v0.18.0

# Summary

This release marks a significant step forward in performance, reliability, and optimization, ensuring a more robust and efficient system for our users. We've expanded our CI testing suite to address multi-threading, lazy evaluation, and async execution issues, ensuring robust performance across an increasing number of supported platforms.

## Matrix Multiplication Improvements

Optimized matrix multiplication kernels with specialized implementations for:
- Matrix-vector (mat@vec)
- Vector-matrix (vec@mat)
- Inner product
- Outer product

And enhanced flexibility in the matrix multiplication kernel generation engine, surpassing traditional GEMM (General Matrix Multiply) approaches.

For more details, including performance benchmarks, check out our [state-of-the-art multiplatform matrix multiplication post](https://burn.dev/blog/sota-multiplatform-matmul/).

## Fusion Enhancements

- Improved reliability and performance of Burn Fusion through advanced optimizations.
- Added support for basic dead code elimination.
- Introduced a new search engine that optimally reorders operations to maximize optimization opportunities, improving resilience to tensor operation ordering.

### Multi-Threading and Memory Management

- Resolved critical multi-threading issues by adopting a new approach to support multiple concurrent streams.
- Burn Fusion's lazy evaluation of registered operations across concurrent streams now places greater demands on memory management. To address this:
  - Implemented a robust memory leak test in our CI pipeline to verify the runtime's internal state, ensuring all handles and concurrent streams are properly cleaned up in all test cases.
  - Fixed bugs related to premature memory deallocation, enhancing memory management stability.

## CubeCL Config

By default, CubeCL loads its configuration from a TOML file (`cubecl.toml` or `CubeCL.toml`) located in your current directory or any parent directory. If no configuration file is found, CubeCL falls back to sensible defaults.

A typical `cubecl.toml` file might look like this:

```toml
[profiling]
logger = { level = "basic", stdout = true }

[autotune]
level = "balanced"
logger = { level = "minimal", stdout = true }

[compilation]
logger = { level = "basic", file = "cubecl.log", append = true }
```

Each section configures a different aspect of CubeCL:

- **profiling**: Controls performance profiling and logging.
- **autotune**: Configures the autotuning system, which benchmarks and selects optimal kernel parameters.
- **compilation**: Manages kernel compilation logging and cache.

For more info, check out the [CubeCL book](https://burn.dev/books/cubecl/advanced-usage/config).


As with previous releases, this version includes various bug fixes, many internal optimizations, and backend upgrades that reinforce the framework's performance and flexibility across platforms.


# Changelog

**Breaking:** the default stride(s) for pooling modules now match the kernel size instead of defaulting to strides of `1`. This will affect output shapes if strides were not explicitly set.

<details open>
<summary>
<strong><code>MaxPool2dConfig</code></strong>
</summary>
<br />

```diff
let pool = MaxPool2dConfig::new(kernel_size)
+   .with_strides([1, 1])
    .with_padding(PaddingConfig2d::Same)
    .init();
```
</details>

<details>
<summary>
<strong><code>MaxPool1dConfig</code></strong>
</summary>
<br />

```diff
let pool = MaxPool1dConfig::new(kernel_size)
+   .with_stride(1)
    .with_padding(PaddingConfig1d::Same)
    .init();
```
</details>

<details>
<summary>
<strong><code>AvgPool2dConfig</code></strong>
</summary>
<br />

```diff
let pool = AvgPool2dConfig::new(kernel_size)
+   .with_strides([1, 1])
    .with_padding(PaddingConfig2d::Same)
    .init();
```
</details>

<details>
<summary>
<strong><code>AvgPool1dConfig</code></strong>
</summary>
<br />

```diff
let pool = AvgPool1dConfig::new(kernel_size)
+   .with_stride(1)
    .with_padding(PaddingConfig1d::Same)
    .init();
```
</details>

