Files
KoboldAI-Client/modeling/inference_models/api.py
2023-03-04 19:02:00 -06:00

87 lines
2.7 KiB
Python

from __future__ import annotations
import time
import json
import torch
import requests
import numpy as np
from typing import List, Union
import utils
from logger import logger
from modeling.inference_model import (
GenerationResult,
GenerationSettings,
InferenceModel,
)
class APIException(Exception):
"""To be used for errors when using the Kobold API as an interface."""
class APIInferenceModel(InferenceModel):
def _load(self, save_model: bool, initial_load: bool) -> None:
tokenizer_id = requests.get(
utils.koboldai_vars.colaburl[:-8] + "/api/v1/model",
).json()["result"]
self.tokenizer = self._get_tokenizer(tokenizer_id)
def _raw_generate(
self,
prompt_tokens: Union[List[int], torch.Tensor],
max_new: int,
gen_settings: GenerationSettings,
single_line: bool = False,
batch_count: int = 1,
**kwargs
):
decoded_prompt = utils.decodenewlines(self.tokenizer.decode(prompt_tokens))
# Store context in memory to use it for comparison with generated content
utils.koboldai_vars.lastctx = decoded_prompt
# Build request JSON data
reqdata = {
"prompt": decoded_prompt,
"max_length": max_new,
"max_context_length": utils.koboldai_vars.max_length,
"rep_pen": gen_settings.rep_pen,
"rep_pen_slope": gen_settings.rep_pen_slope,
"rep_pen_range": gen_settings.rep_pen_range,
"temperature": gen_settings.temp,
"top_p": gen_settings.top_p,
"top_k": gen_settings.top_k,
"top_a": gen_settings.top_a,
"tfs": gen_settings.tfs,
"typical": gen_settings.typical,
"n": batch_count,
}
# Create request
while True:
req = requests.post(
utils.koboldai_vars.colaburl[:-8] + "/api/v1/generate",
json=reqdata,
)
if (
req.status_code == 503
): # Server is currently generating something else so poll until it's our turn
time.sleep(1)
continue
js = req.json()
if req.status_code != 200:
logger.error(json.dumps(js, indent=4))
raise APIException(f"Bad API status code {req.status_code}")
genout = [obj["text"] for obj in js["results"]]
return GenerationResult(
model=self,
out_batches=np.array([self.tokenizer.encode(x) for x in genout]),
prompt=prompt_tokens,
is_whole_generation=True,
single_line=single_line,
)