Skip to content

Update Rust crate burn to 0.19.0 #35

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Open
renovate wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update Rust crate burn to 0.19.0 #35

renovate wants to merge 1 commit into from

Conversation

@renovate
Copy link
Contributor

@renovate renovate bot commented Nov 2, 2025 •
edited
Loading

This PR contains the following updates:

Package Type Update Change
burn workspace.dependencies minor 0.18.0 -> 0.19.0

Release Notes

tracel-ai/burn (burn)

v0.19.1

Compare Source

Bug Fixes & Improvements

v0.19.0

Compare Source

Summary

This release brings major improvements to enable efficient distributed training, quantization, and CPU support in Burn.

To achieve true multi-GPU parallelism, we had to rethink several core systems: we implemented multi-stream execution to keep all GPUs busy, optimized device transfers to avoid unnecessary synchronization, and redesigned our locking strategies to eliminate bottlenecks in autotuning, fusion, and autodiff. We also introduced burn-collective for gradient synchronization and refactored our training loop to support different distributed training strategies.

Additionally, we added comprehensive quantization support, allowing models to use significantly less memory while maintaining performance through fused dequantization and optimized quantized operations.

Finally, we introduced a new CPU backend powered by MLIR and LLVM, bringing the same JIT compilation, autotuning, and fusion capabilities from our GPU backends to CPU execution.

As with previous releases, this version includes various bug fixes, further optimizations and enhanced documentation. Support for ONNX models has also been expanded, with additional operators and bug fixes for better operator coverage.

For more details, check out the release post on our website.

Changelog

Breaking

We've introduced a couple of breaking API changes with this release. The affected interfaces are detailed in the sections below.

Learning Strategy

We refactored the Learner to support better distributed training strategies. Instead of registering a list of device(s), you now specify a training strategy.

  let learner = LearnerBuilder::new(artifact_dir)
      .metric_train_numeric(AccuracyMetric::new())
      .metric_valid_numeric(AccuracyMetric::new())
      .metric_train_numeric(LossMetric::new())
      .metric_valid_numeric(LossMetric::new())
      .with_file_checkpointer(CompactRecorder::new())
-     .devices(vec![device.clone()])
+     .learning_strategy(LearningStrategy::SingleDevice(device.clone()))
      .num_epochs(config.num_epochs)
      .summary()
      .build(
          config.model.init::<B>(&device),
          config.optimizer.init(),
          config.learning_rate,
      );

Learner Training Result

The Learner previously lacked an evaluation loop. We extended its return type to include all training states in a TrainingResult, which includes the trained model and a metrics renderer.

- let model_trained = learner.fit(dataloader_train, dataloader_valid);
+ let result = learner.fit(dataloader_train, dataloader_valid);

- model_trained
+ result
+    .model
     .save_file(format!("{artifact_dir}/model"), &CompactRecorder::new())
     .expect("Trained model should be saved successfully");

This enables the renderer to be reused by the new evaluator so that training and evaluation metrics appear together in the TUI dashboard:

let mut renderer = result.renderer;
let evaluator = EvaluatorBuilder::new(artifact_dir)
    .renderer(renderer)
    .metrics((AccuracyMetric::new(), LossMetric::new()))
    .build(result.model.clone());

evaluator.eval(name, dataloader_test);

Interface Changes

Config

The Config trait now requires Debug:

- #[derive(Config)]
+ #[derive(Config, Debug)]
  pub struct TrainingConfig {
      // ...
  }
BatchNorm

BatchNorm no longer requires the spatial dimension generic:

  #[derive(Module, Debug)]
  pub struct ConvBlock<B: Backend> {
      conv: nn::conv::Conv2d<B>,
-     norm: BatchNorm<B, 2>,
+     norm: BatchNorm<B>,
      pool: Option<MaxPool2d>,
      activation: nn::Relu,
  }
Backend::seed

Seeding is now device-specific:

- B::seed(seed);
+ B::seed(&device, seed);
Tensor

For consistency with other methods like unsqueeze() / unsqueeze_dim(dim), squeeze(dim) was renamed:

- tensor.squeeze(dim)
+ tensor.squeeze_dim(dim)

We've also added a tensor.squeeze() method which squeezes all singleton dimensions.

Finally, we removed tensor ^ T syntax, which was clunky.

- use burn::tensor::T;
- tensor ^ T
+ tensor.t()

tensor.t() is also a simple alias for tensor.transpose().

Module & Tensor

Datasets & Training

Backends

Bug Fixes

Documentation & Examples

Fixes

ONNX Support

Enhancements

Refactoring
Miscellaneous

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

  • If you want to rebase/retry this PR, check this box

This PR was generated by Mend Renovate. View the repository job log.

@renovate
Copy link
Contributor Author

renovate bot commented Nov 2, 2025 •
edited
Loading

⚠️ Artifact update problem

Renovate failed to update an artifact related to this branch. You probably do not want to merge this PR as-is.

♻ Renovate will retry this branch, including artifacts, only when one of the following happens:

  • any of the package files in this branch needs updating, or
  • the branch becomes conflicted, or
  • you click the rebase/retry checkbox if found above, or
  • you rename this PR's title to start with "rebase!" to trigger it manually

The artifact failure details are included below:

File name: Cargo.lock
Command failed: cargo update --config net.git-fetch-with-cli=true --manifest-path Cargo.toml --package burn@0.18.0 --precise 0.19.1
    Updating crates.io index
error: failed to select a version for the requirement `burn = "^0.18.0"`
candidate versions found which didn't match: 0.19.1
location searched: crates.io index
required by package `burn-import v0.18.0`
    ... which satisfies dependency `burn-import = "^0.18.0"` of package `birefnet-util v0.1.0 (/tmp/renovate/repos/github/nusu-github/BiRefNet-Burn/crates/birefnet-util)`
    ... which satisfies path dependency `birefnet-util` (locked to 0.1.0) of package `birefnet v0.1.0 (/tmp/renovate/repos/github/nusu-github/BiRefNet-Burn/crates/birefnet)`

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Reviewers

No reviews

Assignees

No one assigned

Labels

None yet

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

1 participant