Accelerator
The Accelerator is the main class for enabling distributed training on any type of training setup. Read the Add Accelerator to your code tutorial to learn more about how to add the Accelerator to your script.
Accelerator
class accelerate.Accelerator
< source >( device_placement: bool = True split_batches: bool = <object object at 0x7fb593100b70> mixed_precision: PrecisionType | str | None = None gradient_accumulation_steps: int = 1 cpu: bool = False dataloader_config: DataLoaderConfiguration | None = None deepspeed_plugin: DeepSpeedPlugin | None = None fsdp_plugin: FullyShardedDataParallelPlugin | None = None megatron_lm_plugin: MegatronLMPlugin | None = None rng_types: list[str | RNGType] | None = None log_with: str | LoggerType | GeneralTracker | list[str | LoggerType | GeneralTracker] | None = None project_dir: str | os.PathLike | None = None project_config: ProjectConfiguration | None = None gradient_accumulation_plugin: GradientAccumulationPlugin | None = None dispatch_batches: bool | None = <object object at 0x7fb593100b90> even_batches: bool = <object object at 0x7fb593100ba0> use_seedable_sampler: bool = <object object at 0x7fb593100bb0> step_scheduler_with_optimizer: bool = True kwargs_handlers: list[KwargsHandler] | None = None dynamo_backend: DynamoBackend | str | None = None )
Parameters
- device_placement (
bool
, optional, defaults toTrue
) — Whether or not the accelerator should put objects on device (tensors yielded by the dataloader, model, etc…). - mixed_precision (
str
, optional) — Whether or not to use mixed precision training. Choose from ‘no’,‘fp16’,‘bf16’ or ‘fp8’. Will default to the value in the environment variableACCELERATE_MIXED_PRECISION
, which will use the default value in the accelerate config of the current system or the flag passed with theaccelerate.launch
command. ‘fp8’ requires the installation of transformers-engine. - gradient_accumulation_steps (
int
, optional, default to 1) — The number of steps that should pass before gradients are accumulated. A number > 1 should be combined withAccelerator.accumulate
. If not passed, will default to the value in the environment variableACCELERATE_GRADIENT_ACCUMULATION_STEPS
. Can also be configured through aGradientAccumulationPlugin
. - cpu (
bool
, optional) — Whether or not to force the script to execute on CPU. Will ignore GPU available if set toTrue
and force the execution on one process only. - dataloader_config (
DataLoaderConfiguration
, optional) — A configuration for how the dataloaders should be handled in distributed scenarios. - deepspeed_plugin (DeepSpeedPlugin, optional) — Tweak your DeepSpeed related args using this argument. This argument is optional and can be configured directly using accelerate config
- fsdp_plugin (FullyShardedDataParallelPlugin, optional) — Tweak your FSDP related args using this argument. This argument is optional and can be configured directly using accelerate config
- megatron_lm_plugin (MegatronLMPlugin, optional) — Tweak your MegatronLM related args using this argument. This argument is optional and can be configured directly using accelerate config
- rng_types (list of
str
or RNGType) — The list of random number generators to synchronize at the beginning of each iteration in your prepared dataloaders. Should be one or several of:"torch"
: the base torch random number generator"cuda"
: the CUDA random number generator (GPU only)"xla"
: the XLA random number generator (TPU only)"generator"
: thetorch.Generator
of the sampler (or batch sampler if there is no sampler in your dataloader) or of the iterable dataset (if it exists) if the underlying dataset is of that type.
Will default to
["torch"]
for PyTorch versions <=1.5.1 and["generator"]
for PyTorch versions >= 1.6. - log_with (list of
str
, LoggerType or GeneralTracker, optional) — A list of loggers to be setup for experiment tracking. Should be one or several of:"all"
"tensorboard"
"wandb"
"comet_ml"
If"all"
is selected, will pick up all available trackers in the environment and initialize them. Can also accept implementations ofGeneralTracker
for custom trackers, and can be combined with"all"
.
- project_config (ProjectConfiguration, optional) — A configuration for how saving the state can be handled.
- project_dir (
str
,os.PathLike
, optional) — A path to a directory for storing data such as logs of locally-compatible loggers and potentially saved checkpoints. - step_scheduler_with_optimizer (
bool
, optional, defaults toTrue
) — SetTrue
if the learning rate scheduler is stepped at the same time as the optimizer,False
if only done under certain circumstances (at the end of each epoch, for instance). - kwargs_handlers (list of KwargsHandler, optional) — A list of KwargsHandler to customize how the objects related to distributed training, profiling or mixed precision are created. See kwargs for more information.
