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import os
import subprocess
import time
from datetime import datetime
import pytest
from src.utils import get_ngpus_vis, makedirs
from tests.utils import wrap_test_forked, get_inf_port, get_inf_server
from tests.test_langchain_units import have_openai_key, have_replicate_key
from src.client_test import run_client_many, test_client_basic_api_lean
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])
@pytest.mark.parametrize("enforce_h2ogpt_api_key", [False, True])
@pytest.mark.parametrize("enforce_h2ogpt_ui_key", [False, True])
@wrap_test_forked
def test_gradio_inference_server(base_model, force_langchain_evaluate, do_langchain,
enforce_h2ogpt_ui_key, enforce_h2ogpt_api_key,
prompt='Who are you?', stream_output=False, max_new_tokens=256,
langchain_mode='Disabled', langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
user_path=None,
langchain_modes=['UserData', 'MyData', 'LLM', 'Disabled'],
docs_ordering_type='reverse_sort'):
if enforce_h2ogpt_api_key and base_model != 'h2oai/h2ogpt-oig-oasst1-512-6_9b':
# no need for so many cases
return
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()
max_seq_len_client = None
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']:
max_seq_len_client = 2048
prompt_type = PromptType.llama2.name
elif base_model in ['gptj']:
max_seq_len_client = 2048
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,
langchain_modes=langchain_modes,
docs_ordering_type=docs_ordering_type,
force_langchain_evaluate=force_langchain_evaluate,
system_prompt='',
verbose=True)
# inference server
from src.gen import main
main(**main_kwargs)
inference_server = get_inf_server()
inf_port = get_inf_port()
# server that consumes inference server has different port
from src.gen import main
client_port = inf_port + 2 # assume will not use + 2 in testing, + 1 reserved for non-gradio inference servers
# only case when GRADIO_SERVER_PORT and HOST should appear in tests because using 2 gradio instances
os.environ['GRADIO_SERVER_PORT'] = str(client_port)
os.environ['HOST'] = "http://127.0.0.1:%s" % client_port
h2ogpt_key = 'foodoo#'
main_kwargs = main_kwargs.copy()
if enforce_h2ogpt_api_key:
main_kwargs.update(dict(enforce_h2ogpt_api_key=True, h2ogpt_api_keys=[h2ogpt_key]))
main_kwargs.update(dict(max_seq_len=max_seq_len_client))
main(**main_kwargs, inference_server=inference_server)
# client test to server that only consumes inference server
from src.client_test import run_client_chat
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
if enforce_h2ogpt_api_key:
# try without key first
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None)
assert 'Invalid Access Key' in ret1['response']
assert 'Invalid Access Key' in ret2['response']
assert 'Invalid Access Key' in ret3['response']
assert 'Invalid Access Key' in ret4['response']
assert 'Invalid Access Key' in ret5['response']
assert 'Invalid Access Key' in ret6['response']
assert 'Invalid Access Key' in ret7['response']
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None, h2ogpt_key='foo')
assert 'Invalid Access Key' in ret1['response']
assert 'Invalid Access Key' in ret2['response']
assert 'Invalid Access Key' in ret3['response']
assert 'Invalid Access Key' in ret4['response']
assert 'Invalid Access Key' in ret5['response']
assert 'Invalid Access Key' in ret6['response']
assert 'Invalid Access Key' in ret7['response']
# try normal or with key if enforcing
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None,
h2ogpt_key=h2ogpt_key) # 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 a helpful assistant' 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 a helpful assistant' 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 a helpful assistant' 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 a helpful assistant' 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 a helpful assistant' 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'] or "I'm LLaMA" 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'] or "I'm LLaMA" 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'] or "I'm LLaMA" 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'] or "I'm LLaMA" 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'] or "I'm LLaMA" 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, low_mem_mode=False, do_shared=True):
datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_")
msg = "Starting HF inference %s..." % datetime_str
print(msg, flush=True)
home_dir = os.path.expanduser('~')
makedirs(os.path.join(home_dir, '.cache/huggingface/hub'))
data_dir = '%s/.cache/huggingface/hub/' % home_dir
n_gpus = get_ngpus_vis()
cmd = ["docker"] + ['run',
'-d',
'--runtime', 'nvidia',
] + gpus_cmd() + [
'--shm-size', '1g',
'-e', 'HUGGING_FACE_HUB_TOKEN=%s' % os.environ['HUGGING_FACE_HUB_TOKEN'],
'-p', '%s:80' % inf_port,
'-v', '%s/.cache/huggingface/hub/:/data' % home_dir,
'-v', '%s:/data' % data_dir,
'ghcr.io/huggingface/text-generation-inference:0.9.3',
'--model-id', base_model,
'--max-stop-sequences', '6',
'--sharded', 'false' if n_gpus == 1 or not do_shared else 'true'
]
if n_gpus > 1 and do_shared:
cmd.extend(['--num-shard', '%s' % n_gpus])
if low_mem_mode:
cmd.extend(['--max-input-length', '1024',
'--max-total-tokens', '2048',
# '--cuda-memory-fraction', '0.3', # for 0.9.4, but too memory hungry
])
else:
cmd.extend(['--max-input-length', '4096',
'--max-total-tokens', '8192',
# '--cuda-memory-fraction', '0.8', # for 0.9.4, but too memory hungry
])
print(cmd, flush=True)
docker_hash = subprocess.check_output(cmd).decode().strip()
import time
connected = False
while not connected:
cmd = 'docker logs %s' % docker_hash
o = subprocess.check_output(cmd, shell=True, timeout=15)
connected = 'Connected' in o.decode("utf-8")
time.sleep(5)
print("Done starting TGI server: %s" % docker_hash, flush=True)
return docker_hash
def gpus_cmd():
n_gpus = get_ngpus_vis()
if n_gpus == 1:
return ['--gpus', 'device=%d' % int(os.getenv('CUDA_VISIBLE_DEVICES', '0'))]
elif n_gpus > 2:
# note below if joined loses ' needed
return ['--gpus', '\"device=%s\"' % os.getenv('CUDA_VISIBLE_DEVICES',
str(list(range(0, n_gpus))).replace(']', '').replace('[',
'').replace(
' ', '')
)]
def run_vllm_docker(inf_port, base_model, tokenizer=None):
if base_model == 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2':
# 7b has 71 heads, not divisible
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
os.system("docker pull gcr.io/vorvan/h2oai/h2ogpt-runtime:0.1.0")
datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_")
msg = "Starting vLLM inference %s..." % datetime_str
print(msg, flush=True)
home_dir = os.path.expanduser('~')
makedirs(os.path.join(home_dir, '.cache/huggingface/hub'))
n_gpus = get_ngpus_vis()
cmd = ["docker"] + ['run',
'-d',
'--runtime', 'nvidia',
] + gpus_cmd() + [
'--shm-size', '10.24g',
'-e', 'HUGGING_FACE_HUB_TOKEN=%s' % os.environ['HUGGING_FACE_HUB_TOKEN'],
'-p', '%s:5000' % inf_port,
'--entrypoint', '/h2ogpt_conda/vllm_env/bin/python3.10',
'-e', 'NCCL_IGNORE_DISABLED_P2P=1',
'-v', '/etc/passwd:/etc/passwd:ro',
'-v', '/etc/group:/etc/group:ro',
'-u', '%s:%s' % (os.getuid(), os.getgid()),
'-v', '%s/.cache:/workspace/.cache' % home_dir,
# '--network', 'host',
'gcr.io/vorvan/h2oai/h2ogpt-runtime:0.1.0',
# 'h2ogpt', # use when built locally with vLLM just freshly added
# 'docker.io/library/h2ogpt', # use when built locally with vLLM just freshly added
'-m', 'vllm.entrypoints.openai.api_server',
'--port=5000',
'--host=0.0.0.0',
'--model=%s' % base_model,
'--tensor-parallel-size=%s' % n_gpus,
'--seed', '1234',
'--trust-remote-code',
'--download-dir=/workspace/.cache/huggingface/hub',
]
os.environ.pop('CUDA_VISIBLE_DEVICES', None)
if tokenizer:
cmd.append('--tokenizer=%s' % tokenizer)
print(cmd, flush=True)
print(' '.join(cmd), flush=True)
docker_hash = subprocess.check_output(cmd).decode().strip()
import time
connected = False
while not connected:
cmd = 'docker logs %s' % docker_hash
o = subprocess.check_output(cmd, shell=True, timeout=15)
connected = 'Uvicorn running on' in o.decode("utf-8")
# somehow above message doesn't come up
connected |= 'GPU blocks' in o.decode("utf-8")
time.sleep(5)
print("Done starting vLLM server: %s" % docker_hash, flush=True)
return docker_hash
