Overview
This release brings major upgrades in performance and platform compatibility (most notably, a new Metal backend via WGPU passthrough). CubeCL now powers backends for CUDA,Metal, ROCm, Vulkan and WebGPU. Tensor operation fusion support has been greatly expanded to optimize element-wise, reductions and matmul operations.
A new compilation cache and improved autotune cache speed up repeated runs by reusing precompiled binaries and tuned kernel configurations. Data parallel training now scales better across multiple GPUs with automatic batch assignment to each worker. A new tensor slice API offers a simpler, more intuitive way to index tensors.
This version also comes with broad performance gains across tensor operations, especially for reductions, matmul, and convolutions. An initial implementation of quantized matmul is now available, with further quantization improvements planned in the future.
As with previous releases, this includes various bug fixes, further optimizations and enhanced documentation.
Be sure to check out the newburn-bench to compare performance across different versions, hardware and backends.
CubeCL Backends
Burn supports CUDA,ROCm, Vulkan and WebGPU, and the newly added Metal backend.
Each backend can be used through their respective type aliases, provided that the appropriate backend feature flag is also enabled.
Metal
TOML
burn = { version = "0.17.0", features = ["metal"] }
Rust
use burn::prelude::*;
use burn::backend::wgpu::{Metal, WgpuDevice};
let tensor = Tensor::<Metal, 2>::zeros([2, 4], &WgpuDevice::default());
Cuda
TOML
burn = { version = "0.17.0", features = ["cuda"] }
Rust
use burn::prelude::*;
use burn::backend::cuda::{Cuda, CudaDevice};
let tensor = Tensor::<Cuda, 2>::zeros([2, 4], &CudaDevice::default());
Rocm
TOML
burn = { version = "0.17.0", features = ["rocm"] }
Rust
use burn::prelude::*;
use burn::backend::rocm::{Rocm, HipDevice};
let tensor = Tensor::<Rocm, 2>::zeros([2, 4], &HipDevice::default());
Vulkan
TOML
burn = { version = "0.17.0", features = ["vulkan"] }
Rust
use burn::prelude::*;
use burn::backend::wgpu::{Vulkan, WgpuDevice};
let tensor = Tensor::<Vulkan, 2>::zeros([2, 4], &WgpuDevice::default());
WebGpu
TOML
burn = { version = "0.17.0", features = ["webgpu"] }
Rust
use burn::prelude::*;
use burn::backend::wgpu::{WebGpu, WgpuDevice};
let tensor = Tensor::<WebGpu, 2>::zeros([2, 4], &WgpuDevice::default());
⚠️ WARNINGWhen using one of the wgpu backends, you may encounter compilation errors related to recursive type evaluation. This is due to complex type nesting within the wgpu dependency chain. To resolve this issue, add the following line at the top of your main.rs orlib.rs file:
recursion_limit = "256"]
The default recursion limit (128) is often just below the required depth (typically 130–150) due to deeply nested associated types and trait bounds.
Data Loader and Batcher
The Batcher trait has been updated to improve multi-device support. Previously, batcher implementations stored a device internally, which could lead to all data being loaded on the same device. The latest changes have the DataLoader generic over the backend, while the device is passed explicitly:
Diff
-impl<B: Backend> Batcher<MyItem, MyBatch<B>> for MyBatcher<B> {
+impl<B: Backend> Batcher<B, MyItem, MyBatch<B>> for MyBatcher {
- fn batch(&self, items: Vec<MyItem>) -> MyBatch<B> {
+ fn batch(&self, items: Vec<MyItem>, device: &B::Device) -> MyBatch<B> {
// The correct `device` is already provided for the batching logic to use
}
}
The device can now be set when building a data loader:
Diff
let dataloader = DataLoaderBuilder::new(batcher)
.batch_size(batch_size)
.shuffle(seed)
.num_workers(num_workers)
+ .set_device(device)
.build(dataset);
This step is not required for the Learner, which handles the device configuration automatically.
Better Tensor Slicing & Indexing
Tensor slicing now fully adopts idiomatic Rust range syntax, replacing the older (i64, i64) and Option tuple forms.
For example:
Diff
let tensor = Tensor::<B, 2>::zeros([m, n], &device);
-let slice = tensor.slice([(0, -1), (0, -2)]);
+let slice = tensor.slice([0..-1, 0..-2]);
For more complex or mixed range types, use the s![] macro:
Diff
let tensor = Tensor::<B, 3>::zeros([b, s, d], &device);
-let slice = tensor.slice([None, Some((t as i64, t as i64 + 1)), None]);
+let slice = tensor.slice(s![.., t..t + 1, ..]);
The macro is inspired by ndarray's s![] (at least, by name) and helps build flexible slice patterns.
