# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import math
import os
import shutil
import sys
import warnings
from contextlib import contextmanager
from functools import wraps
from typing import List, Optional, Union
import torch
from .checkpointing import load_accelerator_state, load_custom_state, save_accelerator_state, save_custom_state
from .data_loader import DataLoaderDispatcher, prepare_data_loader
from .logging import get_logger
from .optimizer import AcceleratedOptimizer
from .scheduler import AcceleratedScheduler
from .state import AcceleratorState, GradientState, parse_flag_from_env
from .tracking import LOGGER_TYPE_TO_CLASS, GeneralTracker, filter_trackers
from .utils import (
MODEL_NAME,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
compare_versions,
convert_outputs_to_fp32,
extract_model_from_parallel,
gather,
get_pretty_name,
is_bf16_available,
is_deepspeed_available,
is_megatron_lm_available,
is_torch_version,
is_tpu_available,
pad_across_processes,
recursively_apply,
reduce,
release_memory,
save,
wait_for_everyone,
)
if is_deepspeed_available():
import deepspeed
from .utils import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
)
if is_megatron_lm_available():
from .utils import (
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
megatron_lm_initialize,
megatron_lm_prepare_data_loader,
megatron_lm_prepare_model,
megatron_lm_prepare_optimizer,
megatron_lm_prepare_scheduler,
)
if is_torch_version(">", "1.10.0"):
from torch.distributed.algorithms.join import Join
if is_tpu_available(check_device=False):
import torch_xla.distributed.xla_multiprocessing as xmp
if is_torch_version("<=", "1.13.5"):
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
else:
from torch.optim.lr_scheduler import LRScheduler as LRScheduler
logger = get_logger(__name__)
class Accelerator:
"""
Creates an instance of an accelerator for distributed training (on multi-GPU, TPU) or mixed precision training.
Args:
device_placement (`bool`, *optional*, defaults to `True`):
Whether or not the accelerator should put objects on device (tensors yielded by the dataloader, model,
etc...).
split_batches (`bool`, *optional*, defaults to `False`):
Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If
`True` the actual batch size used will be the same on any kind of distributed processes, but it must be a
round multiple of the `num_processes` you are using. If `False`, actual batch size used will be the one set
in your script multiplied by the number of processes.
mixed_precision (`str`, *optional*):
Whether or not to use mixed precision training (fp16 or bfloat16). Choose from 'no','fp16','bf16'. Will
default to the value in the environment variable `ACCELERATE_MIXED_PRECISION`, which will use the default
value in the accelerate config of the current system or the flag passed with the `accelerate.launch`
command. 'fp16' requires pytorch 1.6 or higher. 'bf16' requires pytorch 1.10 or higher.
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 with
`Accelerator.accumulate`.
cpu (`bool`, *optional*):
Whether or not to force the script to execute on CPU. Will ignore GPU available if set to `True` and force
the execution on one process only.
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 [`~utils.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"`: the `torch.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`, [`~utils.LoggerType`] or [`~tracking.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 of `GeneralTracker` 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.
dispatch_batches (`bool`, *optional*):
If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process
and then the batches are split and broadcast to each process. Will default to `True` for `DataLoader` whose
underlying dataset is an `IterableDataset`, `False` otherwise.
even_batches (`bool`, *optional*, defaults to `True`):
If set to `True`, in cases where the total batch size across all processes does not exactly divide the
dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among
all workers.
step_scheduler_with_optimizer (`bool`, *optional`, defaults to `True`):
Set `True` 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[KwargHandler]`, *optional*)
A list of `KwargHandler` to customize how the objects related to distributed training or mixed precision
are created. See [kwargs](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.
**Available attributes:**
- **device** (`torch.device`) -- The device to use.
- **distributed_type** ([`~utils.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** ([`~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.
"""
def __init__(
self,
device_placement: bool = True,
split_batches: bool = False,
mixed_precision: Union[PrecisionType, str] = None,
gradient_accumulation_steps: int = 1,
cpu: bool = False,
deepspeed_plugin: DeepSpeedPlugin = None,
fsdp_plugin: FullyShardedDataParallelPlugin = None,
megatron_lm_plugin: MegatronLMPlugin = None,
rng_types: Optional[List[Union[str, RNGType]]] = None,
log_with: Optional[List[Union[str, LoggerType, GeneralTracker]]] = None,
project_dir: Optional[Union[str, os.PathLike]] = None,
project_config: Optional[ProjectConfiguration] = None,
logging_dir: Optional[Union[str, os.PathLike]] = None,
dispatch_batches: Optional[bool] = None,
even_batches: bool = True,
step_scheduler_with_optimizer: bool = True,
kwargs_handlers: Optional[List[KwargsHandler]] = None,
dynamo_backend: Union[DynamoBackend, str] = None,
):
if project_config is not None:
self.project_configuration = project_config
else:
self.project_configuration = ProjectConfiguration(project_dir=project_dir)
if logging_dir is not None:
warnings.warn(
"`logging_dir` is deprecated and will be removed in version 0.18.0 of 🤗 Accelerate. Use `project_dir` instead.",
FutureWarning,
)
self.project_configuration.logging_dir = logging_dir
if project_dir is not None and self.project_dir is None:
self.project_configuration.project_dir = project_dir
if mixed_precision is not None:
mixed_precision = str(mixed_precision)
if mixed_precision not in PrecisionType:
raise ValueError(
f"Unknown mixed_precision mode: {mixed_precision}. Choose between {PrecisionType.list()}"
)
if dynamo_backend is not None:
dynamo_backend = DynamoBackend(dynamo_backend.upper())
if deepspeed_plugin is None: # init from env variables
deepspeed_plugin = (
DeepSpeedPlugin() if os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "true" else None
)
else:
assert isinstance(
deepspeed_plugin, DeepSpeedPlugin
), "`deepspeed_plugin` must be an `accelerate.utils.DeepSpeedPlugin` object."
os.environ["ACCELERATE_USE_DEEPSPEED"] = "true" # use DeepSpeed if plugin is provided
if deepspeed_plugin:
if not is_deepspeed_available():
raise ImportError("DeepSpeed is not installed => run `pip install deepspeed` or build it from source.")
if compare_versions("deepspeed", "<", "0.6.5"):
raise ImportError("DeepSpeed version must be >= 0.6.5. Please update DeepSpeed.")
mixed_precision = (
os.environ.get("ACCELERATE_MIXED_PRECISION", "no") if mixed_precision is None else mixed_precision
)
deepspeed_plugin.set_mixed_precision(mixed_precision)
deepspeed_plugin.set_deepspeed_weakref()
if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true" or isinstance(
fsdp_plugin, FullyShardedDataParallelPlugin
):
if is_torch_version("<", "1.12.0"):
raise ValueError("FSDP requires PyTorch >= 1.12.0")
if fsdp_plugin is None: # init from env variables
fsdp_plugin = (
FullyShardedDataParallelPlugin() if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true" else None
)
else:
if not isinstance(fsdp_plugin, FullyShardedDataParallelPlugin):
raise TypeError("`fsdp_plugin` must be a FullyShardedDataParallelPlugin object.")
os.environ["ACCELERATE_USE_FSDP"] = "true" # use FSDP if plugin is provided
if megatron_lm_plugin is None: # init from env variables
megatron_lm_plugin = (
MegatronLMPlugin() if os.environ.get("ACCELERATE_USE_MEGATRON_LM", "false") == "true" else None
)
else:
if not isinstance(megatron_lm_plugin, MegatronLMPlugin):
raise TypeError("`megatron_lm_plugin` must be a MegatronLMPlugin object.")
os.environ["ACCELERATE_USE_MEGATRON_LM"] = "true" # use MegatronLM if plugin is provided
if megatron_lm_plugin:
if not is_megatron_lm_available():
raise ImportError("Megatron is not installed. please build it from source.")
