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import dataclasses
import importlib
from types import ModuleType
from typing import Any, Dict, List, Type
import yaml
from llm_studio.python_configs.base import DefaultConfigProblemBase
from llm_studio.src.utils.type_annotations import KNOWN_TYPE_ANNOTATIONS
def rreload(module):
"""Recursively reload modules.
Args:
module: module to reload
"""
for attribute_name in dir(module):
if "Config" in attribute_name:
attribute1 = getattr(module, attribute_name)
for attribute_name in dir(attribute1):
attribute2 = getattr(attribute1, attribute_name)
if type(attribute2) is ModuleType:
importlib.reload(attribute2)
def _load_cls(module_path: str, cls_name: str) -> Any:
"""Loads the python class.
Args:
module_path: path to the module
cls_name: name of the class
Returns:
Loaded python class
"""
module_path_fixed = module_path
if module_path_fixed.endswith(".py"):
module_path_fixed = module_path_fixed[:-3]
module_path_fixed = module_path_fixed.replace("/", ".")
module = importlib.import_module(module_path_fixed)
module = importlib.reload(module)
rreload(module)
module = importlib.reload(module)
assert hasattr(module, cls_name), "{} file should contain {} class".format(
module_path, cls_name
)
cls = getattr(module, cls_name)
return cls
def load_config_py(config_path: str, config_name: str = "Config"):
"""Loads the config class.
Args:
config_path: path to the config file
config_name: name of the config class
Returns:
Loaded config class
"""
return _load_cls(config_path, config_name)()
def _get_type_annotation_error(v: Any, type_annotation: Type) -> ValueError:
return ValueError(
f"Cannot show {v}: not a dataclass"
f" and {type_annotation} is not a known type annotation."
)
def convert_cfg_base_to_nested_dictionary(cfg: DefaultConfigProblemBase) -> dict:
"""Returns a grouped config settings dict for a given configuration
Args:
cfg: configuration
q: Q
Returns:
Dict of configuration settings
"""
cfg_dict = cfg.__dict__
type_annotations = cfg.get_annotations()
cfg_dict = {key: cfg_dict[key] for key in cfg._get_order()}
grouped_cfg_dict = {}
for k, v in cfg_dict.items():
if k.startswith("_"):
continue
if any([x in k for x in ["api", "secret"]]):
raise AssertionError(
"Config item must not contain the word 'api' or 'secret'"
)
type_annotation = type_annotations[k]
if type_annotation in KNOWN_TYPE_ANNOTATIONS:
grouped_cfg_dict.update({k: v})
elif dataclasses.is_dataclass(v):
group_items = parse_cfg_dataclass(cfg=v)
group_items = {
k: list(v) if isinstance(v, tuple) else v
for d in group_items
for k, v in d.items()
}
grouped_cfg_dict.update({k: group_items})
else:
raise _get_type_annotation_error(v, type_annotations[k])
# not an explicit field in the config
grouped_cfg_dict["problem_type"] = cfg.problem_type
return grouped_cfg_dict
def convert_nested_dictionary_to_cfg_base(
cfg_dict: Dict[str, Any]
) -> DefaultConfigProblemBase:
"""
Inverse operation of convert_cfg_base_to_nested_dictionary
"""
problem_type = cfg_dict["problem_type"]
module_name = f"llm_studio.python_configs.{problem_type}_config"
try:
module = importlib.import_module(module_name)
except ModuleNotFoundError:
raise NotImplementedError(f"Problem Type {problem_type} not implemented")
return module.ConfigProblemBase.from_dict(cfg_dict)
def get_parent_element(cfg):
if hasattr(cfg, "_parent_experiment") and cfg._parent_experiment != "":
key = "Parent Experiment"
value = cfg._parent_experiment
return {key: value}
return None
def parse_cfg_dataclass(cfg) -> List[Dict]:
"""Returns all single config settings for a given configuration
Args:
cfg: configuration
"""
items = []
parent_element = get_parent_element(cfg)
if parent_element:
items.append(parent_element)
cfg_dict = cfg.__dict__
type_annotations = cfg.get_annotations()
cfg_dict = {key: cfg_dict[key] for key in cfg._get_order()}
for k, v in cfg_dict.items():
if k.startswith("_"):
continue
if any([x in k for x in ["api"]]):
continue
type_annotation = type_annotations[k]
if type_annotation in KNOWN_TYPE_ANNOTATIONS:
if type_annotation == float:
v = float(v)
t = [{k: v}]
elif dataclasses.is_dataclass(v):
elements_group = parse_cfg_dataclass(cfg=v)
t = elements_group
else:
continue
items += t
return items
def save_config_yaml(path: str, cfg: DefaultConfigProblemBase) -> None:
"""Saves config as yaml file
Args:
path: path of file to save to
cfg: config to save
"""
"""
Returns a dictionary representation of the config object.
Protected attributes (starting with an underscore) are not included.
Nested configs are converted to nested dictionaries.
"""
cfg_dict = convert_cfg_base_to_nested_dictionary(cfg)
with open(path, "w") as fp:
yaml.dump(cfg_dict, fp, indent=4)
def load_config_yaml(path: str):
"""Loads config from yaml file
Args:
path: path of file to load from
Returns:
config object
"""
with open(path, "r") as fp:
cfg_dict = yaml.load(fp, Loader=yaml.FullLoader)
return convert_nested_dictionary_to_cfg_base(cfg_dict)
# Note that importing ConfigProblemBase from the python_configs
# and using cfg.problem_type below will not work because of circular imports
NON_GENERATION_PROBLEM_TYPES = ["text_causal_classification_modeling"]
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