Rust
use burn::prelude::*;
let tensor = Tensor::<B, 4>::zeros([8, 4, 2, 3], &device);
let slice = tensor.slice(s![..=4, 0..=3, .., -1]);
assert_eq!(slice.dims(), [5, 4, 2, 1]);
Changelog
Module & Tensor
Feature add new one hot function meeting multi-dimensions (ranks)#2613@tiruka Expand GRU support #2704@nwhitehead feat: bitwise-ops-for-tensors#2498@quinton11 Feat: Add PoissonNLL loss#2765@salvomcl Add metric parametrized name#2808@laggui Add boolean and/or to bool tensors#2802@wingertge Add ATOL/RTOL defaults#2824@crutcher Feat: Add tan trig function#2854@Msa360 Refactor quantization schemes#2849#3036@laggui@maxtremblay Vectorize pooling for optimization#2905@wingertge Feat: Add Cosh and Sinh#2959@Msa360 Refactor in-memory recorder load args#2892@BjornTheProgrammer Improve gradient checkpointing#2997@nathanielsimard Improve tensor.slice(...) to support multiple range types#3061@laggui Bug Fixes
Fix bce loss log #2741@laggui Fix repeat_dim backward w/ dim size > 1#2777@laggui [Fix] tch upgrade #2834@wingertge Check channels_in matches in convolution layers#2944@chlobes Fixed GroupNorm implementation#2945@computer-whisperer Backends
Migrate to type magic autotune#2710@wingertge Feat/fused matmul tune#2726@nathanielsimard Improve fusion for broadcasting, mix vectorization and reshape operation#2773#2833@nathanielsimard Remove from_data conversions in backends#2783@laggui [Feature] reduce fuse on read#2870@nathanielsimard [Feat] SIMD acceleration for ndarray backend#2851@wingertge Perf/reduce fuse on write#2937@nathanielsimard [metal] Add CubeCL metal compiler support#2993@syl20bnr Cubecl quantize matmul#3022#3030@maxtremblay Bug Fixes
Fix constant creation in fusion to cast at compile time, not runtime#2782@wingertge Fix two autotune issues on wasm#2899@ArthurBrussee Fix/reduce out of bounds#2906@nathanielsimard Fix metal backend name#3040@nathanielsimard Fix matmul dynamic line size support#3056@nathanielsimard Fix: matmul lower precision / flex32#3059@nathanielsimard Fix/autotune cache conflicts#3070@nathanielsimard Documentation & Examples
Wasserstein Generative Adversarial Network#2660@wangjiawen2013 Improve tensor docs #2951@PtiLuky Fixes
chore: fix some comments#2717@sunxunle Add hardsigmoid formula and fix WGAN doc + default lr#2706@laggui Fix db-pedia-infer backend#2736@laggui Fixed typo in the burn book chapter advanced unit no-std.#2731@xmy314 typo - correct smp_serde to rmp_serde as per crate's name in url#2744@cameronbraid typo - missing tick which was breaking formatting#2745@cameronbraid Remove autodiff from generate#2759@laggui Remove empty format precision specifier#2785@hkBst Update tch instructions#2844#2976@laggui Fix from_embedded and bool ops docs#2848@laggui Fix tiny typo in mathematical expression#2867@janhohenheim Fix: burn-book getting-started Use Declarations#2966@jerryshell ONNX Support
Code generation bug fix for ONNX import#2708@antimora Onnx op topk #2305@oojo12 Fix output elem type for unsqueeze and reshape#2807@christeefy Feat/Split ONNX Import #2568@agelas Refactor GatherNode to support scalar outputs.#2828@loloxwg Rename dim to rank for ONNX import#2831@antimora Add rank inference for tan#2868@Msa360 Fix RandomNormalLike ONNX node output rank#2936@Knight-Ops Support multiple outputs being tracked in BurnGraph during ONNX conversion#2938@Knight-Ops Ignore ONNX optional node inputs/outputs#2935@Knight-Ops Fix ONNX flatten to match spec#2940@catch-twenty-two burn-import: add some tests for ConstantNode#2623@jameshiew@laggui Update SUPPORTED-ONNX-OPS.md with the latest info#3064@antimora Enhancements
Add new burn-vision crate#2753#2810#2842@wingertge Improve Burn compilation times#2815#2994@nathanielsimard Support training in no-std#2830@ivila Perf: Speed up element and TensorData conversion#2913@wingertge Improve multi-device data loading strategy#2890#3035@laggui Autotune level matmul double buffering#2988@nathanielsimard@louisfd Refactoring
Remove deprecated Data and DataSerialize#2703@laggui Clean up train system metrics#2707@laggui Move IR to its own crate#2796#2798@laggui Refactor burn jit => burn-cubecl#2809@nathanielsimard Cleanup Tensor Registry in fusion#2826@nathanielsimard Migrate conv2d to cubecl#2908#3018@wingertge Update to edition 2024 #2931@laggui Migrate backend comparison#2961@laggui Improve test tolerance assertions#3024@maxtremblay@laggui [hip] Move burn-hip to burn-rocm and rename backend to ROCm#3062@syl20bnr Miscellaneous
Fix no default features flags + update cubecl#2725@laggui Replace return with terminate#2742@maxtremblay Clean up -jit suffix in feature flags and modules#2705@laggui Fix types under autotune flag#2750@laggui Fix BackendValues in backend-comparison after removal of jit suffix#2756@syl20bnr Fix optional burn-import dep + impl module types for isize#2774@laggui Update cubecl with fix to shared_sum#2779@maxtremblay feat: using rustls instead of native-tls#2799@ShoofLLC bump cubecl version with dummy implementations#2814@maxtremblay Add data_dir optional argument to Huggingface DataLoader to enable some manual download use cases#2817@Pablo1785 Bump xtask to 1.1.9 #2896@syl20bnr Fix test checks for macos#2952@PtiLuky Add train end event #2967@laggui Update cubecl bitcast -> reinterpret#2985@maxtremblay Update wgpu to v25 #3007@syl20bnr update cubecl: sync full cyclic checked#3025@louisfd Fix autotune measurement#3043@nathanielsimard 
