repo stringlengths 6 65 | file_url stringlengths 81 311 | file_path stringlengths 6 227 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 15:31:58 2026-01-04 20:25:31 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/whisper/model.rs | candle-transformers/src/models/whisper/model.rs | use super::Config;
use crate::models::with_tracing::{linear, linear_no_bias, Linear};
use candle::{Device, IndexOp, Result, Tensor, D};
use candle_nn::{embedding, Conv1d, Conv1dConfig, Embedding, LayerNorm, Module, VarBuilder};
fn conv1d(
in_channels: usize,
out_channels: usize,
kernel_size: usize,
con... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/whisper/mod.rs | candle-transformers/src/models/whisper/mod.rs | //! Whisper Model Implementation
//!
//! Whisper is an automatic speech recognition (ASR) system trained on large amounts
//! of multilingual and multitask supervised data collected from the web. It can be used to
//! convert audio files (in the `.wav` format) to text. Supported features include
//! language detection ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/whisper/audio.rs | candle-transformers/src/models/whisper/audio.rs | // Audio processing code, adapted from whisper.cpp
// https://github.com/ggerganov/whisper.cpp
use candle::utils::get_num_threads;
use std::sync::Arc;
use std::thread;
pub trait Float:
num_traits::Float + num_traits::FloatConst + num_traits::NumAssign + Send + Sync
{
}
impl Float for f32 {}
impl Float for f64 {}... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/resnet.rs | candle-transformers/src/models/stable_diffusion/resnet.rs | //! ResNet Building Blocks
//!
//! Some Residual Network blocks used in UNet models.
//!
//! Denoising Diffusion Implicit Models, K. He and al, 2015.
//! - [Paper](https://arxiv.org/abs/1512.03385)
//!
use crate::models::with_tracing::{conv2d, Conv2d};
use candle::{Result, Tensor, D};
use candle_nn as nn;
use candle_nn... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/embeddings.rs | candle-transformers/src/models/stable_diffusion/embeddings.rs | use candle::{Result, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
#[derive(Debug)]
pub struct TimestepEmbedding {
linear_1: nn::Linear,
linear_2: nn::Linear,
}
impl TimestepEmbedding {
// act_fn: "silu"
pub fn new(vs: nn::VarBuilder, channel: usize, time_embed_dim: usize) -> Result<Self> {
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/attention.rs | candle-transformers/src/models/stable_diffusion/attention.rs | //! Attention Based Building Blocks
use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
#[derive(Debug)]
struct GeGlu {
proj: nn::Linear,
span: tracing::Span,
}
impl GeGlu {
fn new(vs: nn::VarBuilder, dim_in: usize, dim_out: usize) -> Result<Self> {
let pro... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/schedulers.rs | candle-transformers/src/models/stable_diffusion/schedulers.rs | #![allow(dead_code)]
//! # Diffusion pipelines and models
//!
//! Noise schedulers can be used to set the trade-off between
//! inference speed and quality.
use candle::{Result, Tensor};
pub trait SchedulerConfig: std::fmt::Debug + Send + Sync {
fn build(&self, inference_steps: usize) -> Result<Box<dyn Scheduler>>... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/vae.rs | candle-transformers/src/models/stable_diffusion/vae.rs | #![allow(dead_code)]
//! # Variational Auto-Encoder (VAE) Models.
//!
//! Auto-encoder models compress their input to a usually smaller latent space
//! before expanding it back to its original shape. This results in the latent values
//! compressing the original information.
use super::unet_2d_blocks::{
DownEncode... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/ddim.rs | candle-transformers/src/models/stable_diffusion/ddim.rs | //! # Denoising Diffusion Implicit Models
//!
//! The Denoising Diffusion Implicit Models (DDIM) is a simple scheduler
//! similar to Denoising Diffusion Probabilistic Models (DDPM). The DDPM
//! generative process is the reverse of a Markovian process, DDIM generalizes
//! this to non-Markovian guidance.
//!
//! Denoi... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/utils.rs | candle-transformers/src/models/stable_diffusion/utils.rs | use candle::{Device, Result, Tensor};
pub fn linspace(start: f64, stop: f64, steps: usize) -> Result<Tensor> {
if steps == 0 {
Tensor::from_vec(Vec::<f64>::new(), steps, &Device::Cpu)
} else if steps == 1 {
Tensor::from_vec(vec![start], steps, &Device::Cpu)
} else {
let delta = (sto... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/mod.rs | candle-transformers/src/models/stable_diffusion/mod.rs | //! Stable Diffusion
//!
//! Stable Diffusion is a latent text-to-image diffusion model capable of
//! generating photo-realistic images given any text input.
//!
//! - 💻 [Original Repository](https://github.com/CompVis/stable-diffusion)
//! - 🤗 [Hugging Face](https://huggingface.co/runwayml/stable-diffusion-v1-5)
//... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/unet_2d.rs | candle-transformers/src/models/stable_diffusion/unet_2d.rs | //! 2D UNet Denoising Models
//!
