Source code for langchain.llms.rwkv
"""Wrapper for the RWKV model.
Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.py
         https://github.com/BlinkDL/ChatRWKV/blob/main/v2/chat.py
"""
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import BaseModel, Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
[docs]class RWKV(LLM, BaseModel):
    r"""Wrapper around RWKV language models.
    To use, you should have the ``rwkv`` python package installed, the
    pre-trained model file, and the model's config information.
    Example:
        .. code-block:: python
            from langchain.llms import RWKV
            model = RWKV(model="./models/rwkv-3b-fp16.bin", strategy="cpu fp32")
            # Simplest invocation
            response = model("Once upon a time, ")
    """
    model: str
    """Path to the pre-trained RWKV model file."""
    tokens_path: str
    """Path to the RWKV tokens file."""
    strategy: str = "cpu fp32"
    """Token context window."""
    rwkv_verbose: bool = True
    """Print debug information."""
    temperature: float = 1.0
    """The temperature to use for sampling."""
    top_p: float = 0.5
    """The top-p value to use for sampling."""
    penalty_alpha_frequency: float = 0.4
    """Positive values penalize new tokens based on their existing frequency
    in the text so far, decreasing the model's likelihood to repeat the same
    line verbatim.."""
    penalty_alpha_presence: float = 0.4
    """Positive values penalize new tokens based on whether they appear
    in the text so far, increasing the model's likelihood to talk about
    new topics.."""
    CHUNK_LEN: int = 256
    """Batch size for prompt processing."""
    max_tokens_per_generation: int = 256
    """Maximum number of tokens to generate."""
    client: Any = None  #: :meta private:
    tokenizer: Any = None  #: :meta private:
    pipeline: Any = None  #: :meta private:
    model_tokens: Any = None  #: :meta private:
    model_state: Any = None  #: :meta private:
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {
            "verbose": self.verbose,
            "top_p": self.top_p,
            "temperature": self.temperature,
            "penalty_alpha_frequency": self.penalty_alpha_frequency,
            "penalty_alpha_presence": self.penalty_alpha_presence,
            "CHUNK_LEN": self.CHUNK_LEN,
            "max_tokens_per_generation": self.max_tokens_per_generation,
        }
    @staticmethod
    def _rwkv_param_names() -> Set[str]:
        """Get the identifying parameters."""
        return {
            "verbose",
        }
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in the environment."""
        try:
            import tokenizers
        except ImportError:
            raise ImportError(
                "Could not import tokenizers python package. "
                "Please install it with `pip install tokenizers`."
            )
        try:
            from rwkv.model import RWKV as RWKVMODEL
            from rwkv.utils import PIPELINE
            values["tokenizer"] = tokenizers.Tokenizer.from_file(values["tokens_path"])
            rwkv_keys = cls._rwkv_param_names()
            model_kwargs = {k: v for k, v in values.items() if k in rwkv_keys}
            model_kwargs["verbose"] = values["rwkv_verbose"]
            values["client"] = RWKVMODEL(
                values["model"], strategy=values["strategy"], **model_kwargs
            )
            values["pipeline"] = PIPELINE(values["client"], values["tokens_path"])
        except ImportError:
            raise ValueError(
                "Could not import rwkv python package. "
                "Please install it with `pip install rwkv`."
            )
        return values
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {
            "model": self.model,
            **self._default_params,
            **{k: v for k, v in self.__dict__.items() if k in RWKV._rwkv_param_names()},
        }
    @property
    def _llm_type(self) -> str:
        """Return the type of llm."""
        return "rwkv"
    def run_rnn(self, _tokens: List[str], newline_adj: int = 0) -> Any:
        AVOID_REPEAT_TOKENS = []
        AVOID_REPEAT = "，：？！"
        for i in AVOID_REPEAT:
            dd = self.pipeline.encode(i)
            assert len(dd) == 1
            AVOID_REPEAT_TOKENS += dd
        tokens = [int(x) for x in _tokens]
        self.model_tokens += tokens
        out: Any = None
        while len(tokens) > 0:
            out, self.model_state = self.client.forward(
                tokens[: self.CHUNK_LEN], self.model_state
            )
            tokens = tokens[self.CHUNK_LEN :]
        END_OF_LINE = 187
        out[END_OF_LINE] += newline_adj  # adjust \n probability
        if self.model_tokens[-1] in AVOID_REPEAT_TOKENS:
            out[self.model_tokens[-1]] = -999999999
        return out
    def rwkv_generate(self, prompt: str) -> str:
        self.model_state = None
        self.model_tokens = []
        logits = self.run_rnn(self.tokenizer.encode(prompt).ids)
        begin = len(self.model_tokens)
        out_last = begin
        occurrence: Dict = {}
        decoded = ""
        for i in range(self.max_tokens_per_generation):
            for n in occurrence:
                logits[n] -= (
                    self.penalty_alpha_presence
                    + occurrence[n] * self.penalty_alpha_frequency
                )
            token = self.pipeline.sample_logits(
                logits, temperature=self.temperature, top_p=self.top_p
            )
            END_OF_TEXT = 0
            if token == END_OF_TEXT:
                break
            if token not in occurrence:
                occurrence[token] = 1
            else:
                occurrence[token] += 1
            logits = self.run_rnn([token])
            xxx = self.tokenizer.decode(self.model_tokens[out_last:])
            if "\ufffd" not in xxx:  # avoid utf-8 display issues
                decoded += xxx
                out_last = begin + i + 1
                if i >= self.max_tokens_per_generation - 100:
                    break
        return decoded
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        r"""RWKV generation
        Args:
            prompt: The prompt to pass into the model.
            stop: A list of strings to stop generation when encountered.
        Returns:
            The string generated by the model.
        Example:
            .. code-block:: python
                prompt = "Once upon a time, "
                response = model(prompt, n_predict=55)
        """
        text = self.rwkv_generate(prompt)
        if stop is not None:
            text = enforce_stop_tokens(text, stop)
        return text