(&Device::new_cuda(0)?)?` |
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| Dtype | `tensor.to(dtype=torch.float16)` | `tensor.to_dtype(&DType::F16)?` |
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| Saving | `torch.save({"A": A}, "model.bin")` | `candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")?` |
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| Loading | `weights = torch.load("model.bin")` | `candle::safetensors::load("model.safetensors", &device)` |
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<!--- ANCHOR_END: cheatsheet --->
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## Structure
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- [candle-core](./candle-core): Core ops, devices, and `Tensor` struct definition
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- [candle-nn](./candle-nn/): Tools to build real models
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- [candle-examples](./candle-examples/): Examples of using the library in realistic settings
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- [candle-kernels](./candle-kernels/): CUDA custom kernels
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- [candle-datasets](./candle-datasets/): Datasets and data loaders.
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- [candle-transformers](./candle-transformers): transformers-related utilities.
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- [candle-flash-attn](./candle-flash-attn): Flash attention v2 layer.
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- [candle-onnx](./candle-onnx/): ONNX model evaluation.
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## FAQ
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### Why should I use Candle?
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Candle's core goal is to *make serverless inference possible*. Full machine learning frameworks like PyTorch
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are very large, which makes creating instances on a cluster slow. Candle allows deployment of lightweight
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binaries.
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Secondly, Candle lets you *remove Python* from production workloads. Python overhead can seriously hurt performance,
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and the [GIL](https://www.backblaze.com/blog/the-python-gil-past-present-and-future/) is a notorious source of headaches.
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Finally, Rust is cool! A lot of the HF ecosystem already has Rust crates, like [safetensors](https://github.com/huggingface/safetensors) and [tokenizers](https://github.com/huggingface/tokenizers).
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### Other ML frameworks
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- [dfdx](https://github.com/coreylowman/dfdx) is a formidable crate, with shapes being included
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in types. This prevents a lot of headaches by getting the compiler to complain about shape mismatches right off the bat.
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However, we found that some features still require nightly, and writing code can be a bit daunting for non rust experts.
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We're leveraging and contributing to other core crates for the runtime so hopefully both crates can benefit from each
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other.
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- [burn](https://github.com/burn-rs/burn) is a general crate that can leverage multiple backends so you can choose the best
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engine for your workload.
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- [tch-rs](https://github.com/LaurentMazare/tch-rs.git) Bindings to the torch library in Rust. Extremely versatile, but they
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bring in the entire torch library into the runtime. The main contributor of `tch-rs` is also involved in the development
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of `candle`.
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### Common Errors
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#### Missing symbols when compiling with the mkl feature.
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If you get some missing symbols when compiling binaries/tests using the mkl
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or accelerate features, e.g. for mkl you get:
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```
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= note: /usr/bin/ld: (....o): in function `blas::sgemm':
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.../blas-0.22.0/src/lib.rs:1944: undefined reference to `sgemm_' collect2: error: ld returned 1 exit status
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= note: some `extern` functions couldn't be found; some native libraries may need to be installed or have their path specified
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= note: use the `-l` flag to specify native libraries to link
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= note: use the `cargo:rustc-link-lib` directive to specify the native libraries to link with Cargo
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```
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or for accelerate:
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```
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Undefined symbols for architecture arm64:
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"_dgemm_", referenced from:
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candle_core::accelerate::dgemm::h1b71a038552bcabe in libcandle_core...
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"_sgemm_", referenced from:
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candle_core::accelerate::sgemm::h2cf21c592cba3c47 in libcandle_core...
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ld: symbol(s) not found for architecture arm64
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```
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This is likely due to a missing linker flag that was needed to enable the mkl library. You
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can try adding the following for mkl at the top of your binary:
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```rust
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extern crate intel_mkl_src;
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```
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or for accelerate:
|
||
```rust
|
||
extern crate accelerate_src;
|
||
```
|
||
|
||
#### Cannot run the LLaMA examples: access to source requires login credentials
|
||
|
||
```
|
||
Error: request error: https://huggingface.co/meta-llama/Llama-2-7b-hf/resolve/main/tokenizer.json: status code 401
|
||
```
|
||
|
||
This is likely because you're not permissioned for the LLaMA-v2 model. To fix
|
||
this, you have to register on the huggingface-hub, accept the [LLaMA-v2 model
|
||
conditions](https://huggingface.co/meta-llama/Llama-2-7b-hf), and set up your
|
||
authentication token. See issue
|
||
[#350](https://github.com/huggingface/candle/issues/350) for more details.
|
||
|
||
#### Missing cute/cutlass headers when compiling flash-attn
|
||
|
||
```
|
||
In file included from kernels/flash_fwd_launch_template.h:11:0,
|
||
from kernels/flash_fwd_hdim224_fp16_sm80.cu:5:
|
||
kernels/flash_fwd_kernel.h:8:10: fatal error: cute/algorithm/copy.hpp: No such file or directory
|
||
#include <cute/algorithm/copy.hpp>
|
||
^~~~~~~~~~~~~~~~~~~~~~~~~
|
||
compilation terminated.
|
||
Error: nvcc error while compiling:
|
||
```
|
||
[cutlass](https://github.com/NVIDIA/cutlass) is provided as a git submodule so you may want to run the following command to check it in properly.
|
||
```bash
|
||
git submodule update --init
|
||
```
|
||
|
||
#### Compiling with flash-attention fails
|
||
|
||
```
|
||
/usr/include/c++/11/bits/std_function.h:530:146: error: parameter packs not expanded with ‘...’:
|
||
```
|
||
|
||
This is a bug in gcc-11 triggered by the Cuda compiler. To fix this, install a different, supported gcc version - for example gcc-10, and specify the path to the compiler in the NVCC_CCBIN environment variable.
|
||
```
|
||
env NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...
|
||
```
|
||
|
||
#### Linking error on windows when running rustdoc or mdbook tests
|
||
|
||
```
|
||
Couldn't compile the test.
|
||
---- .\candle-book\src\inference\hub.md - Using_the_hub::Using_in_a_real_model_ (line 50) stdout ----
|
||
error: linking with `link.exe` failed: exit code: 1181
|
||
//very long chain of linking
|
||
= note: LINK : fatal error LNK1181: cannot open input file 'windows.0.48.5.lib'
|
||
```
|
||
|
||
Make sure you link all native libraries that might be located outside a project target, e.g., to run mdbook tests, you should run:
|
||
|
||
```
|
||
mdbook test candle-book -L .\target\debug\deps\ `
|
||
-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.42.2\lib `
|
||
-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.48.5\lib
|
||
```
|
||
|
||
#### Extremely slow model load time with WSL
|
||
|
||
This may be caused by the models being loaded from `/mnt/c`, more details on
|
||
[stackoverflow](https://stackoverflow.com/questions/68972448/why-is-wsl-extremely-slow-when-compared-with-native-windows-npm-yarn-processing).
|
||
|
||
#### Tracking down errors
|
||
|
||
You can set `RUST_BACKTRACE=1` to be provided with backtraces when a candle
|
||
error is generated.
|
||
|
||
#### CudaRC error
|
||
|
||
If you encounter an error like this one `called `Result::unwrap()` on an `Err` value: LoadLibraryExW { source: Os { code: 126, kind: Uncategorized, message: "The specified module could not be found." } }` on windows. To fix copy and rename these 3 files (make sure they are in path). The paths depend on your cuda version.
|
||
`c:\Windows\System32\nvcuda.dll` -> `cuda.dll`
|
||
`c:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin\cublas64_12.dll` -> `cublas.dll`
|
||
`c:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin\curand64_10.dll` -> `curand.dll`
|