import copy import torch from evaluate_params import eval_func_param_names, input_args_list from gen import get_score_model, get_model, evaluate, check_locals, get_model_retry from prompter import non_hf_types from utils import clear_torch_cache, NullContext, get_kwargs def run_cli( # for local function: base_model=None, lora_weights=None, inference_server=None, regenerate_clients=None, debug=None, examples=None, memory_restriction_level=None, # for get_model: score_model=None, load_8bit=None, load_4bit=None, low_bit_mode=None, load_half=None, use_flash_attention_2=None, load_gptq=None, use_autogptq=None, load_awq=None, load_exllama=None, use_safetensors=None, revision=None, use_gpu_id=None, tokenizer_base_model=None, gpu_id=None, n_jobs=None, n_gpus=None, local_files_only=None, resume_download=None, use_auth_token=None, trust_remote_code=None, offload_folder=None, rope_scaling=None, max_seq_len=None, compile_model=None, llamacpp_dict=None, llamacpp_path=None, exllama_dict=None, gptq_dict=None, attention_sinks=None, sink_dict=None, hf_model_dict=None, truncation_generation=None, use_pymupdf=None, use_unstructured_pdf=None, use_pypdf=None, enable_pdf_ocr=None, enable_pdf_doctr=None, enable_imagegen_high_sd=None, try_pdf_as_html=None, # for some evaluate args stream_output=None, async_output=None, num_async=None, prompt_type=None, prompt_dict=None, system_prompt=None, temperature=None, top_p=None, top_k=None, penalty_alpha=None, num_beams=None, max_new_tokens=None, min_new_tokens=None, early_stopping=None, max_time=None, repetition_penalty=None, num_return_sequences=None, do_sample=None, chat=None, langchain_mode=None, langchain_action=None, langchain_agents=None, document_subset=None, document_choice=None, document_source_substrings=None, document_source_substrings_op=None, document_content_substrings=None, document_content_substrings_op=None, top_k_docs=None, chunk=None, chunk_size=None, pre_prompt_query=None, prompt_query=None, pre_prompt_summary=None, prompt_summary=None, hyde_llm_prompt=None, image_audio_loaders=None, pdf_loaders=None, url_loaders=None, jq_schema=None, extract_frames=None, llava_prompt=None, visible_models=None, h2ogpt_key=None, add_search_to_context=None, chat_conversation=None, text_context_list=None, docs_ordering_type=None, min_max_new_tokens=None, max_input_tokens=None, max_total_input_tokens=None, docs_token_handling=None, docs_joiner=None, hyde_level=None, hyde_template=None, hyde_show_only_final=None, hyde_show_intermediate_in_accordion=None, doc_json_mode=None, chatbot_role=None, speaker=None, tts_language=None, tts_speed=None, # for evaluate kwargs captions_model=None, caption_loader=None, doctr_loader=None, pix2struct_loader=None, llava_model=None, image_gen_loader=None, image_gen_loader_high=None, image_change_loader=None, asr_model=None, asr_loader=None, image_audio_loaders_options0=None, pdf_loaders_options0=None, url_loaders_options0=None, jq_schema0=None, keep_sources_in_context=None, gradio_errors_to_chatbot=None, allow_chat_system_prompt=None, src_lang=None, tgt_lang=None, concurrency_count=None, save_dir=None, sanitize_bot_response=None, model_state0=None, max_max_new_tokens=None, is_public=None, max_max_time=None, raise_generate_gpu_exceptions=None, load_db_if_exists=None, use_llm_if_no_docs=None, my_db_state0=None, selection_docs_state0=None, dbs=None, langchain_modes=None, langchain_mode_paths=None, detect_user_path_changes_every_query=None, use_openai_embedding=None, use_openai_model=None, hf_embedding_model=None, migrate_embedding_model=None, auto_migrate_db=None, cut_distance=None, answer_with_sources=None, append_sources_to_answer=None, append_sources_to_chat=None, show_accordions=None, top_k_docs_max_show=None, show_link_in_sources=None, langchain_instruct_mode=None, add_chat_history_to_context=None, context=None, iinput=None, db_type=None, first_para=None, text_limit=None, verbose=None, gradio=None, cli=None, use_cache=None, auto_reduce_chunks=None, max_chunks=None, headsize=None, model_lock=None, force_langchain_evaluate=None, model_state_none=None, # unique to this function: cli_loop=None, ): # avoid noisy command line outputs import warnings warnings.filterwarnings("ignore") import logging logging.getLogger("torch").setLevel(logging.ERROR) logging.getLogger("transformers").setLevel(logging.ERROR) from_ui = False check_locals(**locals()) score_model = "" # FIXME: For now, so user doesn't have to pass n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0 device = 'cpu' if n_gpus == 0 else 'cuda' context_class = NullContext if n_gpus > 1 or n_gpus == 0 else torch.device with context_class(device): from functools import partial # get score model smodel, stokenizer, sdevice = get_score_model(reward_type=True, **get_kwargs(get_score_model, exclude_names=['reward_type'], **locals())) model, tokenizer, device = get_model_retry(reward_type=False, **get_kwargs(get_model, exclude_names=['reward_type'], **locals())) model_dict = dict(base_model=base_model, tokenizer_base_model=tokenizer_base_model, lora_weights=lora_weights, inference_server=inference_server, prompt_type=prompt_type, prompt_dict=prompt_dict, visible_models=None, h2ogpt_key=None) model_state = dict(model=model, tokenizer=tokenizer, device=device) model_state.update(model_dict) requests_state0 = {} roles_state0 = None args = (model_state, my_db_state0, selection_docs_state0, requests_state0, roles_state0) assert len(args) == len(input_args_list) fun = partial(evaluate, *args, **get_kwargs(evaluate, exclude_names=input_args_list + eval_func_param_names, **locals())) example1 = examples[-1] # pick reference example all_generations = [] if not context: context = '' while True: clear_torch_cache(allow_skip=True) instruction = input("\nEnter an instruction: ") if instruction == "exit": break eval_vars = copy.deepcopy(example1) eval_vars[eval_func_param_names.index('instruction')] = \ eval_vars[eval_func_param_names.index('instruction_nochat')] = instruction eval_vars[eval_func_param_names.index('iinput')] = \ eval_vars[eval_func_param_names.index('iinput_nochat')] = iinput eval_vars[eval_func_param_names.index('context')] = context # grab other parameters, like langchain_mode for k in eval_func_param_names: if k in locals(): eval_vars[eval_func_param_names.index(k)] = locals()[k] gener = fun(*tuple(eval_vars)) outr = '' res_old = '' for gen_output in gener: res = gen_output['response'] sources = gen_output.get('sources', 'Failure of Generation') if base_model not in non_hf_types or base_model in ['llama']: if not stream_output: print(res) else: # then stream output for gradio that has full output each generation, so need here to show only new chars diff = res[len(res_old):] print(diff, end='', flush=True) res_old = res outr = res # don't accumulate else: outr += res # just is one thing if sources: # show sources at end after model itself had streamed to std rest of response print('\n\n' + str(sources), flush=True) all_generations.append(outr + '\n') if not cli_loop: break return all_generations