LMAsJudge reward

LMAsJudge

Bases: ProgramAsJudge

Evaluate the output of a program using a LanguageModel.

Example:

async def main():
    # ... program definition

    program.compile(
        reward=synalinks.rewards.LMAsJudge(
            language_model=language_model,
        )
        optimizer=synalinks.optimizers.RandomFewShot(),
    )

    history = await program.fit(...)

Parameters:

Name	Type	Description	Default
language_model	LanguageModel	The language model to use.	None
prompt_template	str	The default jinja2 prompt template to use (see Generator).	None
static_system_prompt	str	A static system prompt that do not evolve during training. This prompt allow the user to provide additional information that won't be changed during training. Allowing to cache it and reduce inference costs during training.	None
examples	list	The default examples to use in the prompt (see Generator).	None
instructions	list	The default instructions to use (see Generator).	None
name	str	Optional. string name of the reward instance.	'lm_as_judge'
in_mask	list	Optional. list of keys to keep to compute the reward.	None
out_mask	list	Optional. list of keys to remove to compute the reward.	None

Source code in synalinks/src/rewards/lm_as_judge.py

@synalinks_export(
    [
        "synalinks.LMAsJudge",
        "synalinks.rewards.LMAsJudge",
    ]
)
class LMAsJudge(ProgramAsJudge):
    """Evaluate the output of a program using a `LanguageModel`.

    Example:

    ```python

    async def main():
        # ... program definition

        program.compile(
            reward=synalinks.rewards.LMAsJudge(
                language_model=language_model,
            )
            optimizer=synalinks.optimizers.RandomFewShot(),
        )

        history = await program.fit(...)

    ```

    Args:
        language_model (LanguageModel): The language model to use.
        prompt_template (str): The default jinja2 prompt template
            to use (see `Generator`).
        static_system_prompt (str): A static system prompt that **do not** evolve
            during training. This prompt allow the user to provide additional
            information that won't be changed during training. Allowing to cache
            it and reduce inference costs during training.
        examples (list): The default examples to use in the prompt
            (see `Generator`).
        instructions (list): The default instructions to use (see `Generator`).
        name (str): Optional. string name of the reward instance.
        in_mask (list): Optional. list of keys to keep to compute the reward.
        out_mask (list): Optional. list of keys to remove to compute the reward.
    """

    def __init__(
        self,
        language_model=None,
        prompt_template=None,
        static_system_prompt=None,
        examples=None,
        instructions=None,
        name="lm_as_judge",
        in_mask=None,
        out_mask=None,
    ):
        program = LMAsJudgeProgram(
            language_model=language_model,
            prompt_template=prompt_template,
            static_system_prompt=static_system_prompt,
            examples=examples,
            instructions=instructions,
        )
        super().__init__(
            program=program,
            name=name,
            in_mask=in_mask,
            out_mask=out_mask,
        )

LMAsJudgeProgram

Bases: Program

Evaluate the output of a program using a LanguageModel.

Parameters:

Name	Type	Description	Default
language_model	LanguageModel	The language model to use.	None
prompt_template	str	The default jinja2 prompt template to use (see Generator).	None
static_system_prompt	str	A static system prompt that do not evolve during training. This prompt allow the user to provide additional information that won't be changed during training. Allowing to cache it and reduce inference costs during training.	None
examples	list	The default examples to use in the prompt (see Generator).	None
instructions	list	The default instructions to use (see Generator).	None
name	str	Optional. The name of the program.	None
description	str	Optional. The description of the program.	None
trainable	bool	Whether the program's variables should be trainable.	True

Source code in synalinks/src/rewards/lm_as_judge.py

class LMAsJudgeProgram(Program):
    """Evaluate the output of a program using a `LanguageModel`.

    Args:
        language_model (LanguageModel): The language model to use.
        prompt_template (str): The default jinja2 prompt template
            to use (see `Generator`).
        static_system_prompt (str): A static system prompt that **do not** evolve
            during training. This prompt allow the user to provide additional
            information that won't be changed during training. Allowing to cache
            it and reduce inference costs during training.
        examples (list): The default examples to use in the prompt
            (see `Generator`).
        instructions (list): The default instructions to use (see `Generator`).
        name (str): Optional. The name of the program.
        description (str): Optional. The description of the program.
        trainable (bool): Whether the program's variables should be trainable.
    """

    def __init__(
        self,
        language_model=None,
        prompt_template=None,
        static_system_prompt=None,
        examples=None,
        instructions=None,
        name=None,
        description=None,
        trainable=True,
    ):
        super().__init__(
            name=name,
            description=description,
            trainable=trainable,
        )
        self.critique = SelfCritique(
            language_model=language_model,
            prompt_template=prompt_template,
            static_system_prompt=static_system_prompt,
            examples=examples,
            instructions=instructions,
            name=self.name + "_self_critique",
        )
        self.language_model = language_model
        self.prompt_template = prompt_template
        self.static_system_prompt = static_system_prompt
        self.examples = examples
        self.instructions = instructions

    async def call(self, inputs):
        if not isinstance(inputs, (list, tuple)):
            raise ValueError("The inputs should be a list or tuple.")
        if len(inputs) != 2:
            raise ValueError("The inputs of the program should have a length of 2.")
        y_true = inputs[0]
        y_pred = inputs[1]
        if y_true:
            y_true = await ops.prefix(
                y_true,
                prefix="gold",
                name="gold_y_true",
            )
            return await self.critique(
                await ops.concat(
                    y_true,
                    y_pred,
                    name="y_true_with_y_pred",
                )
            )
        else:
            return await self.critique(y_pred)

    def get_config(self):
        config = {
            "prompt_template": self.prompt_template,
            "static_system_prompt": self.static_system_prompt,
            "examples": self.examples,
            "instructions": self.instructions,
            "name": self.name,
            "description": self.description,
            "trainable": self.trainable,
        }
        language_model_config = {
            "language_model": serialization_lib.serialize_synalinks_object(
                self.language_model
            )
        }
        return {**language_model_config, **config}

    @classmethod
    def from_config(cls, config):
        language_model = serialization_lib.deserialize_synalinks_object(
            config.pop("language_model")
        )
        return cls(language_model=language_model, **config)