def run_h2ogpt_docker(port, base_model, inference_server=None, max_new_tokens=None):
os.system("docker pull gcr.io/vorvan/h2oai/h2ogpt-runtime:0.1.0")
datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_")
msg = "Starting h2oGPT %s..." % datetime_str
print(msg, flush=True)
home_dir = os.path.expanduser('~')
makedirs(os.path.join(home_dir, '.cache/huggingface/hub'))
makedirs(os.path.join(home_dir, 'save'))
cmd = ["docker"] + ['run',
'-d',
'--runtime', 'nvidia',
] + gpus_cmd() + [
'--shm-size', '1g',
'-p', '%s:7860' % port,
'-v', '%s/.cache:/workspace/.cache/' % home_dir,
'-v', '%s/save:/workspace/save' % home_dir,
'-v', '/etc/passwd:/etc/passwd:ro',
'-v', '/etc/group:/etc/group:ro',
'-u', '%s:%s' % (os.getuid(), os.getgid()),
'-e', 'HUGGING_FACE_HUB_TOKEN=%s' % os.environ['HUGGING_FACE_HUB_TOKEN'],
'--network', 'host',
'gcr.io/vorvan/h2oai/h2ogpt-runtime:0.1.0',
# 'h2ogpt', # use when built locally with vLLM just freshly added
'/workspace/generate.py',
'--base_model=%s' % base_model,
'--use_safetensors=True',
'--save_dir=/workspace/save/',
'--score_model=None',
'--max_max_new_tokens=%s' % (max_new_tokens or 2048),
'--max_new_tokens=%s' % (max_new_tokens or 1024),
'--num_async=10',
'--num_beams=1',
'--top_k_docs=-1',
'--chat=True',
'--stream_output=True',
# '--debug=True',
]
if inference_server:
cmd.extend(['--inference_server=%s' % inference_server])
print(cmd, flush=True)
docker_hash = subprocess.check_output(cmd).decode().strip()
print("Done starting h2oGPT server: %s" % docker_hash, flush=True)
return docker_hash
@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,
langchain_modes=['UserData', 'MyData', 'LLM', 'Disabled'],
docs_ordering_type='reverse_sort'):
# HF inference server
gradio_port = get_inf_port()
inf_port = gradio_port + 1
inference_server = 'http://127.0.0.1:%s' % inf_port
docker_hash = run_docker(inf_port, base_model, low_mem_mode=True, do_shared=False)
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,
system_prompt='',
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,
langchain_modes=langchain_modes,
docs_ordering_type=docs_ordering_type,
force_langchain_evaluate=force_langchain_evaluate,
inference_server=inference_server,
model_lock=model_lock)
try:
# server that consumes inference server
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
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'] or 'year old' 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'] or 'year old' 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'] or 'year old' 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'] or 'year old' 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'] or 'year old' 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 a helpful assistant' in \
ret1['response'] or 'a chat-based assistant' in ret1['response'] or 'am a student' in ret1[
'response'] or 'I am an AI language model' 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 a helpful assistant' in \
ret4['response'] or 'a chat-based assistant' in ret4['response'] or 'am a student' in ret4[
'response'] or 'I am an AI language model' in ret4['response']
assert 'I am a language model trained' in ret5['response'] or 'I am a helpful assistant' in \
ret5['response'] or 'a chat-based assistant' in ret5['response'] or 'am a student' in ret5[
'response'] or 'I am an AI language model' in ret5['response']
assert 'I am a language model trained' in ret6['response'] or 'I am a helpful assistant' in \
ret6['response'] or 'a chat-based assistant' in ret6['response'] or 'am a student' in ret6[
'response'] or 'I am an AI language model' in ret6['response']
assert 'I am a language model trained' in ret7['response'] or 'I am a helpful assistant' in \
ret7['response'] or 'a chat-based assistant' in ret7['response'] or 'am a student' in ret7[
'response'] or 'I am an AI language model' in ret7['response']
print("DONE", flush=True)
finally:
os.system("docker stop %s" % docker_hash)
chat_conversation1 = [['Who are you?',
'I am an AI language model created by OpenAI, designed to assist with various tasks such as answering questions, generating text, and providing information.']]
