Helpful Utilities
Below are a variety of utility functions that 🤗 Accelerate provides, broken down by use-case.
Constants
Constants used throughout 🤗 Accelerate for reference
The following are constants used when utilizing [Accelerator.save_state]
utils.MODEL_NAME: "pytorch_model"
utils.OPTIMIZER_NAME: "optimizer"
utils.RNG_STATE_NAME: "random_states"
utils.SCALER_NAME: "scaler.pt
utils.SCHEDULER_NAME: "scheduler
The following are constants used when utilizing [Accelerator.save_model]
utils.WEIGHTS_NAME: "pytorch_model.bin"
utils.SAFE_WEIGHTS_NAME: "model.safetensors"
utils.WEIGHTS_INDEX_NAME: "pytorch_model.bin.index.json"
utils.SAFE_WEIGHTS_INDEX_NAME: "model.safetensors.index.json"
Data Classes
These are basic dataclasses used throughout 🤗 Accelerate and they can be passed in as parameters.
[[autodoc]] utils.DistributedType
[[autodoc]] utils.DynamoBackend
[[autodoc]] utils.LoggerType
[[autodoc]] utils.PrecisionType
[[autodoc]] utils.FP8RecipeKwargs
[[autodoc]] utils.ProjectConfiguration
Environmental Variables
These are environmental variables that can be enabled for different use cases
ACCELERATE_DEBUG_MODE(str): Whether to run accelerate in debug mode. More info available here.
Plugins
These are plugins that can be passed to the [Accelerator] object. While they are defined elsewhere in the documentation,
for convience all of them are available to see here:
[[autodoc]] utils.DeepSpeedPlugin
[[autodoc]] utils.FullyShardedDataParallelPlugin
[[autodoc]] utils.GradientAccumulationPlugin
[[autodoc]] utils.MegatronLMPlugin
[[autodoc]] utils.TorchDynamoPlugin
Data Manipulation and Operations
These include data operations that mimic the same torch ops but can be used on distributed processes.
[[autodoc]] utils.broadcast
[[autodoc]] utils.concatenate
[[autodoc]] utils.gather
[[autodoc]] utils.pad_across_processes
[[autodoc]] utils.reduce
[[autodoc]] utils.send_to_device
Environment Checks
These functionalities check the state of the current working environment including information about the operating system itself, what it can support, and if particular dependencies are installed.
[[autodoc]] utils.is_bf16_available
[[autodoc]] utils.is_ipex_available
[[autodoc]] utils.is_mps_available
[[autodoc]] utils.is_npu_available
[[autodoc]] utils.is_torch_version
[[autodoc]] utils.is_tpu_available
[[autodoc]] utils.is_xpu_available
Environment Manipulation
[[autodoc]] utils.patch_environment
[[autodoc]] utils.clear_environment
[[autodoc]] utils.write_basic_config
When setting up 🤗 Accelerate for the first time, rather than running accelerate config [~utils.write_basic_config] can be used as an alternative for quick configuration.
Memory
[[autodoc]] utils.get_max_memory
[[autodoc]] utils.find_executable_batch_size
Modeling
These utilities relate to interacting with PyTorch models
[[autodoc]] utils.extract_model_from_parallel
[[autodoc]] utils.get_max_layer_size
[[autodoc]] utils.offload_state_dict
Parallel
These include general utilities that should be used when working in parallel.
[[autodoc]] utils.extract_model_from_parallel
[[autodoc]] utils.save
[[autodoc]] utils.wait_for_everyone
Random
These utilities relate to setting and synchronizing of all the random states.
[[autodoc]] utils.set_seed
[[autodoc]] utils.synchronize_rng_state
[[autodoc]] utils.synchronize_rng_states
PyTorch XLA
These include utilities that are useful while using PyTorch with XLA.
[[autodoc]] utils.install_xla
Loading model weights
These include utilities that are useful to load checkpoints.
[[autodoc]] utils.load_checkpoint_in_model
Quantization
These include utilities that are useful to quantize model.
[[autodoc]] utils.load_and_quantize_model
[[autodoc]] utils.BnbQuantizationConfig