//! The 2D Unet models take as input a noisy sample and the current diffusion
//! timestep and return a denoised version of the input.
use super::embeddings::{TimestepEmbedding, Timesteps};
use super::unet_2d_blocks::*;
use crate::models::with_tracing::{conv2d, Conv2d};
use candle::{Res... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/clip.rs | candle-transformers/src/models/stable_diffusion/clip.rs | //! Contrastive Language-Image Pre-Training
//!
//! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - [CLIP](https://github.com/openai/CLIP)
use candle::{DType, Device, Result, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
#[derive(... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/ddpm.rs | candle-transformers/src/models/stable_diffusion/ddpm.rs | use super::schedulers::{betas_for_alpha_bar, BetaSchedule, PredictionType};
use candle::{Result, Tensor};
#[derive(Debug, Default, Clone, PartialEq, Eq)]
pub enum DDPMVarianceType {
#[default]
FixedSmall,
FixedSmallLog,
FixedLarge,
FixedLargeLog,
Learned,
}
#[derive(Debug, Clone)]
pub struct D... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/euler_ancestral_discrete.rs | candle-transformers/src/models/stable_diffusion/euler_ancestral_discrete.rs | //! Ancestral sampling with Euler method steps.
//!
//! Based on the original [`k-diffusion` implementation by Katherine Crowson]( https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72).
//!
use super::{
schedulers::{
betas_for_alpha_bar, BetaSche... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/uni_pc.rs | candle-transformers/src/models/stable_diffusion/uni_pc.rs | //! # UniPC Scheduler
//!
//! UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a
//! corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.
//!
//! UniPC is by design model-agnostic, supporting pixel-space/latent... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs | candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs | //! 2D UNet Building Blocks
//!
use super::attention::{
AttentionBlock, AttentionBlockConfig, SpatialTransformer, SpatialTransformerConfig,
};
use super::resnet::{ResnetBlock2D, ResnetBlock2DConfig};
use crate::models::with_tracing::{conv2d, Conv2d};
use candle::{Module, Result, Tensor, D};
use candle_nn as nn;
#[... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/nvembed_v2/embedding.rs | candle-transformers/src/models/nvembed_v2/embedding.rs | /// Mistral LLM, https://github.com/mistralai/mistral-src
use crate::models::{
mistral::Config,
with_tracing::{linear_no_bias, Linear, RmsNorm},
};
use crate::utils::repeat_kv;
use candle::{DType, Device, Module, Result, Tensor};
use candle_nn::{Activation, VarBuilder};
use std::sync::Arc;
#[derive(Debug, Clon... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/nvembed_v2/model.rs | candle-transformers/src/models/nvembed_v2/model.rs | use super::embedding::Model as EmbeddingModel;
use crate::models::{
mistral::Config,
with_tracing::{layer_norm, linear, linear_no_bias, LayerNorm, Linear},
};
use candle::{DType, Device, Result, Tensor, D};
use candle_nn::{ops::softmax_last_dim, LayerNormConfig, Module, VarBuilder};
// Geglu and feedforward fr... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/nvembed_v2/mod.rs | candle-transformers/src/models/nvembed_v2/mod.rs | //! NV-Embed-v2
//!
//! NV-Embed-v2 is a text embedding model that combines a Mistral decoder with a latent attention mechanism to produce high-quality text embeddings.
//!
//! This implementation is based on the [paper](https://arxiv.org/pdf/2405.17428) and [weights](https://huggingface.co/nvidia/NV-Embed-v2)
//!
//! ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mimi/mod.rs | candle-transformers/src/models/mimi/mod.rs | //! mimi model
//!
//! [Mimi](https://huggingface.co/kyutai/mimi) is a state of the art audio
//! compression model using an encoder/decoder architecture with residual vector
//! quantization. The candle implementation supports streaming meaning that it's
//! possible to encode or decode a stream of audio tokens on the... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mimi/seanet.rs | candle-transformers/src/models/mimi/seanet.rs | // Copyright (c) Kyutai, all rights reserved.
// This source code is licensed under the license found in the
// LICENSE file in the root directory of this source tree.
use candle::{streaming, Module, Result, StreamTensor, StreamingModule, Tensor};
use candle_nn::VarBuilder;
use super::conv::{StreamableConv1d, Streama... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mimi/conv.rs | candle-transformers/src/models/mimi/conv.rs | // Copyright (c) Kyutai, all rights reserved.
// This source code is licensed under the license found in the
// LICENSE file in the root directory of this source tree.
use candle::{Module, Result, StreamTensor, StreamingModule, Tensor, D};
use candle_nn::{Conv1d, VarBuilder};
#[allow(clippy::enum_variant_names)]
#[de... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mimi/encodec.rs | candle-transformers/src/models/mimi/encodec.rs | // Copyright (c) Kyutai, all rights reserved.
// This source code is licensed under the license found in the
// LICENSE file in the root directory of this source tree.
use super::{conv, quantization, seanet, transformer};
use candle::{DType, Device, Module, Result, StreamTensor, StreamingModule, Tensor};
use candle_nn... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mimi/transformer.rs | candle-transformers/src/models/mimi/transformer.rs | // Copyright (c) Kyutai, all rights reserved.
