v0.12.0 New doc, gather_for_metrics, balanced device map and M1 support

New documentation

The whole documentation has been revamped, just go look at it here!

• Complete revamp of the docs by @muellerzr in #495

New gather_for_metrics method

When doing distributed evaluation, the dataloader loops back at the beginning of the dataset to make batches that have a round multiple of the number of processes. This causes the predictions to be slightly bigger than the length of the dataset, which used to require some truncating. This is all done behind the scenes now if you replace the gather your did in evaluation by gather_for_metrics.

• Reenable Gather for Metrics by @muellerzr in #590
• Fix gather_for_metrics by @muellerzr in #578
• Add a gather_for_metrics capability by @muellerzr in #540

Balanced device maps

When loading big models for inference, device_map="auto" used to fill the GPUs sequentially, making it hard to use a batch size > 1. It now balances the weights evenly on the GPUs so if you have more GPU space than the model size, you can do predictions with a bigger batch size!

M1 GPU support

Accelerate now supports M1 GPUs, to learn more about how to setup your environment, see the documentation.

• M1 GPU mps device integration by @pacman100 in #596

What's new?

• Small fixed for balanced device maps by @sgugger in #583
• Add balanced option for auto device map creation by @sgugger in #534
• fixing deepspeed slow tests issue by @pacman100 in #604
• add more conditions on casting by @younesbelkada in #606
• Remove redundant .run in WandBTracker. by @zh-plus in #605
• Fix some typos + wordings by @muellerzr in #603
• reorg of test scripts and minor changes to tests by @pacman100 in #602
• Move warning by @muellerzr in #598
• Shorthand way to grab a tracker by @muellerzr in #594
• Pin deepspeed by @muellerzr in #595
• Improve docstring by @muellerzr in #591
• TESTS! by @muellerzr in #589
• Fix DispatchDataloader by @sgugger in #588
• Use main_process_first in the examples by @muellerzr in #581
• Skip and raise NotImplementedError for gather_for_metrics for now by @muellerzr in #580
• minor FSDP launcher fix by @pacman100 in #579
• Refine test in set_module_tensor_to_device by @sgugger in #577
• Fix set_module_tensor_to_device by @sgugger in #576
• Add 8 bit support - chapter II by @younesbelkada in #539
• Fix tests, add wandb to gitignore by @muellerzr in #573
• Fix step by @muellerzr in #572
• Speed up main CI by @muellerzr in #571
• ccl version check and import different module according to version by @sywangyi in #567
• set default num_cpu_threads_per_process to improve oob performance by @sywangyi in #562
• Add a tqdm helper by @muellerzr in #564
• Rename actions to be a bit more accurate by @muellerzr in #568
• Fix clean by @muellerzr in #569
• enhancements and fixes for FSDP and DeepSpeed by @pacman100 in #532
• fix: saving model weights by @csarron in #556
• add on_main_process decorators by @ZhiyuanChen in #488
• Update imports.py by @KimBioInfoStudio in #554
• unpin datasets by @lhoestq in #563
• Create good defaults in accelerate launch by @muellerzr in #553
• Fix a few minor issues with example code in docs by @BenjaminBossan in #551
• deepspeed version 0.6.7 fix by @pacman100 in #544
• Rename test extras to testing by @muellerzr in #545
• Add production testing + fix failing CI by @muellerzr in #547
• Add a gather_for_metrics capability by @muellerzr in #540
• Allow for kwargs to be passed to trackers by @muellerzr in #542
• Add support for downcasting bf16 on TPUs by @muellerzr in #523
• Add more documentation for device maps computations by @sgugger in #530
• Restyle prepare one by @muellerzr in #531
• Pick a better default for offload_state_dict by @sgugger in #529
• fix some parameter setting does not work for CPU DDP and bf16 fail in… by @sywangyi in #527
• Fix accelerate tests command by @sgugger in #528

Significant community contributions

The following contributors have made significant changes to the library over the last release:

• @sywangyi
  • ccl version check and import different module according to version (#567)
  • set default num_cpu_threads_per_process to improve oob performance (#562)
  • fix some parameter setting does not work for CPU DDP and bf16 fail in… (#527)
• @ZhiyuanChen
  • add on_main_process decorators (#488)

v0.11.0 Gradient Accumulation and SageMaker Data Parallelism

Gradient Accumulation

Accelerate now handles gradient accumulation if you want, just pass along gradient_accumulation_steps=xxx when instantiating the Accelerator and put all your training loop step under a with accelerator.accumulate(model):. Accelerate will then handle the loss re-scaling and gradient accumulation for you (avoiding slowdowns in distributed training when gradients only need to be synced when you want to step). More details in the documentation.

• Add gradient accumulation doc by @muellerzr in #511
• Make gradient accumulation work with dispatched dataloaders by @muellerzr in #510
• Introduce automatic gradient accumulation wrapper + fix a few test issues by @muellerzr in #484

Support for SageMaker Data parallelism

Accelerate now support SageMaker specific brand of data parallelism.

• SageMaker enhancements to allow custom docker image, input channels referring to s3/remote data locations and metrics logging by @pacman100 in #504
• SageMaker DP Support by @pacman100 in #494

What's new?

• Fix accelerate tests command by @sgugger in #528
• FSDP integration enhancements and fixes by @pacman100 in #522
• Warn user if no trackers are installed by @muellerzr in #524
• Fixup all example CI tests and properly fail by @muellerzr in #517
• fixing deepspeed multi-node launcher by @pacman100 in #514
• Add special Parameters modules support by @younesbelkada in #519
• Don't unwrap in save_state() by @cccntu in #489
• Fix a bug when reduce a tensor. by @wwhio in #513
• Add benchmarks by @sgugger in #506
• Fix DispatchDataLoader length when split_batches=True by @sgugger in #509
• Fix scheduler in gradient accumulation example by @muellerzr in #500
• update dataloader wrappers to have total_batch_size attribute by @pacman100 in #493
• Introduce automatic gradient accumulation wrapper + fix a few test issues by @muellerzr in #484
• add use_distributed property by @ZhiyuanChen in #487
• fixing fsdp autowrap functionality by @pacman100 in #475
• Use datasets 2.2.0 for now by @muellerzr in #481
• Rm gradient accumulation on TPU by @muellerzr in #479
• Revert "Pin datasets for now by @muellerzr in #477)"
• Pin datasets for now by @muellerzr in #477
• Some typos and cosmetic fixes by @douwekiela in #472
• Fix when TPU device check is ran by @muellerzr in #469
• Refactor Utility Documentation by @muellerzr in #467
• Add docbuilder to quality by @muellerzr in #468
• Expose some is_*_available utils in docs by @muellerzr in #466
• Cleanup CI Warnings by @muellerzr in #465
• Link CI slow runners to the commit by @muellerzr in #464
• Fix subtle bug in BF16 by @muellerzr in #463
• Include bf16 support for TPUs and CPUs, and a better check for if a CUDA device supports BF16 by @muellerzr in #462
• Handle bfloat16 weights in disk offload without adding memory overhead by @noamwies in #460)
• Handle bfloat16 weights in disk offload by @sgugger in #460
• Raise a clear warning if a user tries to modify the AcceleratorState by @muellerzr in #458
• Right step point by @muellerzr in #459
• Better checks for if a TPU device exists by @muellerzr in #456
• Offload and modules with unused submodules by @sgugger in #442

V0.10.0 DeepSpeed integration revamp and TPU speedup

This release adds two major new features: the DeepSpeed integration has been revamped to match the one in Transformers Trainer, with multiple new options unlocked, and the TPU integration has been sped up.

This version also officially stops supporting Python 3.6 and requires Python 3.7+

DeepSpeed integration revamp

Users can now specify a DeepSpeed config file when they want to use DeepSpeed, which unlocks many new options. More details in the new documentation.