## Module & Tensor
- Add tensor `grid::meshgrid` (#3107 #3191) @crutcher
- Add scalar tensor operations (#3127) @ArthurBrussee
- Orthogonal initialization (#3109) @dymat
- Support importing safetensors format (#2721) @wandbrandon @antimora
- Add `burn::linalg` norms (#3131) @crutcher
- Extract Linear.forward to nn::functional::linear (#3147) @crutcher
- Base impl of matmul for Int tensor (#3201) @crutcher
- (perf) generate_mask functions optimizations (#3203) @tafia
- Add CosineEmbeddingLoss module and cosine_similarity function (#3207) @antimora
- Tensor::slice_fill() (#3221 #3223) @crutcher
- Base impl of `tensor.slice_dim(dim, range)` (#3235) @crutcher
- Support shifting pre-computed RoPE values (#3275) @laggui
- Improve RoPE partial shift case (#3290) @laggui
- Add `tensor.roll()` and improve `AsIndex` (renamed `IndexConversion`) (#3281) @crutcher
- [Breaking] Update pooling default strides to match kernel size (#3338) @lucianyao
- Add `is_finite` tensor element wise op and fix `is_close/all_close` inf (#3341) @jonboh

## Backends
- [Perf] Interpolate optimizations (#3077) @wingertge
- [Perf] Slice assign (#3069) @wingertge
- Add multi stage conv (#3105) @wingertge
- [Perf] Convolution migration to NHWC (#3090) @wingertge
- Merge different convolution dimensional kernels (#3115) @wingertge
- Support reduce mixed precision accumulation w/ fusion (#3132) @nathanielsimard
- Update remote backend (#3175) @Cielbird
- Feat/autotune optional (#3188) @nathanielsimard
- cubecl unit matmul (#3214) @louisfd
- Update CubeCL for client based profiling (#3222) @ArthurBrussee
- Update cubecl unit matmul double buffered (#3233) @louisfd
- Burn-remote to_device function (#3189) @Cielbird
- Add Drop operation for fusion (#3263) @nathanielsimard
- Lazy tensor downloading in burn-remote (#3276) @Cielbird
- Improve specialized matmul (#3304) @louisfd
- Add autotune priority (#3347 #3378) @nathanielsimard
- Fix local tuner deadlock (#3384) @nathanielsimard
- Fix fusion wasm unsafe input (#3385 #3386) @nathanielsimard

### Bug Fixes
- Fix WASM deadlock by really properly not capturing locks (#3123) @ArthurBrussee
- Fix burn-cubecl with autotune disabled (#3141) @wingertge
- Fix fusion multiple reshapes (#3220) @nathanielsimard
- Fix/fusion multiple streams (#3297) @nathanielsimard
- Fix gather broadcasted indices in kernel impl and fusion (#3337) @laggui
- Fix rand interval (#3321) @laggui
- Restrict binary op lhs/rhs alias (#3349) @laggui
- Fix sum fallback when atomic add is not supported (#3369) @laggui

## Documentation & Examples
- Update pytorch-model.md with a new troubleshooting help (#3081) @antimora
- Contributor example instructions (#3153) @AshAnand34
- Update README.md with DeepWiki badge (#3192) @antimora
- Add recursion_limit macro to getting started exemples code (#3238) @Marc-AnthonyG
- KaTeX for Mathematical expressions in docstrings (#3278) @BhavyeMathur
- Add Metal backend support to custom-image-dataset (#3335 #3354) @TsaoLun
- Add link to license in README badge (#3356) @Olexandr88

### Fixes
- Fix typo in Burn Book (#3113) @danny-burrows
- fix typos (#3186) @omahs
- Fix Typos in Documentation Comments (#3280) @leopardracer
- Fix typo in code documentation for BurnGraph codegen (#3286) @kilavvy
- Fix error messages from tensor checks for flatten (#3319) @NoVegetable
- Fix broken link to burn-tch (#3365) @dbdr
- Update documentation description for nonzero and nonzero_async (#3368) @catch-twenty-two