- dynamo_backend (
str
or DynamoBackend, optional, defaults to"no"
) — Set to one of the possible dynamo backends to optimize your training with torch dynamo. - gradient_accumulation_plugin (GradientAccumulationPlugin, optional) —
A configuration for how gradient accumulation should be handled, if more tweaking than just the
gradient_accumulation_steps
is needed.
Creates an instance of an accelerator for distributed training (on multi-GPU, TPU) or mixed precision training.
Available attributes:
- device (
torch.device
) — The device to use. - distributed_type (DistributedType) — The distributed training configuration.
- local_process_index (
int
) — The process index on the current machine. - mixed_precision (
str
) — The configured mixed precision mode. - num_processes (
int
) — The total number of processes used for training. - optimizer_step_was_skipped (
bool
) — Whether or not the optimizer update was skipped (because of gradient overflow in mixed precision), in which case the learning rate should not be changed. - process_index (
int
) — The overall index of the current process among all processes. - state (AcceleratorState) — The distributed setup state.
- sync_gradients (
bool
) — Whether the gradients are currently being synced across all processes. - use_distributed (
bool
) — Whether the current configuration is for distributed training.
accumulate
< source >( *models )
A context manager that will lightly wrap around and perform gradient accumulation automatically
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator(gradient_accumulation_steps=1)
>>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)
>>> for input, output in dataloader:
... with accelerator.accumulate(model):
... outputs = model(input)
... loss = loss_func(outputs)
... loss.backward()
... optimizer.step()
... scheduler.step()
... optimizer.zero_grad()
Will apply automatic mixed-precision inside the block inside this context manager, if it is enabled. Nothing different will happen otherwise.
A different autocast_handler
can be passed in to override the one set in the Accelerator
object. This is
useful in blocks under autocast
where you want to revert to fp32.
Scales the gradients in accordance to the GradientAccumulationPlugin
and calls the correct backward()
based
on the configuration.
Should be used in lieu of loss.backward()
.
Checks if the internal trigger tensor has been set to 1 in any of the processes. If so, will return True
and
reset the trigger tensor to 0.
Note:
Does not require wait_for_everyone()
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> # Assume later in the training script
>>> # `should_do_breakpoint` is a custom function to monitor when to break,
>>> # e.g. when the loss is NaN
>>> if should_do_breakpoint(loss):
... accelerator.set_trigger()
>>> # Assume later in the training script
>>> if accelerator.check_trigger():
... break
Alias for Accelerate.free_memory
, releases all references to the internal objects stored and call the
garbage collector. You should call this method between two trainings with different models/optimizers.
clip_grad_norm_
< source >( parameters max_norm norm_type = 2 ) → torch.Tensor
Returns
torch.Tensor
Total norm of the parameter gradients (viewed as a single vector).
Should be used in place of torch.nn.utils.clip_grad_norm_
.
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator(gradient_accumulation_steps=2)
>>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)
>>> for input, target in dataloader:
... optimizer.zero_grad()
... output = model(input)
... loss = loss_func(output, target)
... accelerator.backward(loss)
... if accelerator.sync_gradients:
... accelerator.clip_grad_norm_(model.parameters(), max_grad_norm)
... optimizer.step()
Should be used in place of torch.nn.utils.clip_grad_value_
.
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator(gradient_accumulation_steps=2)
>>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)
>>> for input, target in dataloader:
... optimizer.zero_grad()
... output = model(input)
... loss = loss_func(output, target)
... accelerator.backward(loss)
... if accelerator.sync_gradients:
... accelerator.clip_grad_value_(model.parameters(), clip_value)
... optimizer.step()
Runs any special end training behaviors, such as stopping trackers on the main process only or destoying process group. Should always be called at the end of your script if using experiment tracking.
Will release all references to the internal objects stored and call the garbage collector. You should call this
method between two trainings with different models/optimizers. Also will reset Accelerator.step
to 0.
gather
< source >( tensor ) → torch.Tensor
, or a nested tuple/list/dictionary of torch.Tensor
Parameters
- tensor (
torch.Tensor
, or a nested tuple/list/dictionary oftorch.Tensor
) — The tensors to gather across all processes.
