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import os
import subprocess
import time
from datetime import datetime
import pytest
from tests.utils import wrap_test_forked
from tests.test_langchain_units import have_openai_key
from src.client_test import run_client_many
from src.enums import PromptType, LangChainAction
@pytest.mark.parametrize("base_model",
['h2oai/h2ogpt-oig-oasst1-512-6_9b',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2',
'llama', 'gptj']
)
@pytest.mark.parametrize("force_langchain_evaluate", [False, True])
@pytest.mark.parametrize("do_langchain", [False, True])
@wrap_test_forked
def test_gradio_inference_server(base_model, force_langchain_evaluate, do_langchain,
prompt='Who are you?', stream_output=False, max_new_tokens=256,
langchain_mode='Disabled', langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
user_path=None,
visible_langchain_modes=['UserData', 'MyData'],
reverse_docs=True):
if force_langchain_evaluate:
langchain_mode = 'MyData'
if do_langchain:
langchain_mode = 'UserData'
from tests.utils import make_user_path_test
user_path = make_user_path_test()
# from src.gpt_langchain import get_some_dbs_from_hf
# get_some_dbs_from_hf()
if base_model in ['h2oai/h2ogpt-oig-oasst1-512-6_9b', 'h2oai/h2ogpt-oasst1-512-12b']:
prompt_type = PromptType.human_bot.name
elif base_model in ['h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2']:
prompt_type = PromptType.prompt_answer.name
elif base_model in ['llama']:
prompt_type = PromptType.wizard2.name
elif base_model in ['gptj']:
prompt_type = PromptType.gptj.name
else:
raise NotImplementedError(base_model)
main_kwargs = dict(base_model=base_model, prompt_type=prompt_type, chat=True,
stream_output=stream_output, gradio=True, num_beams=1, block_gradio_exit=False,
max_new_tokens=max_new_tokens,
langchain_mode=langchain_mode, langchain_action=langchain_action,
langchain_agents=langchain_agents,
user_path=user_path,
visible_langchain_modes=visible_langchain_modes,
reverse_docs=reverse_docs,
force_langchain_evaluate=force_langchain_evaluate)
# inference server
inf_port = os.environ['GRADIO_SERVER_PORT'] = "7860"
from src.gen import main
main(**main_kwargs)
# server that consumes inference server
client_port = os.environ['GRADIO_SERVER_PORT'] = "7861"
from src.gen import main
main(**main_kwargs, inference_server='http://127.0.0.1:%s' % inf_port)
# client test to server that only consumes inference server
from src.client_test import run_client_chat
os.environ['HOST'] = "http://127.0.0.1:%s" % client_port
res_dict, client = run_client_chat(prompt=prompt, prompt_type=prompt_type, stream_output=stream_output,
max_new_tokens=max_new_tokens, langchain_mode=langchain_mode,
langchain_action=langchain_action, langchain_agents=langchain_agents)
assert res_dict['prompt'] == prompt
assert res_dict['iinput'] == ''
# will use HOST from above
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None) # client shouldn't have to specify
if base_model == 'h2oai/h2ogpt-oig-oasst1-512-6_9b':
assert 'h2oGPT' in ret1['response']
assert 'Birds' in ret2['response']
assert 'Birds' in ret3['response']
assert 'h2oGPT' in ret4['response']
assert 'h2oGPT' in ret5['response']
assert 'h2oGPT' in ret6['response']
assert 'h2oGPT' in ret7['response']
elif base_model == 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2':
assert 'I am a language model trained' in ret1['response'] or \
'I am an AI language model developed by' in ret1['response'] or \
'I am a chatbot.' in ret1['response'] or \
'a chat-based assistant that can answer questions' in ret1['response'] or \
'I am an AI language model' in ret1['response'] or \
'I am an AI assistant.' in ret1['response']
assert 'Once upon a time' in ret2['response']
assert 'Once upon a time' in ret3['response']
assert 'I am a language model trained' in ret4['response'] or 'I am an AI language model developed by' in \
ret4['response'] or 'I am a chatbot.' in ret4['response'] or \
'a chat-based assistant that can answer questions' in ret4['response'] or \
'I am an AI language model' in ret4['response'] or \