// This source code is licensed under the license found in the
// LICENSE file in the root directory of this source tree.
use candle::{DType, Device, IndexOp, Module, Result, StreamTensor, StreamingModule, Tensor, D};
use candle_nn::{linear_no_bias, Linear, VarBuilder};
us... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mimi/quantization.rs | candle-transformers/src/models/mimi/quantization.rs | // Copyright (c) Kyutai, all rights reserved.
// This source code is licensed under the license found in the
// LICENSE file in the root directory of this source tree.
use candle::{IndexOp, Layout, Result, Shape, Tensor, D};
use candle_nn::{linear, Linear, VarBuilder};
struct CodebookEncode;
impl candle::CustomOp2 f... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/pipelines/text_generation.rs | candle-transformers/src/pipelines/text_generation.rs | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false | |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/pipelines/mod.rs | candle-transformers/src/pipelines/mod.rs | pub mod text_generation;
| rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/generation/mod.rs | candle-transformers/src/generation/mod.rs | //! Logit Processing and Sampling
//!
//! Functionality for modeling sampling strategies and logits processing in text generation
//! with support for temperature-based sampling, top-k filtering, nucleus sampling (top-p),
//! and combinations thereof.
use candle::{DType, Error, Result, Tensor};
use rand::{distr::Distri... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/tests/generation_tests.rs | candle-transformers/tests/generation_tests.rs | use candle::{Device, Result, Tensor};
use candle_transformers::generation::LogitsProcessor;
#[test]
fn sample_with_zero_temperature() -> Result<()> {
let mut logits_process = LogitsProcessor::new(1337, None, None);
let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
let token = logits_process.s... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/tests/nms_tests.rs | candle-transformers/tests/nms_tests.rs | use candle::Result;
use candle_transformers::object_detection::{
non_maximum_suppression, soft_non_maximum_suppression, Bbox,
};
#[test]
fn nms_basic() -> Result<()> {
// Boxes based upon https://thepythoncode.com/article/non-maximum-suppression-using-opencv-in-python
let mut bboxes = vec![vec![
Bb... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-pyo3/build.rs | candle-pyo3/build.rs | fn main() {
pyo3_build_config::add_extension_module_link_args();
}
| rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-pyo3/src/shape.rs | candle-pyo3/src/shape.rs | use ::candle::Tensor;
use pyo3::prelude::*;
#[derive(Clone, Debug)]
/// Represents an absolute shape e.g. (1, 2, 3)
pub struct PyShape(Vec<usize>);
impl pyo3::FromPyObject<'_, '_> for PyShape {
type Error = PyErr;
fn extract(obj: Borrowed<'_, '_, PyAny>) -> PyResult<Self> {
if obj.is_none() {
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-pyo3/src/lib.rs | candle-pyo3/src/lib.rs | #![allow(clippy::redundant_closure_call)]
#![allow(clippy::useless_conversion)]
use float8::F8E4M3;
use half::{bf16, f16};
use pyo3::exceptions::{PyTypeError, PyValueError};
use pyo3::prelude::*;
use pyo3::pyclass::CompareOp;
use pyo3::types::{IntoPyDict, PyDict, PyString, PyTuple};
use pyo3::{IntoPyObject, IntoPyObjec... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-pyo3/src/utils.rs | candle-pyo3/src/utils.rs | use pyo3::exceptions::PyValueError;
use pyo3::prelude::*;
pub fn wrap_err(err: ::candle::Error) -> PyErr {
PyErr::new::<PyValueError, _>(format!("{err:?}"))
}
| rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-pyo3/src/onnx.rs | candle-pyo3/src/onnx.rs | use std::collections::HashMap;
use crate::utils::wrap_err;
use crate::{PyDType, PyTensor};
use candle_onnx::eval::{dtype, get_tensor, simple_eval};
use candle_onnx::onnx::tensor_proto::DataType;
use candle_onnx::onnx::tensor_shape_proto::dimension::Value;
use candle_onnx::onnx::type_proto::{Tensor as ONNXTensor, Value... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/op.rs | candle-core/src/op.rs | //! Tensor Operation Enums and Traits
//!
#![allow(clippy::redundant_closure_call)]
use crate::Tensor;
use float8::F8E4M3 as f8e4m3;
use half::{bf16, f16};
use num_traits::float::Float;
#[derive(Clone, Copy, PartialEq, Eq)]
pub enum CmpOp {
Eq,
Ne,
Le,
Ge,
Lt,
Gt,
}
#[derive(Debug, Clone, Copy... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/dummy_metal_backend.rs | candle-core/src/dummy_metal_backend.rs | #![allow(dead_code)]
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Error, Layout, Result, Shape};
#[derive(Debug, Clone)]
pub struct MetalDevice;
#[derive(Debug)]
pub struct MetalStorage;
#[derive(thiserror::Error, Debug)]
pub enum MetalError {
#[error("{0}")]
Message(... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/tensor_cat.rs | candle-core/src/tensor_cat.rs | use crate::{shape::Dim, Context, Error, Result, Shape, Tensor};
impl Tensor {
/// Concatenates two or more tensors along a particular dimension.
///
/// All tensors must of the same rank, and the output will have
/// the same rank
///
/// ```rust
/// # use candle_core::{Tensor, DType, Devic... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/variable.rs | candle-core/src/variable.rs | // Variables are wrappers around tensors that can be modified, they are typically used for holding
// weights and being modified by gradient descent.