Returns
torch.Tensor
, or a nested tuple/list/dictionary of torch.Tensor
The gathered tensor(s). Note that the first dimension of the result is num_processes multiplied by the first dimension of the input tensors.
Gather the values in tensor across all processes and concatenate them on the first dimension. Useful to regroup the predictions from all processes when doing evaluation.
Note: This gather happens in all processes.
gather_for_metrics
< source >( input_data use_gather_object = False )
Parameters
- input (
torch.Tensor
,object
, a nested tuple/list/dictionary oftorch.Tensor
, or a nested tuple/list/dictionary ofobject
) — The tensors or objects for calculating metrics across all processes use_gather_object(bool
) — Whether to forcibly use gather_object instead of gather (which is already done if all objects passed do not contain tensors). This flag can be useful for gathering tensors with different sizes that we don’t want to pad and concatenate along the first dimension. Using it with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled.
Gathers input_data
and potentially drops duplicates in the last batch if on a distributed system. Should be
used for gathering the inputs and targets for metric calculation.
Example:
>>> # Assuming two processes, with a batch size of 5 on a dataset with 9 samples
>>> import torch
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> dataloader = torch.utils.data.DataLoader(range(9), batch_size=5)
>>> dataloader = accelerator.prepare(dataloader)
>>> batch = next(iter(dataloader))
>>> gathered_items = accelerator.gather_for_metrics(batch)
>>> len(gathered_items)
9
get_state_dict
< source >( model unwrap = True ) → dict
Parameters
- model (
torch.nn.Module
) — A PyTorch model sent through Accelerator.prepare() - unwrap (
bool
, optional, defaults toTrue
) — Whether to return the original underlying state_dict ofmodel
or to return the wrapped state_dict
Returns
dict
The state dictionary of the model potentially without full precision.
Returns the state dictionary of a model sent through Accelerator.prepare() potentially without full precision.
get_tracker
< source >( name: str unwrap: bool = False ) → GeneralTracker
Returns a tracker
from self.trackers
based on name
on the main process only.
join_uneven_inputs
< source >( joinables even_batches = None )
Parameters
- joinables (
list[torch.distributed.algorithms.Joinable]
) — A list of models or optimizers that subclasstorch.distributed.algorithms.Joinable
. Most commonly, a PyTorch Module that was prepared withAccelerator.prepare
for DistributedDataParallel training. - even_batches (
bool
, optional) — If set, this will override the value ofeven_batches
set in theAccelerator
. If it is not provided, the defaultAccelerator
value wil be used.
A context manager that facilitates distributed training or evaluation on uneven inputs, which acts as a wrapper
around torch.distributed.algorithms.join
. This is useful when the total batch size does not evenly divide the
length of the dataset.
join_uneven_inputs
is only supported for Distributed Data Parallel training on multiple GPUs. For any other
configuration, this method will have no effect.
Overidding even_batches
will not affect iterable-style data loaders.
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator(even_batches=True)
>>> ddp_model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
>>> with accelerator.join_uneven_inputs([ddp_model], even_batches=False):
... for input, output in dataloader:
... outputs = model(input)
... loss = loss_func(outputs)
... loss.backward()
... optimizer.step()
... optimizer.zero_grad()
load_state
< source >( input_dir: str = None **load_model_func_kwargs )
Parameters
- input_dir (
str
oros.PathLike
) — The name of the folder all relevant weights and states were saved in. Can beNone
ifautomatic_checkpoint_naming
is used, and will pick up from the latest checkpoint. - load_model_func_kwargs (
dict
, optional) — Additional keyword arguments for loading model which can be passed to the underlying load function, such as optional arguments for DeepSpeed’sload_checkpoint
function or amap_location
to load the model and optimizer on.
Loads the current states of the model, optimizer, scaler, RNG generators, and registered objects.
Should only be used in conjunction with Accelerator.save_state(). If a file is not registered for checkpointing, it will not be loaded if stored in the directory.
Lets the local main process go inside a with block.
The other processes will enter the with block after the main process exits.
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> with accelerator.local_main_process_first():
... # This will be printed first by local process 0 then in a seemingly
... # random order by the other processes.
... print(f"This will be printed by process {accelerator.local_process_index}")
Runs backward pass on LOMO optimizers.
Lets the main process go first inside a with block.
The other processes will enter the with block after the main process exits.