'I am an AI assistant.' in ret4['response']
assert 'I am a language model trained' in ret5['response'] or 'I am an AI language model developed by' in \
ret5['response'] or 'I am a chatbot.' in ret5['response'] or \
'a chat-based assistant that can answer questions' in ret5['response'] or \
'I am an AI language model' in ret5['response'] or \
'I am an AI assistant.' in ret5['response']
assert 'I am a language model trained' in ret6['response'] or 'I am an AI language model developed by' in \
ret6['response'] or 'I am a chatbot.' in ret6['response'] or \
'a chat-based assistant that can answer questions' in ret6['response'] or \
'I am an AI language model' in ret6['response'] or \
'I am an AI assistant.' in ret6['response']
assert 'I am a language model trained' in ret7['response'] or 'I am an AI language model developed by' in \
ret7['response'] or 'I am a chatbot.' in ret7['response'] or \
'a chat-based assistant that can answer questions' in ret7['response'] or \
'I am an AI language model' in ret7['response'] or \
'I am an AI assistant.' in ret7['response']
elif base_model == 'llama':
assert 'I am a bot.' in ret1['response'] or 'can I assist you today?' in ret1[
'response'] or 'How can I assist you?' in ret1['response']
assert 'Birds' in ret2['response'] or 'Once upon a time' in ret2['response']
assert 'Birds' in ret3['response'] or 'Once upon a time' in ret3['response']
assert 'I am a bot.' in ret4['response'] or 'can I assist you today?' in ret4[
'response'] or 'How can I assist you?' in ret4['response']
assert 'I am a bot.' in ret5['response'] or 'can I assist you today?' in ret5[
'response'] or 'How can I assist you?' in ret5['response']
assert 'I am a bot.' in ret6['response'] or 'can I assist you today?' in ret6[
'response'] or 'How can I assist you?' in ret6['response']
assert 'I am a bot.' in ret7['response'] or 'can I assist you today?' in ret7[
'response'] or 'How can I assist you?' in ret7['response']
elif base_model == 'gptj':
assert 'I am a bot.' in ret1['response'] or 'can I assist you today?' in ret1[
'response'] or 'a student at' in ret1['response'] or 'am a person who' in ret1['response'] or 'I am' in \
ret1['response'] or "I'm a student at" in ret1['response']
assert 'Birds' in ret2['response'] or 'Once upon a time' in ret2['response']
assert 'Birds' in ret3['response'] or 'Once upon a time' in ret3['response']
assert 'I am a bot.' in ret4['response'] or 'can I assist you today?' in ret4[
'response'] or 'a student at' in ret4['response'] or 'am a person who' in ret4['response'] or 'I am' in \
ret4['response'] or "I'm a student at" in ret4['response']
assert 'I am a bot.' in ret5['response'] or 'can I assist you today?' in ret5[
'response'] or 'a student at' in ret5['response'] or 'am a person who' in ret5['response'] or 'I am' in \
ret5['response'] or "I'm a student at" in ret5['response']
assert 'I am a bot.' in ret6['response'] or 'can I assist you today?' in ret6[
'response'] or 'a student at' in ret6['response'] or 'am a person who' in ret6['response'] or 'I am' in \
ret6['response'] or "I'm a student at" in ret6['response']
assert 'I am a bot.' in ret7['response'] or 'can I assist you today?' in ret7[
'response'] or 'a student at' in ret7['response'] or 'am a person who' in ret7['response'] or 'I am' in \
ret7['response'] or "I'm a student at" in ret7['response']
print("DONE", flush=True)
def run_docker(inf_port, base_model):
datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_")
msg = "Starting HF inference %s..." % datetime_str
print(msg, flush=True)
home_dir = os.path.expanduser('~')
data_dir = '%s/.cache/huggingface/hub/' % home_dir
cmd = ["docker"] + ['run',
'--gpus', 'device=0',
'--shm-size', '1g',
'-e', 'TRANSFORMERS_CACHE="/.cache/"',
'-p', '%s:80' % inf_port,
'-v', '%s/.cache:/.cache/' % home_dir,
'-v', '%s:/data' % data_dir,
'ghcr.io/huggingface/text-generation-inference:0.8.2',
'--model-id', base_model,
'--max-input-length', '2048',
'--max-total-tokens', '4096',
'--max-stop-sequences', '6',
]
print(cmd, flush=True)
p = subprocess.Popen(cmd,