// We do not expose a public way to create variables as this would break the invariant that the
// tensor within a variable is actually with `is_variable` set to `true`.
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/dummy_cuda_backend.rs | candle-core/src/dummy_cuda_backend.rs | //! Implementation of the Cuda backend when Cuda support has not been compiled in.
//!
#![allow(dead_code)]
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Error, Layout, Result, Shape};
#[derive(Debug, Clone)]
pub struct CudaDevice;
#[derive(Debug)]
pub struct CudaStorage;
macr... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/accelerate.rs | candle-core/src/accelerate.rs | #![allow(dead_code)]
use libc::{c_char, c_double, c_float, c_int, c_long, c_ulong};
mod ffi {
use super::*;
extern "C" {
// It would be nice to be able to switch to the NEWLAPACK version of the function but this
// seems to trigger some link error. Available function names can be seen here:
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/shape.rs | candle-core/src/shape.rs | //! The shape of a tensor is a tuple with the size of each of its dimensions.
#![allow(clippy::redundant_closure_call)]
use crate::{Error, Result};
#[derive(Clone, PartialEq, Eq)]
pub struct Shape(Vec<usize>);
pub const SCALAR: Shape = Shape(vec![]);
impl std::fmt::Debug for Shape {
fn fmt(&self, f: &mut std::fm... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/lib.rs | candle-core/src/lib.rs | //! ML framework for Rust
//!
//! ```rust
//! use candle_core::{Tensor, DType, Device};
//! # use candle_core::Error;
//! # fn main() -> Result<(), Error>{
//!
//! let a = Tensor::arange(0f32, 6f32, &Device::Cpu)?.reshape((2, 3))?;
//! let b = Tensor::arange(0f32, 12f32, &Device::Cpu)?.reshape((3, 4))?;
//! let c = a.m... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/device.rs | candle-core/src/device.rs | use crate::backend::BackendDevice;
use crate::cpu_backend::CpuDevice;
use crate::{CpuStorage, DType, Result, Shape, Storage, WithDType};
/// A `DeviceLocation` represents a physical device whereas multiple `Device`
/// can live on the same location (typically for cuda devices).
#[derive(Debug, Copy, Clone, PartialEq, ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/mkl.rs | candle-core/src/mkl.rs | #![allow(dead_code)]
use libc::{c_char, c_double, c_float, c_int};
mod ffi {
use super::*;
extern "C" {
pub fn vsTanh(n: c_int, a: *const c_float, y: *mut c_float);
pub fn vdTanh(n: c_int, a: *const c_double, y: *mut c_double);
pub fn vsExp(n: c_int, a: *const c_float, y: *mut c_float);... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/convert.rs | candle-core/src/convert.rs | //! Implement conversion traits for tensors
use crate::{DType, Device, Error, Tensor, WithDType};
use half::{bf16, f16, slice::HalfFloatSliceExt};
use std::convert::TryFrom;
impl<T: WithDType> TryFrom<&Tensor> for Vec<T> {
type Error = Error;
fn try_from(tensor: &Tensor) -> Result<Self, Self::Error> {
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/dtype.rs | candle-core/src/dtype.rs | //! Types for elements that can be stored and manipulated using tensors.
#![allow(clippy::redundant_closure_call)]
use crate::backend::BackendStorage;
use crate::{CpuStorage, CpuStorageRef, Error, Result};
/// The different types of elements allowed in tensors.
#[derive(Debug, Copy, Clone, PartialEq, Eq, Hash)]
pub en... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/sort.rs | candle-core/src/sort.rs | use crate::{Result, Tensor};
use rayon::prelude::*;
#[derive(Debug, Clone, Copy)]
struct ArgSort {
asc: bool,
last_dim: usize,
}
impl ArgSort {
fn asort<T: crate::WithDType>(&self, vs: &[T], layout: &crate::Layout) -> Vec<u32> {
#[allow(clippy::uninit_vec)]
// Safety: indexes are set later... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/backend.rs | candle-core/src/backend.rs | //! Traits to Define Backend Behavior
//!
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Layout, Result, Shape};
pub trait BackendStorage: Sized {
type Device: BackendDevice;
fn try_clone(&self, _: &Layout) -> Result<Self>;
fn dtype(&self) -> DType;
fn device(&... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/strided_index.rs | candle-core/src/strided_index.rs | use crate::Layout;
/// An iterator over offset position for items of an N-dimensional arrays stored in a
/// flat buffer using some potential strides.
