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Changelog

All notable changes to fairseq2 are documented in this file.

The format is based on Keep a Changelog.

[0.8.0] - March 25th, 2026

  • fsspec integration for remote filesystem support. Checkpoints can be saved to and loaded from S3 via --checkpoint-dir s3://bucket/path/. Requires s3fs. (#1126)
  • New GlobalFileSystem replaces LocalFileSystem as default, dispatching to the appropriate backend based on URI scheme. (#1126)
  • PyTorch 2.9.1 and 2.10 (forward compatibility) are now supported. PyTorch 2.9 introduced breaking changes to LR scheduler return types, which have been addressed. (#1477, #1491, #1456)
  • Breaking: Trainer, evaluator, generator, validator, and task moved from fairseq2.recipe to fairseq2 package root. (#1417)
  • Breaking: LM recipes restructured: text_generate renamed to generate, SFT configs removed/renamed, recipe config classes changed. (#1431, #1432, #1433)
  • Breaking: RecipeModel is deprecated. Access the model directly via .module instead. (#1403)
  • Breaking: pq.ParquetDataset replaced with pyarrow.dataset interface. (#1490)
  • Breaking: resolve_optional renamed to maybe_resolve. (#1462)
  • Breaking: Revised ModelCheckpointLoader API. (#1475)
  • Breaking: Refactored tensor sharded modules (embedding, projection, FFN, attention). (#1476)
  • New context managers for procedural programming: GangContext, DeviceContext, DataTypeContext, current_dtype. Eliminates need to pass state through nested function calls. (#1474, #1473, #1464)
  • CheckpointManager, Optimizer, and LRScheduler now exposed in RecipeContext. (#1461)
  • Synchronous asset loading across ranks for models and tokenizers. Use when all ranks need identical assets loaded simultaneously. (#1429, #1426)
  • CheckpointManager.register_save_hook allows custom logic during checkpoint saves. (#1439)
  • Config files now support ${env:<NAME>} to interpolate environment variables. (#1435)
  • --no-rich CLI flag disables rich text output for log parsing. (#1421)
  • Hugging Face export now runs in isolated process with saved command line and logs for debugging. (#1459, #1458, #1437, #1434)
  • Improved support for gated Hugging Face models. (#1422)
  • get_family utility functions for detecting model families. (#1454)
  • Gemma3n model family (E2B/E4B) with text + audio inference and SFT training. (#1496)
  • Generic HuggingFace model integration: load, shard, and train any HuggingFace CausalLM model directly through HgCausalLMAdapter without requiring a native fairseq2 reimplementation. Includes FSDP sharding, HF tokenizer integration, and SFT recipe support. (#1479)
  • AssetDownloadManager gains local_only parameter and custom download subpath support. (#1423, #1425)
  • Recipes now set Python random and numpy seeds for reproducibility. (#1419)
  • Wandb metric recorder now respects wandb environment variables. (#1440)
  • Improved share_parameters implementation. (#1484)
  • Fixed cross_entropy with reduction="mean" to properly exclude padding tokens from the denominator. (#1455)
  • Fixed Flash3SDPA to support the flash-attn-3 v3.0.0 package API (flash_attn_3._C / torch.ops.flash_attn_3) in addition to the legacy flash_attn_3_cuda module. (#1495)
  • Fixed data pipeline sampling bug when allow_repeats=False with many pipelines. (#1471)
  • Fixed DataParallelFacade weakref errors. (#1447, #1436)
  • Fixed WER calculation to use lists instead of tensors. (#1413)

[0.7.0] - Nov 4th, 2025

  • RecipeModel is now callable and forwards the call to RecipeModel.module for a cleaner, more convenient syntax.
  • A new get_asset_download_manager helper function to download assets in procedural code.
  • A new register_recipe_assets helper function that can be used to register recipe-specific asset cards that cannot be (accidentally) overwritten by users. More info
  • Reference API documentation has been flattened and updated for better readability.
  • Revised Wav2Vec 2.0 recipes have been merged back and are available under the recipes/wav2vec2 directory.

[0.6.0] - Oct 7th, 2025

  • fairseq2.sharder is deprecated. fairseq2 now expects parallelism strategies to be applied within model factories. This gives model authors full control over how parallelism is applied to their models. More info

  • Gangs can now be used as a context manager, along with a new maybe_get_current_gangs() helper function. This feature is particularly useful in procedural programming, as it eliminates the need to pass a Gangs instance through every function call. More info

  • An experimental implementation of LLaMA 4 Scout model is now available.

  • The recipe command line interface now accepts a new --no-exit-on-error flag to allow post-mortem debugging of recipe processes. More info

  • The optimizer and learning rate scheduler recipe configurations now support multiple parameter groups. This is in particular convenient for models that require more than one learning rate to train (e.g. GAN models). More info

  • The regime.save_model_only recipe option now accepts 'all' and 'all_but_last' as alternatives to a boolean value. Setting the option to 'all' is equivalent to True and means that only the model state is saved during checkpointing. This is beneficial for short-lived training jobs where the user does not expect to resume the job but requires frequent snapshots of the model for evaluation purposes. In this mode, checkpointing is faster and disk space is saved by avoiding the storage of trainer, optimizer, and data reader states.

    The 'all_but_last' option is similar to 'all', except that the full state is saved only for the last checkpoint while all previous checkpoints will store only the model state, as in the 'all' mode. This is helpful to avoid unnecessary disk space use if the user does not plan to branch off the training from a previous checkpoint.

  • The default resume mode for Weights & Biases metric recorder changed from 'allow' to None to avoid noisy, safe-to-ignore warnings when resuming a preempted job.