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> with accelerator.main_process_first():
... # This will be printed first by process 0 then in a seemingly
... # random order by the other processes.
... print(f"This will be printed by process {accelerator.process_index}")
no_sync
< source >( model )
A context manager to disable gradient synchronizations across DDP processes by calling
torch.nn.parallel.DistributedDataParallel.no_sync
.
If model
is not in DDP, this context manager does nothing
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> dataloader, model, optimizer = accelerator.prepare(dataloader, model, optimizer)
>>> input_a = next(iter(dataloader))
>>> input_b = next(iter(dataloader))
>>> with accelerator.no_sync():
... outputs = model(input_a)
... loss = loss_func(outputs)
... accelerator.backward(loss)
... # No synchronization across processes, only accumulate gradients
>>> outputs = model(input_b)
>>> accelerator.backward(loss)
>>> # Synchronization across all processes
>>> optimizer.step()
>>> optimizer.zero_grad()
on_last_process
< source >( function: Callable[..., Any] )
A decorator that will run the decorated function on the last process only. Can also be called using the
PartialState
class.
on_local_main_process
< source >( function: Callable[..., Any] = None )
A decorator that will run the decorated function on the local main process only. Can also be called using the
PartialState
class.
Example:
# Assume we have 2 servers with 4 processes each.
from accelerate import Accelerator
accelerator = Accelerator()
@accelerator.on_local_main_process
def print_something():
print("This will be printed by process 0 only on each server.")
print_something()
# On server 1:
"This will be printed by process 0 only"
# On server 2:
"This will be printed by process 0 only"
on_local_process
< source >( function: Callable[..., Any] = None local_process_index: int = None )
A decorator that will run the decorated function on a given local process index only. Can also be called using
the PartialState
class.
Example:
# Assume we have 2 servers with 4 processes each.
from accelerate import Accelerator
accelerator = Accelerator()
@accelerator.on_local_process(local_process_index=2)
def print_something():
print(f"Printed on process {accelerator.local_process_index}")
print_something()
# On server 1:
"Printed on process 2"
# On server 2:
"Printed on process 2"
on_main_process
< source >( function: Callable[..., Any] = None )
A decorator that will run the decorated function on the main process only. Can also be called using the
PartialState
class.
on_process
< source >( function: Callable[..., Any] = None process_index: int = None )
A decorator that will run the decorated function on a given process index only. Can also be called using the
PartialState
class.
pad_across_processes
< source >( tensor dim = 0 pad_index = 0 pad_first = False ) → torch.Tensor
, or a nested tuple/list/dictionary of torch.Tensor
Parameters
- tensor (nested list/tuple/dictionary of
torch.Tensor
) — The data to gather. - dim (
int
, optional, defaults to 0) — The dimension on which to pad. - pad_index (
int
, optional, defaults to 0) — The value with which to pad. - pad_first (
bool
, optional, defaults toFalse
) — Whether to pad at the beginning or the end.
Returns
torch.Tensor
, or a nested tuple/list/dictionary of torch.Tensor
The padded tensor(s).
Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so they can safely be gathered.
Example:
>>> # Assuming two processes, with the first processes having a tensor of size 1 and the second of size 2
>>> import torch
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> process_tensor = torch.arange(accelerator.process_index + 1).to(accelerator.device)
>>> padded_tensor = accelerator.pad_across_processes(process_tensor)
>>> padded_tensor.shape
torch.Size([2])
prepare
< source >( *args device_placement = None )
Parameters
- *args (list of objects) —
Any of the following type of objects:
torch.utils.data.DataLoader
: PyTorch Dataloadertorch.nn.Module
: PyTorch Moduletorch.optim.Optimizer
: PyTorch Optimizertorch.optim.lr_scheduler.LRScheduler
: PyTorch LR Scheduler
- device_placement (
list[bool]
, optional) — Used to customize whether automatic device placement should be performed for each object passed. Needs to be a list of the same length asargs
. Not compatible with DeepSpeed or FSDP.
Prepare all objects passed in args
for distributed training and mixed precision, then return them in the same
order.
You don’t need to prepare a model if you only use it for inference without any kind of mixed precision
Examples:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> # Assume a model, optimizer, data_loader and scheduler are defined
>>> model, optimizer, data_loader, scheduler = accelerator.prepare(model, optimizer, data_loader, scheduler)
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> # Assume a model, optimizer, data_loader and scheduler are defined
>>> device_placement = [True, True, False, False]
>>> # Will place the first to items passed in automatically to the right device but not the last two.