#[derive(Debug)]
pub struct StridedIndex<'a> {
next_storage_index: Option<usize>,
multi_index: Vec<usize>,
dims: &'a [usize],
stride: &'a [usize],
r... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/test_utils.rs | candle-core/src/test_utils.rs | use crate::{Result, Tensor};
#[macro_export]
macro_rules! test_device {
// TODO: Switch to generating the two last arguments automatically once concat_idents is
// stable. https://github.com/rust-lang/rust/issues/29599
($fn_name: ident, $test_cpu: ident, $test_cuda: ident, $test_metal: ident) => {
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/storage.rs | candle-core/src/storage.rs | use crate::backend::BackendStorage;
use crate::op::{self, CmpOp, ReduceOp};
use crate::scalar::Scalar;
use crate::{CpuStorage, CudaStorage, DType, Device, Error, Layout, MetalStorage, Result, Shape};
use crate::{CustomOp1, CustomOp2, CustomOp3, InplaceOp1, InplaceOp2, InplaceOp3};
// We do not want to implement Clone ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/dummy_dtype.rs | candle-core/src/dummy_dtype.rs | //! Dummy data types for experimental/future float formats
//!
//! These are placeholder types for experimental floating-point formats
//! that are defined in the safetensors spec but not yet fully implemented.
use crate::{DType, Error, Result, WithDType};
/// 6-bit float with 2 exponent bits and 3 mantissa bits (MX6... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/error.rs | candle-core/src/error.rs | //! Candle-specific Error and Result
use std::{convert::Infallible, fmt::Display};
use crate::{DType, DeviceLocation, Layout, MetalError, Shape};
#[derive(Debug, Clone)]
pub struct MatMulUnexpectedStriding {
pub lhs_l: Layout,
pub rhs_l: Layout,
pub bmnk: (usize, usize, usize, usize),
pub msg: &'stati... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/safetensors.rs | candle-core/src/safetensors.rs | //! Module to load `safetensor` files into CPU/GPU memory.
//!
//! There are multiple ways to load tensors from safetensor files:
//! - `load` function for loading directly into memory and returning a HashMap of tensors
//! - `MmapedSafetensors` for memory mapping files and avoiding full allocation
//! - `SliceSafetens... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/display.rs | candle-core/src/display.rs | //! Pretty printing of tensors
//!
//! This implementation should be in line with the [PyTorch version](https://github.com/pytorch/pytorch/blob/7b419e8513a024e172eae767e24ec1b849976b13/torch/_tensor_str.py).
//!
use crate::{DType, Result, Tensor, WithDType};
use half::{bf16, f16};
impl Tensor {
fn fmt_dt<T: WithDT... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/tensor.rs | candle-core/src/tensor.rs | //! Tensors are N-dimensional matrixes of elements using a single data type.
#![allow(clippy::redundant_closure_call)]
use crate::backend::{BackendDevice, BackendStorage};
use crate::op::{BackpropOp, BinaryOp, CmpOp, Op, ReduceOp, UnaryOp};
use crate::scalar::TensorOrScalar;
use crate::shape::{Dim, Dims, ShapeWithOneHo... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/npy.rs | candle-core/src/npy.rs | //! Numpy support for tensors.
//!
//! The spec for the npy format can be found in
//! [npy-format](https://docs.scipy.org/doc/numpy-1.14.2/neps/npy-format.html).
//! The functions from this module can be used to read tensors from npy/npz files
//! or write tensors to these files. A npy file contains a single tensor (u... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/scalar.rs | candle-core/src/scalar.rs | //! TensorScalar Enum and Trait
//!
use crate::{DType, Result, Tensor, WithDType};
use float8::F8E4M3 as f8e4m3;
use half::{bf16, f16};
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum Scalar {
U8(u8),
U32(u32),
I16(i16),
I32(i32),
I64(i64),
BF16(bf16),
F16(f16),
F32(f32),
F64(f64)... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/utils.rs | candle-core/src/utils.rs | //! Useful functions for checking features.
use std::str::FromStr;
pub fn get_num_threads() -> usize {
// Respond to the same environment variable as rayon.
match std::env::var("RAYON_NUM_THREADS")
.ok()
.and_then(|s| usize::from_str(&s).ok())
{
Some(x) if x > 0 => x,
Some(_... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/layout.rs | candle-core/src/layout.rs | //! Tensor Layouts including contiguous or sparse strides
use crate::{Error, Result, Shape};
#[derive(Debug, PartialEq, Eq, Clone)]
pub struct Layout {
shape: Shape,
// The strides are given in number of elements and not in bytes.
stride: Vec<usize>,
start_offset: usize,
}
impl Layout {
pub fn new... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/conv.rs | candle-core/src/conv.rs | //! 1D and 2D Convolutions
//!
use crate::{op::BackpropOp, op::Op, Error, Result, Tensor};
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ParamsConv1D {
pub(crate) b_size: usize,
// Maybe we should have a version without l_in as this bit depends on the input and not only on
// the weights.
pub(crate... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/backprop.rs | candle-core/src/backprop.rs | //! Methods for backpropagation of gradients.
use crate::op::{BinaryOp, Op, ReduceOp, UnaryOp};
use crate::{Error, Result, Tensor, TensorId};
use std::collections::HashMap;
// arg has been reduced to node via reduce_dims, expand it back to arg.
// This has to handle keepdims.
fn broadcast_back(arg: &Tensor, node: &Ten... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/custom_op.rs | candle-core/src/custom_op.rs | use crate::op::{BackpropOp, Op};
use crate::tensor::from_storage;
use crate::{CpuStorage, CudaStorage, Layout, MetalStorage, Result, Shape, Tensor};
use std::sync::Arc;
/// Unary ops that can be defined in user-land.
pub trait CustomOp1 {
// Box<dyn> does not support const yet, so use a function to get the name.