## ONNX Support
- ONNX Import: switch to rank inferencing, rename shape to static_shape, decouple tensor shape info (#3037) @antimora
- Restrict ONNX opset to 16 and up (#3051) @antimora
- Allow Shape input type for Slice operation (#3092) @antimora
- Support onnx and, or & xor nodes (#3173) @tye-singwa
- Add support ONNX instance norm (#3177) @tye-singwa
- Onnx ceil & round (#3225) @tye-singwa
- Add support onnx group norm (#3245) @tye-singwa
- Add onnx SpaceToDepth / DepthToSpace (#3277) @tye-singwa
- Fix onnx topological sort check (#3284) @tye-singwa
- Add onnx ArgMin node (#3285) @tye-singwa
- Add support onnx size (#3301) @tye-singwa
- Support flexible backend selection for import tests (#3372 #3380) @lucianyao
- Fix ONNX node name sanitization and allow ai.onnx.ml domain (#3371) @antimora

## Enhancements
- Replace some powf->powi (#3152) @ArthurBrussee
- Improve fusion compilation speed (#3155) @nathanielsimard
- Perf/remove repeat dim (#3183) @nathanielsimard
- Perf: Fusion search for composed optimization (#3258) @nathanielsimard
- Improve matmul selector (#3307 #3343 #3350 #3376) @nathanielsimard

### Refactoring
- Refactor CubeCL slices (#3104) @nathanielsimard
- CubeCL init refactor (#3128) @nathanielsimard
- Refactor narrow, chunk and split (#3137) @laggui
- Refactor quantization scheme (#3042) @maxtremblay
- Migrated prng (random) to CubeCL (#3165 #3170) @Cielbird
- Break down `test_onnx.rs` into test subdirectories (#3144) @antimora
- Refactor: Move op_configuration.rs from burn-import to onnx-ir (#3126) @antimora
- Fix relative cmp + debug tools (#3197) @nathanielsimard
- Refactor cubecl line size matmul (#3219) @louisfd
- Absolute tolerance is too tight for strict/balanced/permissive (#3242) @laggui
- Fix clippy rust 1.88 and cargo run checks usage (#3325 #3320) @laggui
- Remove hip os cfg flags (#3336) @laggui
- Update cubecl matmul refactor / docs (#3366) @louisfd

### Miscellaneous
- Fix conv2d test tolerance & disable crates cache on stable linux-std runner (#3114) @laggui
- Replace run-checks scripts with command alias (#3118) @laggui
- Relax tolerance transformer autoregressive test (ndarray failure) (#3143) @crutcher
- Add cubecl.toml config (#3150) @nathanielsimard
- Use `CUBECL_DEBUG_OPTION=profile` macos ci (#3164) @laggui
- Update cubecl: sync_cube (#3163) @louisfd
- Fix autotune recursive (#3161)  @nathanielsimard
- Bump zip dependency (#3199) @swfsql
- Import `derive_new::new` for `safetensors` feat (#3205) @swfsql
- Add CUDA, Vulkan and WGPU on-demand self-hosted runners (#3190 #3215 #3334 #3348 #3351 #3352) @syl20bnr
- Fix: size_of import in quantization tests (#3195) @louisfd
- burn-dataset: Catch import.py unsuccessful exits (#3236) @drozdziak1
- Adding image dimensions to ImageDatasetItem (#3251) @catch-twenty-two
- burn-dataset: Make virtualenv optional when running importer.py (#3255) @drozdziak1
- Fix cubecl std usage (#3306) @laggui
- Fix tui legend label placement (#3327) @BenFradet
- Move blanket `Adaptor` impl to metrics base (#3346) @dbdr
- Make metric order consistent in summaries (#3353) @dbdr
- Fix cubecl `normal_respects_68_95_99_rule` (#3377) @laggui
- Bump deps (#3367) @ArthurBrussee
- Fix fusion rollback, disable autotune checks and other CI issues (#3362) @laggui