# Kwargs handlers
self.ddp_handler = None
self.scaler_handler = None
self.init_handler = None
if kwargs_handlers is not None:
for handler in kwargs_handlers:
assert isinstance(
handler, KwargsHandler
), f"Unsupported kwargs handler passed: {handler}, must be one that inherits `accelerate.utils.KwargsHandler`."
if isinstance(handler, DistributedDataParallelKwargs):
if self.ddp_handler is not None:
raise ValueError("You can only pass one `DistributedDataParallelKwargs` in `kwargs_handler`.")
else:
self.ddp_handler = handler
elif isinstance(handler, GradScalerKwargs):
if self.scaler_handler is not None:
raise ValueError("You can only pass one `GradScalerKwargs` in `kwargs_handler`.")
else:
self.scaler_handler = handler
elif isinstance(handler, InitProcessGroupKwargs):
if self.init_handler is not None:
raise ValueError("You can only pass one `InitProcessGroupKwargs` in `kwargs_handler`.")
else:
self.init_handler = handler
kwargs = self.init_handler.to_kwargs() if self.init_handler is not None else {}
self.state = AcceleratorState(
mixed_precision=mixed_precision,
cpu=cpu,
dynamo_backend=dynamo_backend,
deepspeed_plugin=deepspeed_plugin,
fsdp_plugin=fsdp_plugin,
megatron_lm_plugin=megatron_lm_plugin,
_from_accelerator=True,
**kwargs,
)
trackers = filter_trackers(log_with, self.logging_dir)
if len(trackers) < 1 and log_with is not None:
warnings.warn(f"`log_with={log_with}` was passed but no supported trackers are currently installed.")
self.log_with = trackers
if (
(mixed_precision != "bf16")
and getattr(self.state, "downcast_bfloat", False)
and (self.state.distributedType != DistributedType.TPU)
):
raise ValueError("Can only use `downcast_bf16` when using `mixed_precision='bf16'` and on a TPU")
if gradient_accumulation_steps > 1:
if self.state.distributed_type == DistributedType.TPU:
raise NotImplementedError(
"Gradient accumulation on TPU is not supported. Pass in `gradient_accumulation_steps=1`"
)
self.gradient_accumulation_steps = gradient_accumulation_steps
self.device_placement = device_placement
self.split_batches = split_batches
self.dispatch_batches = dispatch_batches
if dispatch_batches is True and is_torch_version("<", "1.8.0"):
raise ImportError(
"Using `DataLoaderDispatcher` requires PyTorch 1.8.0 minimum. You have {torch.__version__}."
)
self.even_batches = even_batches
self.step_scheduler_with_optimizer = step_scheduler_with_optimizer
# Mixed precision attributes
self.scaler = None
self.native_amp = False
err = "{mode} mixed precision requires {requirement}"
if (
self.state.mixed_precision == "fp16"
and self.device.type != "cpu"
and self.distributed_type not in (DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM)
):
self.native_amp = True
if not torch.cuda.is_available() and not parse_flag_from_env("ACCELERATE_USE_MPS_DEVICE"):
raise ValueError(err.format(mode="fp16", requirement="a GPU"))
kwargs = self.scaler_handler.to_kwargs() if self.scaler_handler is not None else {}
if self.distributed_type == DistributedType.FSDP:
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
self.scaler = ShardedGradScaler(**kwargs)
else:
self.scaler = torch.cuda.amp.GradScaler(**kwargs)
elif self.state.mixed_precision == "bf16" and self.distributed_type not in (
DistributedType.DEEPSPEED,
DistributedType.FSDP,
DistributedType.MEGATRON_LM,
):
if self.device.type == "cpu":
self.native_amp = is_torch_version(">=", "1.10")
else:
self.native_amp = is_bf16_available(True)
if mixed_precision == "bf16" and not self.native_amp and not is_tpu_available():
raise ValueError(err.format(mode="bf16", requirement="PyTorch >= 1.10 and a supported device."))
# Only on the GPU do we care about scaling the gradients
if torch.cuda.is_available() and self.device.type != "cpu":
kwargs = self.scaler_handler.to_kwargs() if self.scaler_handler is not None else {}
self.scaler = torch.cuda.amp.GradScaler(**kwargs)
# Start of internal step tracking
self.step = 0
self.gradient_state = GradientState()
# Internal references to the training objects
self._optimizers = []
self._models = []
self._schedulers = []
self._dataloaders = []
self._custom_objects = []
# RNG Types
self.rng_types = rng_types
if self.rng_types is None:
self.rng_types = ["generator"]
@property
def use_distributed(self):
"""
Whether the Accelerator is configured for distributed training
"""
return self.distributed_type != DistributedType.NO and self.num_processes > 1
@property
def distributed_type(self):
return self.state.distributed_type
@property
def num_processes(self):
return self.state.num_processes
@property
def process_index(self):
return self.state.process_index
@property
def local_process_index(self):
return self.state.local_process_index
@property
def device(self):
return self.state.device
@property
def project_dir(self):
return self.project_configuration.project_dir
@property
def logging_dir(self):
return self.project_configuration.logging_dir
@property
def save_iteration(self):
return self.project_configuration.iteration
@property
def is_main_process(self):
"""True for one process only."""
return (
self.process_index == 0 if self.distributed_type != DistributedType.MEGATRON_LM else self.is_last_process
)
@property
def is_local_main_process(self):
"""True for one process per server."""
return (
self.local_process_index == 0
if self.distributed_type != DistributedType.MEGATRON_LM
else self.is_last_process
)
@property
def use_fp16(self):
return self.mixed_precision != "no"
@property
def is_last_process(self):
return self.process_index == self.num_processes - 1
@property
def mixed_precision(self):
return self.state.mixed_precision
def on_main_process(func):
"""
A decorator that will run the decorated function on the main process only.
"""
@wraps(func)
def wrapper(self, *args, **kwargs):
if self.is_main_process or not self.use_distributed:
return func(self, *args, **kwargs)
return wrapper
def on_local_main_process(func):
"""
A decorator that will run the decorated function on the local main process only.
"""
@wraps(func)
def wrapper(self, *args, **kwargs):
if self.is_local_main_process or not self.use_distributed:
return func(self, *args, **kwargs)
return wrapper
def on_last_process(func):
"""
A decorator that will run the decorated function on the last process only.
"""
@wraps(func)
def wrapper(self, *args, **kwargs):
if self.is_last_process or not self.use_distributed:
return func(self, *args, **kwargs)
return wrapper
def on_process(process_idx):
"""
A decorator that will run the decorated function on a given process index only.
"""
def decorator(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
if self.process_idx == process_idx or not self.use_distributed:
return func(self, *args, **kwargs)
return wrapper
return decorator
def on_local_process(local_process_idx):
"""
A decorator that will run the decorated function on a given local process index only.
