import ast import copy import functools import glob import inspect import queue import sys import os import time import traceback import types import typing import warnings from datetime import datetime import filelock import requests import psutil from requests import ConnectTimeout, JSONDecodeError from urllib3.exceptions import ConnectTimeoutError, MaxRetryError, ConnectionError from requests.exceptions import ConnectionError as ConnectionError2 from requests.exceptions import ReadTimeout as ReadTimeout2 from evaluate_params import eval_func_param_names, no_default_param_names if os.path.dirname(os.path.abspath(__file__)) not in sys.path: sys.path.append(os.path.dirname(os.path.abspath(__file__))) os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' os.environ['BITSANDBYTES_NOWELCOME'] = '1' warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated') from enums import DocumentChoices, LangChainMode, no_lora_str, model_token_mapping, no_model_str, source_prefix, \ source_postfix, LangChainAction from loaders import get_loaders from utils import set_seed, clear_torch_cache, save_generate_output, NullContext, wrapped_partial, EThread, get_githash, \ import_matplotlib, get_device, makedirs, get_kwargs, start_faulthandler, get_hf_server, FakeTokenizer, remove start_faulthandler() import_matplotlib() SEED = 1236 set_seed(SEED) from typing import Union import fire import torch from transformers import GenerationConfig, AutoModel, TextIteratorStreamer from prompter import Prompter, inv_prompt_type_to_model_lower, non_hf_types, PromptType, get_prompt, generate_prompt from stopping import get_stopping langchain_modes = [x.value for x in list(LangChainMode)] langchain_actions = [x.value for x in list(LangChainAction)] scratch_base_dir = '/tmp/' def main( load_8bit: bool = False, load_4bit: bool = False, load_half: bool = True, infer_devices: bool = True, base_model: str = '', tokenizer_base_model: str = '', lora_weights: str = "", gpu_id: int = 0, compile_model: bool = True, use_cache: bool = None, inference_server: str = "", prompt_type: Union[int, str] = None, prompt_dict: typing.Dict = None, model_lock: typing.List[typing.Dict[str, str]] = None, model_lock_columns: int = None, fail_if_cannot_connect: bool = False, # input to generation temperature: float = None, top_p: float = None, top_k: int = None, num_beams: int = None, repetition_penalty: float = None, num_return_sequences: int = None, do_sample: bool = None, max_new_tokens: int = None, min_new_tokens: int = None, early_stopping: Union[bool, str] = None, max_time: float = None, memory_restriction_level: int = None, debug: bool = False, save_dir: str = None, share: bool = True, local_files_only: bool = False, resume_download: bool = True, use_auth_token: Union[str, bool] = False, trust_remote_code: Union[str, bool] = True, offload_folder: str = "offline_folder", src_lang: str = "English", tgt_lang: str = "Russian", cli: bool = False, cli_loop: bool = True, gradio: bool = True, gradio_offline_level: int = 0, chat: bool = True, chat_context: bool = False, stream_output: bool = True, show_examples: bool = None, verbose: bool = False, h2ocolors: bool = True, height: int = 600, show_lora: bool = True, login_mode_if_model0: bool = False, block_gradio_exit: bool = True, concurrency_count: int = 1, api_open: bool = False, allow_api: bool = True, input_lines: int = 1, gradio_size: str = None, auth: typing.List[typing.Tuple[str, str]] = None, max_max_time=None, max_max_new_tokens=None, sanitize_user_prompt: bool = False, sanitize_bot_response: bool = False, extra_model_options: typing.List[str] = [], extra_lora_options: typing.List[str] = [], extra_server_options: typing.List[str] = [], score_model: str = 'OpenAssistant/reward-model-deberta-v3-large-v2', eval_filename: str = None, eval_prompts_only_num: int = 0, eval_prompts_only_seed: int = 1234, eval_as_output: bool = False, langchain_mode: str = 'Disabled', langchain_action: str = LangChainAction.QUERY.value, force_langchain_evaluate: bool = False, visible_langchain_modes: list = ['UserData', 'MyData'], # WIP: # visible_langchain_actions: list = langchain_actions.copy(), visible_langchain_actions: list = [LangChainAction.QUERY.value, LangChainAction.SUMMARIZE_MAP.value], document_choice: list = [DocumentChoices.All_Relevant.name], user_path: str = None, detect_user_path_changes_every_query: bool = False, load_db_if_exists: bool = True, keep_sources_in_context: bool = False, db_type: str = 'chroma', use_openai_embedding: bool = False, use_openai_model: bool = False, hf_embedding_model: str = None, allow_upload_to_user_data: bool = True, allow_upload_to_my_data: bool = True, enable_url_upload: bool = True, enable_text_upload: bool = True, enable_sources_list: bool = True, chunk: bool = True, chunk_size: int = 512, top_k_docs: int = None, reverse_docs: bool = True, auto_reduce_chunks: bool = True, max_chunks: int = 100, n_jobs: int = -1, enable_captions: bool = True, captions_model: str = "Salesforce/blip-image-captioning-base", pre_load_caption_model: bool = False, caption_gpu: bool = True, enable_ocr: bool = False, ): """ :param load_8bit: load model in 8-bit using bitsandbytes :param load_4bit: load model in 4-bit using bitsandbytes :param load_half: load model in float16 :param infer_devices: whether to control devices with gpu_id. If False, then spread across GPUs :param base_model: model HF-type name. If use --base_model to preload model, cannot unload in gradio in models tab :param tokenizer_base_model: tokenizer HF-type name. Usually not required, inferred from base_model. :param lora_weights: LORA weights path/HF link :param gpu_id: if infer_devices, then use gpu_id for cuda device ID, or auto mode if gpu_id != -1 :param compile_model Whether to compile the model :param use_cache: Whether to use caching in model (some models fail when multiple threads use) :param inference_server: Consume base_model as type of model at this address Address can be text-generation-server hosting that base_model e.g. python generate.py --inference_server="http://192.168.1.46:6112" --base_model=h2oai/h2ogpt-oasst1-512-12b Or Address can be "openai_chat" or "openai" for OpenAI API e.g. python generate.py --inference_server="openai_chat" --base_model=gpt-3.5-turbo e.g. python generate.py --inference_server="openai" --base_model=text-davinci-003 :param prompt_type: type of prompt, usually matched to fine-tuned model or plain for foundational model :param prompt_dict: If prompt_type=custom, then expects (some) items returned by get_prompt(..., return_dict=True) :param model_lock: Lock models to specific combinations, for ease of use and extending to many models Only used if gradio = True List of dicts, each dict has base_model, tokenizer_base_model, lora_weights, inference_server, prompt_type, and prompt_dict If all models have same prompt_type, and prompt_dict, can still specify that once in CLI outside model_lock as default for dict Can specify model_lock instead of those items on CLI As with CLI itself, base_model can infer prompt_type and prompt_dict if in prompter.py. Also, tokenizer_base_model and lora_weights are optional. Also, inference_server is optional if loading model from local system. All models provided will automatically appear in compare model mode Model loading-unloading and related choices will be disabled. Model/lora/server adding will be disabled :param model_lock_columns: How many columns to show if locking models (and so showing all at once) If None, then defaults to up to 3 if -1, then all goes into 1 row Maximum value is 4 due to non-dynamic gradio rendering elements :param fail_if_cannot_connect: if doing model locking (e.g. with many models), fail if True. Otherwise ignore. Useful when many endpoints and want to just see what works, but still have to wait for timeout. :param temperature: generation temperature :param top_p: generation top_p :param top_k: generation top_k :param num_beams: generation number of beams :param repetition_penalty: generation repetition penalty :param num_return_sequences: generation number of sequences (1 forced for chat) :param do_sample: generation sample :param max_new_tokens: generation max new tokens :param min_new_tokens: generation min tokens :param early_stopping: generation early stopping :param max_time: maximum time to allow for generation :param memory_restriction_level: 0 = no restriction to tokens or model, 1 = some restrictions on token 2 = HF like restriction 3 = very low memory case :param debug: enable debug mode :param save_dir: directory chat data is saved to :param share: whether to share the gradio app with sharable URL :param local_files_only: whether to only use local files instead of doing to HF for models :param resume_download: whether to resume downloads from HF for models :param use_auth_token: whether to use HF auth token (requires CLI did huggingface-cli login before) :param trust_remote_code: whether to use trust any code needed for HF model :param offload_folder: path for spilling model onto disk :param src_lang: source languages to include if doing translation (None = all) :param tgt_lang: target languages to include if doing translation (None = all) :param cli: whether to use CLI (non-gradio) interface. :param cli_loop: whether to loop for CLI (False usually only for testing) :param gradio: whether to enable gradio, or to enable benchmark mode :param gradio_offline_level: > 0, then change fonts so full offline == 1 means backend won't need internet for fonts, but front-end UI might if font not cached == 2 means backend and frontend don't need internet to download any fonts. Note: Some things always disabled include HF telemetry, gradio telemetry, chromadb posthog that involve uploading. This option further disables google fonts for downloading, which is less intrusive than uploading, but still required in