Skip to content

dspy.BootstrapFinetune

dspy.BootstrapFinetune(metric: Callable | None = None, multitask: bool = True, train_kwargs: Dict[str, Any] | Dict[LM, Dict[str, Any]] | None = None, adapter: Adapter | Dict[LM, Adapter] | None = None, exclude_demos: bool = False, num_threads: int | None = None)

Bases: FinetuneTeleprompter

Source code in dspy/teleprompt/bootstrap_finetune.py 
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
def __init__(
    self,
    metric: Callable | None = None,
    multitask: bool = True,
    train_kwargs: Dict[str, Any] | Dict[LM, Dict[str, Any]] | None = None,
    adapter: Adapter | Dict[LM, Adapter] | None = None,
    exclude_demos: bool = False,
    num_threads: int | None = 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
    # takingthese as parameters for the time being. However, they can be
    # attached to LMs themselves -- an LM could know which adapter it should
    # be used with along with the train_kwargs. This will lead the only
    # required argument for LM.finetune() to be the train dataset.

    super().__init__(train_kwargs=train_kwargs)
    self.metric = metric
    self.multitask = multitask
    self.adapter: Dict[LM, Adapter] = self.convert_to_lm_dict(adapter)
    self.exclude_demos = exclude_demos
    self.num_threads = num_threads

Functions

compile(student: Module, trainset: List[Example], teacher: Module | List[Module] | None = None) -> Module

Source code in dspy/teleprompt/bootstrap_finetune.py 
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
def compile(
    self, student: Module, trainset: List[Example], teacher: Module | List[Module] | None = None
) -> Module:
    # TODO: Print statements can be converted to logger.info if we ensure
    # that the default DSPy logger logs info level messages in notebook
    # environments.
    logger.info("Preparing the student and teacher programs...")
    all_predictors_have_lms(student)

    logger.info("Bootstrapping data...")
    trace_data = []

    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)

    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
            )
            logger.info(f"Using {len(train_data)} data points for fine-tuning the model: {pred.lm.model}")
            finetune_kwargs = {
                "lm": pred.lm,
                "train_data": train_data,
                "train_data_format": data_format,
                "train_kwargs": self.train_kwargs[pred.lm],
            }
            key_to_data[training_key] = finetune_kwargs

    logger.info("Starting LM fine-tuning...")
    # TODO(feature): We could run batches of fine-tuning jobs in sequence
    # to avoid exceeding the number of threads.
    if len(key_to_data) > num_threads:
        raise ValueError(
            "BootstrapFinetune requires `num_threads` to be bigger than or equal to the number of fine-tuning "
            f"jobs. There are {len(key_to_data)} fine-tuning jobs to start, but the number of threads is: "
            f"{num_threads}! If the `multitask` flag is set to False, the number of fine-tuning jobs will "
            "be equal to the number of predictors in the student program. If the `multitask` flag is set to True, "
            "the number of fine-tuning jobs will be equal to: 1 if there is only a context LM, or the number of "
            "unique LMs attached to the predictors in the student program. In any case, the number of fine-tuning "
            "jobs will be less than or equal to the number of predictors."
        )
    logger.info(f"{len(key_to_data)} fine-tuning job(s) to start")
    key_to_lm = self.finetune_lms(key_to_data)

    logger.info("Updating the student program with the fine-tuned LMs...")
    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)
        finetuned_lm = key_to_lm[training_key]
        if isinstance(finetuned_lm, Exception):
            raise RuntimeError(f"Finetuned LM for predictor {pred_ind} failed.") from finetuned_lm
        pred.lm = finetuned_lm
        # TODO: What should the correct behavior be here? Should
        # BootstrapFinetune modify the prompt demos according to the
        # train data?
        pred.demos = [] if self.exclude_demos else pred.demos

    logger.info("BootstrapFinetune has finished compiling the student program")
    student._compiled = True
    return student

convert_to_lm_dict(arg) -> Dict[LM, Any] staticmethod

Source code in dspy/teleprompt/bootstrap_finetune.py 
29
30
31
32
33
34
35
@staticmethod
def convert_to_lm_dict(arg) -> Dict[LM, Any]:
    non_empty_dict = arg and isinstance(arg, dict)
    if non_empty_dict and all(isinstance(k, LM) for k in arg.keys()):
        return arg
    # Default to using the same value for all LMs
    return defaultdict(lambda: arg)

finetune_lms(finetune_dict) -> Dict[Any, LM] staticmethod

Source code in dspy/teleprompt/bootstrap_finetune.py 
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
@staticmethod
def finetune_lms(finetune_dict) -> Dict[Any, LM]:
    num_jobs = len(finetune_dict)
    logger.info(f"Starting {num_jobs} fine-tuning job(s)...")
    # TODO(nit) Pass an identifier to the job so that we can tell the logs
    # coming from different fine-tune threads.

    key_to_job = {}
    for key, finetune_kwargs in finetune_dict.items():
        lm: LM = finetune_kwargs.pop("lm")
        # TODO: The following line is a hack. We should re-think how to free
        # up resources for fine-tuning. This might mean introducing a new
        # provider method (e.g. prepare_for_finetune) that can be called
        # before fine-tuning is started.
        logger.info(
            "Calling lm.kill() on the LM to be fine-tuned to free up resources. This won't have any effect if the "
            "LM is not running."
        )
        lm.kill()
        key_to_job[key] = lm.finetune(**finetune_kwargs)

    key_to_lm = {}
    for ind, (key, job) in enumerate(key_to_job.items()):
        result = job.result()
        if isinstance(result, Exception):
            raise result
        key_to_lm[key] = result
        job.thread.join()
        logger.info(f"Job {ind + 1}/{num_jobs} is done")

    return key_to_lm

get_params() -> dict[str, Any]

Get the parameters of the teleprompter.

Returns:

Type Description
dict[str, Any]

The parameters of the teleprompter.
Source code in dspy/teleprompt/teleprompt.py 
25
26
27
28
29
30
31
32
def get_params(self) -> dict[str, Any]:
    """
    Get the parameters of the teleprompter.

    Returns:
        The parameters of the teleprompter.
    """
    return self.__dict__

:::