"""
def decorator(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
if self.local_process_idx == local_process_idx or not self.use_distributed:
return func(self, *args, **kwargs)
return wrapper
return decorator
def _goes_first(self, is_main):
if not is_main:
self.wait_for_everyone()
yield
if is_main:
self.wait_for_everyone()
@contextmanager
def main_process_first(self):
"""
Lets the main process go first inside a with block.
The other processes will enter the with block after the main process exits.
"""
yield from self._goes_first(self.is_main_process)
@contextmanager
def local_main_process_first(self):
"""
Lets the local main process go inside a with block.
The other processes will enter the with block after the main process exits.
"""
yield from self._goes_first(self.is_local_main_process)
@contextmanager
def no_sync(self, 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
Args:
model (`torch.nn.Module`):
PyTorch Module that was prepared with `Accelerator.prepare`
Example:
```python
>>> 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()
```
"""
context = contextlib.nullcontext
if self.use_distributed:
context = getattr(model, "no_sync", context)
with context():
yield
def _do_sync(self):
"Sets the right `sync_gradients` context and either resets or increases `self.step`"
if self.gradient_state.end_of_dataloader:
self.step = 0
self.gradient_state._set_sync_gradients(True)
else:
self.step += 1
self.gradient_state._set_sync_gradients((self.step % self.gradient_accumulation_steps) == 0)
@property
def sync_gradients(self):
return self.gradient_state.sync_gradients
@contextmanager
def accumulate(self, model):
"""
A context manager that will lightly wrap around and perform gradient accumulation automatically
Args:
model (`torch.nn.Module`):
PyTorch Module that was prepared with `Accelerator.prepare`
Example:
```python
>>> from accelerate import Accelerator
>>> accelerator = Accelerator(gradient_accumulation_steps=2)
>>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)
>>> with accelerator.accumulate():
... for input, output in dataloader:
... outputs = model(input)
... loss = loss_func(outputs)
... loss.backward()
... optimizer.step()
... scheduler.step()
... optimizer.zero_grad()
```
"""
self._do_sync()
if self.sync_gradients:
context = contextlib.nullcontext
else:
context = self.no_sync
with context(model):
yield
@contextmanager
def join_uneven_inputs(self, joinables, even_batches=None):
"""
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.
Args:
joinables (`List[torch.distributed.algorithms.Joinable]`):
A list of models or optimizers that subclass `torch.distributed.algorithms.Joinable`. Most commonly, a
PyTorch Module that was prepared with `Accelerator.prepare` for DistributedDataParallel training.
even_batches (`bool`, *optional*)
If set, this will override the value of `even_batches` set in the `Accelerator`. If it is not provided,
the default `Accelerator` value wil be used.
`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:
```python
>>> 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()
```
"""
if is_torch_version("<", "1.10.0"):
raise ValueError(f"Joining uneven inputs requires PyTorch >= 1.10.0, You have {torch.__version__}.")
if self.distributed_type == DistributedType.MULTI_GPU:
dl_even_batches_values = []
if even_batches is not None:
iterable_dl_seen = False
# override value in batch sampler for map-style datasets
for dl_idx, dl in enumerate(self._dataloaders):
if isinstance(dl, DataLoaderDispatcher):
iterable_dl_seen = True
continue
dl_even_batches_values.append((dl_idx, dl.batch_sampler.even_batches))
dl.batch_sampler.even_batches = even_batches
if iterable_dl_seen:
warnings.warn(
"Overridding even_batches is only supported for map-style datasets, yet some dataloaders given were iterable"
)
else:
even_batches = self.even_batches
enable_join = False if even_batches else True
try:
with Join(joinables, enable=enable_join, throw_on_early_termination=False):
yield
finally:
# reset any batch samplers that have been modified
for dl_idx, even_batches_value in dl_even_batches_values:
self._dataloaders[dl_idx].batch_sampler.even_batches = even_batches_value
else:
# Even when disabled, Join expects models to subclass Joinable, so skip entirely for single process runs
if self.distributed_type != DistributedType.NO:
warnings.warn(
"Joining uneven inputs is only supported for multi-GPU training, as a result `join_uneven_inputs` will have no effect."
)
with contextlib.nullcontext(joinables):
yield
def print(self, *args, **kwargs):
"""
Use in replacement of `print()` to only print once per server.
"""
if self.is_local_main_process:
print(*args, **kwargs)
def _prepare_one(self, obj, first_pass=False, device_placement=None):
# First pass of preparation: DataLoader, model, optimizer
if first_pass:
if isinstance(obj, torch.utils.data.DataLoader):
return self.prepare_data_loader(obj, device_placement=device_placement)
elif isinstance(obj, torch.nn.Module):
return self.prepare_model(obj, device_placement=device_placement)
elif isinstance(obj, torch.optim.Optimizer):
optimizer = self.prepare_optimizer(obj, device_placement=device_placement)
return optimizer
# Second pass of preparation: LR scheduler (which need the full list of optimizers)
elif isinstance(obj, LRScheduler):
scheduler = self.prepare_scheduler(obj)
return scheduler
# Return the unprocessed object if previous criteria was not met
return obj
def _prepare_fsdp(self, *args):
result = []
for obj in args:
if isinstance(obj, torch.nn.Module):
model = obj
break
optimizers = []
self._schedulers = []
self._models = []
intermediate_result = []
for obj in args:
if isinstance(obj, torch.optim.Optimizer):
if len(obj.param_groups) > 1:
logger.warning(
"FSDP Warning: When using FSDP, several parameter groups will be conflated into "
"a single one due to nested module wrapping and parameter flattening."
)
try:
optimizer = obj.optimizer.__class__(model.parameters(), **obj.optimizer.defaults)
except TypeError:
if "differentiable" in obj.optimizer.defaults:
# https://github.com/huggingface/accelerate/issues/801
defaults = {k: v for k, v in obj.optimizer.defaults.items() if k != "differentiable"}
optimizer = obj.optimizer.__class__(model.parameters(), **defaults)
else:
raise
obj = self.prepare_optimizer(optimizer)
optimizers.append(obj)
elif isinstance(obj, torch.nn.Module):
self._models.append(obj)
intermediate_result.append(obj)
for obj in intermediate_result:
if isinstance(obj, AcceleratedScheduler):
obj.optimizer = optimizers
for i, opt in enumerate(self._optimizers):
if getattr(obj.scheduler, "optimizer", None) == opt.optimizer:
obj.scheduler.optimizer = optimizers[i]
obj.optimizers = [optimizers[i]]
break
self._schedulers.append(obj)
result.append(obj)
self._optimizers = optimizers
return tuple(result)
def prepare(self, *args, device_placement=None):
"""
Prepare all objects passed in `args` for distributed training and mixed precision, then return them in the same
order.
Args:
*args (list of objects):
Any of the following type of objects:
- `torch.utils.data.DataLoader`: PyTorch Dataloader
- `torch.nn.Module`: PyTorch Module
- `torch.optim.Optimizer`: PyTorch Optimizer
- `torch.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 as `args`.
You don't need to prepare a model if you only use it for inference without any kind of mixed precision
"""
if device_placement is None:
device_placement = [None for _ in args]
elif self.distributed_type in (DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM):
raise ValueError("You can't customize device placements with DeepSpeed or Megatron-LM.")
elif len(device_placement) != len(args):
raise ValueError(
f"`device_placement` should be a list with {len(args)} elements (the number of objects passed)."