air-gapped case. The fonts don't look as nice as google fonts, but ensure full offline behavior. Also set --share=False to avoid sharing a gradio live link. :param chat: whether to enable chat mode with chat history :param chat_context: whether to use extra helpful context if human_bot :param stream_output: whether to stream output :param show_examples: whether to show clickable examples in gradio :param verbose: whether to show verbose prints :param h2ocolors: whether to use H2O.ai theme :param height: height of chat window :param show_lora: whether to show LORA options in UI (expert so can be hard to understand) :param login_mode_if_model0: set to True to load --base_model after client logs in, to be able to free GPU memory when model is swapped :param block_gradio_exit: whether to block gradio exit (used for testing) :param concurrency_count: gradio concurrency count (1 is optimal for LLMs) :param api_open: If False, don't let API calls skip gradio queue :param allow_api: whether to allow API calls at all to gradio server :param input_lines: how many input lines to show for chat box (>1 forces shift-enter for submit, else enter is submit) :param gradio_size: Overall size of text and spaces: "xsmall", "small", "medium", "large". Small useful for many chatbots in model_lock mode :param auth: gradio auth for launcher in form [(user1, pass1), (user2, pass2), ...] e.g. --auth=[('jon','password')] with no spaces :param max_max_time: Maximum max_time for gradio slider :param max_max_new_tokens: Maximum max_new_tokens for gradio slider :param sanitize_user_prompt: whether to remove profanity from user input (slows down input processing) :param sanitize_bot_response: whether to remove profanity and repeat lines from bot output (about 2x slower generation for long streaming cases due to better_profanity being slow) :param extra_model_options: extra models to show in list in gradio :param extra_lora_options: extra LORA to show in list in gradio :param extra_server_options: extra servers to show in list in gradio :param score_model: which model to score responses (None means no scoring) :param eval_filename: json file to use for evaluation, if None is sharegpt :param eval_prompts_only_num: for no gradio benchmark, if using eval_filename prompts for eval instead of examples :param eval_prompts_only_seed: for no gradio benchmark, seed for eval_filename sampling :param eval_as_output: for no gradio benchmark, whether to test eval_filename output itself :param langchain_mode: Data source to include. Choose "UserData" to only consume files from make_db.py. WARNING: wiki_full requires extra data processing via read_wiki_full.py and requires really good workstation to generate db, unless already present. :param langchain_action: Mode langchain operations in on documents. Query: Make query of document(s) Summarize or Summarize_map_reduce: Summarize document(s) via map_reduce Summarize_all: Summarize document(s) using entire document at once Summarize_refine: Summarize document(s) using entire document, and try to refine before returning summary :param force_langchain_evaluate: Whether to force langchain LLM use even if not doing langchain, mostly for testing. :param user_path: user path to glob from to generate db for vector search, for 'UserData' langchain mode. If already have db, any new/changed files are added automatically if path set, does not have to be same path used for prior db sources :param detect_user_path_changes_every_query: whether to detect if any files changed or added every similarity search (by file hashes). Expensive for large number of files, so not done by default. By default only detect changes during db loading. :param visible_langchain_modes: dbs to generate at launch to be ready for LLM Can be up to ['wiki', 'wiki_full', 'UserData', 'MyData', 'github h2oGPT', 'DriverlessAI docs'] But wiki_full is expensive and requires preparation To allow scratch space only live in session, add 'MyData' to list Default: If only want to consume local files, e.g. prepared by make_db.py, only include ['UserData'] FIXME: Avoid 'All' for now, not implemented :param visible_langchain_actions: Which actions to allow :param document_choice: Default document choice when taking subset of collection :param load_db_if_exists: Whether to load chroma db if exists or re-generate db :param keep_sources_in_context: Whether to keep url sources in context, not helpful usually :param db_type: 'faiss' for in-memory or 'chroma' or 'weaviate' for persisted on disk :param use_openai_embedding: Whether to use OpenAI embeddings for vector db :param use_openai_model: Whether to use OpenAI model for use with vector db :param hf_embedding_model: Which HF embedding model to use for vector db Default is instructor-large with 768 parameters per embedding if have GPUs, else all-MiniLM-L6-v1 if no GPUs Can also choose simpler model with 384 parameters per embedding: "sentence-transformers/all-MiniLM-L6-v2" Can also choose even better embedding with 1024 parameters: 'hkunlp/instructor-xl' We support automatically changing of embeddings for chroma, with a backup of db made if this is done :param allow_upload_to_user_data: Whether to allow file uploads to update shared vector db :param allow_upload_to_my_data: Whether to allow file uploads to update scratch vector db :param enable_url_upload: Whether to allow upload from URL :param enable_text_upload: Whether to allow upload of text :param enable_sources_list: Whether to allow list (or download for non-shared db) of list of sources for chosen db :param chunk: Whether to chunk data (True unless know data is already optimally chunked) :param chunk_size: Size of chunks, with typically top-4 passed to LLM, so neesd to be in context length :param top_k_docs: number of chunks to give LLM :param reverse_docs: whether to reverse docs order so most relevant is closest to question. Best choice for sufficiently smart model, and truncation occurs for oldest context, so best then too. But smaller 6_9 models fail to use newest context and can get stuck on old information. :param auto_reduce_chunks: Whether to automatically reduce top_k_docs to fit context given prompt :param max_chunks: If top_k_docs=-1, maximum number of chunks to allow :param n_jobs: Number of processors to use when consuming documents (-1 = all, is default) :param enable_captions: Whether to support captions using BLIP for image files as documents, then preloads that model :param captions_model: Which model to use for captions. captions_model: str = "Salesforce/blip-image-captioning-base", # continue capable captions_model: str = "Salesforce/blip2-flan-t5-xl", # question/answer capable, 16GB state captions_model: str = "Salesforce/blip2-flan-t5-xxl", # question/answer capable, 60GB state Note: opt-based blip2 are not permissive license due to opt and Meta license restrictions :param pre_load_caption_model: Whether to preload caption model, or load after forking parallel doc loader parallel loading disabled if preload and have images, to prevent deadlocking on cuda context Recommended if using larger caption model :param caption_gpu: If support caption, then use GPU if exists :param enable_ocr: Whether to support OCR on images :return: """ if base_model is None: base_model = '' if tokenizer_base_model is None: tokenizer_base_model = '' if lora_weights is None: lora_weights = '' if inference_server is None: inference_server = '' # listen to env if set model_lock = os.getenv('model_lock', str(model_lock)) model_lock = ast.literal_eval(model_lock) if model_lock: assert gradio, "model_lock only supported for gradio=True" if len(model_lock) > 1: assert chat, "model_lock only works for multiple models for chat=True" assert not cli, "model_lock only supported for cli=False" assert not (not cli and not gradio), "model_lock only supported for eval (cli=gradio=False)" assert not base_model, "Don't specify model_lock and base_model" assert not tokenizer_base_model, "Don't specify model_lock and tokenizer_base_model" assert not lora_weights, "Don't specify model_lock and lora_weights" assert not inference_server, "Don't specify model_lock and inference_server" # assert not prompt_type, "Don't specify model_lock and prompt_type" # assert not prompt_dict, "Don't specify model_lock and prompt_dict" n_jobs = int(os.getenv('n_jobs', str(n_jobs))) is_hf = bool(int(os.getenv("HUGGINGFACE_SPACES", '0'))) is_gpth2oai = bool(int(os.getenv("GPT_H2O_AI", '0'))) is_public = is_hf or is_gpth2oai # multi-user case with fixed model and disclaimer if memory_restriction_level is None: memory_restriction_level = 2 if is_hf else 0 # 2 assumes run on 24GB consumer GPU else: assert 0 <= memory_restriction_level <= 3, "Bad memory_restriction_level=%s" % memory_restriction_level if is_public and os.getenv('n_jobs') is None: n_jobs = max(1, min(os.cpu_count() // 2, 8)) admin_pass = os.getenv("ADMIN_PASS") # will sometimes appear in UI or sometimes actual generation, but maybe better than empty result # but becomes unrecoverable sometimes if raise, so just be silent for now raise_generate_gpu_exceptions = True # allow set token directly use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token) allow_upload_to_user_data = bool( int(os.environ.get("allow_upload_to_user_data", str(int(allow_upload_to_user_data))))) allow_upload_to_my_data = bool(int(os.environ.get("allow_upload_to_my_data", str(int(allow_upload_to_my_data))))) height = int(os.environ.get("HEIGHT", height)) h2ocolors = bool(int(os.getenv('h2ocolors', h2ocolors))) # allow enabling langchain via ENV # FIRST PLACE where LangChain referenced, but no imports related to it langchain_mode = os.environ.get("LANGCHAIN_MODE", langchain_mode) assert langchain_mode in langchain_modes, "Invalid langchain_mode %s" % langchain_mode visible_langchain_modes = ast.literal_eval(os.environ.get("visible_langchain_modes", str(visible_langchain_modes))) if