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/pickle.rs | candle-core/src/pickle.rs | //! Just enough pickle support to be able to read PyTorch checkpoints.
// This hardcodes objects that are required for tensor reading, we may want to make this a bit more
// composable/tensor agnostic at some point.
use crate::{Context, DType, Error as E, Layout, Result, Tensor};
use byteorder::{LittleEndian, ReadBytes... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/indexer.rs | candle-core/src/indexer.rs | use crate::{Error, Tensor};
use std::ops::{
Bound, Range, RangeBounds, RangeFrom, RangeFull, RangeInclusive, RangeTo, RangeToInclusive,
};
impl Tensor {
/// Intended to be use by the trait `.i()`
///
/// ```
/// # use candle_core::{Tensor, DType, Device, IndexOp};
/// let a = Tensor::zeros((2, ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/streaming.rs | candle-core/src/streaming.rs | //! StreamTensror useful for streaming ops.
//!
use crate::{Result, Shape, Tensor};
pub trait Dim: crate::shape::Dim + Copy {}
impl<T: crate::shape::Dim + Copy> Dim for T {}
/// A stream tensor is used in streaming module. It can either contain an actual tensor or be
/// empty.
#[derive(Clone)]
pub struct StreamTenso... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/k_quants.rs | candle-core/src/quantized/k_quants.rs | use super::utils::{
get_scale_min_k4, group_for_dequantization, group_for_quantization, make_q3_quants,
make_qkx1_quants, make_qx_quants, nearest_int,
};
use super::GgmlDType;
use crate::quantized::utils::{make_qkx3_quants, make_qp_quants};
use crate::Result;
use byteorder::{ByteOrder, LittleEndian};
use half::... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/metal.rs | candle-core/src/quantized/metal.rs | use super::{GgmlDType, QStorage};
use crate::backend::BackendStorage;
use crate::{DType, MetalDevice, MetalStorage, Result, Shape, D};
use candle_metal_kernels::metal::Buffer;
use std::sync::Arc;
pub struct QMetalStorage {
dtype: GgmlDType,
device: MetalDevice,
buffer: Arc<Buffer>,
}
impl QMetalStorage {
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/avx.rs | candle-core/src/quantized/avx.rs | use super::k_quants::{
BlockQ2K, BlockQ3K, BlockQ4K, BlockQ4_0, BlockQ5K, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K,
};
use byteorder::{ByteOrder, LittleEndian};
use half::f16;
#[cfg(target_arch = "x86")]
use core::arch::x86::*;
#[cfg(target_arch = "x86_64")]
use core::arch::x86_64::*;
#[inline(always)]
pub(crat... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/dummy_cuda.rs | candle-core/src/quantized/dummy_cuda.rs | #![allow(unused)]
use super::GgmlDType;
use crate::{CudaDevice, CudaStorage, Error, Result};
pub struct QCudaStorage {
dtype: GgmlDType,
device: CudaDevice,
}
impl QCudaStorage {
pub fn zeros(_: &CudaDevice, _: usize, _: GgmlDType) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/gguf_file.rs | candle-core/src/quantized/gguf_file.rs | //! Support for the [GGUF file format](https://github.com/philpax/ggml/blob/gguf-spec/docs/gguf.md).
//!
//! Spec: https://github.com/ggml-org/ggml/blob/master/docs/gguf.md
use super::{GgmlDType, QTensor};
use crate::{Context, Device, Result};
use byteorder::{LittleEndian, ReadBytesExt, WriteBytesExt};
use std::coll... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/imatrix_file.rs | candle-core/src/quantized/imatrix_file.rs | use std::collections::HashMap;
use std::fs::File;
use std::io::{Cursor, Read};
use std::path::Path;
use byteorder::{LittleEndian, ReadBytesExt};
use crate::Result;
pub fn load_imatrix<P: AsRef<Path>>(fname: P) -> Result<HashMap<String, Vec<f32>>> {
let mut all_data = HashMap::new();
let mut file = File::ope... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/ggml_file.rs | candle-core/src/quantized/ggml_file.rs | //! Support for the GGML file format.
use super::{k_quants, GgmlDType, QStorage};
use crate::{Device, Result};
use byteorder::{LittleEndian, ReadBytesExt};
use std::collections::HashMap;
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.h#L37
#[derive(Debug, Clone, Copy, Pa... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/utils.rs | candle-core/src/quantized/utils.rs | pub(super) fn nearest_int(v: f32) -> i32 {
v.round() as i32
}
/// Validates that the input and output are the right size and returns an iterator which maps each
/// input region `xs` to its corresponding output block in `ys`. Each output region is guaranteed
/// to be `T::BLCK_SIZE` long.
