291 lines
9.4 KiB
Python
291 lines
9.4 KiB
Python
import logging
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import time
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from typing import Callable, List, Optional, Union
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import numpy as np
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from sglang.backend.base_backend import BaseBackend
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from sglang.lang.chat_template import ChatTemplate, get_chat_template_by_model_path
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from sglang.lang.interpreter import StreamExecutor
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from sglang.lang.ir import SglSamplingParams
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try:
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import openai
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import tiktoken
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except ImportError as e:
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openai = tiktoken = e
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logger = logging.getLogger("openai")
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def create_logit_bias_int(tokenizer):
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"""Get logit bias for integer numbers."""
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int_token_ids = []
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tokens = tokenizer._mergeable_ranks
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for token, token_id in tokens.items():
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s = tokenizer.decode([token_id])
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if all([c.isdigit() for c in s]) or s in [" "]:
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int_token_ids.append(token_id)
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if len(int_token_ids) >= 300: # OpenAI API limit
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break
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special_tokens = tokenizer._special_tokens
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mask = {t: 100 for t in int_token_ids[:299]}
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mask[special_tokens["<|endoftext|>"]] = 100
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return mask
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INSTRUCT_MODEL_NAMES = [
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"gpt-3.5-turbo-instruct",
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]
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class OpenAI(BaseBackend):
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def __init__(
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self,
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model_name: str,
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is_chat_model: Optional[bool] = None,
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chat_template: Optional[ChatTemplate] = None,
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is_azure: bool = False,
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*args,
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**kwargs,
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):
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super().__init__()
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if isinstance(openai, Exception):
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raise openai
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if is_azure:
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self.client = openai.AzureOpenAI(*args, **kwargs)
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else:
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self.client = openai.OpenAI(*args, **kwargs)
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self.model_name = model_name
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try:
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self.tokenizer = tiktoken.encoding_for_model(model_name)
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except KeyError:
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self.tokenizer = tiktoken.get_encoding("cl100k_base")
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self.logit_bias_int = create_logit_bias_int(self.tokenizer)
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self.chat_template = chat_template or get_chat_template_by_model_path(
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model_name
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)
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if is_chat_model is not None:
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self.is_chat_model = is_chat_model
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else:
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if model_name in INSTRUCT_MODEL_NAMES:
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self.is_chat_model = False
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else:
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self.is_chat_model = True
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self.chat_begin_str = self.chat_template.role_prefix_and_suffix["assistant"][0]
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def get_chat_template(self):
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return self.chat_template
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def generate(
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self,
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s: StreamExecutor,
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sampling_params: SglSamplingParams,
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):
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if sampling_params.dtype is None:
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if self.is_chat_model:
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if not s.text_.endswith(self.chat_begin_str):
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raise RuntimeError(
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"This use case is not supported. "
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"For OpenAI chat models, sgl.gen must be right after sgl.assistant"
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)
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prompt = s.messages_
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else:
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prompt = s.text_
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kwargs = sampling_params.to_openai_kwargs()
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comp = openai_completion(
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client=self.client,
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is_chat=self.is_chat_model,
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model=self.model_name,
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prompt=prompt,
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**kwargs,
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)
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elif sampling_params.dtype in [str, "str", "string"]:
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kwargs = sampling_params.to_openai_kwargs()
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kwargs.pop("stop")
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comp = openai_completion(
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client=self.client,
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is_chat=self.is_chat_model,
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model=self.model_name,
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prompt=s.text_ + '"',
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stop='"',
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**kwargs,
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)
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comp = '"' + comp + '"'
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elif sampling_params.dtype in [int, "int"]:
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kwargs = sampling_params.to_openai_kwargs()
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kwargs.pop("stop")
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comp = openai_completion(
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client=self.client,
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is_chat=self.is_chat_model,
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model=self.model_name,
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prompt=s.text_,
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logit_bias=self.logit_bias_int,
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stop=[" "],
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**kwargs,
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)
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else:
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raise ValueError(f"Unknown dtype: {sampling_params.dtype}")
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return comp, {}
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def generate_stream(
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self,
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s: StreamExecutor,
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sampling_params: SglSamplingParams,
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):
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if sampling_params.dtype is None:
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if self.is_chat_model:
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if not s.text_.endswith(self.chat_begin_str):
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raise RuntimeError(
