CHANGELOG.md

# Changelog
All notable changes to this project will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## NEXT - TBD
### Fixed

### Added


## [0.4.0] - 2021-07-31
### Fixed
- FSDP: fixed final backward callback in certain activation checkpointed cases. Before this fix,
        if a model is activation checkpointed in a certain way, the final backward
        callback can fire incorrectly. That's due to autograd and reentrant backward
        graphs. With this fix, the final callback is always registered on the outer
        most root FSDP instance (i.e. the outer most backward graph), which result
        in reliably firing. This makes FSDP much more robust with respect to different
        models and activation checkpoints. [#753]

### Added
- FSDP: support gradient accumulation without the `no_sync` context. This is useful
        in training with smaller number of GPU with same overall batch size as large
        number of GPUs. Compared with the `no_sync` context, this mode consumes less
        GPU memory but uses more networking bandwidth. [#752]


## [0.3.9] - 2021-07-26
### Fixed
- FSDP: fixed metadata saving and shard consolidation for MoE cases. When a model has
        shared parameters or mixture of expert layers, the handling of state dict
        metadata was broken. This release fixes that. [#746]
- OSS: fixed the buckets which would stay in fp16 if `broadcast fp16` was required [#751]

### Added
- FSDP: better performance; use `_allgather_base` and `_reduce_scatter_base` when they are
        available from pytorch nightly version (will be in 1.10 releases) [#729]
- FSDP: prepared FSDP internals for supporting multiple groups of flatten parameters (to support more general optimization) [#746]

## [0.3.8] - 2021-07-12
### Fixed
- checkpointing: Use dummy tensor to ensure backward pass is called. [#701]
- checkpointing: Ensure internal fwd counter is not incremented in eval mode. [#709]
- checkpointing: Use non-blocking CPU transfer to improve perf. [#719]
- FSDP: Fixed bug where buffers returned in `state_dict()` could still be half precision when `mixed_precision` is set to `True`. [#705]
- FSDP: Ensure requires_grad of FlatParameter is consistent with requires_grad of the original parameters. [#721]
- doc: Thoroughly improved the doc for FSDP. [#711]
- cleanup: Remove examples/ doc from the repo. [#712]
- cleanup: Future proof storage size test. [#735]
- cleanup: Migrate away from legacy torchtext iterators. [#713]
- chore: Updated torch 1.9 to release version. [#717]

### Added
- FSDP: supporting multiple flatten parameter groups [#708] [#711]
- chore: Add the latest numpy version to requirements-test.txt to prevent mypy errors on certain PR commits [#732]

## [0.3.7] - 2021-05-17
### Fixed
- setup.py: hide CUDA extensions behind `BUILD_CUDA_EXTENSIONS` envvar [#634]
- checkpointing: rename and move the `checkpoint_activations` wrapper [#654]
- FSDP: fix `local_state_dict` potentially called child class's `state_dict` [#574]
- FSDP: fix extra process groups being created by default. Old behavior can cause excessive GPU memory usage [#678] [#681]
- FSDP: fix forward pass not overlapping compute and allgather [#671]
- FSDP: improved frozen weight support [#657]
- FSDP: workaround AMP autocast cache issue with `clear_autocast_cache` flag [#650]
- FSDP: Rename API arg `cpu_offload` to `move_params_to_cpu` to better reflect functionality. We will deprecate `cpu_offload` in an upcoming release [#676]
- MoE: several fixes [#666] [#667] [#668]
- SDP: re-expose the module property [#647]
- wrap: support wrapping based on `wrapper_config` [#685]

### Added
- FSDP: added `force_input_to_fp32` flag for SyncBatchNorm [#659]
- FSDP: better memory usage for reduce bucket [#633]
- FSDP: added `local_metadata_dict` to save sharding relating information [#683]
- FSDP: added `consolidate_shard_weights` to reconstruct the consolidated (non-sharded) model weights from saved sharded weights and metadata on the disk [#683]
- Experimental SyncBatchNorm [#662] [#680]

## [0.3.6] - 2021-04-26
### Added
- FSDP: Consolidate cpu\_adam optimizer state dict ([#607](https://github.com/facebookresearch/fairscale/pull/607))