)
if self.distributed_type == DistributedType.FSDP:
model_count = 0
optimizer_present = False
for obj in args:
if isinstance(obj, torch.nn.Module):
model_count += 1
if isinstance(obj, torch.optim.Optimizer):
optimizer_present = True
if model_count > 1 and optimizer_present:
raise ValueError(
"For FSDP to work with multiple models (>1), "
"prepare must be called for all the models before optimizers are created. "
"Then pass the optimizers to the prepare call in the same order as corresponding models."
)
elif model_count == 1 and optimizer_present:
logger.warning(
"FSDP Warning: When using FSDP, "
"it is efficient and recommended to call prepare for the model before creating the optimizer"
)
# On TPUs, putting the model on the XLA device will create new parameters, so the corresponding optimizer will
# have parameters disconnected from the model (so no training :-( ).
# If the model and optimizer have parameters on different devices we raise an error.
if self.distributed_type == DistributedType.TPU:
model_device, optimizer_device = self._get_devices()
if model_device is not None and optimizer_device is not None and model_device != optimizer_device:
raise ValueError(
"The model and the optimizer parameters are not on the same device, which probably means you "
"created an optimizer around your model **before** putting on the device. Make sure the line "
"model.to(device) is before the optimizer creation in your script or remove it entirely and use "
"the flag default value for `device_placement` in your `Accelerator` to let it handle that "
"part for you."
)
# If we're dealing with device placement, this deals with that by...
tpu_should_fix_optimizer = self.device_placement and self.distributed_type == DistributedType.TPU
if tpu_should_fix_optimizer:
# 1. grabbing old model parameters
old_named_params = self._get_named_parameters(*args)
if self.distributed_type == DistributedType.DEEPSPEED:
result = self._prepare_deepspeed(*args)
elif self.distributed_type == DistributedType.MEGATRON_LM:
result = self._prepare_megatron_lm(*args)
else:
result = tuple(
self._prepare_one(obj, first_pass=True, device_placement=d) for obj, d in zip(args, device_placement)
)
result = tuple(self._prepare_one(obj, device_placement=d) for obj, d in zip(result, device_placement))
if tpu_should_fix_optimizer:
# 2. grabbing new model parameters
new_named_params = self._get_named_parameters(*result)
# 3. building a map from the first to the second
mapping = {p: new_named_params[n] for n, p in old_named_params.items()}
# 4. using that map to update the parameters of the optimizer
for obj in result:
if isinstance(obj, torch.optim.Optimizer):
obj._switch_parameters(mapping)
if self.distributed_type == DistributedType.FSDP and model_count == 1 and optimizer_present:
result = self._prepare_fsdp(*result)
return result if len(result) > 1 else result[0]
def prepare_model(self, model: torch.nn.Module, device_placement=None):
"""
Prepares a PyTorch model for training in any distributed setup. It is recommended to use
[`Accelerator.prepare`] instead.
Args:
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 to `self.device_placement`.
"""
if device_placement is None:
device_placement = self.device_placement and self.distributed_type != DistributedType.FSDP
self._models.append(model)
if device_placement:
model = model.to(self.device)
if self.state.dynamo_backend != DynamoBackend.NO:
import torch._dynamo as dynamo
model = dynamo.optimize(self.state.dynamo_backend.value.lower())(model)
if self.distributed_type == DistributedType.MULTI_GPU:
if any(p.requires_grad for p in model.parameters()):
kwargs = self.ddp_handler.to_kwargs() if self.ddp_handler is not None else {}
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[self.local_process_index], output_device=self.local_process_index, **kwargs
)
elif self.distributed_type == DistributedType.FSDP:
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
# Check if the model is already a FSDP model due to `Manual Wrapping` and if so,
# don't wrap it again
if type(model) != FSDP:
self.state.fsdp_plugin.set_auto_wrap_policy(model)
fsdp_plugin = self.state.fsdp_plugin
model = FSDP(
model,
sharding_strategy=fsdp_plugin.sharding_strategy,
cpu_offload=fsdp_plugin.cpu_offload,
auto_wrap_policy=fsdp_plugin.auto_wrap_policy,
backward_prefetch=fsdp_plugin.backward_prefetch,
mixed_precision=fsdp_plugin.mixed_precision_policy,
ignored_modules=fsdp_plugin.ignored_modules,
device_id=self.device,
limit_all_gathers=fsdp_plugin.limit_all_gathers,
)
self._models[-1] = model
elif self.distributed_type == DistributedType.MULTI_CPU:
kwargs = self.ddp_handler.to_kwargs() if self.ddp_handler is not None else {}
model = torch.nn.parallel.DistributedDataParallel(model, **kwargs)
if self.native_amp:
model._original_forward = model.forward
if self.mixed_precision == "fp16" and is_torch_version(">=", "1.10"):
model.forward = torch.cuda.amp.autocast(dtype=torch.float16)(model.forward)
elif self.mixed_precision == "bf16" and self.distributed_type != DistributedType.TPU:
model.forward = torch.autocast(device_type=self.device.type, dtype=torch.bfloat16)(model.forward)
else:
model.forward = torch.cuda.amp.autocast()(model.forward)
model.forward = convert_outputs_to_fp32(model.forward)
if self.distributed_type == DistributedType.TPU and self.state.fork_launched:
model = xmp.MpModelWrapper(model).to(self.device)
return model
def _prepare_deepspeed(self, *args):
deepspeed_plugin = self.state.deepspeed_plugin
if deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"] == "auto":
result = [
self._prepare_one(obj, first_pass=True) if isinstance(obj, torch.utils.data.DataLoader) else obj
for obj in args
]
batch_sizes = [obj.batch_size for obj in args if hasattr(obj, "batch_size")]
if self.split_batches:
batch_sizes = [batch_size // self.num_processes for batch_size in batch_sizes]
if len(batch_sizes) == 0:
raise ValueError(
"When using DeepSpeed `accelerate.prepare()` requires you to pass at least one of training or evaluation dataloaders "
"or alternatively set an integer value in `train_micro_batch_size_per_gpu` in the deepspeed config file"
"or assign integer value to `AcceleratorState().deepspeed_plugin.deepspeed_config['train_micro_batch_size_per_gpu']`."
)
batch_size_per_device = min(batch_sizes) if deepspeed_plugin.is_train_batch_min else max(batch_sizes)
if len(batch_sizes) > 1:
logger.info(
"Since you passed both train and evaluation dataloader, `is_train_batch_min` (here "
f"{deepspeed_plugin.is_train_batch_min} will decide the `train_batch_size` ({batch_size_per_device})."
)
else:
batch_size_per_device = deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"]
result = [obj for obj in args]
if self.gradient_accumulation_steps != deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"]:
logger.info(
f"Updating DeepSpeed's gradient accumulation steps to {self.gradient_accumulation_steps} from "
f"{deepspeed_plugin.deepspeed_config['gradient_accumulation_steps']}."
