Spaces:
Runtime error
Runtime error
| """ | |
| Client test. | |
| Run server: | |
| python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b | |
| NOTE: For private models, add --use-auth_token=True | |
| NOTE: --use_gpu_id=True (default) must be used for multi-GPU in case see failures with cuda:x cuda:y mismatches. | |
| Currently, this will force model to be on a single GPU. | |
| Then run this client as: | |
| python src/client_test.py | |
| For HF spaces: | |
| HOST="https://h2oai-h2ogpt-chatbot.hf.space" python src/client_test.py | |
| Result: | |
| Loaded as API: https://h2oai-h2ogpt-chatbot.hf.space ✔ | |
| {'instruction_nochat': 'Who are you?', 'iinput_nochat': '', 'response': 'I am h2oGPT, a large language model developed by LAION.', 'sources': ''} | |
| For demo: | |
| HOST="https://gpt.h2o.ai" python src/client_test.py | |
| Result: | |
| Loaded as API: https://gpt.h2o.ai ✔ | |
| {'instruction_nochat': 'Who are you?', 'iinput_nochat': '', 'response': 'I am h2oGPT, a chatbot created by LAION.', 'sources': ''} | |
| NOTE: Raw output from API for nochat case is a string of a python dict and will remain so if other entries are added to dict: | |
| {'response': "I'm h2oGPT, a large language model by H2O.ai, the visionary leader in democratizing AI.", 'sources': ''} | |
| """ | |
| import ast | |
| import time | |
| import os | |
| import markdown # pip install markdown | |
| import pytest | |
| from bs4 import BeautifulSoup # pip install beautifulsoup4 | |
| from enums import DocumentSubset, LangChainAction | |
| debug = False | |
| os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' | |
| def get_client(serialize=True): | |
| from gradio_client import Client | |
| client = Client(os.getenv('HOST', "http://localhost:7860"), serialize=serialize) | |
| if debug: | |
| print(client.view_api(all_endpoints=True)) | |
| return client | |
| def get_args(prompt, prompt_type, chat=False, stream_output=False, | |
| max_new_tokens=50, | |
| top_k_docs=3, | |
| langchain_mode='Disabled', | |
| add_chat_history_to_context=True, | |
| langchain_action=LangChainAction.QUERY.value, | |
| langchain_agents=[], | |
| prompt_dict=None): | |
| from collections import OrderedDict | |
| kwargs = OrderedDict(instruction=prompt if chat else '', # only for chat=True | |
| iinput='', # only for chat=True | |
| context='', | |
| # streaming output is supported, loops over and outputs each generation in streaming mode | |
| # but leave stream_output=False for simple input/output mode | |
| stream_output=stream_output, | |
| prompt_type=prompt_type, | |
| prompt_dict=prompt_dict, | |
| temperature=0.1, | |
| top_p=0.75, | |
| top_k=40, | |
| num_beams=1, | |
| max_new_tokens=max_new_tokens, | |
| min_new_tokens=0, | |
| early_stopping=False, | |
| max_time=20, | |
| repetition_penalty=1.0, | |
| num_return_sequences=1, | |
| do_sample=True, | |
| chat=chat, | |
| instruction_nochat=prompt if not chat else '', | |
| iinput_nochat='', # only for chat=False | |
| langchain_mode=langchain_mode, | |
| add_chat_history_to_context=add_chat_history_to_context, | |
| langchain_action=langchain_action, | |
| langchain_agents=langchain_agents, | |
| top_k_docs=top_k_docs, | |
| chunk=True, | |
| chunk_size=512, | |
| document_subset=DocumentSubset.Relevant.name, | |
| document_choice=[], | |
| ) | |
| from evaluate_params import eval_func_param_names | |
| assert len(set(eval_func_param_names).difference(set(list(kwargs.keys())))) == 0 | |
| if chat: | |
| # add chatbot output on end. Assumes serialize=False | |
| kwargs.update(dict(chatbot=[])) | |
| return kwargs, list(kwargs.values()) | |
| def test_client_basic(prompt_type='human_bot'): | |
| return run_client_nochat(prompt='Who are you?', prompt_type=prompt_type, max_new_tokens=50) | |
| def run_client_nochat(prompt, prompt_type, max_new_tokens): | |
| kwargs, args = get_args(prompt, prompt_type, chat=False, max_new_tokens=max_new_tokens) | |
| api_name = '/submit_nochat' | |
| client = get_client(serialize=True) | |
| res = client.predict( | |
| *tuple(args), | |
| api_name=api_name, | |
| ) | |
| print("Raw client result: %s" % res, flush=True) | |
