Skip to content

base

LLM

Bases: ABC

Source code in src/distilabel/llm/base.py 
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 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
130
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
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
class LLM(ABC):
    def __init__(
        self,
        task: Task,
        num_threads: Union[int, None] = None,
        prompt_format: Union["SupportedFormats", None] = None,
        prompt_formatting_fn: Union[Callable[..., str], None] = None,
    ) -> None:
        """Initializes the LLM base class.

        Note:
            This class is intended to be used internally, but you anyone can still create
            a subclass, implement the `abstractmethod`s and use it.

        Args:
            task (Task): the task to be performed by the LLM.
            num_threads (Union[int, None], optional): the number of threads to be used
                for parallel generation. If `None`, no parallel generation will be performed.
                Defaults to `None`.
            prompt_format (Union["SupportedFormats", None], optional): the format to be used
                for the prompt. If `None`, the default format of the task will be used, available
                formats are `openai`, `chatml`, `llama2`, `zephyr`, and `default`. Defaults to `None`,
                but `default` (concatenation of `system_prompt` and `formatted_prompt` with a line-break)
                will be used if no `prompt_formatting_fn` is provided.
            prompt_formatting_fn (Union[Callable[..., str], None], optional): a function to be
                applied to the prompt before generation. If `None`, no formatting will be applied.
                Defaults to `None`.
        """
        self.task = task

        self.thread_pool_executor = (
            ThreadPoolExecutor(max_workers=num_threads)
            if num_threads is not None
            else None
        )

        self.prompt_format = prompt_format
        self.prompt_formatting_fn = prompt_formatting_fn

    def __del__(self) -> None:
        """Shuts down the thread pool executor if it is not `None`."""
        if self.thread_pool_executor is not None:
            self.thread_pool_executor.shutdown()

    @property
    def num_threads(self) -> Union[int, None]:
        if self.thread_pool_executor:
            return self.thread_pool_executor._max_workers

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(task={self.task.__class__.__name__}, num_threads={self.num_threads}, promp_format='{self.prompt_format}', model='{self.model_name}')"

    def __rich_repr__(self) -> Generator[Any, None, None]:
        yield "task", self.task
        yield "num_threads", self.num_threads
        yield "prompt_format", self.prompt_format
        if self.prompt_formatting_fn is not None:
            args = f"({', '.join(self.prompt_formatting_fn.__code__.co_varnames)})"
            representation = self.prompt_formatting_fn.__name__ + args
            yield "prompt_formatting_fn", representation
        yield "model", self.model_name

    @property
    @abstractmethod
    def model_name(self) -> str:
        pass

    def _generate_prompts(
        self,
        inputs: List[Dict[str, Any]],
        default_format: Union["SupportedFormats", None] = None,
        expected_output_type: Type = str,
    ) -> List[Any]:
        """Generates the prompts to be used for generation.

        Args:
            inputs (List[Dict[str, Any]]): the inputs to be used for generation.
            default_format (Union["SupportedFormats", None], optional): the default format to be used
                for the prompt if no `prompt_format` is specified. Defaults to `None`.
            expected_output_type (Type, optional): the expected type of the prompt. Defaults to `str`.

        Returns:
            List[Any]: the generated prompts.

        Raises:
            ValueError: if the generated prompt is not of the expected type.
        """
        prompts = []
        for input in inputs:
            prompt = self.task.generate_prompt(**input)
            if not isinstance(prompt, Prompt) and self.prompt_formatting_fn is not None:
                warnings.warn(
                    "The method `generate_prompt` is not returning a `Prompt` class but a prompt"
                    f" of `type={type(prompt)}`, meaning that a pre-formatting has already been"
                    " applied in the `task.generate_prompt` method, so the usage of a `prompt_formatting_fn`"
                    " is discouraged.",
                    UserWarning,
                    stacklevel=2,
                )
                prompt = self.prompt_formatting_fn(prompt)
            elif isinstance(prompt, Prompt) and self.prompt_formatting_fn is None:
                if self.prompt_format is not None or default_format is not None:
                    prompt = prompt.format_as(
                        format=self.prompt_format or default_format  # type: ignore
                    )
                else:
                    warnings.warn(
                        "No `prompt_format` has been specified and no `default_format` is set, so"
                        " the prompt will be concatenated with a line-break and no specific formatting"
                        " by default.",
                        UserWarning,
                        stacklevel=2,
                    )
                    prompt = prompt.format_as(format="default")
            if not isinstance(prompt, expected_output_type):
                raise ValueError(
                    f"The provided `prompt={prompt}` is of `type={type(prompt)}`, but it must be of"
                    f" `type={expected_output_type}`, so make sure that `task.generate_prompt` returns"
                    f" a `{expected_output_type}` or that the `formatting_fn` formats the prompt as a "
                    f" `{expected_output_type}`."
                )
            prompts.append(prompt)
        return prompts

    @abstractmethod
    def _generate(
        self, inputs: List[Dict[str, Any]], num_generations: int = 1
    ) -> List[List["LLMOutput"]]:
        pass

    def generate(
        self,
        inputs: List[Dict[str, Any]],
        num_generations: int = 1,
        progress_callback_func: Union[Callable, None] = None,
    ) -> Union[List[Future[List["LLMOutput"]]], List[List["LLMOutput"]]]:
        """Generates the outputs for the given inputs using the LLM.

        Args:
            inputs (List[Dict[str, Any]]): the inputs to be used for generation.
            num_generations (int, optional): the number of generations to be performed for each input.
                Defaults to `1`.
            progress_callback_func (Union[Callable, None], optional): a function to be called at each
                generation step. Defaults to `None`.