)
deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"] = self.gradient_accumulation_steps
config_kwargs = {
"train_micro_batch_size_per_gpu": batch_size_per_device,
"train_batch_size": batch_size_per_device
* deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"]
* self.num_processes,
"gradient_clipping": 1.0,
"zero_optimization.stage3_gather_16bit_weights_on_model_save": False,
}
model = None
optimizer = None
scheduler = None
for obj in result:
if isinstance(obj, torch.nn.Module):
model = obj
elif isinstance(obj, (torch.optim.Optimizer, DummyOptim)):
optimizer = obj
elif (isinstance(obj, (LRScheduler, DummyScheduler))) or (
type(obj).__name__ in deepspeed.runtime.lr_schedules.VALID_LR_SCHEDULES
):
scheduler = obj
if optimizer is not None:
if "optimizer" in deepspeed_plugin.deepspeed_config and not isinstance(optimizer, (DummyOptim)):
raise ValueError(
"You cannot specify an optimizer in the config file and in the code at the same time. "
"Please remove the optimizer from the config file or "
"create `accelerate.utils.DummyOptim` in the code."
)
elif "optimizer" not in deepspeed_plugin.deepspeed_config and isinstance(optimizer, (DummyOptim)):
raise ValueError(
"You cannot create a `DummyOptim` without specifying an optimizer in the config file."
)
if isinstance(optimizer, (torch.optim.Optimizer)):
deepspeed_plugin.deepspeed_config["zero_allow_untested_optimizer"] = True
if scheduler is not None:
if "scheduler" in deepspeed_plugin.deepspeed_config and not isinstance(scheduler, (DummyScheduler)):
raise ValueError(
"You cannot specify a scheduler in the config file and in the code at the same time. "
"Please remove the scheduler from the config file or "
"create `accelerate.utils.DummyScheduler` in the code."
)
elif "scheduler" not in deepspeed_plugin.deepspeed_config and isinstance(scheduler, (DummyScheduler)):
raise ValueError(
"You cannot create a `DummyScheduler` without specifying a scheduler in the config file."
)
if optimizer is not None and scheduler is not None:
if isinstance(optimizer, (DummyOptim)) and not isinstance(scheduler, (DummyScheduler)):
raise ValueError(
"You can only specify `accelerate.utils.DummyScheduler` in the code when using "
"`accelerate.utils.DummyOptim`."
)
if model is not None:
if hasattr(model, "config") and hasattr(model.config, "hidden_size"):
hidden_size = model.config.hidden_size
config_kwargs.update(
{
"zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
"zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
"zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
}
)
if isinstance(optimizer, (DummyOptim)):
config_kwargs.update(
{"optimizer.params.lr": optimizer.lr, "optimizer.params.weight_decay": optimizer.weight_decay}
)
if isinstance(scheduler, (DummyScheduler)):
config_kwargs.update(
{
"scheduler.params.warmup_min_lr": 0,
"scheduler.params.warmup_max_lr": scheduler.optimizer.lr,
"scheduler.params.warmup_num_steps": scheduler.warmup_num_steps,
}
)
if scheduler.total_num_steps is not None:
config_kwargs["scheduler.params.total_num_steps"] = (
math.ceil(scheduler.total_num_steps / self.num_processes)
if not self.split_batches
else scheduler.total_num_steps
)
deepspeed_plugin.deepspeed_config_process(must_match=False, **config_kwargs)
self.deepspeed_config = deepspeed_plugin.deepspeed_config
kwargs = dict(model=model, config_params=self.deepspeed_config)
if optimizer is not None:
if isinstance(optimizer, (DummyOptim)):
kwargs["model_parameters"] = optimizer.params
else:
kwargs["optimizer"] = optimizer
if scheduler is not None:
if type(scheduler).__name__ in deepspeed.runtime.lr_schedules.VALID_LR_SCHEDULES:
kwargs["lr_scheduler"] = scheduler
engine, optimizer, _, lr_scheduler = deepspeed.initialize(**kwargs)
if optimizer is not None:
optimizer = DeepSpeedOptimizerWrapper(optimizer)
if scheduler is not None:
if lr_scheduler is None:
scheduler = AcceleratedScheduler(
scheduler,
optimizer,
step_with_optimizer=self.step_scheduler_with_optimizer,
split_batches=self.split_batches,
)
else:
scheduler = DeepSpeedSchedulerWrapper(lr_scheduler, optimizer)
for i in range(len(result)):
if isinstance(result[i], torch.nn.Module):
result[i] = engine
elif isinstance(result[i], (torch.optim.Optimizer, DummyOptim)):
result[i] = optimizer
elif (isinstance(result[i], (LRScheduler, DummyScheduler))) or (
type(result[i]).__name__ in deepspeed.runtime.lr_schedules.VALID_LR_SCHEDULES
):
result[i] = scheduler
# pointing for deepspeed_engine_wrapped.backward()
self.deepspeed_engine_wrapped = DeepSpeedEngineWrapper(engine)
self._models.append(engine)
if optimizer is not None:
self._optimizers.append(optimizer)
if scheduler is not None:
self._schedulers.append(scheduler)
if len(self._models) > 1:
raise AssertionError(
"You can't use same `Accelerator()` instance with multiple models when using DeepSpeed"
)
return tuple(result)
def _prepare_megatron_lm(self, *args):
megatron_lm_plugin = self.state.megatron_lm_plugin
if not megatron_lm_plugin.megatron_dataset_flag:
batch_sizes = [obj.batch_size for obj in args if hasattr(obj, "batch_size")]
if len(batch_sizes) == 0:
raise ValueError(
"You must specify a training or evaluation dataloader in `accelerate.prepare()` when using Megatron-LM."
)
micro_batch_size = min(batch_sizes) if megatron_lm_plugin.is_train_batch_min else max(batch_sizes)
if len(batch_sizes) > 1:
logger.info(
"Since you passed both train and evaluation dataloader, `is_train_batch_min` (here "
f"{megatron_lm_plugin.is_train_batch_min} will decide the `train_batch_size` ({micro_batch_size})."
)
else:
for obj in args:
if isinstance(obj, MegatronLMDummyDataLoader):
micro_batch_size = obj.dataset_args["micro_batch_size"]
break
dp_degree = self.num_processes // (megatron_lm_plugin.tp_degree * megatron_lm_plugin.pp_degree)
megatron_lm_plugin.set_training_args(micro_batch_size, dp_degree)
model = None
optimizer = None
scheduler = None
is_dummy_scheduler = False
batch_data = None
for obj in args:
if isinstance(obj, torch.utils.data.DataLoader) and batch_data is None:
batch_data = next(iter(obj))
if isinstance(obj, torch.nn.Module):
model = obj
elif isinstance(obj, (torch.optim.Optimizer)):
optimizer = obj
elif isinstance(obj, (LRScheduler, MegatronLMDummyScheduler)):
scheduler = obj
if model is not None:
megatron_lm_plugin.set_network_size_args(model, batch_data)
if optimizer is not None:
megatron_lm_plugin.set_optimizer_type(optimizer)
if scheduler is not None:
is_dummy_scheduler = isinstance(scheduler, MegatronLMDummyScheduler)
if not is_dummy_scheduler:
raise ValueError(
"You can't use a custom scheduler with Megatron-LM. Please use the `accelerate.utils.MegatronLMDummyScheduler` instead."