| res_dict = dict(prompt=kwargs['instruction_nochat'], iinput=kwargs['iinput_nochat'], | |
| response=md_to_text(res)) | |
| print(res_dict) | |
| return res_dict, client | |
| def test_client_basic_api(prompt_type='human_bot'): | |
| return run_client_nochat_api(prompt='Who are you?', prompt_type=prompt_type, max_new_tokens=50) | |
| def run_client_nochat_api(prompt, prompt_type, max_new_tokens): | |
| kwargs, args = get_args(prompt, prompt_type, chat=False, max_new_tokens=max_new_tokens) | |
| api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing | |
| client = get_client(serialize=True) | |
| res = client.predict( | |
| str(dict(kwargs)), | |
| api_name=api_name, | |
| ) | |
| print("Raw client result: %s" % res, flush=True) | |
| res_dict = dict(prompt=kwargs['instruction_nochat'], iinput=kwargs['iinput_nochat'], | |
| response=md_to_text(ast.literal_eval(res)['response']), | |
| sources=ast.literal_eval(res)['sources']) | |
| print(res_dict) | |
| return res_dict, client | |
| def test_client_basic_api_lean(prompt_type='human_bot'): | |
| return run_client_nochat_api_lean(prompt='Who are you?', prompt_type=prompt_type, max_new_tokens=50) | |
| def run_client_nochat_api_lean(prompt, prompt_type, max_new_tokens): | |
| kwargs = dict(instruction_nochat=prompt) | |
| api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing | |
| client = get_client(serialize=True) | |
| res = client.predict( | |
| str(dict(kwargs)), | |
| api_name=api_name, | |
| ) | |
| print("Raw client result: %s" % res, flush=True) | |
| res_dict = dict(prompt=kwargs['instruction_nochat'], | |
| response=md_to_text(ast.literal_eval(res)['response']), | |
| sources=ast.literal_eval(res)['sources']) | |
| print(res_dict) | |
| return res_dict, client | |
| def test_client_basic_api_lean_morestuff(prompt_type='human_bot'): | |
| return run_client_nochat_api_lean_morestuff(prompt='Who are you?', prompt_type=prompt_type, max_new_tokens=50) | |
| def run_client_nochat_api_lean_morestuff(prompt, prompt_type='human_bot', max_new_tokens=512): | |
| kwargs = dict( | |
| instruction='', | |
| iinput='', | |
| context='', | |
| stream_output=False, | |
| prompt_type=prompt_type, | |
| temperature=0.1, | |
| top_p=0.75, | |
| top_k=40, | |
| num_beams=1, | |
| max_new_tokens=256, | |
| min_new_tokens=0, | |
| early_stopping=False, | |
| max_time=20, | |
| repetition_penalty=1.0, | |
| num_return_sequences=1, | |
| do_sample=True, | |
| chat=False, | |
| instruction_nochat=prompt, | |
| iinput_nochat='', | |
| langchain_mode='Disabled', | |
| add_chat_history_to_context=True, | |
| langchain_action=LangChainAction.QUERY.value, | |
| langchain_agents=[], | |
| top_k_docs=4, | |
| document_subset=DocumentSubset.Relevant.name, | |
| document_choice=[], | |
| ) | |
| api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing | |
| client = get_client(serialize=True) | |
| res = client.predict( | |
| str(dict(kwargs)), | |
| api_name=api_name, | |
| ) | |
| print("Raw client result: %s" % res, flush=True) | |
| res_dict = dict(prompt=kwargs['instruction_nochat'], | |
| response=md_to_text(ast.literal_eval(res)['response']), | |
| sources=ast.literal_eval(res)['sources']) | |
| print(res_dict) | |
| return res_dict, client | |
| def test_client_chat(prompt_type='human_bot'): | |
| return run_client_chat(prompt='Who are you?', prompt_type=prompt_type, stream_output=False, max_new_tokens=50, | |
| langchain_mode='Disabled', | |
| langchain_action=LangChainAction.QUERY.value, | |
| langchain_agents=[]) | |
| def test_client_chat_stream(prompt_type='human_bot'): | |
| return run_client_chat(prompt="Tell a very long kid's story about birds.", prompt_type=prompt_type, | |
| stream_output=True, max_new_tokens=512, | |
| langchain_mode='Disabled', | |
| langchain_action=LangChainAction.QUERY.value, | |
| langchain_agents=[]) | |
| def run_client_chat(prompt, prompt_type, stream_output, max_new_tokens, | |
| langchain_mode, langchain_action, langchain_agents, | |
| prompt_dict=None): | |
| client = get_client(serialize=False) | |
| kwargs, args = get_args(prompt, prompt_type, chat=True, 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) | |