stdout=None, stderr=subprocess.STDOUT,
)
print("Done starting autoviz server", flush=True)
return p.pid
@pytest.mark.parametrize("base_model",
# FIXME: Can't get 6.9 or 12b (quantized or not) to work on home system, so do falcon only for now
# ['h2oai/h2ogpt-oig-oasst1-512-6_9b', 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2']
['h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2']
)
@pytest.mark.parametrize("force_langchain_evaluate", [False, True])
@pytest.mark.parametrize("do_langchain", [False, True])
@pytest.mark.parametrize("pass_prompt_type", [False, True, 'custom'])
@pytest.mark.parametrize("do_model_lock", [False, True])
@wrap_test_forked
def test_hf_inference_server(base_model, force_langchain_evaluate, do_langchain, pass_prompt_type, do_model_lock,
prompt='Who are you?', stream_output=False, max_new_tokens=256,
langchain_mode='Disabled',
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
user_path=None,
visible_langchain_modes=['UserData', 'MyData'],
reverse_docs=True):
# HF inference server
inf_port = "6112"
inference_server = 'http://127.0.0.1:%s' % inf_port
inf_pid = run_docker(inf_port, base_model)
time.sleep(60)
if force_langchain_evaluate:
langchain_mode = 'MyData'
if do_langchain:
langchain_mode = 'UserData'
from tests.utils import make_user_path_test
user_path = make_user_path_test()
# from src.gpt_langchain import get_some_dbs_from_hf
# get_some_dbs_from_hf()
if base_model in ['h2oai/h2ogpt-oig-oasst1-512-6_9b', 'h2oai/h2ogpt-oasst1-512-12b']:
prompt_type = PromptType.human_bot.name
else:
prompt_type = PromptType.prompt_answer.name
if isinstance(pass_prompt_type, str):
prompt_type = 'custom'
prompt_dict = """{'promptA': None, 'promptB': None, 'PreInstruct': None, 'PreInput': None, 'PreResponse': None, 'terminate_response': [], 'chat_sep': '', 'chat_turn_sep': '', 'humanstr': None, 'botstr': None, 'generates_leading_space': False}"""
else:
prompt_dict = None
if not pass_prompt_type:
prompt_type = None
if do_model_lock:
model_lock = [{'inference_server': inference_server, 'base_model': base_model}]
base_model = None
inference_server = None
else:
model_lock = None
main_kwargs = dict(base_model=base_model,
prompt_type=prompt_type,
prompt_dict=prompt_dict,
chat=True,
stream_output=stream_output, gradio=True, num_beams=1, block_gradio_exit=False,
max_new_tokens=max_new_tokens,
langchain_mode=langchain_mode,
langchain_action=langchain_action,
langchain_agents=langchain_agents,
user_path=user_path,
visible_langchain_modes=visible_langchain_modes,
reverse_docs=reverse_docs,
force_langchain_evaluate=force_langchain_evaluate,
inference_server=inference_server,
model_lock=model_lock)
try:
# server that consumes inference server
client_port = os.environ['GRADIO_SERVER_PORT'] = "7861"
from src.gen import main
main(**main_kwargs)
# client test to server that only consumes inference server
from src.client_test import run_client_chat
os.environ['HOST'] = "http://127.0.0.1:%s" % client_port
res_dict, client = run_client_chat(prompt=prompt, prompt_type=prompt_type,
stream_output=stream_output,
max_new_tokens=max_new_tokens, langchain_mode=langchain_mode,
langchain_action=langchain_action,
langchain_agents=langchain_agents,
prompt_dict=prompt_dict)
assert res_dict['prompt'] == prompt
assert res_dict['iinput'] == ''
# will use HOST from above
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None) # client shouldn't have to specify
# here docker started with falcon before personalization
if isinstance(pass_prompt_type, str):
assert 'year old student from the' in ret1['response'] or 'I am a person who is asking you a question' in \
ret1['response']
assert 'bird' in ret2['response']
assert 'bird' in ret3['response']
assert 'year old student from the' in ret4['response'] or 'I am a person who is asking you a question' in \
ret4['response']
assert 'year old student from the' in ret5['response'] or 'I am a person who is asking you a question' in \
ret5['response']
assert 'year old student from the' in ret6['response'] or 'I am a person who is asking you a question' in \
ret6['response']
