# 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.
from __future__ import annotations
import math
import os
import threading
import warnings
from contextlib import contextmanager
from functools import partial
from typing import Any, Callable, Optional
import torch
from .utils import (
DistributedType,
DynamoBackend,
GradientAccumulationPlugin,
get_ccl_version,
get_int_from_env,
is_ccl_available,
is_deepspeed_available,
is_fp8_available,
is_ipex_available,
is_mps_available,
is_npu_available,
is_tpu_available,
is_xpu_available,
parse_choice_from_env,
parse_flag_from_env,
)
from .utils.dataclasses import SageMakerDistributedType
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def is_initialized() -> bool:
"""
Checks if the `AcceleratorState` has been initialized from `Accelerator`. Same as `AcceleratorState.initialized`,
but works as a module method.
"""
return AcceleratorState._shared_state != {}
# Lambda function that does nothing
def do_nothing(*args, **kwargs):
return None
class ThreadLocalSharedDict(threading.local):
"""
Descriptor that holds a dict shared between instances of a class in the same thread.
Note: Descriptors have slightly different semantics than just a dict field on its own.
`PartialState(...)._shared_state` and `PartialState._shared_state` (instance vs class) give the same value: the
underlying _storage dict. Likewise, `PartialState(...)._shared_state = {...}` overrides the _storage dict inside
the descriptor as you would expect. However, `PartialState._shared_state = {}` actually replaces the descriptor
object with a dict instead Thus, you should modify the _storage dict in-place (e.g. `_shared_state.clear()`).
See Python documentation for an explanation of descriptors: https://docs.python.org/3/howto/descriptor.html
This is required for using PyTorch/XLA with PJRT in multithreaded mode (required for TPU v2 and v3).
See https://github.com/pytorch/xla/blob/r2.0/docs/pjrt.md#multithreading-on-tpu-v2v3
"""
def __init__(self, thread_local: bool = False):
self._storage = {}
def __get__(self, obj, objtype=None):
return self._storage
def __set__(self, obj, value):
self._storage = value
# Prefer global shared dictionary, except when using TPU.
SharedDict = dict if not is_tpu_available(check_device=False) else ThreadLocalSharedDict
# Inspired by Alex Martelli's 'Borg'.
class PartialState:
"""
Singleton class that has information about the current training environment and functions to help with process
control. Designed to be used when only process control and device execution states are needed. Does *not* need to
be initialized from `Accelerator`.
**Available attributes:**
- **device** (`torch.device`) -- The device to use.
- **distributed_type** ([`~accelerate.state.DistributedType`]) -- The type of distributed environment currently
in use.
- **local_process_index** (`int`) -- The index of the current process on the current server.
- **mixed_precision** (`str`) -- Whether or not the current script will use mixed precision, and if so the type
of mixed precision being performed.
- **num_processes** (`int`) -- The number of processes currently launched in parallel.
- **process_index** (`int`) -- The index of the current process.
- **is_last_process** (`bool`) -- Whether or not the current process is the last one.
- **is_main_process** (`bool`) -- Whether or not the current process is the main one.
- **is_local_main_process** (`bool`) -- Whether or not the current process is the main one on the local node.