• Migrate HFDeepSpeedConfig from trfrs to accelerate by @pacman100 in #432
• DeepSpeed Revamp by @pacman100 in #405

TPU speedup

If you're using TPUs we have sped up the dataloaders and models quite a bit, on top of a few bug fixes.

• Revamp TPU internals to be more efficient + enable mixed precision types by @muellerzr in #441

What's new?

• Fix docstring by @muellerzr in #447
• Add psutil as depenedency by @sgugger in #445
• fix fsdp torch version dependency by @pacman100 in #437
• Create Gradient Accumulation Example by @muellerzr in #431
• init by @muellerzr in #429
• Introduce no_sync context wrapper + clean up some more warnings for DDP by @muellerzr in #428
• updating tests to resolve runner failures wrt deepspeed revamp by @pacman100 in #427
• Fix secrets in Docker workflow by @muellerzr in #426
• Introduce a Dependency Checker to trigger new Docker Builds on main by @muellerzr in #424
• Enable slow tests nightly by @muellerzr in #421
• Push out python 3.6 + fix all tests related to the upgrade by @muellerzr in #420
• Speedup main CI by @muellerzr in #419
• Switch to evaluate for metrics by @sgugger in #417
• Create an issue template for Accelerate by @muellerzr in #415
• Introduce post-merge runners by @muellerzr in #416
• Fix debug_launcher issues by @muellerzr in #413
• Use main egg by @muellerzr in #414
• Introduce nightly runners by @muellerzr in #410
• Update requirements to pin tensorboard and include psutil by @muellerzr in #408
• Fix CUDA examples tests by @muellerzr in #407
• Move datasets and transformers to under func by @muellerzr in #411
• Fix CUDA Dockerfile by @muellerzr in #409
• Hotfix all failing GPU tests by @muellerzr in #401
• improve metrics logged in examples by @pacman100 in #399
• Refactor offload_state_dict and fix in offload_weight by @sgugger in #398
• Refactor version checking into a utility by @muellerzr in #395
• Include fastai in frameworks by @muellerzr in #396
• Add packaging to requirements by @muellerzr in #394
• Better dispatch for submodules by @sgugger in #392
• Build Docker Images nightly by @muellerzr in #391
• Small bugfix for the stalebot workflow by @muellerzr in #390
• Introduce stalebot by @muellerzr in #387
• Create Dockerfiles for Accelerate by @muellerzr in #377
• Mix precision -> Mixed precision by @muellerzr in #388
• Fix OneCycle step length when in multiprocess by @muellerzr in #385

v0.9.0: Refactor utils to use in Transformers

v0.9.0: Refactor utils to use in Transformers

This release offers no significant new API, it is just needed to have access to some utils in Transformers.

• Handle deprication errors in launch by @muellerzr in #360
• Update launchers.py by @tmabraham in #363
• fix tracking by @pacman100 in #361
• Remove tensor call by @muellerzr in #365
• Add a utility for writing a barebones config file by @muellerzr in #371
• fix deepspeed model saving by @pacman100 in #370
• deepspeed save model temp fix by @pacman100 in #374
• Refactor tests to use accelerate launch by @muellerzr in #373
• fix zero stage-1 by @pacman100 in #378
• fix shuffling for ShufflerIterDataPipe instances by @loubnabnl in #376
• Better check for deepspeed availability by @sgugger in #379
• Refactor some parts in utils by @sgugger in #380

v0.8.0: Big model inference

v0.8.0: Big model inference

Big model inference

To handle very large models, new functionality has been added in Accelerate:

• a context manager to initalize empty models
• a function to load a sharded checkpoint directly on the right devices
• a set of custom hooks that allow execution of a model split on different devices, as well as CPU or disk offload
• a magic method that auto-determines a device map for a given model, maximizing the GPU spaces, available RAM before using disk offload as a last resort.
• a function that wraps the last three blocks in one simple call (load_checkpoint_and_dispatch)