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"This use case is not supported. "
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"For OpenAI chat models, sgl.gen must be right after sgl.assistant"
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)
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prompt = s.messages_
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else:
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prompt = s.text_
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kwargs = sampling_params.to_openai_kwargs()
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generator = openai_completion_stream(
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client=self.client,
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is_chat=self.is_chat_model,
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model=self.model_name,
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prompt=prompt,
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**kwargs,
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)
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return generator
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else:
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raise ValueError(f"Unknown dtype: {sampling_params.dtype}")
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def select(
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self,
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s: StreamExecutor,
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choices: List[str],
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temperature: float,
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):
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if self.is_chat_model:
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raise NotImplementedError(
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"select/choices is not supported for chat models. "
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"Please try to use a non-chat model such as gpt-3.5-turbo-instruct"
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)
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n_choices = len(choices)
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token_ids = [self.tokenizer.encode(x) for x in choices]
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scores = [0] * n_choices
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valid = [len(x) > 0 for x in token_ids]
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prompt_tokens = self.tokenizer.encode(s.text_)
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max_len = max([len(x) for x in token_ids])
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for step in range(max_len):
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# Build logit bias
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logit_bias = {}
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for i in range(n_choices):
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if valid[i]:
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logit_bias[token_ids[i][step]] = 100
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# Call API
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ret = self.client.completions.create(
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model=self.model_name,
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prompt=prompt_tokens,
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logit_bias=logit_bias,
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max_tokens=1,
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temperature=temperature,
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)
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ret_str = ret.choices[0].text
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ret_token = self.tokenizer.encode(ret_str)[0]
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# TODO:
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# 1. return logits as the scores
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# 2. compute logits of the full choice
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# 3. consider chunk-based decoding
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# Update valid
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hit = False
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for i in range(n_choices):
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if valid[i]:
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if step == len(token_ids[i]) - 1:
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valid[i] = False
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if ret_token == token_ids[i][step]:
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scores[i] += 1
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hit = True
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else:
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valid[i] = False
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assert hit
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if np.sum(valid) <= 1:
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break
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prompt_tokens.append(ret_token)
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decision = choices[np.argmax(scores)]
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return decision, scores, scores
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def openai_completion(client, retries=3, is_chat=None, prompt=None, **kwargs):
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for attempt in range(retries):
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try:
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if is_chat:
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if "stop" in kwargs and kwargs["stop"] is None:
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kwargs.pop("stop")
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ret = client.chat.completions.create(messages=prompt, **kwargs)
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comp = ret.choices[0].message.content
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else:
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ret = client.completions.create(prompt=prompt, **kwargs)
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if isinstance(prompt, (list, tuple)):
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comp = [c.text for c in ret.choices]
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else:
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comp = ret.choices[0].text
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break
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except (openai.APIError, openai.APIConnectionError, openai.RateLimitError) as e:
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logger.error(f"OpenAI Error: {e}. Waiting 5 seconds...")
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time.sleep(5)
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if attempt == retries - 1:
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raise e
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except Exception as e:
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logger.error(f"RuntimeError {e}.")
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raise e
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return comp
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def openai_completion_stream(client, retries=3, is_chat=None, prompt=None, **kwargs):
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for attempt in range(retries):
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try:
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if is_chat:
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if "stop" in kwargs and kwargs["stop"] is None:
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kwargs.pop("stop")
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generator = client.chat.completions.create(
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messages=prompt, stream=True, **kwargs
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)
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for ret in generator:
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try:
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content = ret.choices[0].delta.content
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except IndexError:
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content = None
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yield content or "", {}
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else:
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generator = client.completions.create(
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prompt=prompt, stream=True, **kwargs
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)
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for ret in generator:
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content = ret.choices[0].text
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yield content or "", {}
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break
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except (openai.APIError, openai.APIConnectionError, openai.RateLimitError) as e:
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logger.error(f"OpenAI Error: {e}. Waiting 5 seconds...")
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time.sleep(5)
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if attempt == retries - 1:
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raise e
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except Exception as e:
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logger.error(f"RuntimeError {e}.")
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raise e
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