"""
_shared_state = SharedDict()
def __init__(self, cpu: bool = False, **kwargs):
self.__dict__ = self._shared_state
if not self.initialized:
self._cpu = cpu
self.backend = None
env_device = os.environ.get("ACCELERATE_TORCH_DEVICE", None)
self.device = torch.device(env_device) if env_device is not None else None
use_sagemaker_dp = kwargs.pop("_use_sagemaker_dp", None)
if use_sagemaker_dp is None:
use_sagemaker_dp = (
os.environ.get("ACCELERATE_USE_SAGEMAKER", "false") == "true"
and os.environ.get("ACCELERATE_SAGEMAKER_DISTRIBUTED_TYPE") != SageMakerDistributedType.NO
)
if use_sagemaker_dp and not cpu:
if (
os.environ.get("ACCELERATE_SAGEMAKER_DISTRIBUTED_TYPE") == SageMakerDistributedType.DATA_PARALLEL
) or use_sagemaker_dp:
self.distributed_type = DistributedType.MULTI_GPU
import smdistributed.dataparallel.torch.torch_smddp # noqa
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="smddp")
self.backend = "smddp"
self.num_processes = torch.distributed.get_world_size()
self.process_index = torch.distributed.get_rank()
self.local_process_index = int(os.environ.get("LOCAL_RANK", -1))
if self.device is None:
self.device = torch.device("cuda", self.local_process_index)
torch.cuda.set_device(self.device)
elif is_tpu_available() and not cpu:
self.distributed_type = DistributedType.TPU
self.num_processes = xm.xrt_world_size()
self.process_index = xm.get_ordinal()
self.local_process_index = xm.get_local_ordinal()
self.device = xm.xla_device()
elif (
os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "true"
and int(os.environ.get("LOCAL_RANK", -1)) != -1
and not cpu
):
assert (
is_deepspeed_available()
), "DeepSpeed is not available => install it using `pip3 install deepspeed` or build it from source"
self.distributed_type = DistributedType.DEEPSPEED
if not torch.distributed.is_initialized():
from deepspeed import comm as dist
# DeepSpeed always uses nccl
kwargs.pop("backend", None)
self.backend = "nccl"
dist.init_distributed(dist_backend=self.backend, auto_mpi_discovery=False, **kwargs)
self.num_processes = torch.distributed.get_world_size()
self.process_index = torch.distributed.get_rank()
self.local_process_index = int(os.environ.get("LOCAL_RANK", -1))
if self.device is None:
if is_xpu_available():
self.device = torch.device("xpu", self.local_process_index)
if self.device is not None:
torch.xpu.set_device(self.device)
else:
self.device = torch.device("cuda", self.local_process_index)
if self.device is not None:
torch.cuda.set_device(self.device)
self._mixed_precision = "no" # deepspeed handles mixed_precision using deepspeed_config
elif int(os.environ.get("LOCAL_RANK", -1)) != -1 and not cpu and torch.cuda.is_available():
self.distributed_type = DistributedType.MULTI_GPU
if not torch.distributed.is_initialized():
self.backend = kwargs.pop("backend", "nccl")
# Special case for `TrainingArguments`, where `backend` will be `None`
if self.backend is None:
self.backend = "nccl"
torch.distributed.init_process_group(backend=self.backend, **kwargs)
self.num_processes = torch.distributed.get_world_size()
self.process_index = torch.distributed.get_rank()
self.local_process_index = int(os.environ.get("LOCAL_RANK", -1))
if self.device is None:
self.device = torch.device("cuda", self.local_process_index)
torch.cuda.set_device(self.device)
elif is_npu_available() and not cpu and int(os.environ.get("LOCAL_RANK", -1)) != -1:
self.distributed_type = DistributedType.MULTI_NPU
if not torch.distributed.is_initialized():
# Backend is not set by the user, we set it here
kwargs.pop("backend", None)
self.backend = "hccl"
torch.distributed.init_process_group(backend=self.backend, **kwargs)
self.num_processes = torch.distributed.get_world_size()
self.process_index = torch.distributed.get_rank()
self.local_process_index = int(os.environ.get("LOCAL_RANK", -1))
if self.device is None:
self.device = torch.device("npu", self.local_process_index)
torch.npu.set_device(self.device)
elif get_int_from_env(["PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE", "WORLD_SIZE"], 1) > 1:
if not cpu and is_xpu_available():
self.distributed_type = DistributedType.MULTI_XPU
else:
self.distributed_type = DistributedType.MULTI_CPU
# Actually, CCL_WORKER_COUNT is a CPU only env var in CCL, no need to set it for XPU.
if is_ccl_available() and (
get_int_from_env(["CCL_WORKER_COUNT"], 0) > 0 or self.distributed_type == DistributedType.MULTI_XPU
):
if get_ccl_version() >= "1.12":
import oneccl_bindings_for_pytorch # noqa: F401
else:
import torch_ccl # noqa: F401
backend = "ccl"
elif torch.distributed.is_mpi_available():
backend = "mpi"
else:
backend = "gloo"
# Try to get launch configuration from environment variables set by MPI launcher - works for Intel MPI, OpenMPI and MVAPICH
rank = get_int_from_env(["RANK", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "MV2_COMM_WORLD_RANK"], 0)
size = get_int_from_env(["WORLD_SIZE", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE"], 1)
local_rank = get_int_from_env(
["LOCAL_RANK", "MPI_LOCALRANKID", "OMPI_COMM_WORLD_LOCAL_RANK", "MV2_COMM_WORLD_LOCAL_RANK"], 0
)
local_size = get_int_from_env(
["MPI_LOCALNRANKS", "OMPI_COMM_WORLD_LOCAL_SIZE", "MV2_COMM_WORLD_LOCAL_SIZE"], 1
)
self.local_process_index = local_rank
os.environ["RANK"] = str(rank)
os.environ["WORLD_SIZE"] = str(size)
os.environ["LOCAL_RANK"] = str(local_rank)
if not os.environ.get("MASTER_PORT", None):
os.environ["MASTER_PORT"] = "29500"
if not os.environ.get("MASTER_ADDR", None):
if local_size != size and backend != "mpi":
raise ValueError(
"Looks like distributed multinode run but MASTER_ADDR env not set, "
"please try exporting rank 0's hostname as MASTER_ADDR"
)
if (
self.distributed_type == DistributedType.MULTI_CPU
and get_int_from_env(["OMP_NUM_THREADS", "MKL_NUM_THREADS"], 0) == 0
):
import psutil
num_cpu_threads_per_process = int(psutil.cpu_count(logical=False) / local_size)
if num_cpu_threads_per_process == 0:
num_cpu_threads_per_process = 1
torch.set_num_threads(num_cpu_threads_per_process)
warnings.warn(
f"OMP_NUM_THREADS/MKL_NUM_THREADS unset, we set it at {num_cpu_threads_per_process} to improve oob"
" performance."
)
if not torch.distributed.is_initialized():
# Backend is not set by the user, we set it here
kwargs.pop("backend", None)
self.backend = backend
torch.distributed.init_process_group(self.backend, rank=rank, world_size=size, **kwargs)
self.num_processes = torch.distributed.get_world_size()
self.process_index = torch.distributed.get_rank()
if cpu:
self.device = torch.device("cpu")
elif is_xpu_available():
self.device = torch.device("xpu", self.local_process_index)
torch.xpu.set_device(self.device)
else:
self.device = self.default_device
else:
self.distributed_type = DistributedType.NO
self.num_processes = 1
self.process_index = self.local_process_index = 0
if self.device is None:
self.device = torch.device("cpu") if cpu else self.default_device
self.fork_launched = parse_flag_from_env("FORK_LAUNCHED", 0)
def __repr__(self) -> str:
return (
f"Distributed environment: {self.distributed_type}{(' Backend: ' + self.backend) if self.backend else ''}\n"
f"Num processes: {self.num_processes}\n"
f"Process index: {self.process_index}\n"
f"Local process index: {self.local_process_index}\n"
f"Device: {self.device}\n"
)
@staticmethod
def _reset_state():
"Resets `_shared_state`, is used internally and should not be called"
PartialState._shared_state.clear()
@property
def initialized(self) -> bool:
"Returns whether the `PartialState` has been initialized"
return self._shared_state != {}
@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 is_last_process(self) -> bool:
"Returns whether the current process is the last one"
return self.process_index == self.num_processes - 1
@property
def is_main_process(self) -> bool:
"Returns whether the current process is the main process"
return (
self.process_index == 0 if self.distributed_type != DistributedType.MEGATRON_LM else self.is_last_process
)
@property
def is_local_main_process(self) -> bool:
"Returns whether the current process is the main process on the local node"
return (
self.local_process_index == 0
if self.distributed_type != DistributedType.MEGATRON_LM
else self.is_last_process
)
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.
Example:
```python
>>> # Assuming two GPU processes
>>> import time
>>> from accelerate.state import PartialState
>>> state = PartialState()
>>> if state.is_main_process:
... time.sleep(2)
>>> else:
... print("I'm waiting for the main process to finish its sleep...")
>>> state.wait_for_everyone()
>>> # Should print on every process at the same time
>>> print("Everyone is here")
```
"""
if self.distributed_type in (
DistributedType.MULTI_GPU,
DistributedType.MULTI_NPU,
DistributedType.MULTI_XPU,
DistributedType.MULTI_CPU,
DistributedType.DEEPSPEED,
DistributedType.FSDP,
):
torch.distributed.barrier()
elif self.distributed_type == DistributedType.TPU:
xm.rendezvous("accelerate.utils.wait_for_everyone")
def _goes_first(self, is_main: bool):
if not is_main:
self.wait_for_everyone()
yield
if is_main:
self.wait_for_everyone()
@contextmanager
def split_between_processes(self, inputs: list | tuple | dict | torch.Tensor, apply_padding: bool = False):
"""
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.