pub(super) fn group_for_q... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/mod.rs | candle-core/src/quantized/mod.rs | use crate::{
backend::BackendStorage, CpuStorage, DType, Device, Result, Shape, Storage, Tensor, D,
};
use k_quants::*;
use std::borrow::Cow;
#[cfg(target_feature = "avx2")]
pub mod avx;
mod dummy_cuda;
mod dummy_metal;
pub mod ggml_file;
pub mod gguf_file;
pub mod imatrix_file;
pub mod k_quants;
#[cfg(feature = "... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/dummy_metal.rs | candle-core/src/quantized/dummy_metal.rs | #![allow(unused)]
use super::GgmlDType;
use crate::{Error, MetalDevice, MetalStorage, Result};
pub struct QMetalStorage {
dtype: GgmlDType,
device: MetalDevice,
}
impl QMetalStorage {
pub fn zeros(_: &MetalDevice, _: usize, _: GgmlDType) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/cuda.rs | candle-core/src/quantized/cuda.rs | use super::{GgmlDType, QStorage};
use crate::quantized::k_quants::GgmlType;
use crate::{backend::BackendDevice, cuda_backend::WrapErr};
use crate::{builder_arg as barg, CudaDevice, CudaStorage, Result};
use half::f16;
use cudarc::driver::{CudaSlice, CudaView, PushKernelArg};
#[derive(Clone, Debug)]
struct PaddedCudaS... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/neon.rs | candle-core/src/quantized/neon.rs | use super::k_quants::{
BlockQ2K, BlockQ3K, BlockQ4K, BlockQ4_0, BlockQ5K, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K,
};
use byteorder::{ByteOrder, LittleEndian};
#[allow(unused_imports)]
#[cfg(target_arch = "arm")]
use core::arch::arm::*;
#[allow(unused_imports)]
#[cfg(target_arch = "aarch64")]
use core::arch::a... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/quantized/simd128.rs | candle-core/src/quantized/simd128.rs | use super::k_quants::{BlockQ2K, BlockQ4K, BlockQ4_0, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K};
use byteorder::{ByteOrder, LittleEndian};
use half::f16;
use core::arch::wasm32::*;
#[inline(always)]
pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) -> f32 {
debug_assert!(
n.is... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cuda_backend/device.rs | candle-core/src/cuda_backend/device.rs | use crate::backend::{BackendDevice, BackendStorage};
use crate::{CpuStorage, CpuStorageRef, DType, Layout, Result, Shape};
pub use candle_kernels as kernels;
pub use cudarc;
use cudarc::driver::CudaFunction;
use float8::F8E4M3;
use half::{bf16, f16};
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cuda_backend/error.rs | candle-core/src/cuda_backend/error.rs | use crate::{DType, Layout};
/// cudarc related errors
#[derive(thiserror::Error, Debug)]
pub enum CudaError {
#[error(transparent)]
Cuda(#[from] cudarc::driver::DriverError),
#[error(transparent)]
Compiler(#[from] cudarc::nvrtc::CompileError),
#[error(transparent)]
Cublas(#[from] cudarc::cubl... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cuda_backend/utils.rs | candle-core/src/cuda_backend/utils.rs | /// Helper functions to plug cuda kernels in candle.
use crate::{Layout, Result, WithDType};
pub use cudarc;
use cudarc::driver::{CudaSlice, DeviceRepr, ValidAsZeroBits};
use super::{CudaDevice, CudaError, WrapErr};
pub type S = super::CudaStorageSlice;
pub trait Map1 {
fn f<T: DeviceRepr + WithDType + ValidAsZe... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cuda_backend/mod.rs | candle-core/src/cuda_backend/mod.rs | //! Implementation of Backend traits for CUDA device
//!
use crate::backend::{BackendDevice, BackendStorage};
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{builder_arg as barg, CpuStorage, DType, Layout, Result, WithDType};
pub use candle_kernels as kernels;
pub use cudarc;
use cudarc::cublas::{Gem... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cuda_backend/cudnn.rs | candle-core/src/cuda_backend/cudnn.rs | use crate::WithDType;
use cudarc;
use cudarc::cudnn::safe::{ConvForward, Cudnn};
use cudarc::driver::{CudaSlice, CudaView, DeviceRepr, ValidAsZeroBits};
use std::cell::RefCell;
use std::collections::HashMap;
use std::sync::Arc;
// The cudnn handles are stored per thread here rather than on the CudaDevice as they are n... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu/erf.rs | candle-core/src/cpu/erf.rs | #![allow(clippy::excessive_precision)]
// Code taken from https://github.com/statrs-dev/statrs
//! Provides the [error](https://en.wikipedia.org/wiki/Error_function) and
//! related functions
mod evaluate {
//! Provides functions that don't have a numerical solution and must
//! be solved computationally (e.g.... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu/avx.rs | candle-core/src/cpu/avx.rs | use super::{Cpu, CpuBF16, CpuF16};
#[cfg(target_arch = "x86")]
use core::arch::x86::*;
#[cfg(target_arch = "x86_64")]
use core::arch::x86_64::*;
use half::{bf16, f16};
pub struct CurrentCpu {}
const STEP: usize = 32;
const EPR: usize = 8;
const ARR: usize = STEP / EPR;
impl Cpu<ARR> for CurrentCpu {
type Unit =... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu/mod.rs | candle-core/src/cpu/mod.rs | //! Traits and methods for CPU-backed Tensors
pub mod erf;
pub mod kernels;
#[allow(unused)]
trait Cpu<const ARR: usize> {
type Unit;
type Array;
const STEP: usize;
const EPR: usize;
fn n() -> usize;
unsafe fn zero() -> Self::Unit;
unsafe fn zero_array() -> Self::Array;
unsafe fn load... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu/neon.rs | candle-core/src/cpu/neon.rs | use super::Cpu;
#[cfg(target_arch = "arm")]
use core::arch::arm::*;
#[cfg(target_arch = "aarch64")]
use core::arch::aarch64::*;
pub struct CurrentCpu {}
const STEP: usize = 16;
const EPR: usize = 4;
const ARR: usize = STEP / EPR;
impl CurrentCpu {
#[cfg(target_arch = "aarch64")]
unsafe fn reduce_one(x: floa... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu/simd128.rs | candle-core/src/cpu/simd128.rs | use super::Cpu;
use core::arch::wasm32::*;
pub struct CurrentCpu {}
const STEP: usize = 16;
const EPR: usize = 4;
const ARR: usize = STEP / EPR;
impl Cpu<ARR> for CurrentCpu {
type Unit = v128;
type Array = [v128; ARR];
const STEP: usize = STEP;
const EPR: usize = EPR;
fn n() -> usize {
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu/kernels.rs | candle-core/src/cpu/kernels.rs | pub trait VecOps: num_traits::NumAssign + Copy {
fn min(self, rhs: Self) -> Self;
fn max(self, rhs: Self) -> Self;
/// Dot-product of two vectors.
