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from lm_eval.api.model import LM |
eval_logger = logging.getLogger('lm-eval') |
MODEL_REGISTRY = {} |
def register_model(*names): |
def decorate(cls): |
for name in names: |
assert issubclass(cls, LM), f"Model '{name}' ({cls.__name__}) must extend LM class" |
assert name not in MODEL_REGISTRY, f"Model named '{name}' conflicts with existing model! Please register with a non-conflicting alias instead." |
MODEL_REGISTRY[name] = cls |
return cls |
return decorate |
def get_model(model_name): |
try: |
return MODEL_REGISTRY[model_name] |
except KeyError: |
raise ValueError(f"Attempted to load model '{model_name}', but no model for this name found! Supported model names: {', '.join(MODEL_REGISTRY.keys())}") |
TASK_REGISTRY = {} |
GROUP_REGISTRY = {} |
ALL_TASKS = set() |
func2task_index = {} |
def register_task(name): |
def decorate(fn): |
assert name not in TASK_REGISTRY, f"task named '{name}' conflicts with existing registered task!" |
TASK_REGISTRY[name] = fn |
ALL_TASKS.add(name) |
func2task_index[fn.__name__] = name |
return fn |
return decorate |
def register_group(name): |
def decorate(fn): |
func_name = func2task_index[fn.__name__] |
if name in GROUP_REGISTRY: |
GROUP_REGISTRY[name].append(func_name) |
else: |
GROUP_REGISTRY[name] = [func_name] |
ALL_TASKS.add(name) |
return fn |
return decorate |
OUTPUT_TYPE_REGISTRY = {} |
METRIC_REGISTRY = {} |
METRIC_AGGREGATION_REGISTRY = {} |
AGGREGATION_REGISTRY: Dict[str, Callable[[], Dict[str, Callable]]] = {} |
HIGHER_IS_BETTER_REGISTRY = {} |
FILTER_REGISTRY = {} |
DEFAULT_METRIC_REGISTRY = {'loglikelihood': ['perplexity', 'acc'], 'loglikelihood_rolling': ['word_perplexity', 'byte_perplexity', 'bits_per_byte'], 'multiple_choice': ['acc', 'acc_norm'], 'generate_until': ['exact_match']} |
def register_metric(**args): |
def decorate(fn): |
assert 'metric' in args |
name = args['metric'] |
for (key, registry) in [('metric', METRIC_REGISTRY), ('higher_is_better', HIGHER_IS_BETTER_REGISTRY), ('aggregation', METRIC_AGGREGATION_REGISTRY)]: |
if key in args: |
value = args[key] |
assert value not in registry, f"{key} named '{value}' conflicts with existing registered {key}!" |
if key == 'metric': |
registry[name] = fn |
elif key == 'aggregation': |
registry[name] = AGGREGATION_REGISTRY[value] |
else: |
registry[name] = value |
return fn |
return decorate |
def get_metric(name: str, hf_evaluate_metric=False) -> Callable: |
if not hf_evaluate_metric: |
if name in METRIC_REGISTRY: |
return METRIC_REGISTRY[name] |
else: |
eval_logger.warning(f"Could not find registered metric '{name}' in lm-eval, searching in HF Evaluate library...") |
try: |
metric_object = hf_evaluate.load(name) |
return metric_object.compute |
except Exception: |
eval_logger.error(f'{name} not found in the evaluate library! Please check https://huggingface.co/evaluate-metric') |
def register_aggregation(name: str): |
def decorate(fn): |
assert name not in AGGREGATION_REGISTRY, f"aggregation named '{name}' conflicts with existing registered aggregation!" |
AGGREGATION_REGISTRY[name] = fn |
return fn |
return decorate |
def get_aggregation(name: str) -> Callable[[], Dict[str, Callable]]: |
try: |
return AGGREGATION_REGISTRY[name] |
except KeyError: |
eval_logger.warning(f'{name} not a registered aggregation metric!') |
def get_metric_aggregation(name: str) -> Callable[[], Dict[str, Callable]]: |
try: |
return METRIC_AGGREGATION_REGISTRY[name] |