>>> model, optimizer, data_loader, scheduler = accelerator.prepare(
... model, optimizer, data_loader, scheduler, device_placement=device_placement
... )
prepare_data_loader
< source >( data_loader: torch.utils.data.DataLoader device_placement = None slice_fn_for_dispatch = None )
Parameters
- data_loader (
torch.utils.data.DataLoader
) — A vanilla PyTorch DataLoader to prepare - device_placement (
bool
, optional) — Whether or not to place the batches on the proper device in the prepared dataloader. Will default toself.device_placement
. - slice_fn_for_dispatch (
Callable
, optional) -- If passed, this function will be used to slice tensors across
num_processes. Will default to [slice_tensors()](/docs/accelerate/v0.34.2/en/package_reference/utilities#accelerate.utils.slice_tensors). This argument is used only when
dispatch_batchesis set to
True` and will be ignored otherwise.
Prepares a PyTorch DataLoader for training in any distributed setup. It is recommended to use Accelerator.prepare() instead.
prepare_model
< source >( model: torch.nn.Module device_placement: bool = None evaluation_mode: bool = False )
Parameters
- model (
torch.nn.Module
) — A PyTorch model to prepare. You don’t need to prepare a model if it is used only for inference without any kind of mixed precision - device_placement (
bool
, optional) — Whether or not to place the model on the proper device. Will default toself.device_placement
. - evaluation_mode (
bool
, optional, defaults toFalse
) — Whether or not to set the model for evaluation only, by just applying mixed precision andtorch.compile
(if configured in theAccelerator
object).
Prepares a PyTorch model for training in any distributed setup. It is recommended to use Accelerator.prepare() instead.
prepare_optimizer
< source >( optimizer: torch.optim.Optimizer device_placement = None )
Prepares a PyTorch Optimizer for training in any distributed setup. It is recommended to use Accelerator.prepare() instead.
prepare_scheduler
< source >( scheduler: LRScheduler )
Prepares a PyTorch Scheduler for training in any distributed setup. It is recommended to use Accelerator.prepare() instead.
Drop in replacement of print()
to only print once per server.
profile
< source >( profile_handler: ProfileKwargs | None = None )
Will profile the code inside the context manager. The profile will be saved to a Chrome Trace file if
profile_handler.output_trace_dir
is set.
A different profile_handler
can be passed in to override the one set in the Accelerator
object.
Example:
# Profile with default settings
from accelerate import Accelerator
from accelerate.utils import ProfileKwargs
accelerator = Accelerator()
with accelerator.profile() as prof:
train()
accelerator.print(prof.key_averages().table())
# Profile with the custom handler
def custom_handler(prof):
print(prof.key_averages().table(sort_by="self_cpu_time_total", row_limit=10))
kwargs = ProfileKwargs(schedule_option=dict(wait=1, warmup=1, active=1), on_trace_ready=custom_handler)
accelerator = Accelerator(kwarg_handler=[kwargs])
with accelerator.profile() as prof:
for _ in range(10):
train_iteration()
prof.step()
# Profile and export to Chrome Trace
kwargs = ProfileKwargs(output_trace_dir="output_trace")
accelerator = Accelerator(kwarg_handler=[kwargs])
with accelerator.profile():
train()
reduce
< source >( tensor reduction = 'sum' scale = 1.0 ) → torch.Tensor
, or a nested tuple/list/dictionary of torch.Tensor
Parameters
- tensor (
torch.Tensor
, or a nested tuple/list/dictionary oftorch.Tensor
) — The tensors to reduce across all processes. - reduction (
str
, optional, defaults to “sum”) — A reduction type, can be one of ‘sum’, ‘mean’, or ‘none’. If ‘none’, will not perform any operation. - scale (
float
, optional, defaults to 1.0) — A default scaling value to be applied after the reduce, only valied on XLA.
Returns
torch.Tensor
, or a nested tuple/list/dictionary of torch.Tensor
The reduced tensor(s).
Reduce the values in tensor across all processes based on reduction.
Note: All processes get the reduced value.
Example:
>>> # Assuming two processes
>>> import torch
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> process_tensor = torch.arange(accelerator.num_processes) + 1 + (2 * accelerator.process_index)
>>> process_tensor = process_tensor.to(accelerator.device)
>>> reduced_tensor = accelerator.reduce(process_tensor, reduction="sum")
>>> reduced_tensor
tensor([4, 6])
Makes note of objects
and will save or load them in during save_state
or load_state
.