)
megatron_lm_plugin.set_scheduler_args(scheduler)
# initialize megatron-lm
megatron_lm_initialize(self, args_defaults=megatron_lm_plugin.megatron_lm_default_args)
counter = 0
result = []
for obj in args:
if isinstance(obj, torch.utils.data.DataLoader):
result.append(megatron_lm_prepare_data_loader(self, obj))
counter += 1
elif isinstance(obj, MegatronLMDummyDataLoader):
if counter == 0:
obj.set_megatron_data_args()
dataloaders = megatron_lm_prepare_data_loader(self, obj)
result.append(dataloaders[counter])
counter += 1
else:
result.append(obj)
if model is not None:
model = megatron_lm_prepare_model(self)
if optimizer is not None:
optimizer = megatron_lm_prepare_optimizer(self, model)
if scheduler is not None:
scheduler = megatron_lm_prepare_scheduler(self, optimizer, scheduler)
if model is not None:
model = MegatronEngine(self, model, optimizer, scheduler)
if optimizer is not None:
optimizer = MegatronLMOptimizerWrapper(optimizer)
if scheduler is not None:
scheduler = MegatronLMSchedulerWrapper(scheduler, optimizer)
for i in range(len(result)):
if isinstance(result[i], torch.nn.Module):
result[i] = model
elif isinstance(result[i], torch.optim.Optimizer):
result[i] = optimizer
elif isinstance(result[i], MegatronLMDummyScheduler):
result[i] = scheduler
if model is not None:
self._models.append(model)
if optimizer is not None:
self._optimizers.append(optimizer)
if scheduler is not None:
self._schedulers.append(scheduler)
if len(self._models) > 1:
raise AssertionError(
"You can't use same `Accelerator()` instance with multiple models when using Megatron-LM"
)
return tuple(result)
def prepare_data_loader(self, data_loader: torch.utils.data.DataLoader, device_placement=None):
"""
Prepares a PyTorch DataLoader for training in any distributed setup. It is recommended to use
[`Accelerator.prepare`] instead.
Args:
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 to
`self.device_placement`.
"""
if device_placement is None:
device_placement = self.device_placement if self.distributed_type != DistributedType.TPU else False
prepared_data_loader = prepare_data_loader(
data_loader,
self.device,
num_processes=self.num_processes,
process_index=self.process_index,
split_batches=self.split_batches,
put_on_device=device_placement,
rng_types=self.rng_types.copy(),
dispatch_batches=self.dispatch_batches,
even_batches=self.even_batches,
)
self._dataloaders.append(prepared_data_loader)
return prepared_data_loader
def prepare_optimizer(self, 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.
Args:
optimizer (`torch.optim.Optimizer`):
A vanilla PyTorch optimizer to prepare
device_placement (`bool`, *optional*):
Whether or not to place the optimizer on the proper device. Will default to `self.device_placement`.
"""
if device_placement is None:
device_placement = self.device_placement
optimizer = AcceleratedOptimizer(optimizer, device_placement=device_placement, scaler=self.scaler)
self._optimizers.append(optimizer)
return optimizer
def prepare_scheduler(self, scheduler: LRScheduler):
"""
Prepares a PyTorch Scheduler for training in any distributed setup. It is recommended to use
[`Accelerator.prepare`] instead.
Args:
scheduler (`torch.optim.lr_scheduler.LRScheduler`):
A vanilla PyTorch scheduler to prepare
"""
# We try to find the optimizer associated with `scheduler`, the default is the full list.
optimizer = self._optimizers
for opt in self._optimizers:
if getattr(scheduler, "optimizer", None) == opt.optimizer:
optimizer = opt
break
scheduler = AcceleratedScheduler(
scheduler,
optimizer,
step_with_optimizer=self.step_scheduler_with_optimizer,
split_batches=self.split_batches,
)
self._schedulers.append(scheduler)
return scheduler
def backward(self, loss, **kwargs):
"""
Scales the gradients in accordance to `Accelerator.gradient_accumulation_steps` and calls the correct
`backward()` based on the configuration.
Should be used in lieu of `loss.backward()`.
"""
if self.distributed_type != DistributedType.DEEPSPEED:
# deepspeed handles loss scaling by gradient_accumulation_steps in its `backward`
loss = loss / self.gradient_accumulation_steps
if self.distributed_type == DistributedType.DEEPSPEED:
self.deepspeed_engine_wrapped.backward(loss, **kwargs)
elif self.distributed_type == DistributedType.MEGATRON_LM:
return
elif self.scaler is not None:
self.scaler.scale(loss).backward(**kwargs)
else:
loss.backward(**kwargs)
def unscale_gradients(self, optimizer=None):
"""
Unscale the gradients in mixed precision training with AMP. This is a noop in all other settings.
Args:
optimizer (`torch.optim.Optimizer` or `List[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 [`~Accelerator.prepare`].
"""
if self.use_fp16 and self.native_amp:
if optimizer is None:
# TODO: this unscales all optimizers where we should only unscale the one where parameters are.
optimizer = self._optimizers
elif not isinstance(optimizer, (tuple, list)):
optimizer = [optimizer]
for opt in optimizer:
while isinstance(opt, AcceleratedOptimizer):
opt = opt.optimizer
self.scaler.unscale_(opt)
def clip_grad_norm_(self, parameters, max_norm, norm_type=2):
"""
Should be used in place of `torch.nn.utils.clip_grad_norm_`.
Returns:
`torch.Tensor`: Total norm of the parameter gradients (viewed as a single vector).
Example:
```python
>>> 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()
```
"""
if self.distributed_type == DistributedType.FSDP:
self.unscale_gradients()
parameters = [p for p in parameters]
for model in self._models:
if parameters == [p for p in model.parameters()]:
return model.clip_grad_norm_(max_norm, norm_type)
elif self.distributed_type == DistributedType.DEEPSPEED:
# `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
# We cannot return the gradient norm because DeepSpeed does it.
return None
self.unscale_gradients()
return torch.nn.utils.clip_grad_norm_(parameters, max_norm, norm_type=norm_type)
def clip_grad_value_(self, parameters, clip_value):
"""
Should be used in place of `torch.nn.utils.clip_grad_value_`.
Example:
```python
>>> 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()
```
"""
if self.distributed_type in [DistributedType.DEEPSPEED, DistributedType.FSDP]:
raise Exception("DeepSpeed and FSDP do not support `clip_grad_value_`. Use `clip_grad_norm_` instead.")
self.unscale_gradients()
torch.nn.utils.clip_grad_value_(parameters, clip_value)
def gather(self, tensor):
"""
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.
Args:
tensor (`torch.Tensor`, or a nested tuple/list/dictionary of `torch.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.
"""
return gather(tensor)
def gather_for_metrics(self, tensor):
"""
Gathers `tensor` 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.
Args:
tensor (`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`):
The tensors for calculating metrics across all processes.
"""
tensor = self.gather(tensor)
if self.use_distributed:
if self.gradient_state.remainder == -1:
logger.info(
"The used dataset had no length, returning gathered tensors. You should drop the remainder yourself."
)
return tensor
try:
# Then see if we're on the last batch of our eval dataloader
if self.gradient_state.end_of_dataloader and self.gradient_state.remainder > 0:
# Last batch needs to be truncated on distributed systems as it contains additional samples
def _adjust_samples(tensor):
return tensor[: self.gradient_state.remainder]
return recursively_apply(_adjust_samples, tensor)
else:
# Not at the end of the dataloader, no need to adjust the tensors
return tensor
except:
# Dataset had no length or raised an error
return tensor
return tensor
def reduce(self, tensor, reduction="sum"):
"""
Reduce the values in *tensor* across all processes based on *reduction*.
