460 lines
16 KiB
Python
460 lines
16 KiB
Python
"""Wrapper around FastChat APIs."""
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from __future__ import annotations
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import logging
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import sys
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import warnings
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from typing import (
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AbstractSet,
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Any,
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Callable,
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Collection,
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Dict,
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Generator,
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List,
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Literal,
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Mapping,
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Optional,
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Set,
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Tuple,
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Union,
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)
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from pydantic import Extra, Field, root_validator
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from tenacity import (
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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from langchain.llms.base import BaseLLM
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from langchain.schema import Generation, LLMResult
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from langchain.utils import get_from_dict_or_env
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import requests
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import json
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logger = logging.getLogger(__name__)
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FAST_CHAT_API = "http://localhost:21002/worker_generate_stream"
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def _streaming_response_template() -> Dict[str, Any]:
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"""
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:return: 响应结构
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"""
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return {
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"text": "",
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"error_code": 0,
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}
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def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
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"""Update response from the stream response."""
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response["text"] += stream_response["text"]
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response["error_code"] += stream_response["error_code"]
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class BaseFastChat(BaseLLM):
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"""Wrapper around FastChat large language models."""
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model_name: str = "text-davinci-003"
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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max_new_tokens: int = 200
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stop: int = 20
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batch_size: int = 20
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Penalizes repeated tokens."""
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n: int = 1
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"""Whether to stream the results or not."""
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allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
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"""Set of special tokens that are allowed。"""
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disallowed_special: Union[Literal["all"], Collection[str]] = "all"
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"""Set of special tokens that are not allowed。"""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.ignore
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in cls.__fields__.values()}
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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logger.warning(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transfered to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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values["model_kwargs"] = extra
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling FastChat API."""
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normal_params = {
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"model": self.model_name,
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"prompt": '',
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"max_new_tokens": self.max_new_tokens,
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"temperature": self.temperature,
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}
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return {**normal_params}
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def _generate(
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self, prompts: List[str], stop: Optional[List[str]] = None
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) -> LLMResult:
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"""Call out to FastChat's endpoint with k unique prompts.
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Args:
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prompts: The prompts to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The full LLM output.
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Example:
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.. code-block:: python
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response = fastchat.generate(["Tell me a joke."])
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"""
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# TODO: write a unit test for this
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params = self._invocation_params
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sub_prompts = self.get_sub_prompts(params, prompts)
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choices = []
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token_usage: Dict[str, int] = {}
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headers = {"User-Agent": "fastchat Client"}
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for _prompts in sub_prompts:
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params["prompt"] = _prompts[0]
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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if self.streaming:
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if len(_prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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response_template = _streaming_response_template()
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response = requests.post(
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FAST_CHAT_API,
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headers=headers,
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json=params,
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stream=True,
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)
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for stream_resp in response.iter_lines(
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chunk_size=8192, decode_unicode=False, delimiter=b"\0"
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):
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if stream_resp:
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data = json.loads(stream_resp.decode("utf-8"))
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skip_echo_len = len(_prompts[0])
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output = data["text"][skip_echo_len:].strip()
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data["text"] = output
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self.callback_manager.on_llm_new_token(
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output,
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verbose=self.verbose,
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logprobs=data["error_code"],
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)
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_update_response(response_template, data)
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choices.append(response_template)
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else:
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response_template = _streaming_response_template()
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response = requests.post(
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FAST_CHAT_API,
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headers=headers,
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json=params,
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stream=True,
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)
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for stream_resp in response.iter_lines(
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chunk_size=8192, decode_unicode=False, delimiter=b"\0"
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):
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if stream_resp:
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data = json.loads(stream_resp.decode("utf-8"))
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skip_echo_len = len(_prompts[0])
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output = data["text"][skip_echo_len:].strip()
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data["text"] = output
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_update_response(response_template, data)
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choices.append(response_template)
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return self.create_llm_result(choices, prompts, token_usage)
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async def _agenerate(
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self, prompts: List[str], stop: Optional[List[str]] = None
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) -> LLMResult:
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"""Call out to FastChat's endpoint async with k unique prompts."""
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params = self._invocation_params
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sub_prompts = self.get_sub_prompts(params, prompts)
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choices = []
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token_usage: Dict[str, int] = {}
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headers = {"User-Agent": "fastchat Client"}
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for _prompts in sub_prompts:
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params["prompt"] = _prompts[0]
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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if self.streaming:
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if len(_prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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response_template = _streaming_response_template()
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response = requests.post(
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FAST_CHAT_API,
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headers=headers,
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json=params,
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stream=True,
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)
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for stream_resp in response.iter_lines(
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chunk_size=8192, decode_unicode=False, delimiter=b"\0"
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):
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if stream_resp:
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data = json.loads(stream_resp.decode("utf-8"))
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skip_echo_len = len(_prompts[0])
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output = data["text"][skip_echo_len:].strip()
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data["text"] = output
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self.callback_manager.on_llm_new_token(
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output,
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verbose=self.verbose,
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logprobs=data["error_code"],
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)
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_update_response(response_template, data)
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choices.append(response_template)
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else:
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response_template = _streaming_response_template()
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response = requests.post(
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FAST_CHAT_API,
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headers=headers,
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json=params,
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stream=True,
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)
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for stream_resp in response.iter_lines(
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chunk_size=8192, decode_unicode=False, delimiter=b"\0"
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):
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if stream_resp:
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data = json.loads(stream_resp.decode("utf-8"))
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skip_echo_len = len(_prompts[0])
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output = data["text"][skip_echo_len:].strip()
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data["text"] = output
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_update_response(response_template, data)
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choices.append(response_template)
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return self.create_llm_result(choices, prompts, token_usage)
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def get_sub_prompts(
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self,
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params: Dict[str, Any],
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prompts: List[str],
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) -> List[List[str]]:
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"""Get the sub prompts for llm call."""
