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Added option for loading pre-saved bootstrapped training data for fine-tuning #8262

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Added option for loading pre-saved bootstrapped training data for fine-tuning #8262

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29 changes: 24 additions & 5 deletions dspy/teleprompt/bootstrap_finetune.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,7 @@ def __init__(
adapter: Optional[Union[Adapter, Dict[LM, Adapter]]] = None,
exclude_demos: bool = False,
num_threads: Optional[int] = None,
bootstrapped_data_path: Optional[str] = None,
):
# TODO(feature): Inputs train_kwargs (a dict with string keys) and
# adapter (Adapter) can depend on the LM they are used with. We are
Expand All @@ -58,6 +59,7 @@ def __init__(
self.adapter: Dict[LM, Adapter] = self.convert_to_lm_dict(adapter)
self.exclude_demos = exclude_demos
self.num_threads = num_threads
self.bootstrapped_data_path = bootstrapped_data_path

def compile(
self, student: Program, trainset: List[Example], teacher: Optional[Union[Program, List[Program]]] = None
Expand All @@ -74,18 +76,26 @@ def compile(
teachers = teacher if isinstance(teacher, list) else [teacher]
teachers = [prepare_teacher(student, t) for t in teachers]
num_threads = self.num_threads or dspy.settings.num_threads
for t in teachers:
trace_data += bootstrap_trace_data(program=t, dataset=trainset, metric=self.metric, num_threads=num_threads)
if self.bootstrapped_data_path:
logger.info(f"Loading bootstrapped training data from: {self.bootstrapped_data_path}")
train_data = self.load_data(self.bootstrapped_data_path)
else:
for t in teachers:
trace_data += bootstrap_trace_data(program=t, dataset=trainset, metric=self.metric, num_threads=num_threads)

logger.info("Preparing the train data...")
key_to_data = {}
for pred_ind, pred in enumerate(student.predictors()):
data_pred_ind = None if self.multitask else pred_ind
training_key = (pred.lm, data_pred_ind)
if training_key not in key_to_data:
train_data, data_format = self._prepare_finetune_data(
trace_data=trace_data, lm=pred.lm, pred_ind=data_pred_ind
)
if self.bootstrapped_data_path:
adapter = self.adapter[pred.lm] or settings.adapter or ChatAdapter()
data_format = infer_data_format(adapter)
else:
train_data, data_format = self._prepare_finetune_data(
trace_data=trace_data, lm=pred.lm, pred_ind=data_pred_ind
)
logger.info(f"Using {len(train_data)} data points for fine-tuning the model: {pred.lm.model}")
finetune_kwargs = {
"lm": pred.lm,
Expand Down Expand Up @@ -128,6 +138,15 @@ def compile(
student._compiled = True
return student

@staticmethod
def load_data(path: str) -> List[Dict[str, Any]]:
import json
data = []
with open(path, "r") as f:
for line in f:
data.append(json.loads(line))
return data

@staticmethod
def finetune_lms(finetune_dict) -> Dict[Any, LM]:
num_jobs = len(finetune_dict)
Expand Down