@pytest.mark.skipif(not have_openai_key, reason="requires OpenAI key to run")
@pytest.mark.parametrize("system_prompt", ['You are a baby cat who likes to talk to people.', ''])
@pytest.mark.parametrize("chat_conversation", [chat_conversation1, []])
@pytest.mark.parametrize("force_langchain_evaluate", [False, True])
@pytest.mark.parametrize("inference_server", ['openai_chat', 'openai_azure_chat'])
@wrap_test_forked
def test_openai_inference_server(inference_server, force_langchain_evaluate, chat_conversation,
system_prompt,
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,
langchain_modes=['UserData', 'MyData', 'LLM', 'Disabled'],
docs_ordering_type='reverse_sort'):
if force_langchain_evaluate:
langchain_mode = 'MyData'
if inference_server == 'openai_azure_chat':
# need at least deployment name added:
deployment_name = 'h2ogpt'
inference_server += ':%s:%s' % (deployment_name, 'h2ogpt.openai.azure.com/')
if 'azure' in inference_server:
assert 'OPENAI_AZURE_KEY' in os.environ, "Missing 'OPENAI_AZURE_KEY'"
os.environ['OPENAI_API_KEY'] = os.environ['OPENAI_AZURE_KEY']
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,
langchain_modes=langchain_modes,
system_prompt='auto',
docs_ordering_type=docs_ordering_type,
# chat_conversation=chat_conversation # not enough if API passes [], API will override
)
# server that consumes inference server
from src.gen import main
main(**main_kwargs, inference_server=inference_server)
if chat_conversation:
prompt = 'What did I ask?'
# client test to server that only consumes inference server
from src.client_test import run_client_chat
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,
chat_conversation=chat_conversation,
system_prompt=system_prompt)
assert res_dict['prompt'] == prompt
assert res_dict['iinput'] == ''
if chat_conversation and system_prompt:
# TODO: don't check yet, system_prompt ignored if response from LLM is as if no system prompt
return
if chat_conversation or system_prompt:
ret6, _ = test_client_basic_api_lean(prompt=prompt, prompt_type=None,
chat_conversation=chat_conversation,
system_prompt=system_prompt)
if system_prompt:
assert 'baby cat' in res_dict['response'] and 'meow' in res_dict['response'].lower()
assert 'baby cat' in ret6['response'] and 'meow' in ret6['response'].lower()
else:
options_response = ['You asked "Who are you?"', """You asked, \"Who are you?\""""]
assert res_dict['response'] in options_response
assert ret6['response'] in options_response
return
if system_prompt:
# don't test rest, too many cases
return
# 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'] or 'I am a helpful assistant designed' in ret1[
'response'] or 'I am an AI assistant designed to help answer questions and provide information' 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'] or 'I am a helpful assistant designed' in ret4[
'response'] or 'I am an AI assistant designed to help answer questions and provide information' in ret4[
'response']
assert 'I am an AI language model' in ret5['response'] or 'I am a helpful assistant designed' in ret5[
'response'] or 'I am an AI assistant designed to help answer questions and provide information' in ret5[
'response']
assert 'I am an AI language model' in ret6['response'] or 'I am a helpful assistant designed' in ret6[
'response'] or 'I am an AI assistant designed to help answer questions and provide information' in ret6[
'response']
assert 'I am an AI language model' in ret7['response'] or 'I am a helpful assistant designed' in ret7[
'response'] or 'I am an AI assistant designed to help answer questions and provide information' in ret7[
'response']
print("DONE", flush=True)
@pytest.mark.parametrize("base_model",
['h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2', 'meta-llama/Llama-2-7b-chat-hf']
)
@wrap_test_forked
def test_gradio_tgi_docker(base_model):
# HF inference server
gradio_port = get_inf_port()
inf_port = gradio_port + 1
inference_server = 'http://127.0.0.1:%s' % inf_port
docker_hash1 = run_docker(inf_port, base_model, low_mem_mode=True, do_shared=False)
os.system('docker logs %s | tail -10' % docker_hash1)
# h2oGPT server
docker_hash2 = run_h2ogpt_docker(gradio_port, base_model, inference_server=inference_server)
time.sleep(30) # assumes image already downloaded, else need more time
os.system('docker logs %s | tail -10' % docker_hash2)
# test this version for now, until docker updated
version = 1
try:
# client test to server that only consumes inference server
prompt = 'Who are you?'