Args:
inputs (`list`, `tuple`, `torch.Tensor`, or `dict` of `list`/`tuple`/`torch.Tensor`):
The input to split between processes.
apply_padding (`bool`, `optional`, defaults to `False`):
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 as `gather()` on the outputs or passing
in less inputs than there are processes. If so, just remember to drop the padded elements afterwards.
Example:
```python
# Assume there are two processes
from accelerate import PartialState
state = PartialState()
with state.split_between_processes(["A", "B", "C"]) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C"]
with state.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C", "C"]
```
"""
if self.num_processes == 1:
yield inputs
return
# Nested dictionary of any types
if isinstance(inputs, dict):
length = len(inputs[list(inputs.keys())[0]])
if not all(len(v) == length for v in inputs.values()):
raise ValueError("All values in the dictionary must have the same length")
num_samples_per_process = math.ceil(len(inputs) / self.num_processes)
start_index = self.process_index * num_samples_per_process
end_index = start_index + num_samples_per_process
if (len(inputs) % self.num_processes != 0) and (self.process_index == self.num_processes - 1):
if isinstance(inputs, (list, tuple, torch.Tensor)):
end_index = len(inputs)
elif isinstance(inputs, dict):
end_index = len(inputs[list(inputs.keys())[0]])
def _split_values(inputs, start_index, end_index):
if isinstance(inputs, (list, tuple, torch.Tensor)):
result = inputs[start_index:end_index]
if apply_padding:
if isinstance(result, torch.Tensor):
from accelerate.utils import pad_across_processes, send_to_device
# The tensor needs to be on the device before we can pad it
tensorized_result = send_to_device(result, self.device)
result = pad_across_processes(tensorized_result, pad_index=inputs[-1])
else:
result += [result[-1]] * (num_samples_per_process - len(result))
return result
elif isinstance(inputs, dict):
for key in inputs.keys():
inputs[key] = _split_values(inputs[key], start_index, end_index)
return inputs
else:
return inputs
yield _split_values(inputs, start_index, end_index)
@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.
Example:
```python
>>> 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}")
```
"""
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.
Example:
```python
>>> from accelerate.state import PartialState
>>> state = PartialState()
>>> with state.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 {state.local_process_index}")
```
"""
yield from self._goes_first(self.is_local_main_process)
def on_main_process(self, function: Callable[..., Any] = None):
"""
Decorator that only runs the decorated function on the main process.
Args:
function (`Callable`): The function to decorate.
Example:
```python
>>> from accelerate.state import PartialState
>>> state = PartialState()
>>> @state.on_main_process
... def print_something():
... print("This will be printed by process 0 only.")
>>> print_something()
"This will be printed by process 0 only"
```
"""
if not self.initialized:
raise ValueError("The `PartialState` or `Accelerator` must be initialized before calling this function.")
if self.is_main_process or not self.use_distributed:
return function
return do_nothing
def on_local_main_process(self, function: Callable[..., Any] = None):
"""
Decorator that only runs the decorated function on the local main process.
Args:
function (`Callable`): The function to decorate.
Example:
```python
# Assume we have 2 servers with 4 processes each.
from accelerate.state import PartialState
state = PartialState()
@state.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"
```
"""
if self.is_local_main_process or not self.use_distributed:
return function
return do_nothing
def on_last_process(self, function: Callable[..., Any]):
"""
Decorator that only runs the decorated function on the last process.
Args:
function (`Callable`): The function to decorate.
Example:
```python
# Assume we have 4 processes.
from accelerate.state import PartialState
state = PartialState()
@state.on_last_process
def print_something():
print(f"Printed on process {state.process_index}")
print_something()
"Printed on process 3"
```
"""
if self.is_last_process or not self.use_distributed:
return function
return do_nothing
def on_process(self, function: Callable[..., Any] = None, process_index: int = None):
"""
Decorator that only runs the decorated function on the process with the given index.
Args:
function (`Callable`, `optional`):
The function to decorate.
process_index (`int`, `optional`):
The index of the process on which to run the function.
