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- #!/usr/bin/env python
- # 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 json
- import os
- from dataclasses import dataclass
- from enum import Enum
- from typing import Optional, Union
- import yaml
- from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
- from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION
- hf_cache_home = os.path.expanduser(
- os.environ.get("HF_HOME", os.path.join(os.environ.get("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 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, 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).
- def _convert_enums(value):
- if isinstance(value, Enum):
- return value.value
- if isinstance(value, dict):
- if not bool(value):
- return None
- for key1, value1 in value.items():
- value[key1] = _convert_enums(value1)
- return value
- for key, value in result.items():
- result[key] = _convert_enums(value)
- result = {k: v for k, v in result.items() if v is not None}
- return result
- @staticmethod
- def process_config(config_dict):
- """
- Processes `config_dict` and sets default values for any missing keys
- """
- if "compute_environment" not in config_dict:
- config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE
- if "distributed_type" not in config_dict:
- raise ValueError("A `distributed_type` must be specified in the config file.")
- if "num_processes" not in config_dict and config_dict["distributed_type"] == DistributedType.NO:
- config_dict["num_processes"] = 1
- 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
- if "enable_cpu_affinity" not in config_dict:
- config_dict["enable_cpu_affinity"] = False
- return config_dict
- @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, encoding="utf-8") as f:
- config_dict = json.load(f)
- config_dict = cls.process_config(config_dict)
- 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, encoding="utf-8") as f:
- config_dict = yaml.safe_load(f)
- config_dict = cls.process_config(config_dict)
- 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 getattr(self, "dynamo_config", None) is None:
- self.dynamo_config = {}
- @dataclass
- class ClusterConfig(BaseConfig):
- num_processes: int = -1 # For instance if we use SLURM and the user manually passes it in
- 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"
- enable_cpu_affinity: bool = False
- # args for FP8 training
- fp8_config: Optional[dict] = None
- # args for deepspeed_plugin
- deepspeed_config: Optional[dict] = None
- # args for fsdp
- fsdp_config: Optional[dict] = None
- # args for parallelism config
- parallelism_config: Optional[dict] = None
- # args for megatron_lm
- megatron_lm_config: Optional[dict] = None
- # args for ipex
- ipex_config: Optional[dict] = None
- # args for mpirun
- mpirun_config: Optional[dict] = None
- # args for TPU
- downcast_bf16: bool = False
- # args for TPU pods
- tpu_name: Optional[str] = None
- tpu_zone: Optional[str] = None
- tpu_use_cluster: bool = False
- tpu_use_sudo: bool = False
- command_file: Optional[str] = None
- commands: list[str] = None
- tpu_vm: list[str] = None
- tpu_env: list[str] = None
- # args for dynamo
- dynamo_config: Optional[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 = {}
- if self.mpirun_config is None:
- self.mpirun_config = {}
- if self.fp8_config is None:
- self.fp8_config = {}
- if self.parallelism_config is None:
- self.parallelism_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: Optional[str] = None
- sagemaker_metrics_file: Optional[str] = None
- additional_args: Optional[dict] = None
- dynamo_config: Optional[dict] = None
- enable_cpu_affinity: bool = False
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