        Returns:
            Union[List[Future[List["LLMOutput"]]], List[List["LLMOutput"]]]: the generated outputs.
        """

        def _progress():
            if progress_callback_func is not None:
                advance = (
                    num_generations * len(inputs)
                    if not self.return_futures
                    else num_generations
                )
                progress_callback_func(advance=advance)

        if self.thread_pool_executor is not None:
            futures = []
            for input in inputs:
                future = self.thread_pool_executor.submit(
                    self._generate, [input], num_generations
                )
                future.add_done_callback(lambda _: _progress())
                futures.append(future)
            return futures

        generations = self._generate(inputs, num_generations)
        _progress()
        return generations

    @property
    def return_futures(self) -> bool:
        """Returns whether the LLM returns futures or not."""
        return self.thread_pool_executor is not None

return_futures: bool property

Returns whether the LLM returns futures or not.

__del__()

Shuts down the thread pool executor if it is not None.

Source code in src/distilabel/llm/base.py 
69
70
71
72
def __del__(self) -> None:
    """Shuts down the thread pool executor if it is not `None`."""
    if self.thread_pool_executor is not None:
        self.thread_pool_executor.shutdown()

__init__(task, num_threads=None, prompt_format=None, prompt_formatting_fn=None)

Initializes the LLM base class.

Note

This class is intended to be used internally, but you anyone can still create a subclass, implement the abstractmethods and use it.

Parameters:

Name Type Description Default
task Task

the task to be performed by the LLM.

 required
num_threads Union[int, None]

the number of threads to be used for parallel generation. If None, no parallel generation will be performed. Defaults to None.

 None
prompt_format Union['SupportedFormats', None]

the format to be used for the prompt. If None, the default format of the task will be used, available formats are openai, chatml, llama2, zephyr, and default. Defaults to None, but default (concatenation of system_prompt and formatted_prompt with a line-break) will be used if no prompt_formatting_fn is provided.

 None
prompt_formatting_fn Union[Callable[..., str], None]

a function to be applied to the prompt before generation. If None, no formatting will be applied. Defaults to None.

 None
Source code in src/distilabel/llm/base.py 
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
def __init__(
    self,
    task: Task,
    num_threads: Union[int, None] = None,
    prompt_format: Union["SupportedFormats", None] = None,
    prompt_formatting_fn: Union[Callable[..., str], None] = None,
) -> None:
    """Initializes the LLM base class.

    Note:
        This class is intended to be used internally, but you anyone can still create
        a subclass, implement the `abstractmethod`s and use it.

    Args:
        task (Task): the task to be performed by the LLM.
        num_threads (Union[int, None], optional): the number of threads to be used
            for parallel generation. If `None`, no parallel generation will be performed.
            Defaults to `None`.
        prompt_format (Union["SupportedFormats", None], optional): the format to be used
            for the prompt. If `None`, the default format of the task will be used, available
            formats are `openai`, `chatml`, `llama2`, `zephyr`, and `default`. Defaults to `None`,
            but `default` (concatenation of `system_prompt` and `formatted_prompt` with a line-break)
            will be used if no `prompt_formatting_fn` is provided.
        prompt_formatting_fn (Union[Callable[..., str], None], optional): a function to be
            applied to the prompt before generation. If `None`, no formatting will be applied.
            Defaults to `None`.
    """
    self.task = task

    self.thread_pool_executor = (
        ThreadPoolExecutor(max_workers=num_threads)
        if num_threads is not None
        else None
    )

    self.prompt_format = prompt_format
    self.prompt_formatting_fn = prompt_formatting_fn

generate(inputs, num_generations=1, progress_callback_func=None)

Generates the outputs for the given inputs using the LLM.

Parameters:

Name Type Description Default
inputs List[Dict[str, Any]]

the inputs to be used for generation.

 required
num_generations int

the number of generations to be performed for each input. Defaults to 1.

 1
progress_callback_func Union[Callable, None]

a function to be called at each generation step. Defaults to None.

 None

Returns:

Type Description
Union[List[Future[List['LLMOutput']]], List[List['LLMOutput']]]

Union[List[Future[List["LLMOutput"]]], List[List["LLMOutput"]]]: the generated outputs.
Source code in src/distilabel/llm/base.py 
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
def generate(
    self,
    inputs: List[Dict[str, Any]],
    num_generations: int = 1,
    progress_callback_func: Union[Callable, None] = None,
) -> Union[List[Future[List["LLMOutput"]]], List[List["LLMOutput"]]]:
    """Generates the outputs for the given inputs using the LLM.

    Args:
        inputs (List[Dict[str, Any]]): the inputs to be used for generation.
        num_generations (int, optional): the number of generations to be performed for each input.
            Defaults to `1`.
        progress_callback_func (Union[Callable, None], optional): a function to be called at each
            generation step. Defaults to `None`.

    Returns:
        Union[List[Future[List["LLMOutput"]]], List[List["LLMOutput"]]]: the generated outputs.
    """

    def _progress():
        if progress_callback_func is not None:
            advance = (
                num_generations * len(inputs)
                if not self.return_futures
                else num_generations
            )
            progress_callback_func(advance=advance)

    if self.thread_pool_executor is not None:
        futures = []
        for input in inputs:
            future = self.thread_pool_executor.submit(
                self._generate, [input], num_generations
            )
            future.add_done_callback(lambda _: _progress())
            futures.append(future)
        return futures

    generations = self._generate(inputs, num_generations)
    _progress()
    return generations