///
/// # Safety
///
/// The length of `lhs` and `rhs` have to be at least `len`. `res` has to point to a valid
/// element.
#[inline(al... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/metal_backend/device.rs | candle-core/src/metal_backend/device.rs | use crate::{DType, Result};
#[cfg(feature = "ug")]
use candle_metal_kernels::metal::ComputePipeline;
use candle_metal_kernels::{
metal::{
BlitCommandEncoder, Buffer, BufferMap, Commands, ComputeCommandEncoder, Device,
MTLResourceOptions,
},
Kernels,
};
use objc2_foundation::NSURL;
use objc2... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/metal_backend/mod.rs | candle-core/src/metal_backend/mod.rs | //! Implementation of Backend traits for Metal
//!
use crate::backend::{BackendDevice, BackendStorage};
use crate::conv::{ParamsConv1D, ParamsConv2D, ParamsConvTranspose1D, ParamsConvTranspose2D};
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, CpuStorageRef, DType, Error, Layout, Result,... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu_backend/utils.rs | candle-core/src/cpu_backend/utils.rs | /// Helper functions to write CPU kernels.
use crate::backend::BackendStorage;
use crate::{Error, Layout, Result, WithDType};
type C = super::CpuStorage;
pub trait Map1 {
fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>>;
fn map(&self, vs: &C, layout: &Layout) -> Result<C> {
match... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu_backend/mod.rs | candle-core/src/cpu_backend/mod.rs | //! Implementation of Backend Fns for CPU
use crate::backend::{BackendDevice, BackendStorage};
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{DType, Error, IntDType, Layout, Result, Shape, WithDType};
use float8::F8E4M3;
use half::{bf16, f16};
use rayon::prelude::*;
mod utils;
pub use utils::{
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/src/cpu_backend/conv2d.rs | candle-core/src/cpu_backend/conv2d.rs | use std::borrow::Cow;
use rayon::iter::{IntoParallelIterator, ParallelIterator};
use crate::{
conv::ParamsConv2D,
cpu_backend::{copy_strided_src_, Im2Col, Map1, Map2, MatMul},
shape::dims4,
Layout, Result, WithDType,
};
pub(super) struct Conv2D<'a>(pub(super) &'a crate::conv::ParamsConv2D);
#[allow(... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/tests/indexing_tests.rs | candle-core/tests/indexing_tests.rs | use anyhow::Result;
use candle_core::{Device, IndexOp, Tensor};
#[test]
fn integer_index() -> Result<()> {
let dev = Device::Cpu;
let tensor = Tensor::arange(0u32, 2 * 3, &dev)?.reshape((2, 3))?;
let result = tensor.i(1)?;
assert_eq!(result.dims(), &[3]);
assert_eq!(result.to_vec1::<u32>()?, &[3, ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/tests/grad_tests.rs | candle-core/tests/grad_tests.rs | #![allow(clippy::approx_constant)]
use anyhow::{Context, Result};
use candle_core::{test_device, test_utils, DType, Device, Shape, Tensor, Var};
fn simple_grad(device: &Device) -> Result<()> {
let x = Var::new(&[3f32, 1., 4.], device)?;
let x = x.as_tensor();
let y = (((x * x)? + x * 5f64)? + 4f64)?;
l... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-core/tests/quantized_tests.rs | candle-core/tests/quantized_tests.rs | use candle_core::{
bail,
quantized::{self, GgmlDType},
test_device,
test_utils::to_vec2_round,
DType, Device, IndexOp, Module, Result, Tensor, Var,
};
use quantized::{k_quants, GgmlType};
use rand::prelude::*;
const GGML_TEST_SIZE: usize = 32 * 128;
const GGML_MAX_QUANTIZATION_TOTAL_ERROR: f32 = 0... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
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