print("Starting client tests with prompt: %s using %s" % (prompt, get_inf_server()))
from src.client_test import run_client_chat
res_dict, client = run_client_chat(prompt=prompt,
stream_output=True,
max_new_tokens=256,
langchain_mode='Disabled',
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
version=version)
assert res_dict['prompt'] == prompt
assert res_dict['iinput'] == ''
# will use HOST from above
# client shouldn't have to specify
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None, version=version)
if 'llama' in base_model.lower():
who = "I'm LLaMA, an AI assistant developed by Meta AI"
assert who in ret1['response']
assert who in ret1['response']
assert 'Once upon a time' in ret2['response']
assert 'Once upon a time' in ret3['response']
assert who in ret4['response']
assert who in ret5['response']
assert who in ret6['response']
assert who in ret7['response']
else:
who = 'I am an AI language model'
assert who in ret1['response']
assert 'Once upon a time' in ret2['response']
assert 'Once upon a time' in ret3['response']
assert who in ret4['response']
assert who in ret5['response']
assert who in ret6['response']
assert who in ret7['response']
print("DONE", flush=True)
finally:
os.system("docker stop %s" % docker_hash1)
os.system("docker stop %s" % docker_hash2)
@pytest.mark.parametrize("base_model",
[
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2',
'h2oai/h2ogpt-4096-llama2-7b-chat'] # avoid meta to avoid hassle of key
)
@wrap_test_forked
def test_gradio_vllm_docker(base_model):
# HF inference server
gradio_port = get_inf_port()
inf_port = gradio_port + 1
inference_server = 'vllm:127.0.0.1:%s' % inf_port
if 'llama' in base_model:
tokenizer = 'hf-internal-testing/llama-tokenizer'
else:
tokenizer = None
docker_hash1 = run_vllm_docker(inf_port, base_model, tokenizer)
os.system('docker logs %s | tail -10' % docker_hash1)
# h2oGPT server
docker_hash2 = run_h2ogpt_docker(gradio_port, base_model, inference_server=inference_server)
time.sleep(30) # assumes image already downloaded, else need more time
os.system('docker logs %s | tail -10' % docker_hash2)
# test this version for now, until docker updated
version = 1
try:
# client test to server that only consumes inference server
prompt = 'Who are you?'