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if params["max_new_tokens"] == -1:
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if len(prompts) != 1:
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raise ValueError(
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"max_new_tokens set to -1 not supported for multiple inputs."
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)
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params["max_new_tokens"] = self.max_new_tokens_for_prompt(prompts[0])
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# append pload
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sub_prompts = [
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prompts[i: i + self.batch_size]
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for i in range(0, len(prompts), self.batch_size)
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]
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return sub_prompts
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def create_llm_result(
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self, choices: Any, prompts: List[str], token_usage: Dict[str, int]
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) -> LLMResult:
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"""Create the LLMResult from the choices and prompts."""
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generations = []
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for i, _ in enumerate(prompts):
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sub_choices = choices[i * self.n: (i + 1) * self.n]
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generations.append(
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[
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Generation(
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text=choice["text"],
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generation_info=dict(
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finish_reason='over',
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logprobs=choice["text"],
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),
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)
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for choice in sub_choices
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]
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)
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llm_output = {"token_usage": token_usage, "model_name": self.model_name}
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return LLMResult(generations=generations, llm_output=llm_output)
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def stream(self, prompt: str, stop: Optional[List[str]] = None) -> Generator:
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"""Call FastChat with streaming flag and return the resulting generator.
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BETA: this is a beta feature while we figure out the right abstraction.
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Once that happens, this interface could change.
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Args:
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prompt: The prompts to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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A generator representing the stream of tokens from OpenAI.
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Example:
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.. code-block:: python
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generator = fastChat.stream("Tell me a joke.")
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for token in generator:
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yield token
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"""
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params = self._invocation_params
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params["prompt"] = prompt
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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headers = {"User-Agent": "fastchat Client"}
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response = requests.post(
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FAST_CHAT_API,
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headers=headers,
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json=params,
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stream=True,
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)
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for stream_resp in response.iter_lines(
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chunk_size=8192, decode_unicode=False, delimiter=b"\0"
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):
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if stream_resp:
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data = json.loads(stream_resp.decode("utf-8"))
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skip_echo_len = len(_prompts[0])
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output = data["text"][skip_echo_len:].strip()
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data["text"] = output
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yield data
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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"""Get the parameters used to invoke the model."""
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return self._default_params
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model_name": self.model_name}, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "fastChat"
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def get_num_tokens(self, text: str) -> int:
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"""Calculate num tokens with tiktoken package."""
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# tiktoken NOT supported for Python < 3.8
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if sys.version_info[1] < 8:
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return super().get_num_tokens(text)
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try:
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import tiktoken
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except ImportError:
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raise ValueError(
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"Could not import tiktoken python package. "
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"This is needed in order to calculate get_num_tokens. "
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"Please install it with `pip install tiktoken`."
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)
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enc = tiktoken.encoding_for_model(self.model_name)
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tokenized_text = enc.encode(
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text,
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allowed_special=self.allowed_special,
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disallowed_special=self.disallowed_special,
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)
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# calculate the number of tokens in the encoded text
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return len(tokenized_text)
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def modelname_to_contextsize(self, modelname: str) -> int:
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"""Calculate the maximum number of tokens possible to generate for a model.
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Args:
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modelname: The modelname we want to know the context size for.
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Returns:
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The maximum context size
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Example:
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.. code-block:: python
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max_new_tokens = openai.modelname_to_contextsize("text-davinci-003")
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"""
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model_token_mapping = {
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"vicuna-13b": 2049,
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"koala": 2049,
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"dolly-v2": 2049,
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"oasst": 2049,
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"stablelm": 2049,
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}
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context_size = model_token_mapping.get(modelname, None)
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if context_size is None:
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raise ValueError(
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f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
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"Known models are: " + ", ".join(model_token_mapping.keys())
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)
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return context_size
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def max_new_tokens_for_prompt(self, prompt: str) -> int:
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"""Calculate the maximum number of tokens possible to generate for a prompt.
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Args:
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prompt: The prompt to pass into the model.
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Returns:
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The maximum number of tokens to generate for a prompt.
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Example:
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.. code-block:: python
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max_new_tokens = openai.max_token_for_prompt("Tell me a joke.")
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"""
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num_tokens = self.get_num_tokens(prompt)
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# get max context size for model by name
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max_size = self.modelname_to_contextsize(self.model_name)
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return max_size - num_tokens
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class FastChat(BaseFastChat):
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"""Wrapper around OpenAI large language models.
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To use, you should have the ``openai`` python package installed, and the
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environment variable ``OPENAI_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain.llms import OpenAI
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openai = FastChat(model_name="vicuna")
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"""
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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return {**{"model": self.model_name}, **super()._invocation_params}
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