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# Utility functions and classes

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.

### Standalone

These are standalone dataclasses used for checks, such as the type of distributed system being used

[[autodoc]] utils.ComputeEnvironment

[[autodoc]] utils.DistributedType

[[autodoc]] utils.DynamoBackend

[[autodoc]] utils.LoggerType

[[autodoc]] utils.PrecisionType

[[autodoc]] utils.RNGType

[[autodoc]] utils.SageMakerDistributedType

### Kwargs

These are configurable arguments for specific interactions throughout the PyTorch ecosystem that Accelerate handles under the hood.

[[autodoc]] utils.AutocastKwargs

[[autodoc]] utils.DistributedDataParallelKwargs

[[autodoc]] utils.FP8RecipeKwargs

[[autodoc]] utils.GradScalerKwargs

[[autodoc]] utils.InitProcessGroupKwargs

[[autodoc]] utils.KwargsHandler

## Plugins

These are plugins that can be passed to the [`Accelerator`] object. While they are defined elsewhere in the documentation, 
for convenience all of them are available to see here:

[[autodoc]] utils.DeepSpeedPlugin

[[autodoc]] utils.FullyShardedDataParallelPlugin

[[autodoc]] utils.GradientAccumulationPlugin

[[autodoc]] utils.MegatronLMPlugin

[[autodoc]] utils.TorchDynamoPlugin

## Configurations

These are classes which can be configured and passed through to the appropriate integration

[[autodoc]] utils.BnbQuantizationConfig

[[autodoc]] utils.DataLoaderConfiguration

[[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](../usage_guides/debug.md).




## 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.broadcast_object_list

[[autodoc]] utils.concatenate

[[autodoc]] utils.convert_outputs_to_fp32

[[autodoc]] utils.convert_to_fp32

[[autodoc]] utils.gather

[[autodoc]] utils.gather_object

[[autodoc]] utils.get_grad_scaler

[[autodoc]] utils.get_mixed_precision_context_manager

[[autodoc]] utils.listify

[[autodoc]] utils.pad_across_processes

[[autodoc]] utils.recursively_apply

[[autodoc]] utils.reduce

[[autodoc]] utils.send_to_device

[[autodoc]] utils.slice_tensors

## 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_torch_xla_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.

[[autodoc]] utils.set_numa_affinity

[[autodoc]] utils.environment.override_numa_affinity

[[autodoc]] utils.purge_accelerate_environment

## Memory

[[autodoc]] utils.find_executable_batch_size

## Modeling

These utilities relate to interacting with PyTorch models

[[autodoc]] utils.calculate_maximum_sizes

[[autodoc]] utils.compute_module_sizes

[[autodoc]] utils.extract_model_from_parallel

[[autodoc]] utils.get_balanced_memory

[[autodoc]] utils.get_max_layer_size

[[autodoc]] utils.infer_auto_device_map

[[autodoc]] utils.load_checkpoint_in_model

[[autodoc]] utils.load_offloaded_weights

[[autodoc]] utils.load_state_dict

[[autodoc]] utils.offload_state_dict

[[autodoc]] utils.retie_parameters

[[autodoc]] utils.set_module_tensor_to_device


## Parallel

These include general utilities that should be used when working in parallel.

[[autodoc]] utils.extract_model_from_parallel

[[autodoc]] utils.save

[[autodoc]] utils.load

[[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