print("Starting client tests with prompt: %s using %s" % (prompt, get_inf_server()))
from src.client_test import run_client_chat
res_dict, client = run_client_chat(prompt=prompt,
stream_output=True,
max_new_tokens=256,
langchain_mode='Disabled',
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
version=version)
assert res_dict['prompt'] == prompt
assert res_dict['iinput'] == ''
# will use HOST from above
# client shouldn't have to specify
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None, version=version)
if 'llama' in base_model.lower():
who = "I'm LLaMA, an AI assistant developed by Meta AI"
assert who in ret1['response']
assert who in ret1['response']
assert 'Once upon a time' in ret2['response']
assert 'Once upon a time' in ret3['response']
assert who in ret4['response']
assert who in ret5['response']
assert who in ret6['response']
assert who in ret7['response']
else:
who = 'I am an AI language model'
assert who in ret1['response']
assert 'Once upon a time' in ret2['response']
assert 'Once upon a time' in ret3['response']
assert who in ret4['response']
assert who in ret5['response']
assert who in ret6['response']
assert who in ret7['response']
print("DONE", flush=True)
finally:
os.system("docker stop %s" % docker_hash1)
os.system("docker stop %s" % docker_hash2)
@pytest.mark.skipif(not have_replicate_key, reason="requires Replicate key to run")
@pytest.mark.parametrize("system_prompt", ['You are a baby cat who likes to talk to people.', ''])
@pytest.mark.parametrize("chat_conversation", [chat_conversation1, []])
@pytest.mark.parametrize("force_langchain_evaluate", [False, True])
@wrap_test_forked
def test_replicate_inference_server(force_langchain_evaluate,
chat_conversation,
system_prompt,
prompt='Who are you?', stream_output=False,
max_new_tokens=128, # limit cost
base_model='h2oai/h2ogpt-4096-llama2-7b-chat',
langchain_mode='Disabled',
langchain_action=LangChainAction.QUERY.value,
langchain_agents=[],
user_path=None,
langchain_modes=['UserData', 'MyData', 'LLM', 'Disabled'],
docs_ordering_type='reverse_sort'):
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,
langchain_modes=langchain_modes,
docs_ordering_type=docs_ordering_type)
# server that consumes inference server
from src.gen import main
# https://replicate.com/lucataco/llama-2-7b-chat
#model_string = "lucataco/llama-2-7b-chat:6ab580ab4eef2c2b440f2441ec0fc0ace5470edaf2cbea50b8550aec0b3fbd38"
model_string = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e"
main(**main_kwargs, inference_server='replicate:%s' % model_string)
if chat_conversation:
prompt = 'What did I ask?'
# client test to server that only consumes inference server
from src.client_test import run_client_chat
res_dict, client = run_client_chat(prompt=prompt, prompt_type='llama2', stream_output=stream_output,
max_new_tokens=max_new_tokens, langchain_mode=langchain_mode,
langchain_action=langchain_action, langchain_agents=langchain_agents,
chat_conversation=chat_conversation,
system_prompt=system_prompt)
assert res_dict['prompt'] == prompt
assert res_dict['iinput'] == ''
if chat_conversation and system_prompt:
# TODO: don't check yet, system_prompt ignored if response from LLM is as if no system prompt
return
if chat_conversation or system_prompt:
ret6, _ = test_client_basic_api_lean(prompt=prompt, prompt_type=None,
chat_conversation=chat_conversation,
system_prompt=system_prompt)
if system_prompt:
assert 'baby cat' in res_dict['response'] and 'meow' in res_dict['response'].lower()
assert 'baby cat' in ret6['response'] and 'meow' in ret6['response'].lower()
else:
options_response = ['You asked "Who are you?"',
"""You asked, \"Who are you?\"""",
"""You asked: \"Who are you?\"""",
]
assert res_dict['response'] in options_response
assert ret6['response'] in options_response
return
if system_prompt:
# don't test rest, too many cases
return
# will use HOST from above
ret1, ret2, ret3, ret4, ret5, ret6, ret7 = run_client_many(prompt_type=None) # client shouldn't have to specify
who = 'an AI assistant'
who2 = 'just an AI'
assert who in ret1['response'] or who2 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 who in ret4['response'] or 'I am a helpful assistant designed' in ret4['response'] or who2 in ret4['response']
assert who in ret5['response'] or 'I am a helpful assistant designed' in ret5['response'] or who2 in ret5['response']
assert who in ret6['response'] or 'I am a helpful assistant designed' in ret6['response'] or who2 in ret6['response']
assert who in ret7['response'] or 'I am a helpful assistant designed' in ret7['response'] or who2 in ret7['response']
print("DONE", flush=True)
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