Example:
```python
# Assume we have 4 processes.
from accelerate.state import PartialState
state = PartialState()
@state.on_process(process_index=2)
def print_something():
print(f"Printed on process {state.process_index}")
print_something()
"Printed on process 2"
```
"""
if function is None:
return partial(self.on_process, process_index=process_index)
if (self.process_index == process_index) or (not self.use_distributed):
return function
return do_nothing
def on_local_process(self, function: Callable[..., Any] = None, local_process_index: int = None):
"""
Decorator that only runs the decorated function on the process with the given index on the current node.
Args:
function (`Callable`, *optional*):
The function to decorate.
local_process_index (`int`, *optional*):
The index of the local process on which to run the function.
Example:
```python
# 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"
```
"""
if function is None:
return partial(self.on_local_process, local_process_index=local_process_index)
if (self.local_process_index == local_process_index) or (not self.use_distributed):
return function
return do_nothing
def print(self, *args, **kwargs):
if self.is_local_main_process:
print(*args, **kwargs)
@property
def default_device(self) -> torch.device:
"""
Returns the default device which is:
- MPS if `torch.backends.mps.is_available()` and `torch.backends.mps.is_built()` both return True.
- CUDA if `torch.cuda.is_available()`
- NPU if `is_npu_available()`
- CPU otherwise
"""
if is_mps_available():
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
return torch.device("mps")
elif torch.cuda.is_available():
return torch.device("cuda")
elif is_xpu_available():
return torch.device("xpu:0")
elif is_npu_available():
return torch.device("npu")
else:
return torch.device("cpu")
class AcceleratorState:
"""
Singleton class that has information about the current training environment.
**Available attributes:**
- **device** (`torch.device`) -- The device to use.
- **distributed_type** ([`~accelerate.state.DistributedType`]) -- The type of distributed environment currently
in use.
- **initialized** (`bool`) -- Whether or not the `AcceleratorState` has been initialized from `Accelerator`.
- **local_process_index** (`int`) -- The index of the current process on the current server.
- **mixed_precision** (`str`) -- Whether or not the current script will use mixed precision, and if so the type
of mixed precision being performed.
- **num_processes** (`int`) -- The number of processes currently launched in parallel.
- **process_index** (`int`) -- The index of the current process.
- **is_last_process** (`bool`) -- Whether or not the current process is the last one.
- **is_main_process** (`bool`) -- Whether or not the current process is the main one.
- **is_local_main_process** (`bool`) -- Whether or not the current process is the main one on the local node.
"""
_shared_state = SharedDict()
def __init__(
self,
mixed_precision: str = None,
cpu: bool = False,
dynamo_plugin=None,
deepspeed_plugin=None,
fsdp_plugin=None,
megatron_lm_plugin=None,
_from_accelerator: bool = False,
**kwargs,
):
self.__dict__ = self._shared_state
if parse_flag_from_env("ACCELERATE_USE_CPU"):
cpu = True
if PartialState._shared_state == {}:
PartialState(cpu, **kwargs)
self.__dict__.update(PartialState._shared_state)
self._check_initialized(mixed_precision, cpu)
if not self.initialized:
self.deepspeed_plugin = None
mixed_precision = (
parse_choice_from_env("ACCELERATE_MIXED_PRECISION", "no")
if mixed_precision is None
else mixed_precision.lower()
)
if mixed_precision == "fp8" and not is_fp8_available():
raise ValueError("Using `fp8` precision requires `transformer_engine` to be installed.")
self.dynamo_plugin = dynamo_plugin
if not _from_accelerator:
raise ValueError(
"Please make sure to properly initialize your accelerator via `accelerator = Accelerator()` "
"before using any functionality from the `accelerate` library."
