Skip to main content
`Ctrl`+`K`

#

# Source code for langchain.llms.petals

    """Wrapper around Petals API."""
    import logging
    from typing import Any, Dict, List, Mapping, Optional

    from pydantic import Extra, Field, root_validator

    from langchain.llms.base import LLM
    from langchain.llms.utils import enforce_stop_tokens
    from langchain.utils import get_from_dict_or_env

    logger = logging.getLogger(__name__)

    class Petals(LLM):
        """Wrapper around Petals Bloom models.

        To use, you should have the ``petals`` python package installed, and the
        environment variable ``HUGGINGFACE_API_KEY`` set with your API key.

        Any parameters that are valid to be passed to the call can be passed
        in, even if not explicitly saved on this class.

        Example:
            .. code-block:: python
                from langchain.llms import petals
                petals = Petals()

        """

        client: Any
        """The client to use for the API calls."""

        tokenizer: Any
        """The tokenizer to use for the API calls."""

        model_name: str = "bigscience/bloom-petals"
        """The model to use."""

        temperature: float = 0.7
        """What sampling temperature to use"""

        max_new_tokens: int = 256
        """The maximum number of new tokens to generate in the completion."""

        top_p: float = 0.9
        """The cumulative probability for top-p sampling."""

        top_k: Optional[int] = None
        """The number of highest probability vocabulary tokens
        to keep for top-k-filtering."""

        do_sample: bool = True
        """Whether or not to use sampling; use greedy decoding otherwise."""

        max_length: Optional[int] = None
        """The maximum length of the sequence to be generated."""

        model_kwargs: Dict[str, Any] = Field(default_factory=dict)
        """Holds any model parameters valid for `create` call
        not explicitly specified."""

        huggingface_api_key: Optional[str] = None

        class Config:
            """Configuration for this pydantic config."""

            extra = Extra.forbid

        @root_validator(pre=True)
        def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
            """Build extra kwargs from additional params that were passed in."""
            all_required_field_names = {field.alias for field in cls.__fields__.values()}

            extra = values.get("model_kwargs", {})
            for field_name in list(values):
                if field_name not in all_required_field_names:
                    if field_name in extra:
                        raise ValueError(f"Found {field_name} supplied twice.")
                    logger.warning(
                        f"""WARNING! {field_name} is not default parameter.
                        {field_name} was transfered to model_kwargs.
                        Please confirm that {field_name} is what you intended."""
                    )
                    extra[field_name] = values.pop(field_name)
            values["model_kwargs"] = extra
            return values

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that api key and python package exists in environment."""
            huggingface_api_key = get_from_dict_or_env(
                values, "huggingface_api_key", "HUGGINGFACE_API_KEY"
            )
            try:
                from petals import DistributedBloomForCausalLM
                from transformers import BloomTokenizerFast

                model_name = values["model_name"]
                values["tokenizer"] = BloomTokenizerFast.from_pretrained(model_name)
                values["client"] = DistributedBloomForCausalLM.from_pretrained(model_name)
                values["huggingface_api_key"] = huggingface_api_key

            except ImportError:
                raise ValueError(
                    "Could not import transformers or petals python package."
                    "Please install with `pip install -U transformers petals`."
                )
            return values

        @property
        def _default_params(self) -> Dict[str, Any]:
            """Get the default parameters for calling Petals API."""
            normal_params = {
                "temperature": self.temperature,
                "max_new_tokens": self.max_new_tokens,
                "top_p": self.top_p,
                "top_k": self.top_k,
                "do_sample": self.do_sample,
                "max_length": self.max_length,
            }
            return {**normal_params, **self.model_kwargs}

        @property
        def _identifying_params(self) -> Mapping[str, Any]:
            """Get the identifying parameters."""
            return {**{"model_name": self.model_name}, **self._default_params}

        @property
        def _llm_type(self) -> str:
            """Return type of llm."""
            return "petals"

        def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
            """Call the Petals API."""
            params = self._default_params
            inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
            outputs = self.client.generate(inputs, **params)
            text = self.tokenizer.decode(outputs[0])
            if stop is not None:
                # I believe this is required since the stop tokens
                # are not enforced by the model parameters
                text = enforce_stop_tokens(text, stop)
            return text