| return run_client(client, prompt, args, kwargs) | |
| def run_client(client, prompt, args, kwargs, do_md_to_text=True, verbose=False): | |
| assert kwargs['chat'], "Chat mode only" | |
| res = client.predict(*tuple(args), api_name='/instruction') | |
| args[-1] += [res[-1]] | |
| res_dict = kwargs | |
| res_dict['prompt'] = prompt | |
| if not kwargs['stream_output']: | |
| res = client.predict(*tuple(args), api_name='/instruction_bot') | |
| res_dict['response'] = res[0][-1][1] | |
| print(md_to_text(res_dict['response'], do_md_to_text=do_md_to_text)) | |
| return res_dict, client | |
| else: | |
| job = client.submit(*tuple(args), api_name='/instruction_bot') | |
| res1 = '' | |
| while not job.done(): | |
| outputs_list = job.communicator.job.outputs | |
| if outputs_list: | |
| res = job.communicator.job.outputs[-1] | |
| res1 = res[0][-1][-1] | |
| res1 = md_to_text(res1, do_md_to_text=do_md_to_text) | |
| print(res1) | |
| time.sleep(0.1) | |
| full_outputs = job.outputs() | |
| if verbose: | |
| print('job.outputs: %s' % str(full_outputs)) | |
| # ensure get ending to avoid race | |
| # -1 means last response if streaming | |
| # 0 means get text_output, ignore exception_text | |
| # 0 means get list within text_output that looks like [[prompt], [answer]] | |
| # 1 means get bot answer, so will have last bot answer | |
| res_dict['response'] = md_to_text(full_outputs[-1][0][0][1], do_md_to_text=do_md_to_text) | |
| return res_dict, client | |
| def test_client_nochat_stream(prompt_type='human_bot'): | |
| return run_client_nochat_gen(prompt="Tell a very long kid's story about birds.", prompt_type=prompt_type, | |
| stream_output=True, max_new_tokens=512, | |
| langchain_mode='Disabled', | |
| langchain_action=LangChainAction.QUERY.value, | |
| langchain_agents=[]) | |
| def run_client_nochat_gen(prompt, prompt_type, stream_output, max_new_tokens, | |
| langchain_mode, langchain_action, langchain_agents): | |
| client = get_client(serialize=False) | |
| kwargs, args = get_args(prompt, prompt_type, chat=False, stream_output=stream_output, | |
| max_new_tokens=max_new_tokens, langchain_mode=langchain_mode, | |
| langchain_action=langchain_action, langchain_agents=langchain_agents) | |
| return run_client_gen(client, prompt, args, kwargs) | |
| def run_client_gen(client, prompt, args, kwargs, do_md_to_text=True, verbose=False): | |
| res_dict = kwargs | |
| res_dict['prompt'] = prompt | |
| if not kwargs['stream_output']: | |
| res = client.predict(str(dict(kwargs)), api_name='/submit_nochat_api') | |
| res_dict['response'] = res[0] | |
| print(md_to_text(res_dict['response'], do_md_to_text=do_md_to_text)) | |
| return res_dict, client | |
| else: | |
| job = client.submit(str(dict(kwargs)), api_name='/submit_nochat_api') | |
| while not job.done(): | |
| outputs_list = job.communicator.job.outputs | |
| if outputs_list: | |
| res = job.communicator.job.outputs[-1] | |
| res_dict = ast.literal_eval(res) | |
| print('Stream: %s' % res_dict['response']) | |
| time.sleep(0.1) | |
| res_list = job.outputs() | |
| assert len(res_list) > 0, "No response, check server" | |
| res = res_list[-1] | |
| res_dict = ast.literal_eval(res) | |
| print('Final: %s' % res_dict['response']) | |
| return res_dict, client | |
| def md_to_text(md, do_md_to_text=True): | |
| if not do_md_to_text: | |
| return md | |
| assert md is not None, "Markdown is None" | |
| html = markdown.markdown(md) | |
| soup = BeautifulSoup(html, features='html.parser') | |
| return soup.get_text() | |
| def run_client_many(prompt_type='human_bot'): | |
| ret1, _ = test_client_chat(prompt_type=prompt_type) | |
| ret2, _ = test_client_chat_stream(prompt_type=prompt_type) | |
| ret3, _ = test_client_nochat_stream(prompt_type=prompt_type) | |
| ret4, _ = test_client_basic(prompt_type=prompt_type) | |
| ret5, _ = test_client_basic_api(prompt_type=prompt_type) | |
| ret6, _ = test_client_basic_api_lean(prompt_type=prompt_type) | |
| ret7, _ = test_client_basic_api_lean_morestuff(prompt_type=prompt_type) | |
| return ret1, ret2, ret3, ret4, ret5, ret6, ret7 | |
| if __name__ == '__main__': | |
| run_client_many() | |