### Fixed
- FSDP: handle model with multiple forward pass and checkpoint ([#621](https://github.com/facebookresearch/fairscale/pull/621))
- FSDP & SDP: check before calling `_specify_ddp_gpu_num` ([#626](https://github.com/facebookresearch/fairscale/pull/626))
- FSDP: relax checking root condition ([#620](https://github.com/facebookresearch/fairscale/pull/620))
- SDP: removing an assert which does not seem always accurate ([#625](https://github.com/facebookresearch/fairscale/pull/625))
- FSDP: changing FSDP init to by pass pg validation ([#619](https://github.com/facebookresearch/fairscale/pull/619))
- OSS: to 100% coverage ([#618](https://github.com/facebookresearch/fairscale/pull/618))

## [0.3.5] - 2021-04-19
### Added
- [offload] Add API, tutorial and smaller doc string changes. ([#576](https://github.com/facebookresearch/fairscale/pull/576))

### Fixed
- FSDP: fixing training with freezing weights ([#614](https://github.com/facebookresearch/fairscale/pull/614))
- SDP: privatizing all the things ([#611](https://github.com/facebookresearch/fairscale/pull/611))
- FSDP: Make `_get_default_cuda_device` more robust to modules without params ([#606](https://github.com/facebookresearch/fairscale/pull/606))
- OffloadModel: Add prev codepath of using OffloadModel without activation checkpointing ([#608](https://github.com/facebookresearch/fairscale/pull/608))

## [0.3.4] - 2021-04-13
### Added
- FSDP: Add no broadcast optim state option ([#560](https://github.com/facebookresearch/fairscale/pull/560))

### Fixed
- ShardedDDP: Properly handle .eval() mode ([#587](https://github.com/facebookresearch/fairscale/pull/587))
- ShardedDDP: Handle model being moved back to CPU prior to state consolidation ([#573](https://github.com/facebookresearch/fairscale/pull/573))
- FSDP: much faster state consolidation ([#595](https://github.com/facebookresearch/fairscale/pull/595))
- FSDP: Add gradient pre-dedivide to prevent overflow with large world sizes ([#565](https://github.com/facebookresearch/fairscale/pull/565))
- Offload: (experimental) Fix activation offloading to CPU ([#588]((https://github.com/facebookresearch/fairscale/pull/588) )

## [0.3.3] - 2021-04-1
### Added
- FSDP: changed `auto_wrap_bn` utility function so that single FSDP group is optional ([#556](https://github.com/facebookresearch/fairscale/pull/556))
- FSDP: optimizer state load/save ([#537](https://github.com/facebookresearch/fairscale/pull/537))
- FSDP: fix weight init when using apply() ([#543](https://github.com/facebookresearch/fairscale/pull/543))
- Multiprocess Pipe: retired old implementation
- Experimental: xpipe

### Fixed
- ShardedDDP deferred init ([#558](https://github.com/facebookresearch/fairscale/pull/558))

## [0.3.2] - 2021-03-18
### Added
- Experimental: Add spectrain support ([#372](https://github.com/facebookresearch/fairscale/issues/372))
- FSDP: enabled pytorch SyncBN (no asserting) ([#527](https://github.com/facebookresearch/fairscale/issues/527))
- FSDP: added `auto_wrap_bn` utility function ([#531](https://github.com/facebookresearch/fairscale/pull/531))

### Fixed
- OSS: fix a compatibily problem with lightning wrt optimizer state dict ([#510](https://github.com/facebookresearch/fairscale/issues/510))
- FSDP: fixed a bug when part of autograd graph is traversed multiple times in mixed precision mode ([#513](https://github.com/facebookresearch/fairscale/pull/513))

## [0.3.1] - 2021-03-09
### Added
- FSDP docs ([#455](https://github.com/facebookresearch/fairscale/issues/455))
- `enable_wrap` and `auto_wrap` APIs ([#446](https://github.com/facebookresearch/fairscale/issues/446))
- Added experimental.nn.OffloadModel API for training large models on a single GPU.([#432](https://github.com/facebookresearch/fairscale/issues/432))

### Fixed
- OSS: fix a broken state dict when using non contiguous param groups
- Several SDP fixes around performance and corner cases
- Many FSDP fixes
- AdaScale & SDP/FSDP test added but not officially supported

