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| import os |
| import sys |
|
|
| import torch |
|
|
| from ..utils import is_torch_version |
| from .dataclasses import DistributedType |
|
|
|
|
| def get_launch_prefix(): |
| """ |
| Grabs the correct launcher for starting a distributed command, such as either `torchrun`, `python -m |
| torch.distributed.run`, etc |
| """ |
| if is_torch_version(">=", "1.10.0"): |
| cmd = ["torchrun"] |
| elif is_torch_version(">=", "1.9.0"): |
| cmd = [sys.executable, "-m", "torch.distributed.run"] |
| else: |
| cmd = [sys.executable, "-m", "torch.distributed.launch", "--use_env"] |
| return cmd |
|
|
|
|
| def _filter_args(args): |
| """ |
| Filters out all `accelerate` specific args |
| """ |
| if is_torch_version(">=", "1.9.1"): |
| import torch.distributed.run as distrib_run |
| distrib_args = distrib_run.get_args_parser() |
| new_args, _ = distrib_args.parse_known_args() |
|
|
| for key, value in vars(args).items(): |
| if key in vars(new_args).keys(): |
| setattr(new_args, key, value) |
| return new_args |
|
|
|
|
| def env_var_path_add(env_var_name, path_to_add): |
| """ |
| Extends a path-based environment variable's value with a new path and returns the updated value. It's up to the |
| caller to set it in os.environ. |
| """ |
| paths = [p for p in os.environ.get(env_var_name, "").split(":") if len(p) > 0] |
| paths.append(str(path_to_add)) |
| return ":".join(paths) |
|
|
|
|
| class PrepareForLaunch: |
| """ |
| Prepare a function that will launched in a distributed setup. |
| |
| Args: |
| launcher (`Callable`): |
| The function to launch. |
| distributed_type ([`~state.DistributedType`]): |
| The distributed type to prepare for. |
| debug (`bool`, *optional*, defaults to `False`): |
| Whether or not this is a debug launch. |
| """ |
|
|
| def __init__(self, launcher, distributed_type="NO", debug=False): |
| self.launcher = launcher |
| self.distributed_type = DistributedType(distributed_type) |
| self.debug = debug |
|
|
| def __call__(self, index, *args): |
| if self.debug: |
| world_size = int(os.environ.get("WORLD_SIZE")) |
| rdv_file = os.environ.get("ACCELERATE_DEBUG_RDV_FILE") |
| torch.distributed.init_process_group( |
| "gloo", |
| rank=index, |
| store=torch.distributed.FileStore(rdv_file, world_size), |
| world_size=world_size, |
| ) |
| elif self.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU): |
| |
| os.environ["LOCAL_RANK"] = str(index) |
| os.environ["RANK"] = str(index) |
|
|
| os.environ["FORK_LAUNCHED"] = str(1) |
| self.launcher(*args) |
|
|
