src/state.py

logger = logging.getLogger(__name__)
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. (Choose from 'no','fp16','bf16 or 'fp8').
        - **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.
        - **debug** (`bool`) -- Whether or not the current script is being run in debug mode.
    """
    _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
            self.debug = parse_flag_from_env("ACCELERATE_DEBUG_MODE")
            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)
                    if is_xpu_available and is_ccl_available():
                        # Set DeepSpeed backend to ccl for xpu
                        self.backend = "ccl"
                    elif is_npu_available():
                        self.backend = "hccl"
                    else:
                        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)
                    elif is_npu_available():
                        self.device = torch.device("npu", self.local_process_index)
                        if self.device is not None:
                            torch.npu.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)
                if self.device.type == "cuda" and not check_cuda_p2p_ib_support():
                    if "NCCL_P2P_DISABLE" not in os.environ or "NCCL_IB_DISABLE" not in os.environ:
                        raise NotImplementedError(
                            "Using RTX 3090 or 4000 series doesn't support faster communication broadband via P2P or IB. "
                            'Please set `NCCL_P2P_DISABLE="1"` and `NCCL_IB_DISABLE="1" or use `accelerate launch` which '
                            "will do this automatically."
                        )
                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)
                if not check_cuda_p2p_ib_support():
                    if "NCCL_P2P_DISABLE" not in os.environ or "NCCL_IB_DISABLE" not in os.environ:
                        raise NotImplementedError(
                            "Using RTX 3090 or 4000 series doesn't support faster communication broadband via P2P or IB. "
                            'Please set `NCCL_P2P_DISABLE="1"` and `NCCL_IB_DISABLE="1" or use `accelerate launch` which '
                            "will do this automatically."
                        )
                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
                    if os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "false"
                    else DistributedType.DEEPSPEED
                )
                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
        length = len(inputs)
        # 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(length / 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):
            end_index = length
    
    def _split_values(inputs, start_index, end_index):
            if isinstance(inputs, (list, tuple, torch.Tensor)):
                if start_index >= len(inputs):
                    result = inputs[-1:]
                else:
                    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. (Choose from 'no','fp16','bf16 or 'fp8').
        - **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.
        - **debug** (`bool`) -- Whether or not the current script is being run in debug mode.
    """
    _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
            self.use_ipex = None
            mixed_precision = (
                parse_choice_from_env("ACCELERATE_MIXED_PRECISION", "no")
                if mixed_precision is None
                else mixed_precision.lower()
            )
            if mixed_precision == "fp8":
                if not is_fp8_available():
                    raise ValueError(
                        "Using `fp8` precision requires `transformer_engine` or `MS-AMP` to be installed."
                    )
                elif not check_fp8_capability():
                    logger.warning(
                        f"The current device has compute capability of {torch.cuda.get_device_capability()} which is "
                        "insufficient for FP8 mixed precision training (requires a GPU Hopper/Ada Lovelace "
                        "or higher, compute capability of 8.9 or higher). Will use FP16 instead."
                    )
                    mixed_precision = "fp16"
            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 == DistributedType.MULTI_NPU:
                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
            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.distributed_type == DistributedType.MULTI_XPU:
                    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 (
                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()