)
# deepspeed handles mixed_precision using deepspeed_config
self._mixed_precision = "no" if self.distributed_type == DistributedType.DEEPSPEED else mixed_precision
if self.distributed_type == DistributedType.TPU:
if mixed_precision == "bf16":
if os.environ.get("ACCELERATE_DOWNCAST_BF16"):
os.environ["XLA_USE_BF16"] = str(0)
os.environ["XLA_DOWNCAST_BF16"] = str(1)
self.downcast_bfloat = True
else:
os.environ["XLA_USE_BF16"] = str(1)
os.environ["XLA_DOWNCAST_BF16"] = str(0)
self.downcast_bfloat = False
elif os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "true" and not cpu:
self.deepspeed_plugin = deepspeed_plugin
elif self.distributed_type == DistributedType.MULTI_GPU:
if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true":
self.distributed_type = DistributedType.FSDP
if self._mixed_precision != "no":
fsdp_plugin.set_mixed_precision(self._mixed_precision)
self.fsdp_plugin = fsdp_plugin
if os.environ.get("ACCELERATE_USE_MEGATRON_LM", "false") == "true":
self.distributed_type = DistributedType.MEGATRON_LM
megatron_lm_plugin.set_mixed_precision(self._mixed_precision)
self.megatron_lm_plugin = megatron_lm_plugin
elif self.distributed_type in [DistributedType.MULTI_CPU, DistributedType.MULTI_XPU, DistributedType.NO]:
if is_ipex_available():
"check if user disables it explicitly"
self.use_ipex = parse_flag_from_env("ACCELERATE_USE_IPEX", default=True)
else:
self.use_ipex = False
if (
self.dynamo_plugin.backend != DynamoBackend.NO
and self._mixed_precision == "no"
and self.device.type == "cuda"
):
torch.backends.cuda.matmul.allow_tf32 = True
PartialState._shared_state["distributed_type"] = self.distributed_type
@property
def initialized(self) -> bool:
return self._shared_state != PartialState._shared_state
def __repr__(self):
repr = PartialState().__repr__() + f"\nMixed precision type: {self.mixed_precision}\n"
if self.distributed_type == DistributedType.DEEPSPEED:
repr += f"ds_config: {self.deepspeed_plugin.deepspeed_config}\n"
return repr
def _check_initialized(self, mixed_precision=None, cpu=None):
"Checks if a modification is trying to be made and the `AcceleratorState` has already been initialized"
if self.initialized:
err = "AcceleratorState has already been initialized and cannot be changed, restart your runtime completely and pass `{flag}` to `Accelerator()`."
if cpu and self.device.type != "cpu":
raise ValueError(err.format(flag="cpu=True"))
if (
mixed_precision is not None
and mixed_precision != self._mixed_precision
and self.distributed_type != DistributedType.DEEPSPEED
):
raise ValueError(err.format(flag=f"mixed_precision='{mixed_precision}'"))
# For backward compatibility
@property
def use_fp16(self):
warnings.warn(
"The `use_fp16` property is deprecated and will be removed in version 1.0 of Accelerate use "
"`AcceleratorState.mixed_precision == 'fp16'` instead.",
FutureWarning,
)
return self._mixed_precision != "no"
@property
def mixed_precision(self):
if self.distributed_type == DistributedType.DEEPSPEED:
config = self.deepspeed_plugin.deepspeed_config
if config.get("fp16", {}).get("enabled", False):
mixed_precision = "fp16"
elif config.get("bf16", {}).get("enabled", False):
mixed_precision = "bf16"
else:
mixed_precision = "no"
else:
mixed_precision = self._mixed_precision
return mixed_precision
@staticmethod
def _reset_state(reset_partial_state: bool = False):
"Resets `_shared_state`, is used internally and should not be called"
AcceleratorState._shared_state.clear()
if reset_partial_state:
PartialState._reset_state()
@property
def use_distributed(self):
"""
Whether the Accelerator is configured for distributed training
"""
return PartialState().use_distributed
@property
def is_last_process(self) -> bool:
"Returns whether the current process is the last one"
return PartialState().is_last_process
@property
def is_main_process(self) -> bool:
"Returns whether the current process is the main process"
return PartialState().is_main_process
@property
def is_local_main_process(self) -> bool:
"Returns whether the current process is the main process on the local node"
return PartialState().is_local_main_process
def wait_for_everyone(self):
PartialState().wait_for_everyone()
@contextmanager
def split_between_processes(self, inputs: list | tuple | dict | torch.Tensor, apply_padding: bool = False):
"""
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.
Args:
inputs (`list`, `tuple`, `torch.Tensor`, or `dict` of `list`/`tuple`/`torch.Tensor`):
The input to split between processes.
apply_padding (`bool`, `optional`, defaults to `False`):
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 as `gather()` on the outputs or passing
in less inputs than there are processes. If so, just remember to drop the padded elements afterwards.