See more in the documentation

• Big model inference by @sgugger in #345

What's new

• Create peak_memory_uasge_tracker.py by @pacman100 in #336
• Fixed a typo to enable running accelerate correctly by @Idodox in #339
• Introduce multiprocess logger by @muellerzr in #337
• Refactor utils into its own module by @muellerzr in #340
• Improve num_processes question in CLI by @muellerzr in #343
• Handle Manual Wrapping in FSDP. Minor fix of fsdp example. by @pacman100 in #342
• Better prompt for number of training devices by @muellerzr in #344
• Fix prompt for num_processes by @pacman100 in #347
• Fix sample calculation in examples by @muellerzr in #352
• Fixing metric eval in distributed setup by @pacman100 in #355
• DeepSpeed and FSDP plugin support through script by @pacman100 in #356

v0.7.1 Patch release

v0.7.1 Patch release

• Fix fdsp config in cluster 331
• Add guards for batch size finder 334
• Patchfix infinite loop 335

v0.7.0: Logging API, FSDP, batch size finder and examples revamp

v0.7.0: Logging API, FSDP, batch size finder and examples revamp

Logging API

Use any of your favorite logging libraries (TensorBoard, Wandb, CometML...) with just a few lines of code inside your training scripts with Accelerate. All details are in the documentation.

• Add logging capabilities by @muellerzr in #293

Support for FSDP (fully sharded DataParallel)

PyTorch recently released a new model wrapper for sharded DDP training called FSDP. This release adds support for it (note that it doesn't work with mixed precision yet). See all caveats in the documentation.

• PyTorch FSDP Feature Incorporation by @pacman100 in #321

Batch size finder

Say goodbye to the CUDA OOM errors with the new find_executable_batch_size decorator. Just decorate your training function and pick a starting batch size, then let Accelerate do the rest.

• Add a memory-aware decorator for CUDA OOM avoidance by @muellerzr in #324

Examples revamp

The Accelerate examples are now split in two: you can find in the base folder a very simple nlp and computer vision examples, as well as complete versions incorporating all features. But you can also browse the examples in the by_feature subfolder, which will show you exactly what code to add for each given feature (checkpointing, tracking, cross-validation etc.)

• Refactor Examples by Feature by @muellerzr in #312

What's Changed

• Document save/load state by @muellerzr in #290
• Refactor precisions to its own enum by @muellerzr in #292
• Load model and optimizet states on CPU to void OOMs by @sgugger in #299
• Fix example for datasets v2 by @sgugger in #298
• Leave default as None in mixed_precision for launch command by @sgugger in #300
• Pass lr_scheduler to Accelerator.prepare by @sgugger in #301
• Create new TestCase classes and clean up W&B tests by @muellerzr in #304
• Have custom trackers work with the API by @muellerzr in #305
• Write tests for comet_ml by @muellerzr in #306
• Fix training in DeepSpeed by @sgugger in #308
• Update example scripts by @muellerzr in #307
• Use --no_local_rank for DeepSpeed launch by @sgugger in #309
• Fix Accelerate CLI CPU option + small fix for W&B tests by @muellerzr in #311
• Fix DataLoader sharding for deepspeed in accelerate by @m3rlin45 in #315
• Create a testing framework for example scripts and fix current ones by @muellerzr in #313
• Refactor Tracker logic and write guards for logging_dir by @muellerzr in #316
• Create Cross-Validation example by @muellerzr in #317
• Create alias for Accelerator.free_memory by @muellerzr in #318
• fix typo in docs of accelerate tracking by @loubnabnl in #320
• Update examples to show how to deal with extra validation copies by @muellerzr in #319
• Fixup all checkpointing examples by @muellerzr in #323
• Introduce reduce operator by @muellerzr in #326

New Contributors

• @m3rlin45 made their first contribution in #315
• @loubnabnl made their first contribution in #320
• @pacman100 made their first contribution in #321

Full Changelog: v0.6.0...v0.7.0

v0.6.2: Fix launcher with mixed precision

The launcher was ignoring the mixed precision attribute of the config since v0.6.0. This patch fixes that.

v0.6.1: Hot fix

Patches an issue with mixed precision (see #286)

v0.6.0: Checkpointing and bfloat16 support

This release adds support for bloat16 mixed precision training (requires PyTorch >= 1.10) and a brand-new checkpoint utility to help with resuming interrupted trainings. We also get a completely revamped documentation frontend.

