testbed/huggingface__accelerate/src/accelerate/launchers.py

# 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 os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, patch_environment


def notebook_launcher(function, args=(), num_processes=None, mixed_precision="no", use_port="29500"):
    """
    Launches a training function, using several processes if it's possible in the current environment (TPU with
    multiple cores for instance).

    Args:
        function (`Callable`):
            The training function to execute. If it accepts arguments, the first argument should be the index of the
            process run.
        args (`Tuple`):
            Tuple of arguments to pass to the function (it will receive `*args`).
        num_processes (`int`, *optional*):
            The number of processes to use for training. Will default to 8 in Colab/Kaggle if a TPU is available, to
            the number of GPUs available otherwise.
        mixed_precision (`str`, *optional*, defaults to `"no"`):
            If `fp16` or `bf16`, will use mixed precision training on multi-GPU.
        use_port (`str`, *optional*, defaults to `"29500"`):
            The port to use to communicate between processes when launching a multi-GPU training.
    """
    # Are we in a google colab or a Kaggle Kernel?
    in_colab = False
    in_kaggle = False
    if any(key.startswith("KAGGLE") for key in os.environ.keys()):
        in_kaggle = True
    elif "IPython" in sys.modules:
        in_colab = "google.colab" in str(sys.modules["IPython"].get_ipython())

    try:
        mixed_precision = PrecisionType(mixed_precision.lower())
    except ValueError:
        raise ValueError(
            f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."
        )

    if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME", None) is not None):
        # TPU launch
        import torch_xla.distributed.xla_multiprocessing as xmp

        if len(AcceleratorState._shared_state) > 0:
            raise ValueError(
                "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                "your training function. Restart your notebook and make sure no cells initializes an "
                "`Accelerator`."
            )
        if num_processes is None:
            num_processes = 8

        launcher = PrepareForLaunch(function, distributed_type="TPU")
        print(f"Launching a training on {num_processes} TPU cores.")
        xmp.spawn(launcher, args=args, nprocs=num_processes, start_method="fork")
    elif in_colab:
        # No need for a distributed launch otherwise as it's either CPU or one GPU.
        if torch.cuda.is_available():
            print("Launching training on one GPU.")
        else:
            print("Launching training on one CPU.")
        function(*args)
    else:
        if num_processes is None:
            raise ValueError(
                "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."
            )

        if num_processes > 1:
            # Multi-GPU launch
            from torch.multiprocessing import start_processes

            if len(AcceleratorState._shared_state) > 0:
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                    "inside your training function. Restart your notebook and make sure no cells initializes an "
                    "`Accelerator`."
                )

            if torch.cuda.is_initialized():
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                    "function."
                )

            # torch.distributed will expect a few environment variable to be here. We set the ones common to each
            # process here (the other ones will be set be the launcher).
            with patch_environment(
                world_size=num_processes, master_addr="127.0.01", master_port=use_port, mixed_precision=mixed_precision
            ):
                launcher = PrepareForLaunch(function, distributed_type="MULTI_GPU")

                print(f"Launching training on {num_processes} GPUs.")
                start_processes(launcher, args=args, nprocs=num_processes, start_method="fork")

        else:
            # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
            use_mps_device = "false"
            if torch.backends.mps.is_available():
                print("Launching training on MPS.")
                use_mps_device = "true"
            elif torch.cuda.is_available():
                print("Launching training on one GPU.")
            else:
                print("Launching training on CPU.")
            with patch_environment(use_mps_device=use_mps_device):
                function(*args)


def debug_launcher(function, args=(), num_processes=2):
    """
    Launches a training function using several processes on CPU for debugging purposes.

    <Tip warning={true}>

    This function is provided for internal testing and debugging, but it's not intended for real trainings. It will
    only use the CPU.

    </Tip>

    Args:
        function (`Callable`):
            The training function to execute.
        args (`Tuple`):
            Tuple of arguments to pass to the function (it will receive `*args`).
        num_processes (`int`, *optional*, defaults to 2):
            The number of processes to use for training.
    """
    from torch.multiprocessing import start_processes

    with tempfile.NamedTemporaryFile() as tmp_file:
        # torch.distributed will expect a few environment variable to be here. We set the ones common to each
        # process here (the other ones will be set be the launcher).
        with patch_environment(
            world_size=num_processes,
            master_addr="127.0.01",
            master_port="29500",
            accelerate_mixed_precision="no",
            accelerate_debug_rdv_file=tmp_file.name,
            accelerate_use_cpu="yes",
        ):
            launcher = PrepareForLaunch(function, debug=True)
            start_processes(launcher, args=args, nprocs=num_processes, start_method="fork")