RWKV-Runner/backend-python/utils/rwkv.py

import os
import pathlib
import copy
from typing import Dict, List
from utils.log import quick_log
from fastapi import HTTPException
from pydantic import BaseModel, Field
from rwkv_pip.utils import PIPELINE
from routes import state_cache


END_OF_TEXT = 0
END_OF_LINE = 187
END_OF_LINE_DOUBLE = 535


os.environ["TORCH_EXTENSIONS_DIR"] = f"{pathlib.Path(__file__).parent.parent.resolve()}"


class RWKV:
    def __init__(self, model: str, strategy: str, tokens_path: str) -> None:
        from rwkv.model import RWKV as Model  # dynamic import to make RWKV_CUDA_ON work

        self.model = Model(model, strategy)
        self.pipeline = PIPELINE(self.model, tokens_path)
        self.model_state = None
        self.model_tokens = []

        self.CHUNK_LEN = 256

        self.max_tokens_per_generation = 500
        self.temperature = 1
        self.top_p = 0.5
        self.penalty_alpha_presence = 0.4
        self.penalty_alpha_frequency = 0.4

        self.interface = ":"
        if "rwkv_vocab" in tokens_path:
            self.user = "Question"
            self.bot = "Answer"
        else:
            self.user = "Bob"
            self.bot = "Alice"

        self.AVOID_REPEAT_TOKENS = []
        AVOID_REPEAT = "，：？！"
        for i in AVOID_REPEAT:
            dd = self.pipeline.encode(i)
            assert len(dd) == 1
            self.AVOID_REPEAT_TOKENS += dd

        self.preload()

    def preload(self):
        interface = self.interface
        user = self.user
        bot = self.bot
        preset_system = (
            f"""
The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. \
{bot} is very intelligent, creative and friendly. \
{bot} is unlikely to disagree with {user}, and {bot} doesn't like to ask {user} questions. \
{bot} likes to tell {user} a lot about herself and her opinions. \
{bot} usually gives {user} kind, helpful and informative advices.\n
"""
            if self.user == "Bob"
            else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
            + "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
        )
        logits = self.run_rnn(self.fix_tokens(self.pipeline.encode(preset_system)))
        try:
            state_cache.add_state(
                state_cache.AddStateBody(
                    prompt=preset_system,
                    tokens=self.model_tokens,
                    state=self.model_state,
                    logits=logits,
                )
            )
        except HTTPException:
            pass

    # Model only saw '\n\n' as [187, 187] before, but the tokenizer outputs [535] for it at the end
    def fix_tokens(self, tokens):
        if len(tokens) > 0 and tokens[-1] == END_OF_LINE_DOUBLE:
            tokens = tokens[:-1] + [END_OF_LINE, END_OF_LINE]
        return tokens

    def run_rnn(self, _tokens: List[str], newline_adj: int = 0):
        tokens = [int(x) for x in _tokens]
        self.model_tokens += tokens

        while len(tokens) > 0:
            out, self.model_state = self.model.forward(
                tokens[: self.CHUNK_LEN], self.model_state
            )
            tokens = tokens[self.CHUNK_LEN :]

        out[END_OF_LINE] += newline_adj  # adjust \n probability

        if self.model_tokens[-1] in self.AVOID_REPEAT_TOKENS:
            out[self.model_tokens[-1]] = -999999999
        return out

    def generate(self, prompt: str, stop: str = None):
        quick_log(None, None, "Generation Prompt:\n" + prompt)
        cache = None
        delta_prompt = prompt
        try:
            cache = state_cache.longest_prefix_state(
                state_cache.LongestPrefixStateBody(prompt=prompt), None
            )
        except HTTPException:
            pass
        if cache is None or cache["prompt"] == "":
            self.model_state = None
            self.model_tokens = []
        else:
            delta_prompt = prompt[len(cache["prompt"]) :]
            self.model_state = copy.deepcopy(cache["state"])
            self.model_tokens = copy.deepcopy(cache["tokens"])
            logits = copy.deepcopy(cache["logits"])

        if delta_prompt != "":
            logits = self.run_rnn(self.fix_tokens(self.pipeline.encode(delta_prompt)))
            try:
                state_cache.add_state(
                    state_cache.AddStateBody(
                        prompt=prompt,
                        tokens=self.model_tokens,
                        state=self.model_state,
                        logits=logits,
                    )
                )
            except HTTPException:
                pass

        begin = len(self.model_tokens)
        out_last = begin

        occurrence: Dict = {}

        response = ""
        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
            )

            if token == END_OF_TEXT:
                yield response, ""
                break
            if token not in occurrence:
                occurrence[token] = 1
            else:
                occurrence[token] += 1

            logits = self.run_rnn([token])
            delta: str = self.pipeline.decode(self.model_tokens[out_last:])
            if "\ufffd" not in delta:  # avoid utf-8 display issues
                response += delta
                if stop is not None:
                    if stop in response:
                        response = response.split(stop)[0]
                        try:
                            state_cache.add_state(
                                state_cache.AddStateBody(
                                    prompt=prompt + response,
                                    tokens=self.model_tokens,
                                    state=self.model_state,
                                    logits=logits,
                                )
                            )
                        except HTTPException:
                            pass
                        yield response, ""
                        break
                out_last = begin + i + 1
                if i == self.max_tokens_per_generation - 1:
                    try:
                        state_cache.add_state(
                            state_cache.AddStateBody(
                                prompt=prompt + response,
                                tokens=self.model_tokens,
                                state=self.model_state,
                                logits=logits,
                            )
                        )
                    except HTTPException:
                        pass
                yield response, delta


class ModelConfigBody(BaseModel):
    max_tokens: int = Field(default=None, gt=0, le=102400)
    temperature: float = Field(default=None, ge=0, le=2)
    top_p: float = Field(default=None, ge=0, le=1)
    presence_penalty: float = Field(default=None, ge=-2, le=2)
    frequency_penalty: float = Field(default=None, ge=-2, le=2)

    class Config:
        schema_extra = {
            "example": {
                "max_tokens": 1000,
                "temperature": 1.2,
                "top_p": 0.5,
                "presence_penalty": 0.4,
                "frequency_penalty": 0.4,
            }
        }


def set_rwkv_config(model: RWKV, body: ModelConfigBody):
    if body.max_tokens is not None:
        model.max_tokens_per_generation = body.max_tokens
    if body.temperature is not None:
        if body.temperature < 0.1:
            model.temperature = 0.1
        else:
            model.temperature = body.temperature
    if body.top_p is not None:
        model.top_p = body.top_p
    if body.presence_penalty is not None:
        model.penalty_alpha_presence = body.presence_penalty
    if body.frequency_penalty is not None:
        model.penalty_alpha_frequency = body.frequency_penalty


def get_rwkv_config(model: RWKV) -> ModelConfigBody:
    return ModelConfigBody(
        max_tokens=model.max_tokens_per_generation,
        temperature=model.temperature,
        top_p=model.top_p,
        presence_penalty=model.penalty_alpha_presence,
        frequency_penalty=model.penalty_alpha_frequency,
    )