File size: 6,957 Bytes
5472531 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 |
"""Call API providers."""
from json import loads
import os
import random
import time
from fastchat.utils import build_logger
from fastchat.constants import WORKER_API_TIMEOUT
logger = build_logger("gradio_web_server", "gradio_web_server.log")
def openai_api_stream_iter(
model_name,
messages,
temperature,
top_p,
max_new_tokens,
api_base=None,
api_key=None,
):
import openai
is_azure = False
if "azure" in model_name:
is_azure = True
openai.api_type = "azure"
openai.api_version = "2023-07-01-preview"
else:
openai.api_type = "open_ai"
openai.api_version = None
openai.api_base = api_base or "https://api.openai.com/v1"
openai.api_key = api_key or os.environ["OPENAI_API_KEY"]
if model_name == "gpt-4-turbo":
model_name = "gpt-4-1106-preview"
# Make requests
gen_params = {
"model": model_name,
"prompt": messages,
"temperature": temperature,
"top_p": top_p,
"max_new_tokens": max_new_tokens,
}
logger.info(f"==== request ====\n{gen_params}")
if is_azure:
res = openai.ChatCompletion.create(
engine=model_name,
messages=messages,
temperature=temperature,
max_tokens=max_new_tokens,
stream=True,
)
else:
res = openai.ChatCompletion.create(
model=model_name,
messages=messages,
temperature=temperature,
max_tokens=max_new_tokens,
stream=True,
)
text = ""
for chunk in res:
if len(chunk["choices"]) > 0:
text += chunk["choices"][0]["delta"].get("content", "")
data = {
"text": text,
"error_code": 0,
}
yield data
def anthropic_api_stream_iter(model_name, prompt, temperature, top_p, max_new_tokens):
import anthropic
c = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
# Make requests
gen_params = {
"model": model_name,
"prompt": prompt,
"temperature": temperature,
"top_p": top_p,
"max_new_tokens": max_new_tokens,
}
logger.info(f"==== request ====\n{gen_params}")
res = c.completions.create(
prompt=prompt,
stop_sequences=[anthropic.HUMAN_PROMPT],
max_tokens_to_sample=max_new_tokens,
temperature=temperature,
top_p=top_p,
model=model_name,
stream=True,
)
text = ""
for chunk in res:
text += chunk.completion
data = {
"text": text,
"error_code": 0,
}
yield data
def init_palm_chat(model_name):
import vertexai # pip3 install google-cloud-aiplatform
from vertexai.preview.language_models import ChatModel
from vertexai.preview.generative_models import GenerativeModel
project_id = os.environ["GCP_PROJECT_ID"]
location = "us-central1"
vertexai.init(project=project_id, location=location)
if model_name in ["palm-2"]:
# According to release note, "chat-bison@001" is PaLM 2 for chat.
# https://cloud.google.com/vertex-ai/docs/release-notes#May_10_2023
model_name = "chat-bison@001"
chat_model = ChatModel.from_pretrained(model_name)
chat = chat_model.start_chat(examples=[])
elif model_name in ["gemini-pro"]:
model = GenerativeModel(model_name)
chat = model.start_chat()
return chat
def palm_api_stream_iter(model_name, chat, message, temperature, top_p, max_new_tokens):
if model_name in ["gemini-pro"]:
max_new_tokens = max_new_tokens * 2
parameters = {
"temperature": temperature,
"top_p": top_p,
"max_output_tokens": max_new_tokens,
}
gen_params = {
"model": model_name,
"prompt": message,
}
gen_params.update(parameters)
if model_name == "palm-2":
response = chat.send_message(message, **parameters)
else:
response = chat.send_message(message, generation_config=parameters, stream=True)
logger.info(f"==== request ====\n{gen_params}")
try:
text = ""
for chunk in response:
text += chunk.text
data = {
"text": text,
"error_code": 0,
}
yield data
except Exception as e:
logger.error(f"==== error ====\n{e}")
yield {
"text": f"**API REQUEST ERROR** Reason: {e}\nPlease try again or increase the number of max tokens.",
"error_code": 1,
}
yield data
def ai2_api_stream_iter(
model_name,
messages,
temperature,
top_p,
max_new_tokens,
api_key=None,
api_base=None,
):
from requests import post
# get keys and needed values
ai2_key = api_key or os.environ.get("AI2_API_KEY")
api_base = api_base or "https://inferd.allen.ai/api/v1/infer"
model_id = "mod_01hhgcga70c91402r9ssyxekan"
# Make requests
gen_params = {
"model": model_name,
"prompt": messages,
"temperature": temperature,
"top_p": top_p,
"max_new_tokens": max_new_tokens,
}
logger.info(f"==== request ====\n{gen_params}")
# AI2 uses vLLM, which requires that `top_p` be 1.0 for greedy sampling:
# https://github.com/vllm-project/vllm/blob/v0.1.7/vllm/sampling_params.py#L156-L157
if temperature == 0.0 and top_p < 1.0:
raise ValueError("top_p must be 1 when temperature is 0.0")
res = post(
api_base,
stream=True,
headers={"Authorization": f"Bearer {ai2_key}"},
json={
"model_id": model_id,
# This input format is specific to the Tulu2 model. Other models
# may require different input formats. See the model's schema
# documentation on InferD for more information.
"input": {
"messages": messages,
"opts": {
"max_tokens": max_new_tokens,
"temperature": temperature,
"top_p": top_p,
"logprobs": 1, # increase for more choices
},
},
},
)
if res.status_code != 200:
logger.error(f"unexpected response ({res.status_code}): {res.text}")
raise ValueError("unexpected response from InferD", res)
text = ""
for line in res.iter_lines():
if line:
part = loads(line)
if "result" in part and "output" in part["result"]:
for t in part["result"]["output"]["text"]:
text += t
else:
logger.error(f"unexpected part: {part}")
raise ValueError("empty result in InferD response")
data = {
"text": text,
"error_code": 0,
}
yield data
|