Spaces:
Running
Running
File size: 6,678 Bytes
dcaa7a1 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 0b3f7b8 e8cf757 dcaa7a1 0b3f7b8 dcaa7a1 0b3f7b8 dcaa7a1 0b3f7b8 dcaa7a1 0b3f7b8 dcaa7a1 0b3f7b8 dcaa7a1 0b3f7b8 dcaa7a1 0b3f7b8 dcaa7a1 85d85d8 dcaa7a1 0b3f7b8 dcaa7a1 0b3f7b8 dcaa7a1 85d85d8 dcaa7a1 85d85d8 |
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 |
def request_gpt_model_in_new_thread_with_ui_alive(inputs, inputs_show_user, top_p, temperature, chatbot, history, sys_prompt, refresh_interval=0.2):
import time
from concurrent.futures import ThreadPoolExecutor
from request_llm.bridge_chatgpt import predict_no_ui_long_connection
# 用户反馈
chatbot.append([inputs_show_user, ""])
msg = '正常'
yield chatbot, [], msg
executor = ThreadPoolExecutor(max_workers=16)
mutable = ["", time.time()]
future = executor.submit(lambda:
predict_no_ui_long_connection(
inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable)
)
while True:
# yield一次以刷新前端页面
time.sleep(refresh_interval)
# “喂狗”(看门狗)
mutable[1] = time.time()
if future.done():
break
chatbot[-1] = [chatbot[-1][0], mutable[0]]
msg = "正常"
yield chatbot, [], msg
return future.result()
def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inputs_array, inputs_show_user_array, top_p, temperature, chatbot, history_array, sys_prompt_array, refresh_interval=0.2, max_workers=10, scroller_max_len=30):
import time
from concurrent.futures import ThreadPoolExecutor
from request_llm.bridge_chatgpt import predict_no_ui_long_connection
assert len(inputs_array) == len(history_array)
assert len(inputs_array) == len(sys_prompt_array)
executor = ThreadPoolExecutor(max_workers=max_workers)
n_frag = len(inputs_array)
# 用户反馈
chatbot.append(["请开始多线程操作。", ""])
msg = '正常'
yield chatbot, [], msg
# 异步原子
mutable = [["", time.time()] for _ in range(n_frag)]
def _req_gpt(index, inputs, history, sys_prompt):
gpt_say = predict_no_ui_long_connection(
inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable[
index]
)
return gpt_say
# 异步任务开始
futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
cnt = 0
while True:
# yield一次以刷新前端页面
time.sleep(refresh_interval)
cnt += 1
worker_done = [h.done() for h in futures]
if all(worker_done):
executor.shutdown()
break
# 更好的UI视觉效果
observe_win = []
# 每个线程都要“喂狗”(看门狗)
for thread_index, _ in enumerate(worker_done):
mutable[thread_index][1] = time.time()
# 在前端打印些好玩的东西
for thread_index, _ in enumerate(worker_done):
print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
replace('\n', '').replace('```', '...').replace(
' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
observe_win.append(print_something_really_funny)
stat_str = ''.join([f'执行中: {obs}\n\n' if not done else '已完成\n\n' for done, obs in zip(
worker_done, observe_win)])
chatbot[-1] = [chatbot[-1][0],
f'多线程操作已经开始,完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
msg = "正常"
yield chatbot, [], msg
# 异步任务结束
gpt_response_collection = []
for inputs_show_user, f in zip(inputs_show_user_array, futures):
gpt_res = f.result()
gpt_response_collection.extend([inputs_show_user, gpt_res])
return gpt_response_collection
def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
def cut(txt_tocut, must_break_at_empty_line): # 递归
if get_token_fn(txt_tocut) <= limit:
return [txt_tocut]
else:
lines = txt_tocut.split('\n')
estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
estimated_line_cut = int(estimated_line_cut)
for cnt in reversed(range(estimated_line_cut)):
if must_break_at_empty_line:
if lines[cnt] != "":
continue
print(cnt)
prev = "\n".join(lines[:cnt])
post = "\n".join(lines[cnt:])
if get_token_fn(prev) < limit:
break
if cnt == 0:
print('what the fuck ?')
raise RuntimeError("存在一行极长的文本!")
# print(len(post))
# 列表递归接龙
result = [prev]
result.extend(cut(post, must_break_at_empty_line))
return result
try:
return cut(txt, must_break_at_empty_line=True)
except RuntimeError:
return cut(txt, must_break_at_empty_line=False)
def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
def cut(txt_tocut, must_break_at_empty_line): # 递归
if get_token_fn(txt_tocut) <= limit:
return [txt_tocut]
else:
lines = txt_tocut.split('\n')
estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
estimated_line_cut = int(estimated_line_cut)
cnt = 0
for cnt in reversed(range(estimated_line_cut)):
if must_break_at_empty_line:
if lines[cnt] != "":
continue
print(cnt)
prev = "\n".join(lines[:cnt])
post = "\n".join(lines[cnt:])
if get_token_fn(prev) < limit:
break
if cnt == 0:
# print('what the fuck ? 存在一行极长的文本!')
raise RuntimeError("存在一行极长的文本!")
# print(len(post))
# 列表递归接龙
result = [prev]
result.extend(cut(post, must_break_at_empty_line))
return result
try:
return cut(txt, must_break_at_empty_line=True)
except RuntimeError:
try:
return cut(txt, must_break_at_empty_line=False)
except RuntimeError:
# 这个中文的句号是故意的,作为一个标识而存在
res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False)
return [r.replace('。\n', '.') for r in res]
|