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@laggui laggui released this 12 Apr 20:12
· 511 commits to main since this release
v0.13.0
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The Burn Release 0.13 is a significant update introducing numerous new features and performance enhancements. One major change is the removal of the Sync trait implementation from most Burn types, see Core User APIs. Additionally, the release introduces several new tensor operations, module features, optimizers, as well as improvements to the autodiff backend. Notably, a new bridge mechanism facilitates runtime switching between backends, and significant work has been done on the Just-in-Time and Wgpu backends. The release also addresses numerous bug fixes, documentation improvements, infrastructure updates, CI enhancements, and miscellaneous changes to improve code quality and usability.

Core User APIs

A major change in this release is that most Burn types no longer implement the Sync trait, such as modules, optimizers, and tensors. This change should not impact users of the Learner struct for model training. However, it may affect those who implemented their own training loop and inference server. While modules, optimizers and tensors can be sent to other threads, they cannot be accessed concurrently by multiple threads. This aligns with Burn's workflow, where each tensor operation requires an owned version of the tensor. The change was made to safely reduce the number of locks needed when modifying the state of the autodiff graph, fusion state, allocation cache, and various other use cases. While not all locks have been removed, the type signature no longer poses a problem for follow-up optimizations. Note that the same tensor can still be sent to multiple threads without copying the underlying data. However it will require cloning before sending a tensor to a thread. (#1575) @nathanielsimard

Tensor

Module

Optimizer

Train

Backend

This release also introduces the backend bridge, a new mechanism for runtime switching between backends.
While an improvement, it remains compatible with previous methods of supporting mixed precision. (#1529) @nathanielsimard

JIT

Significant effort has been devoted over the past few months to refactor the previous Wgpu backend into a shader-agnostic Just-in-Time backend.
All lower-level dependencies have been abstracted into the Just-in-Time Runtime trait, requiring a compiler, compute server, and storage.
The bulk of this work was carried out by @nathanielsimard and @louisfd.

Commits: #1274 #1280 #1313 #1340 #1356 #1359 #1378 #1391 #1396 #1398 #1417 #1429 #1423 #1424 #1433 #1456 #1474 #1457 #1480 #1472 #1493 #1509 #1530 #1528 #1541 #1550 #1569

Wgpu

Autodiff

Extensive work has also been undertaken on Burn's autodiff backend.
The backend now supports gradient checkpointing to reduce memory usage and has been refactored into a client/server architecture.
These updates result in significantly less blocking when tracking gradients, enhancing performance particularly on smaller models.
Furthermore, various bugs have been fixed where some graph nodes weren't used, potentially truncating the autodiff graph.
Overall, these changes make the autodiff process more reliable and efficient. (#1575) (#1358) @louisfd @nathanielsimard

Candle

Data

Import

Benchmarks

We have implemented a system that enables the comparison of backends across a variety of tasks.
Currently, most of these tasks consist of micro-benchmarks, but we plan to expand the range of benchmarks in the future.
To ensure Burn's portability and performance across different devices, the community can run and upload benchmarks! 🔥

Bug Fix

Infrastructure

The minimum Rust version has been updated to 1.75. (#1297) @syl20bnr

Docs

CI

Tests

  • Add NaN and Inf detection in assert_approx_eq to catch potential numerical bugs. (#1209) @skewballfox

Misc

Contributors

errordeveloper, antimora, and 27 other contributors
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