Update accelerate requirement from 0.33.0 to 1.0.0

[dependabot]

Updates the requirements on accelerate to permit the latest version.

Release notes

Sourced from accelerate's releases.

Accelerate 1.0.0 is here!

🚀 Accelerate 1.0 🚀

With accelerate 1.0, we are officially stating that the core parts of the API are now "stable" and ready for the future of what the world of distributed training and PyTorch has to handle. With these release notes, we will focus first on the major breaking changes to get your code fixed, followed by what is new specifically between 0.34.0 and 1.0.

To read more, check out our official blog here

Migration assistance

• Passing in dispatch_batches, split_batches, even_batches, and use_seedable_sampler to the Accelerator() should now be handled by creating an accelerate.utils.DataLoaderConfiguration() and passing this to the Accelerator() instead (Accelerator(dataloader_config=DataLoaderConfiguration(...)))
• Accelerator().use_fp16 and AcceleratorState().use_fp16 have been removed; this should be replaced by checking accelerator.mixed_precision == "fp16"
• Accelerator().autocast() no longer accepts a cache_enabled argument. Instead, an AutocastKwargs() instance should be used which handles this flag (among others) passing it to the Accelerator (Accelerator(kwargs_handlers=[AutocastKwargs(cache_enabled=True)]))
• accelerate.utils.is_tpu_available should be replaced with accelerate.utils.is_torch_xla_available
• accelerate.utils.modeling.shard_checkpoint should be replaced with split_torch_state_dict_into_shards from the huggingface_hub library
• accelerate.tqdm.tqdm() no longer accepts True/False as the first argument, and instead, main_process_only should be passed in as a named argument

Multiple Model DeepSpeed Support

After long request, we finally have multiple model DeepSpeed support in Accelerate! (though it is quite early still). Read the full tutorial here, however essentially:

When using multiple models, a DeepSpeed plugin should be created for each model (and as a result, a separate config). a few examples are below:

Knowledge distillation

(Where we train only one model, zero3, and another is used for inference, zero2)

from accelerate import Accelerator
from accelerate.utils import DeepSpeedPlugin
zero2_plugin = DeepSpeedPlugin(hf_ds_config="zero2_config.json")
zero3_plugin = DeepSpeedPlugin(hf_ds_config="zero3_config.json")
deepspeed_plugins = {"student": zero2_plugin, "teacher": zero3_plugin}
accelerator = Accelerator(deepspeed_plugins=deepspeed_plugins)

To then select which plugin to be used at a certain time (aka when calling prepare), we call `accelerator.state.select_deepspeed_plugin("name"), where the first plugin is active by default:

accelerator.state.select_deepspeed_plugin("student")
student_model, optimizer, scheduler = ...
student_model, optimizer, scheduler, train_dataloader = accelerator.prepare(student_model, optimizer, scheduler, train_dataloader)
accelerator.state.select_deepspeed_plugin("teacher") # This will automatically enable zero init
teacher_model = AutoModel.from_pretrained(...)
teacher_model = accelerator.prepare(teacher_model)

... (truncated)

Commits

• 5d71646 Fix merge
• 97d413e Release: v1.0.0
• 0c46240 Release: v1.0.0rc1
• 873b623 Release: v1.0.0rc0
• 127818f MNT Permission for PRs for GH token in stale.yml (#3112)
• bcc13c0 typo of "scalar" instead of "scaler" (#3116)
• d4d6b6e fix tip brackets typo (#3129)
• 1077611 only move model to device when model is in cpu and target device is xpu (#3133)
• cd93e35 🐛 [HotFix] Handle Profiler Activities Based on PyTorch Version (#3136)
• e93b056 fix deprecated torch.cuda.amp.GradScaler FutureWarning for pytorch 2.4+ (#3...
• Additional commits viewable in compare view

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