chore(deps): bump the rs-dependencies group in /burn-algorithms with 2 updates

[dependabot]

Updates the requirements on burn and burn-common to permit the latest version.
Updates burn to 0.19.0

Release notes

Sourced from burn's releases.

v0.19.0

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);
</tr></table> 

... (truncated)

Commits

• 0767a9a Add burn-cpu publish
• 48d498a Fix burn-train publish
• 56632f0 Fix burn-store publish
• 4f342ba Manually include katex header for now
• 07136a6 Fix burn-import store publish
• 6e3a4b2 Use cubecl released version + smaller text classification configs (#3937)
• 97273a0 Fix train result summary (#3935)
• f63015d Update cubecl with fixed matmul & reduce configs for web (#3933)
• a36f630 Bump cc from 1.2.41 to 1.2.43 (#3928)
• f9cbfec Combined PRs (#3931)
• Additional commits viewable in compare view

Updates burn-common to 0.19.0

Release notes

Sourced from burn-common's releases.

v0.19.0

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);
</tr></table> 

... (truncated)

Commits

• 56632f0 Fix burn-store publish
• 4f342ba Manually include katex header for now
• 6e3a4b2 Use cubecl released version + smaller text classification configs (#3937)
• 97273a0 Fix train result summary (#3935)
• a36f630 Bump cc from 1.2.41 to 1.2.43 (#3928)
• 87aa0be Update wgpu/vulkan CI tests (#3918)
• 50b9136 Fix matmul quantized autotune key (#3917)
• 4361961 Fix powf vectorization on WGPU (#3916)
• 6b74771 Fix q matmul naive (#3915)
• ecf88ef feat: Allow linear to use quantized matmul (#3913)
• Additional commits viewable in compare view

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