## [0.3.0] - 2021-02-22
### Added
- FullyShardedDataParallel (FSDP) ([#413](https://github.com/facebookresearch/fairscale/issues/413))
- ShardedDDP fp16 grad reduction option ([#402](https://github.com/facebookresearch/fairscale/issues/402))
- Expose experimental algorithms within the pip package ([#410](https://github.com/facebookresearch/fairscale/pull/410))

### Fixed
- Catch corner case when the model is too small with respect to the world size, and shards are empty ([#406](https://github.com/facebookresearch/fairscale/pull/406))
- Memory leak in `checkpoint_wrapper` ([#412](https://github.com/facebookresearch/fairscale/pull/412))

## [0.1.7] - 2021-02-19
### Fixed
- ShardedDDP and OSS handle model trainability changes during training ([#369](https://github.com/facebookresearch/fairscale/issues/369))
- ShardedDDP state dict load/save bug ([#386](https://github.com/facebookresearch/fairscale/issues/386))
- ShardedDDP handle train/eval modes ([#393](https://github.com/facebookresearch/fairscale/issues/393))
- AdaScale handling custom scaling factors ([#401](https://github.com/facebookresearch/fairscale/issues/401))

### Added
- ShardedDDP manual reduce option for checkpointing ([#389](https://github.com/facebookresearch/fairscale/issues/389))

## [0.1.6] - 2021-02-10
### Added
- Checkpointing model wrapper (#376)
- Faster OSS, flatbuffers (#371)
- Small speedup in OSS clipgradnorm (#363)

### Fixed
- Bug in ShardedDDP with 0.1.5 depending the init (KeyError / OSS)
- Much refactoring in Pipe (#357, #358, #360, #362, #370, #373)
- Better pip integration / resident pytorch (#375)

## [0.1.5] - 2021-02-03
### Added
- Pytorch compatibility for OSS checkpoints (#310)
- Elastic checkpoints for OSS, world size can vary in between save and loads (#310)
- Tensor views for OSS bucketing, reduced CPU use (#300)
- Bucket calls in ShardedDDP, for faster inter node communications (#327)
- FlattenParamWrapper, which flattens module parameters into a single tensor seamlessly (#317)
- AMPnet experimental support (#304)

### Fixed
- ShardedDDP properly handles device changes via `.to()` (#353)
- Add a new interface for AdaScale, AdaScaleWrapper, which makes it compatible with OSS (#347)


## [0.1.4] - 2021-01-07
### Fixed
- Missing cu files in the pip package


## [0.1.3] - 2021-01-04
### Fixed
- Release numbering within python and from pypi

## [0.1.2] - 2021-01-04
### Added
- AdaScale:
  . Added gradient accumulation feature (#202)
  . Added support of `torch.lr_scheduler` (#229)
  . Added support for `add_param_groups` (#266)
  . Added support for `scale != world_size` (#266)

### Fixed
- AdaScale: smoothing factor value fixed when using gradient accumulation (#235)
- Pipe: documentation on balancing functions (#243)
- ShardedDDP: handle typical NLP models
- ShardedDDP: better partitioning when finetuning


## [0.1.1] - 2020-12-01
### Fixed
- make sure pip package includes header files (#221)

## [0.1.0] - 2020-12-01
### Added
- ShardedDataParallel with autoreduce (#157)
- cpu support for Pipe (#188)
- ShardedOptim: Distributed Grad Scaler (for torch AMP)  (#182)
- OSS-aware clip grads, bridge sharded states (#167)
- oss: add `rank_local_state_dict` staticmethod (#174)
- support for PyTorch 1.7.0 (#171)
- Add implementation of AdaScale (#139)

### Fixed
- pip package install (#196, #200)

## [0.0.3] - 2020-10-14
### Added
- multi-process pipe

### Fixed
- multiple OSS fixes
- MegaTron+OSS DDP fix

## [0.0.2] - 2020-08-28
### Added
- add ddp that works with oss with `reduce()` not `all_reduce()` (#19)
- support for PyTorch v1.6
- add mixed precision Adam (#40)
- Adam optimizer state scaling (#44)

### Fixed
- properly restore a sharded optim state (#39)
- OSS restore state to proper device (#46)
- optim/oss: support optimizers with additional step kwargs (#53)
- optim/oss: fix state cast (#56)
- fix eval for `oss_ddp` (#55)
- optim/oss: work correctly with LRScheduler (#58)

## [0.0.1] - 2020-07-31
- Initial release.