Note:
All processes get the reduced value.
Args:
tensor (`torch.Tensor`, or a nested tuple/list/dictionary of `torch.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.
Returns:
`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`: The reduced tensor(s).
"""
return reduce(tensor, reduction)
def pad_across_processes(self, tensor, dim=0, pad_index=0, pad_first=False):
"""
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.
Args:
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 to `False`):
Whether to pad at the beginning or the end.
"""
return pad_across_processes(tensor, dim=dim, pad_index=pad_index, pad_first=pad_first)
def unwrap_model(self, model, keep_fp32_wrapper: bool = False):
"""
Unwraps the `model` from the additional layer possible added by [`~Accelerator.prepare`]. Useful before saving
the model.
Args:
model (`torch.nn.Module`):
The model to unwrap.
keep_fp32_wrapper (`bool`, *optional*, defaults to `False`):
Whether to not remove the mixed precision hook if it was added.
"""
return extract_model_from_parallel(model, keep_fp32_wrapper)
def wait_for_everyone(self):
"""
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.
"""
wait_for_everyone()
@on_main_process
def init_trackers(self, project_name: str, config: Optional[dict] = None, init_kwargs: Optional[dict] = {}):
"""
Initializes a run for all trackers stored in `self.log_with`, potentially with starting configurations
Args:
project_name (`str`):
The name of the project. All trackers will save their data based on this
config (`dict`, *optional*):
Optional starting configuration to be logged.
init_kwargs (`dict`, *optional*):
A nested dictionary of kwargs to be passed to a specific tracker's `__init__` function. Should be
formatted like so:
```python
{"wandb": {"tags": ["tag_a", "tag_b"]}}
```
"""
self.trackers = []
for tracker in self.log_with:
if issubclass(type(tracker), GeneralTracker):
# Custom trackers are already initialized
self.trackers.append(tracker)
else:
tracker_init = LOGGER_TYPE_TO_CLASS[str(tracker)]
if getattr(tracker_init, "requires_logging_directory"):
# We can skip this check since it was done in `__init__`
self.trackers.append(
tracker_init(project_name, self.logging_dir, **init_kwargs.get(str(tracker), {}))
)
else:
self.trackers.append(tracker_init(project_name, **init_kwargs.get(str(tracker), {})))
if config is not None:
for tracker in self.trackers:
tracker.store_init_configuration(config)
@on_main_process
def get_tracker(self, name: str):
"""
Returns a `tracker` from `self.trackers` based on `name` on the main process only.
Args:
name (`str`):
The name of a tracker, corresponding to the `.name` property.
"""
for tracker in self.trackers:
if tracker.name == name:
return tracker.tracker
raise ValueError(f"{name} is not an available tracker stored inside the `Accelerator`.")
@on_main_process
def log(self, values: dict, step: Optional[int] = None, log_kwargs: Optional[dict] = {}):
"""
Logs `values` to all stored trackers in `self.trackers` on the main process only.
Args:
values (`dict`):
Values should be a dictionary-like object containing only types `int`, `float`, or `str`.
step (`int`, *optional*):
The run step. If included, the log will be affiliated with this step.
log_kwargs (`dict`, *optional*):
A nested dictionary of kwargs to be passed to a specific tracker's `log` function. Should be formatted
like so:
```python
{"wandb": {"tags": ["tag_a", "tag_b"]}}
```
"""
for tracker in self.trackers:
tracker.log(values, step=step, **log_kwargs.get(tracker.name, {}))
@on_main_process
def end_training(self):
"""
Runs any special end training behaviors, such as stopping trackers on the main process only. Should always be
called at the end of your script if using experiment tracking.
"""
for tracker in self.trackers:
tracker.finish()
def save(self, obj, f):
"""
Save the object passed to disk once per machine. Use in place of `torch.save`.
Args:
obj: The object to save.
f (`str` or `os.PathLike`):
Where to save the content of `obj`.
"""
save(obj, f)
def save_state(self, output_dir: str = None, **save_model_func_kwargs):
"""
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_`.
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.
Args:
output_dir (`str` or `os.PathLike`):
The name of the folder to save all relevant weights and states.
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's `save_checkpoint` function.
"""
if self.project_configuration.automatic_checkpoint_naming:
output_dir = os.path.join(self.project_dir, "checkpoints")
os.makedirs(output_dir, exist_ok=True)
if self.project_configuration.automatic_checkpoint_naming:
folders = [os.path.join(output_dir, folder) for folder in os.listdir(output_dir)]
if self.project_configuration.total_limit is not None and (
len(folders) + 1 > self.project_configuration.total_limit
):
folders.sort()
logger.warning(
f"Deleting {len(folders) + 1 - self.project_configuration.total_limit} checkpoints to make room for new checkpoint."
)
for folder in folders[: len(folders) + 1 - self.project_configuration.total_limit]:
shutil.rmtree(folder)
output_dir = os.path.join(output_dir, f"checkpoint_{self.save_iteration}")
if os.path.exists(output_dir):
raise ValueError(
f"Checkpoint directory {output_dir} ({self.save_iteration}) already exists. Please manually override `self.save_iteration` with what iteration to start with."
)
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving current state to {output_dir}")
# Save the models taking care of FSDP and DeepSpeed nuances
weights = []
for i, model in enumerate(self._models):
if self.distributed_type == DistributedType.FSDP:
logger.info("Saving FSDP model")
self.state.fsdp_plugin.save_model(self, model, output_dir, i)
logger.info(f"FSDP Model saved to output dir {output_dir}")
elif self.distributed_type == DistributedType.DEEPSPEED:
logger.info("Saving DeepSpeed Model and Optimizer")
ckpt_id = f"{MODEL_NAME}" if i == 0 else f"{MODEL_NAME}_{i}"
model.save_checkpoint(output_dir, ckpt_id, **save_model_func_kwargs)
logger.info(f"DeepSpeed Model and Optimizer saved to output dir {os.path.join(output_dir, ckpt_id)}")
elif self.distributed_type == DistributedType.MEGATRON_LM:
logger.info("Saving Megatron-LM Model, Optimizer and Scheduler")
model.save_checkpoint(output_dir)
logger.info(f"Megatron-LM Model , Optimizer and Scheduler saved to output dir {output_dir}")
else:
weights.append(self.get_state_dict(model, unwrap=False))
# Save the optimizers taking care of FSDP and DeepSpeed nuances
optimizers = []
if self.distributed_type == DistributedType.FSDP:
for opt in self._optimizers:
logger.info("Saving FSDP Optimizer")
self.state.fsdp_plugin.save_optimizer(self, opt, self._models[i], output_dir, i)
logger.info(f"FSDP Optimizer saved to output dir {output_dir}")
elif self.distributed_type not in [DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM]:
optimizers = self._optimizers
# Save the lr schedulers taking care of DeepSpeed nuances
schedulers = []
if self.distributed_type == DistributedType.DEEPSPEED:
for i, scheduler in enumerate(self._schedulers):
if isinstance(scheduler, DeepSpeedSchedulerWrapper):
continue
schedulers.append(scheduler)
elif self.distributed_type not in [DistributedType.MEGATRON_LM]:
schedulers = self._schedulers
save_location = save_accelerator_state(
output_dir, weights, optimizers, schedulers, self.state.process_index, self.scaler
)
for i, obj in enumerate(self._custom_objects):
save_custom_state(obj, output_dir, i)
self.project_configuration.iteration += 1
return save_location
def load_state(self, input_dir: str, **load_model_func_kwargs):
"""
Loads the current states of the model, optimizer, scaler, RNG generators, and registered objects.