These should be utilized when the state is being loaded or saved in the same script. It is not designed to be used in different scripts.
Every object
must have a load_state_dict
and state_dict
function to be stored.
register_load_state_pre_hook
< source >( hook: Callable[..., None] ) → torch.utils.hooks.RemovableHandle
Parameters
- hook (
Callable
) — A function to be called in Accelerator.load_state() beforeload_checkpoint
.
Returns
torch.utils.hooks.RemovableHandle
a handle that can be used to remove the added hook by calling
handle.remove()
Registers a pre hook to be run before load_checkpoint
is called in Accelerator.load_state().
The hook should have the following signature:
hook(models: list[torch.nn.Module], input_dir: str) -> None
The models
argument are the models as saved in the accelerator state under accelerator._models
, and the
input_dir
argument is the input_dir
argument passed to Accelerator.load_state().
Should only be used in conjunction with Accelerator.register_save_state_pre_hook(). Can be useful to load configurations in addition to model weights. Can also be used to overwrite model loading with a customized method. In this case, make sure to remove already loaded models from the models list.
register_save_state_pre_hook
< source >( hook: Callable[..., None] ) → torch.utils.hooks.RemovableHandle
Parameters
- hook (
Callable
) — A function to be called in Accelerator.save_state() beforesave_checkpoint
.
Returns
torch.utils.hooks.RemovableHandle
a handle that can be used to remove the added hook by calling
handle.remove()
Registers a pre hook to be run before save_checkpoint
is called in Accelerator.save_state().
The hook should have the following signature:
hook(models: list[torch.nn.Module], weights: list[dict[str, torch.Tensor]], input_dir: str) -> None
The models
argument are the models as saved in the accelerator state under accelerator._models
, weigths
argument are the state dicts of the models
, and the input_dir
argument is the input_dir
argument passed
to Accelerator.load_state().
Should only be used in conjunction with Accelerator.register_load_state_pre_hook(). Can be useful to save configurations in addition to model weights. Can also be used to overwrite model saving with a customized method. In this case, make sure to remove already loaded weights from the weights list.
save
< source >( obj f safe_serialization = False )
Save the object passed to disk once per machine. Use in place of torch.save
.
Note:
If save_on_each_node
was passed in as a ProjectConfiguration
, will save the object once per node,
rather than only once on the main node.
save_model
< source >( model: torch.nn.Module save_directory: Union[str, os.PathLike] max_shard_size: Union[int, str] = '10GB' safe_serialization: bool = True )
Parameters
- save_directory (
str
oros.PathLike
) — Directory to which to save. Will be created if it doesn’t exist. - max_shard_size (
int
orstr
, optional, defaults to"10GB"
) — The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like"5MB"
).If a single weight of the model is bigger than
max_shard_size
, it will be in its own checkpoint shard which will be bigger thanmax_shard_size
. - safe_serialization (
bool
, optional, defaults toTrue
) — Whether to save the model usingsafetensors
or the traditional PyTorch way (that usespickle
).
Save a model so that it can be re-loaded using load_checkpoint_in_model
save_state
< source >( output_dir: str = None safe_serialization: bool = True **save_model_func_kwargs )
Parameters
- output_dir (
str
oros.PathLike
) — The name of the folder to save all relevant weights and states. - safe_serialization (
bool
, optional, defaults toTrue
) — Whether to save the model usingsafetensors
or the traditional PyTorch way (that usespickle
). - save_model_func_kwargs (
dict
, optional) — Additional keyword arguments for saving model which can be passed to the underlying save function, such as optional arguments for DeepSpeed’ssave_checkpoint
function.
Saves the current states of the model, optimizer, scaler, RNG generators, and registered objects to a folder.
If a ProjectConfiguration
was passed to the Accelerator
object with automatic_checkpoint_naming
enabled
then checkpoints will be saved to self.project_dir/checkpoints
. If the number of current saves is greater
than total_limit
then the oldest save is deleted. Each checkpoint is saved in seperate folders named
checkpoint_<iteration>
.
Otherwise they are just saved to output_dir
.
Should only be used when wanting to save a checkpoint during training and restoring the state in the same environment.
