Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support) and ease of use. Try our online demos: whisper, LLaMA2, yolo.
Make sure that you have candle-core correctly installed as described in Installation.
Let's see how to run a simple matrix multiplication.
Write the following to your myapp/src/main.rs file:
use candle_core::{Device, Tensor};
fn main() -> Result<(), Box<dyn std::error::Error>> {
let device = Device::Cpu;
let a = Tensor::randn(0f32, 1., (2, 3), &device)?;
let b = Tensor::randn(0f32, 1., (3, 4), &device)?;
let c = a.matmul(&b)?;
println!("{c}");
Ok(())
}cargo run should display a tensor of shape Tensor[[2, 4], f32].
Having installed candle with Cuda support, simply define the device to be on GPU:
- let device = Device::Cpu;
+ let device = Device::new_cuda(0)?;For more advanced examples, please have a look at the following section.
Check out our examples:
- Whisper: speech recognition model.
- LLaMA and LLaMA-v2: general LLM.
- Falcon: general LLM.
- Bert: useful for sentence embeddings.
- StarCoder: LLM specialized to code generation.
- Stable Diffusion: text to image generative model, support for the 1.5, 2.1, and SDXL 1.0 versions.
- DINOv2: computer vision model trained using self-supervision (can be used for imagenet classification, depth evaluation, segmentation).
- Quantized LLaMA: quantized version of the LLaMA model using the same quantization techniques as llama.cpp.
- yolo-v3 and yolo-v8: object detection and pose estimation models.
- segment-anything: image segmentation model with prompt. Run them using the following commands:
cargo run --example whisper --release
cargo run --example llama --release
cargo run --example falcon --release
cargo run --example bert --release
cargo run --example bigcode --release
cargo run --example stable-diffusion --release -- --prompt "a rusty robot holding a fire torch"
cargo run --example dinov2 --release -- --image path/to/myinput.jpg
cargo run --example quantized --release
cargo run --example yolo-v3 --release -- myimage.jpg
cargo run --example yolo-v8 --release -- myimage.jpg # for pose estimation, add --task pose
cargo run --example segment-anything --release -- --image myimage.jpg
In order to use CUDA add --features cuda to the example command line. If
you have cuDNN installed, use --features cudnn for even more speedups.
There are also some wasm examples for whisper and
llama2.c. You can either build them with
trunk or try them online:
whisper,
llama2.
For LLaMA2, run the following command to retrieve the weight files and start a test server:
cd candle-wasm-examples/llama2-c
wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/model.bin
wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/tokenizer.json
trunk serve --release --port 8081And then head over to http://localhost:8081/.
- Simple syntax, looks and feels like PyTorch.
- Model training.
- Embed user-defined ops/kernels, such as flash-attention v2.
- Backends.
- Optimized CPU backend with optional MKL support for x86 and Accelerate for macs.
- CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
- WASM support, run your models in a browser.
- Included models.
- LLMs: LLaMA v1 and v2, Falcon, StarCoder.
- Whisper (multi-lingual support).
- Stable Diffusion.
- Computer Vision: DINOv2, EfficientNet, yolo-v3, yolo-v8.
- File formats: load models from safetensors, npz, ggml, or PyTorch files.
- Serverless (on CPU), small and fast deployments.
- Quantization support using the llama.cpp quantized types.
Cheatsheet:
| Using PyTorch | Using Candle | |
|---|---|---|
| Creation | torch.Tensor([[1, 2], [3, 4]]) |
Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)? |
| Creation | torch.zeros((2, 2)) |
Tensor::zeros((2, 2), DType::F32, &Device::Cpu)? |
| Indexing | tensor[:, :4] |
tensor.i((.., ..4))? |
| Operations | tensor.view((2, 2)) |
tensor.reshape((2, 2))? |
| Operations | a.matmul(b) |
a.matmul(&b)? |
| Arithmetic | a + b |
&a + &b |
| Device | tensor.to(device="cuda") |
tensor.to_device(&Device::new_cuda(0)?)? |
| Dtype | tensor.to(dtype=torch.float16) |
tensor.to_dtype(&DType::F16)? |
| Saving | torch.save({"A": A}, "model.bin") |
candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")? |
| Loading | weights = torch.load("model.bin") |
candle::safetensors::load("model.safetensors", &device) |
- candle-core: Core ops, devices, and
Tensorstruct definition - candle-nn: Tools to build real models
- candle-examples: Examples of using the library in realistic settings
- candle-kernels: CUDA custom kernels
- candle-datasets: Datasets and data loaders.
- candle-transformers: transformers-related utilities.
- candle-flash-attn: Flash attention v2 layer.
Candle's core goal is to make serverless inference possible. Full machine learning frameworks like PyTorch are very large, which makes creating instances on a cluster slow. Candle allows deployment of lightweight binaries.
Secondly, Candle lets you remove Python from production workloads. Python overhead can seriously hurt performance, and the GIL is a notorious source of headaches.
Finally, Rust is cool! A lot of the HF ecosystem already has Rust crates, like safetensors and tokenizers.
-
dfdx is a formidable crate, with shapes being included in types. This prevents a lot of headaches by getting the compiler to complain about shape mismatches right off the bat. However, we found that some features still require nightly, and writing code can be a bit daunting for non rust experts.
We're leveraging and contributing to other core crates for the runtime so hopefully both crates can benefit from each other.
-
burn is a general crate that can leverage multiple backends so you can choose the best engine for your workload.
-
tch-rs Bindings to the torch library in Rust. Extremely versatile, but they bring in the entire torch library into the runtime. The main contributor of
tch-rsis also involved in the development ofcandle.
If you get some missing symbols when compiling binaries/tests using the mkl or accelerate features, e.g. for mkl you get:
= note: /usr/bin/ld: (....o): in function `blas::sgemm':
.../blas-0.22.0/src/lib.rs:1944: undefined reference to `sgemm_' collect2: error: ld returned 1 exit status
= note: some `extern` functions couldn't be found; some native libraries may need to be installed or have their path specified
= note: use the `-l` flag to specify native libraries to link
= note: use the `cargo:rustc-link-lib` directive to specify the native libraries to link with Cargo
or for accelerate:
Undefined symbols for architecture arm64:
"_dgemm_", referenced from:
candle_core::accelerate::dgemm::h1b71a038552bcabe in libcandle_core...
"_sgemm_", referenced from:
candle_core::accelerate::sgemm::h2cf21c592cba3c47 in libcandle_core...
ld: symbol(s) not found for architecture arm64
This is likely due to a missing linker flag that was needed to enable the mkl library. You can try adding the following for mkl at the top of your binary:
extern crate intel_mkl_src;or for accelerate:
extern crate accelerate_src;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, and set up your authentication token. See issue #350 for more details.
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 is provided as a git submodule so you may want to run the following command to check it in properly.
git submodule update --init/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 CANDLE_NVCC_CCBIN environment variable.
env CANDLE_NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...
You can set RUST_BACKTRACE=1 to be provided with backtraces when a candle
error is generated.