Checkpoints

Save the current state of all your objects (models, optimizers, RNG states) with accelerator.save_state(path_to_checkpoint) and reload everything by calling accelerator.load_state(path_to_checkpoint)

• Add in checkpointing capability by @muellerzr in #255
• Implementation of saving and loading custom states by @muellerzr in #270

BFloat16 support

Accelerate now supports bfloat16 mixed precision training. As a result the old --fp16 argument has been deprecated to be replaced by the more generic --mixed-precision.

• Add bfloat16 support #243 by @ikergarcia1996 in #247

New env subcommand

You can now type accelerate env to have a copy-pastable summary of your environment and default configuration. Very convenient when opening a new issue!

• add env command by @johnnv1 in #280

New doc frontend

The documentation has been switched to the new Hugging Face frontend, like Transformers and Datasets.

• Convert documentation to the new front by @sgugger in #271

What's Changed

• Fix send_to_device with non-tensor data by @sgugger in #177
• Handle UserDict in all utils by @sgugger in #179
• Use collections.abc.Mapping to handle both the dict and the UserDict types by @mariosasko in #180
• fix: use store_true on argparse in nlp example by @monologg in #183
• Update README.md by @TevenLeScao in #187
• Add signature check for set_to_none in Optimizer.zero_grad by @sgugger in #189
• fix typo in code snippet by @MrZilinXiao in #199
• Add high-level API reference to README by @Chris-hughes10 in #204
• fix rng_types in accelerator by @s-kumano in #206
• Pass along drop_last in DispatchDataLoader by @sgugger in #212
• Rename state to avoid name conflicts with pytorch's Optimizer class. by @yuxinyuan in #224
• Fix lr scheduler num samples by @sgugger in #227
• Add customization point for init_process_group kwargs by @sgugger in #228
• Fix typo in installation docs by @jaketae in #234
• make deepspeed optimizer match parameters of passed optimizer by @jmhessel in #246
• Upgrade black to version ~=22.0 by @LysandreJik in #250
• add support of gather_object by @ZhiyuanChen in #238
• Add launch flags --module and --no_python (#256) by @parameter-concern in #258
• Accelerate + Animus/Catalyst = 🚀 by @Scitator in #249
• Add debug_launcher by @sgugger in #259
• enhance compatibility of honor type by @ZhiyuanChen in #241
• Add a flag to use CPU only in the config by @sgugger in #263
• Basic fixes for DeepSpeed by @sgugger in #264
• Ability to set the seed with randomness from inside Accelerate by @muellerzr in #266
• Don't use dispatch_batches when torch is < 1.8.0 by @sgugger in #269
• Make accelerated model with AMP possible to pickle by @BenjaminBossan in #274
• Contributing guide by @LysandreJik in #254
• replace texts and link (master -> main) by @johnnv1 in #282
• Use workflow from doc-builder by @sgugger in #275
• Pass along execution info to the exit of autocast by @sgugger in #284

New Contributors

• @mariosasko made their first contribution in #180
• @monologg made their first contribution in #183
• @TevenLeScao made their first contribution in #187
• @MrZilinXiao made their first contribution in #199
• @Chris-hughes10 made their first contribution in #204
• @s-kumano made their first contribution in #206
• @yuxinyuan made their first contribution in #224
• @jaketae made their first contribution in #234
• @jmhessel made their first contribution in #246
• @ikergarcia1996 made their first contribution in #247
• @ZhiyuanChen made their first contribution in #238
• @parameter-concern made their first contribution in #258
• @Scitator made their first contribution in #249
• @muellerzr made their first contribution in #255
• @BenjaminBossan made their first contribution in #274
• @johnnv1 made their first contribution in #280

Full Changelog: v0.5.1...v0.6.0