assert 'year old student from the' in ret7['response'] or 'I am a person who is asking you a question' in \
ret7['response']
elif base_model == 'h2oai/h2ogpt-oig-oasst1-512-6_9b':
assert 'h2oGPT' in ret1['response']
assert 'Birds' in ret2['response']
assert 'Birds' in ret3['response']
assert 'h2oGPT' in ret4['response']
assert 'h2oGPT' in ret5['response']
assert 'h2oGPT' in ret6['response']
assert 'h2oGPT' in ret7['response']
else:
assert 'I am a language model trained' in ret1['response'] or 'I am an AI language model developed by' in \
ret1['response'] or 'a chat-based assistant' in ret1['response'] or 'am a student' in ret1[
'response']
assert 'Once upon a time' in ret2['response']
assert 'Once upon a time' in ret3['response']
assert 'I am a language model trained' in ret4['response'] or 'I am an AI language model developed by' in \
ret4['response'] or 'a chat-based assistant' in ret4['response'] or 'am a student' in ret4[
'response']
assert 'I am a language model trained' in ret5['response'] or 'I am an AI language model developed by' in \
ret5['response'] or 'a chat-based assistant' in ret5['response'] or 'am a student' in ret5[
'response']
assert 'I am a language model trained' in ret6['response'] or 'I am an AI language model developed by' in \
ret6['response'] or 'a chat-based assistant' in ret6['response'] or 'am a student' in ret6[
'response']
assert 'I am a language model trained' in ret7['response'] or 'I am an AI language model developed by' in \
ret7['response'] or 'a chat-based assistant' in ret7['response'] or 'am a student' in ret7[
'response']
print("DONE", flush=True)
finally:
# take down docker server
import signal
try:
os.kill(inf_pid, signal.SIGTERM)
os.kill(inf_pid, signal.SIGKILL)
except:
pass
os.system("docker ps | grep text-generation-inference | awk '{print $1}' | xargs docker stop ")
@pytest.mark.skipif(not have_openai_key, reason="requires OpenAI key to run")
@pytest.mark.parametrize("force_langchain_evaluate", [False, True])
@wrap_test_forked
def test_openai_inference_server(force_langchain_evaluate,
prompt='Who are you?', stream_output=False, max_new_tokens=256,
base_model='gpt-3.5-turbo',
langchain_mode='Disabled',
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
user_path=None,
visible_langchain_modes=['UserData', 'MyData'],
reverse_docs=True):
if force_langchain_evaluate:
langchain_mode = 'MyData'
main_kwargs = dict(base_model=base_model, chat=True,
stream_output=stream_output, gradio=True, num_beams=1, block_gradio_exit=False,
max_new_tokens=max_new_tokens,
langchain_mode=langchain_mode,
langchain_action=langchain_action,
langchain_agents=langchain_agents,
user_path=user_path,
visible_langchain_modes=visible_langchain_modes,
reverse_docs=reverse_docs)
# server that consumes inference server
client_port = os.environ['GRADIO_SERVER_PORT'] = "7861"
from src.gen import main
main(**main_kwargs, inference_server='openai_chat')
# client test to server that only consumes inference server
from src.client_test import run_client_chat
os.environ['HOST'] = "http://127.0.0.1:%s" % client_port
res_dict, client = run_client_chat(prompt=prompt, prompt_type='openai_chat', stream_output=stream_output,
max_new_tokens=max_new_tokens, langchain_mode=langchain_mode,
langchain_action=langchain_action, langchain_agents=langchain_agents)
assert res_dict['prompt'] == prompt
assert res_dict['iinput'] == ''
# will use HOST from above
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None) # client shouldn't have to specify
assert 'I am an AI language model' in ret1['response']
assert 'Once upon a time, in a far-off land,' in ret2['response'] or 'Once upon a time' in ret2['response']
assert 'Once upon a time, in a far-off land,' in ret3['response'] or 'Once upon a time' in ret3['response']
assert 'I am an AI language model' in ret4['response']
assert 'I am an AI language model' in ret5['response']
assert 'I am an AI language model' in ret6['response']
assert 'I am an AI language model' in ret7['response']
print("DONE", flush=True)
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