Example:
```python
# Assume there are two processes
from accelerate.state import AcceleratorState
state = AcceleratorState()
with state.split_between_processes(["A", "B", "C"]) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C"]
with state.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C", "C"]
```
"""
with PartialState().split_between_processes(inputs, apply_padding=apply_padding) as inputs:
yield inputs
@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.
"""
with PartialState().main_process_first():
yield
@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.
"""
with PartialState().local_main_process_first():
yield
def print(self, *args, **kwargs):
PartialState().print(*args, **kwargs)
class GradientState:
"""
Singleton class that has information related to gradient synchronization for gradient accumulation
**Available attributes:**
- **end_of_dataloader** (`bool`) -- Whether we have reached the end the current dataloader
- **remainder** (`int`) -- The number of extra samples that were added from padding the dataloader
- **sync_gradients** (`bool`) -- Whether the gradients should be synced across all devices
- **active_dataloader** (`Optional[DataLoader]`) -- The dataloader that is currently being iterated over
- **dataloader_references** (`List[Optional[DataLoader]]`) -- A list of references to the dataloaders that are
being iterated over
- **num_steps** (`int`) -- The number of steps to accumulate over
- **adjust_scheduler** (`bool`) -- Whether the scheduler should be adjusted to account for the gradient
accumulation
- **sync_with_dataloader** (`bool`) -- Whether the gradients should be synced at the end of the dataloader
iteration and the number of total steps reset
"""
_shared_state = SharedDict()
def __init__(self, gradient_accumulation_plugin: Optional[GradientAccumulationPlugin] = None):
self.__dict__ = self._shared_state
if not self.initialized:
self.sync_gradients = True
self.active_dataloader = None
self.dataloader_references = [None]
self.plugin_kwargs = (
gradient_accumulation_plugin.to_kwargs() if gradient_accumulation_plugin is not None else {}
)
# Plugin args are different and can be updated
if gradient_accumulation_plugin is not None and self.plugin_kwargs != gradient_accumulation_plugin.to_kwargs():
self.plugin_kwargs = gradient_accumulation_plugin.to_kwargs()
@property
def num_steps(self) -> int:
"Returns the number of steps to accumulate over"
return self.plugin_kwargs.get("num_steps", 1)
@property
def adjust_scheduler(self) -> bool:
"Returns whether the scheduler should be adjusted"
return self.plugin_kwargs.get("adjust_scheduler", False)
@property
def sync_with_dataloader(self) -> bool:
"Returns whether the gradients should be synced at the end of the dataloader iteration and the number of total steps reset"
return self.plugin_kwargs.get("sync_with_dataloader", True)
@property
def initialized(self) -> bool:
"Returns whether the `GradientState` has been initialized"
return GradientState._shared_state != {}
@property
def end_of_dataloader(self) -> bool:
"Returns whether we have reached the end of the current dataloader"
if not self.in_dataloader:
return False
return self.active_dataloader.end_of_dataloader
@property
def remainder(self) -> int:
"Returns the number of extra samples that were added from padding the dataloader"
if not self.in_dataloader:
return -1
return self.active_dataloader.remainder
def __repr__(self):
return (
f"Sync Gradients: {self.sync_gradients}\n"
f"At end of current dataloader: {self.end_of_dataloader}\n"
f"Extra samples added: {self.remainder}\n"
f"Gradient accumulation plugin: {self.plugin_kwargs}\n"
)
def _set_sync_gradients(self, sync_gradients):
"Private function that sets whether gradients should be synchronized. Users should not have to call this."
self.sync_gradients = sync_gradients
def _add_dataloader(self, dataloader):
"Private function that adds a dataloader to `self.dataloader_references` and sets `in_dataloader` to `True`. Users should not have to call this."
self.active_dataloader = dataloader
self.dataloader_references.append(self.active_dataloader)
def _remove_dataloader(self, dataloader):
"Private function that removes a dataloader from `self.dataloader_references` and sets `in_dataloader` to `False` if there are no more dataloaders. Users should not have to call this."
self.dataloader_references.remove(dataloader)
self.active_dataloader = self.dataloader_references[-1]
@property
def in_dataloader(self) -> bool:
"Returns whether the current process is in a dataloader"
return self.active_dataloader is not None
@staticmethod
def _reset_state():
"Resets `_shared_state`, is used internally and should not be called"
GradientState._shared_state.clear()