Should only be used in conjunction with [`Accelerator.save_state`].
Args:
input_dir (`str` or `os.PathLike`):
The name of the folder all relevant weights and states were saved in.
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's `load_checkpoint` function.
"""
# Check if folder exists
input_dir = os.path.expanduser(input_dir)
if not os.path.isdir(input_dir):
raise ValueError(f"Tried to find {input_dir} but folder does not exist")
logger.info(f"Loading states from {input_dir}")
# Load the models taking care of FSDP and DeepSpeed nuances
models = []
for i, model in enumerate(self._models):
if self.distributed_type == DistributedType.FSDP:
logger.info("Loading FSDP model")
self.state.fsdp_plugin.load_model(self, model, input_dir, i)
logger.info(f"FSDP Model loaded from input dir {input_dir}")
elif self.distributed_type == DistributedType.DEEPSPEED:
logger.info("Loading DeepSpeed Model and Optimizer")
ckpt_id = f"{MODEL_NAME}" if i == 0 else f"{MODEL_NAME}_{i}"
model.load_checkpoint(input_dir, ckpt_id, **load_model_func_kwargs)
logger.info(f"DeepSpeed Model and Optimizer loaded from input dir {os.path.join(input_dir, ckpt_id)}")
elif self.distributed_type == DistributedType.MEGATRON_LM:
logger.info("Loading Megatron-LM Model, Optimizer and Scheduler")
model.load_checkpoint(input_dir)
logger.info(f"Megatron-LM Model , Optimizer and Scheduler loaded from input dir {input_dir}")
else:
models.append(model)
# Load the optimizers taking care of FSDP and DeepSpeed nuances
optimizers = []
if self.distributed_type == DistributedType.FSDP:
for i, opt in enumerate(self._optimizers):
logger.info("Loading FSDP Optimizer")
self.state.fsdp_plugin.load_optimizer(self, opt, self._models[i], input_dir, i)
logger.info(f"FSDP Optimizer loaded from input dir {input_dir}")
elif self.distributed_type not in [DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM]:
optimizers = self._optimizers
# Load the lr schedulers taking care of DeepSpeed nuances
schedulers = []
if self.distributed_type == DistributedType.DEEPSPEED:
for i, scheduler in enumerate(self._schedulers):
if isinstance(scheduler, DeepSpeedSchedulerWrapper):
continue
schedulers.append(scheduler)
elif self.distributed_type not in [DistributedType.MEGATRON_LM]:
schedulers = self._schedulers
load_accelerator_state(
input_dir, models, optimizers, schedulers, self.state.process_index, self.scaler, **load_model_func_kwargs
)
custom_checkpoints = [f for f in os.listdir(input_dir) if "custom_checkpoint" in f]
if len(custom_checkpoints) != len(self._custom_objects):
err = "Warning! Number of found checkpoints does not match the number of registered objects:"
err += f"\n\tFound checkpoints: {len(custom_checkpoints)}"
err += f"\n\tRegistered objects: {len(self._custom_objects)}\nSkipping."
logger.warning(err)
else:
logger.info(f"Loading in {len(custom_checkpoints)} custom states")
for index, obj in enumerate(self._custom_objects):
load_custom_state(obj, input_dir, index)
def free_memory(self):
"""
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.
"""
self._schedulers = []
self._optimizers = []
self._models = []
self._dataloaders = []
self.deepspeed_engine_wrapped = None
release_memory()
def clear(self):
"""
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.
"""
self.free_memory()
def _get_named_parameters(self, *args):
named_parameters = {}
for obj in args:
if isinstance(obj, torch.nn.Module):
obj = extract_model_from_parallel(obj)
named_parameters.update({n: p for n, p in obj.named_parameters()})
return named_parameters
def _get_devices(self, *args):
model_device = None
optimizer_device = None
for obj in args:
# Loop through model parameters and stop at the first once we have its device.
if isinstance(obj, torch.nn.Module):
for param in obj.parameters():
model_device = param.device
break
# Loop through optimizer parameters groups and stop at the first once we have its device.
if isinstance(obj, torch.optim.Optimizer):
for param_group in obj.param_groups:
if len(param_group["params"]) > 0:
optimizer_device = param_group["params"][0].device
break
return (model_device, optimizer_device)
def get_state_dict(self, model, unwrap=True):
"""
Returns the state dictionary of a model sent through [`Accelerator.prepare`] in full precision
Args:
model (`torch.nn.Module`):
A PyTorch model sent through [`Accelerator.prepare`]
unwrap (`bool`, *optional*, defaults to `True`):
Whether to return the original underlying state_dict of `model` or to return the wrapped state_dict
"""
is_zero_3 = False
if self.distributed_type == DistributedType.DEEPSPEED:
is_zero_3 = self.deepspeed_config["zero_optimization"]["stage"] == 3
if is_zero_3:
if model.zero_gather_16bit_weights_on_model_save():
state_dict = model._zero3_consolidated_16bit_state_dict()
else:
raise ValueError(
"Cannot get 16bit model weights because `stage3_gather_16bit_weights_on_model_save` in DeepSpeed config is False. "
"To save the model weights in 16bit, set `stage3_gather_16bit_weights_on_model_save` to True in DeepSpeed config file or "
"set `zero3_save_16bit_model` to True when using `accelerate config`. "
"To save the full checkpoint, run `model.save_checkpoint(save_dir)` and use `zero_to_fp32.py` to recover weights."
)
else:
if unwrap:
model = self.unwrap_model(model)
state_dict = model.state_dict()
if state_dict is not None:
for k in state_dict:
if state_dict[k].dtype == torch.float16:
state_dict[k] = state_dict[k].float()
return state_dict
def register_for_checkpointing(self, *objects):
"""
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.
"""
invalid_objects = []
for obj in objects:
if not hasattr(obj, "state_dict") or not hasattr(obj, "load_state_dict"):
invalid_objects.append(obj)
if len(invalid_objects) > 0:
err = "All `objects` must include a `state_dict` and `load_state_dict` function to be stored. The following inputs are invalid:"
for index, obj in enumerate(invalid_objects):
err += f"\n\t- Item at index {index}, `{get_pretty_name(obj)}`"
raise ValueError(err)
self._custom_objects.extend(objects)
@contextmanager
def autocast(self):
"""
Will apply automatic mixed-precision inside the block inside this context manager, if it is enabled. Nothing
different will happen otherwise.
"""
if self.native_amp:
if self.mixed_precision == "fp16" and is_torch_version(">=", "1.10"):
autocast_context = torch.cuda.amp.autocast(dtype=torch.float16)
elif self.mixed_precision == "bf16":
if self.distributed_type in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
autocast_context = torch.autocast(dtype=torch.bfloat16, device_type=self.device.type)
else:
autocast_context = torch.cuda.amp.autocast()
autocast_context.__enter__()
yield
autocast_context.__exit__(*sys.exc_info())
else:
yield
@property
def optimizer_step_was_skipped(self):
"""
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.
"""
for optimizer in self._optimizers:
if optimizer.step_was_skipped:
return True
return False