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#!/usr/bin/env python
hf_cache_home = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
cache_dir = os.path.join(hf_cache_home, "accelerate")
default_json_config_file = os.path.join(cache_dir, "default_config.yaml")
default_yaml_config_file = os.path.join(cache_dir, "default_config.yaml")
# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
default_config_file = default_yaml_config_file
else:
default_config_file = default_json_config_file
def load_config_from_file(config_file):
if config_file is not None:
if not os.path.isfile(config_file):
raise FileNotFoundError(
f"The passed configuration file `{config_file}` does not exist. "
"Please pass an existing file to `accelerate launch`, or use the the default one "
"created through `accelerate config` and run `accelerate launch` "
"without the `--config_file` argument."
)
else:
config_file = default_config_file
with open(config_file, "r", encoding="utf-8") as f:
if config_file.endswith(".json"):
if (
json.load(f).get("compute_environment", ComputeEnvironment.LOCAL_MACHINE)
== ComputeEnvironment.LOCAL_MACHINE
):
config_class = ClusterConfig
else:
config_class = SageMakerConfig
return config_class.from_json_file(json_file=config_file)
else:
if (
yaml.safe_load(f).get("compute_environment", ComputeEnvironment.LOCAL_MACHINE)
== ComputeEnvironment.LOCAL_MACHINE
):
config_class = ClusterConfig
else:
config_class = SageMakerConfig
return config_class.from_yaml_file(yaml_file=config_file)
@dataclass
class BaseConfig:
compute_environment: ComputeEnvironment
distributed_type: Union[DistributedType, SageMakerDistributedType]
mixed_precision: str
use_cpu: bool
debug: bool
def to_dict(self):
result = self.__dict__
# For serialization, it's best to convert Enums to strings (or their underlying value type).
for key, value in result.items():
if isinstance(value, Enum):
result[key] = value.value
if isinstance(value, dict) and not bool(value):
result[key] = None
result = {k: v for k, v in result.items() if v is not None}
return result
@classmethod
def from_json_file(cls, json_file=None):
json_file = default_json_config_file if json_file is None else json_file
with open(json_file, "r", encoding="utf-8") as f:
config_dict = json.load(f)
if "compute_environment" not in config_dict:
config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE
if "mixed_precision" not in config_dict:
config_dict["mixed_precision"] = "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
if "fp16" in config_dict: # Convert the config to the new format.
del config_dict["fp16"]
if "dynamo_backend" in config_dict: # Convert the config to the new format.
dynamo_backend = config_dict.pop("dynamo_backend")
config_dict["dynamo_config"] = {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
if "use_cpu" not in config_dict:
config_dict["use_cpu"] = False
if "debug" not in config_dict:
config_dict["debug"] = False
extra_keys = sorted(set(config_dict.keys()) - set(cls.__dataclass_fields__.keys()))
if len(extra_keys) > 0:
raise ValueError(
f"The config file at {json_file} had unknown keys ({extra_keys}), please try upgrading your `accelerate`"
" version or fix (and potentially remove) these keys from your config file."
)
return cls(**config_dict)
def to_json_file(self, json_file):
with open(json_file, "w", encoding="utf-8") as f:
content = json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
f.write(content)
@classmethod
def from_yaml_file(cls, yaml_file=None):
yaml_file = default_yaml_config_file if yaml_file is None else yaml_file
with open(yaml_file, "r", encoding="utf-8") as f:
config_dict = yaml.safe_load(f)
if "compute_environment" not in config_dict:
config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE
if "mixed_precision" not in config_dict:
config_dict["mixed_precision"] = "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
if isinstance(config_dict["mixed_precision"], bool) and not config_dict["mixed_precision"]:
config_dict["mixed_precision"] = "no"
if "fp16" in config_dict: # Convert the config to the new format.
del config_dict["fp16"]
if "dynamo_backend" in config_dict: # Convert the config to the new format.
dynamo_backend = config_dict.pop("dynamo_backend")
config_dict["dynamo_config"] = {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
if "use_cpu" not in config_dict:
config_dict["use_cpu"] = False
if "debug" not in config_dict:
config_dict["debug"] = False
extra_keys = sorted(set(config_dict.keys()) - set(cls.__dataclass_fields__.keys()))
if len(extra_keys) > 0:
raise ValueError(
f"The config file at {yaml_file} had unknown keys ({extra_keys}), please try upgrading your `accelerate`"
" version or fix (and potentially remove) these keys from your config file."
)
return cls(**config_dict)
def to_yaml_file(self, yaml_file):
with open(yaml_file, "w", encoding="utf-8") as f:
yaml.safe_dump(self.to_dict(), f)
def __post_init__(self):
if isinstance(self.compute_environment, str):
self.compute_environment = ComputeEnvironment(self.compute_environment)
if isinstance(self.distributed_type, str):
if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
self.distributed_type = SageMakerDistributedType(self.distributed_type)
else:
self.distributed_type = DistributedType(self.distributed_type)
if self.dynamo_config is None:
self.dynamo_config = {}
@dataclass
class ClusterConfig(BaseConfig):
num_processes: int
machine_rank: int = 0
num_machines: int = 1
gpu_ids: Optional[str] = None
main_process_ip: Optional[str] = None
main_process_port: Optional[int] = None
rdzv_backend: Optional[str] = "static"
same_network: Optional[bool] = False
main_training_function: str = "main"
# args for deepspeed_plugin
deepspeed_config: dict = None
# args for fsdp
fsdp_config: dict = None
# args for megatron_lm
megatron_lm_config: dict = None
# args for ipex
ipex_config: dict = None
# args for TPU
downcast_bf16: bool = False
# args for TPU pods
tpu_name: str = None
tpu_zone: str = None
tpu_use_cluster: bool = False
tpu_use_sudo: bool = False
command_file: str = None
commands: List[str] = None
tpu_vm: List[str] = None
tpu_env: List[str] = None
# args for dynamo
dynamo_config: dict = None
def __post_init__(self):
if self.deepspeed_config is None:
self.deepspeed_config = {}
if self.fsdp_config is None:
self.fsdp_config = {}
if self.megatron_lm_config is None:
self.megatron_lm_config = {}
if self.ipex_config is None:
self.ipex_config = {}
return super().__post_init__()
@dataclass
class SageMakerConfig(BaseConfig):
ec2_instance_type: str
iam_role_name: str
image_uri: Optional[str] = None
profile: Optional[str] = None
region: str = "us-east-1"
num_machines: int = 1
gpu_ids: str = "all"
base_job_name: str = f"accelerate-sagemaker-{num_machines}"
pytorch_version: str = SAGEMAKER_PYTORCH_VERSION
transformers_version: str = SAGEMAKER_TRANSFORMERS_VERSION
py_version: str = SAGEMAKER_PYTHON_VERSION
sagemaker_inputs_file: str = None
sagemaker_metrics_file: str = None
additional_args: dict = None
dynamo_config: dict = None