Sets the internal trigger tensor to 1 on the current process. A latter check should follow using this which will check across all processes.
Note:
Does not require wait_for_everyone()
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> # Assume later in the training script
>>> # `should_do_breakpoint` is a custom function to monitor when to break,
>>> # e.g. when the loss is NaN
>>> if should_do_breakpoint(loss):
... accelerator.set_trigger()
>>> # Assume later in the training script
>>> if accelerator.check_breakpoint():
... break
skip_first_batches
< source >( dataloader num_batches: int = 0 )
Creates a new torch.utils.data.DataLoader
that will efficiently skip the first num_batches
.
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)
>>> skipped_dataloader = accelerator.skip_first_batches(dataloader, num_batches=2)
>>> # for the first epoch only
>>> for input, target in skipped_dataloader:
... optimizer.zero_grad()
... output = model(input)
... loss = loss_func(output, target)
... accelerator.backward(loss)
... optimizer.step()
>>> # subsequent epochs
>>> for input, target in dataloader:
... optimizer.zero_grad()
... ...
split_between_processes
< source >( inputs: list | tuple | dict | torch.Tensor apply_padding: bool = False )
Parameters
- inputs (
list
,tuple
,torch.Tensor
, ordict
oflist
/tuple
/torch.Tensor
) — The input to split between processes. - apply_padding (
bool
,optional
, defaults toFalse
) — Whether to apply padding by repeating the last element of the input so that all processes have the same number of elements. Useful when trying to perform actions such asAccelerator.gather()
on the outputs or passing in less inputs than there are processes. If so, just remember to drop the padded elements afterwards.
Splits input
between self.num_processes
quickly and can be then used on that process. Useful when doing
distributed inference, such as with different prompts.
Note that when using a dict
, all keys need to have the same number of elements.
Example:
# Assume there are two processes
from accelerate import Accelerator
accelerator = Accelerator()
with accelerator.split_between_processes(["A", "B", "C"]) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C"]
with accelerator.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C", "C"]
trigger_sync_in_backward
< source >( model )
Trigger the sync of the gradients in the next backward pass of the model after multiple forward passes under
Accelerator.no_sync
(only applicable in multi-GPU scenarios).
If the script is not launched in distributed mode, this context manager does nothing.
Example:
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> dataloader, model, optimizer = accelerator.prepare(dataloader, model, optimizer)
>>> with accelerator.no_sync():
... loss_a = loss_func(model(input_a)) # first forward pass
... loss_b = loss_func(model(input_b)) # second forward pass
>>> accelerator.backward(loss_a) # No synchronization across processes, only accumulate gradients
>>> with accelerator.trigger_sync_in_backward(model):
... accelerator.backward(loss_b) # Synchronization across all processes
>>> optimizer.step()
>>> optimizer.zero_grad()
unscale_gradients
< source >( optimizer = None )
Parameters
- optimizer (
torch.optim.Optimizer
orlist[torch.optim.Optimizer]
, optional) — The optimizer(s) for which to unscale gradients. If not set, will unscale gradients on all optimizers that were passed to prepare().
Unscale the gradients in mixed precision training with AMP. This is a noop in all other settings.
Likely should be called through Accelerator.clipgrad_norm() or Accelerator.clipgrad_value()
unwrap_model
< source >( model keep_fp32_wrapper: bool = True ) → torch.nn.Module
Unwraps the model
from the additional layer possible added by prepare(). Useful before saving
the model.
Example:
>>> # Assuming two GPU processes
>>> from torch.nn.parallel import DistributedDataParallel
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> model = accelerator.prepare(MyModel())
>>> print(model.__class__.__name__)
DistributedDataParallel
>>> model = accelerator.unwrap_model(model)
>>> print(model.__class__.__name__)
MyModel
Verifies that model
has not been prepared with big model inference with a device-map resembling auto
.
Will stop the execution of the current process until every other process has reached that point (so this does nothing when the script is only run in one process). Useful to do before saving a model.
Example:
>>> # Assuming two GPU processes
>>> import time
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> if accelerator.is_main_process:
... time.sleep(2)
>>> else:
... print("I'm waiting for the main process to finish its sleep...")
>>> accelerator.wait_for_everyone()
>>> # Should print on every process at the same time
>>> print("Everyone is here")
Utilities
accelerate.utils.gather_object
< source >( object: Any )
Recursively gather object in a nested list/tuple/dictionary of objects from all devices.