langchain_mode not in visible_langchain_modes and langchain_mode in langchain_modes: visible_langchain_modes += [langchain_mode] assert langchain_action in langchain_actions, "Invalid langchain_action %s" % langchain_action # if specifically chose not to show My or User Data, disable upload, so gradio elements are simpler if LangChainMode.MY_DATA.value not in visible_langchain_modes: allow_upload_to_my_data = False if LangChainMode.USER_DATA.value not in visible_langchain_modes: allow_upload_to_user_data = False if is_public: allow_upload_to_user_data = False input_lines = 1 # ensure set, for ease of use temperature = 0.2 if temperature is None else temperature top_p = 0.85 if top_p is None else top_p top_k = 70 if top_k is None else top_k if is_hf: do_sample = True if do_sample is None else do_sample top_k_docs = 3 if top_k_docs is None else top_k_docs else: # by default don't sample, too chatty do_sample = False if do_sample is None else do_sample top_k_docs = 4 if top_k_docs is None else top_k_docs if memory_restriction_level == 2: if not base_model and not inference_server and not model_lock: base_model = 'h2oai/h2ogpt-oasst1-512-12b' # don't set load_8bit if passed base_model, doesn't always work so can't just override load_8bit = True load_4bit = False # FIXME - consider using 4-bit instead of 8-bit elif not inference_server: top_k_docs = 10 if top_k_docs is None else top_k_docs if memory_restriction_level >= 2: load_8bit = True load_4bit = False # FIXME - consider using 4-bit instead of 8-bit if hf_embedding_model is None: hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2" top_k_docs = 3 if top_k_docs is None else top_k_docs if top_k_docs is None: top_k_docs = 3 if is_public: if not max_time: max_time = 60 * 2 if not max_max_time: max_max_time = max_time if not max_new_tokens: max_new_tokens = 256 if not max_max_new_tokens: max_max_new_tokens = 256 else: if not max_max_time: max_max_time = 60 * 20 if not max_max_new_tokens: max_max_new_tokens = 512 if is_hf: # must override share if in spaces share = False if not max_time: max_time = 60 * 1 if not max_max_time: max_max_time = max_time # HF accounted for later in get_max_max_new_tokens() save_dir = os.getenv('SAVE_DIR', save_dir) score_model = os.getenv('SCORE_MODEL', score_model) if score_model == 'None' or score_model is None: score_model = '' concurrency_count = int(os.getenv('CONCURRENCY_COUNT', concurrency_count)) api_open = bool(int(os.getenv('API_OPEN', str(int(api_open))))) allow_api = bool(int(os.getenv('ALLOW_API', str(int(allow_api))))) n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0 if n_gpus == 0: gpu_id = None load_8bit = False load_4bit = False load_half = False infer_devices = False torch.backends.cudnn.benchmark = True torch.backends.cudnn.enabled = False torch.set_default_dtype(torch.float32) if psutil.virtual_memory().available < 94 * 1024 ** 3 and not inference_server and not model_lock: # 12B uses ~94GB # 6.9B uses ~47GB base_model = 'h2oai/h2ogpt-oig-oasst1-512-6_9b' if not base_model else base_model if hf_embedding_model is None: # if no GPUs, use simpler embedding model to avoid cost in time hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2" else: if hf_embedding_model is None: # if still None, then set default hf_embedding_model = 'hkunlp/instructor-large' # get defaults if base_model: model_lower = base_model.lower() elif model_lock: # have 0th model be thought of as normal model assert len(model_lock) > 0 and model_lock[0]['base_model'] model_lower = model_lock[0]['base_model'].lower() else: model_lower = '' if not gradio: # force, else not single response like want to look at stream_output = False # else prompt removal can mess up output chat = False # hard-coded defaults first_para = False text_limit = None if offload_folder: makedirs(offload_folder) if user_path: makedirs(user_path) placeholder_instruction, placeholder_input, \ stream_output, show_examples, \ prompt_type, prompt_dict, \ temperature, top_p, top_k, num_beams, \ max_new_tokens, min_new_tokens, early_stopping, max_time, \ repetition_penalty, num_return_sequences, \ do_sample, \ src_lang, tgt_lang, \ examples, \ task_info = \ get_generate_params(model_lower, chat, stream_output, show_examples, prompt_type, prompt_dict, temperature, top_p, top_k, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample, top_k_docs, chunk, chunk_size, verbose, ) git_hash = get_githash() locals_dict = locals() locals_print = '\n'.join(['%s: %s' % (k, v) for k, v in locals_dict.items()]) if verbose: print(f"Generating model with params:\n{locals_print}", flush=True) print("Command: %s\nHash: %s" % (str(' '.join(sys.argv)), git_hash), flush=True) if langchain_mode != "Disabled": # SECOND PLACE where LangChain referenced, but all imports are kept local so not required from gpt_langchain import prep_langchain, get_some_dbs_from_hf if is_hf: get_some_dbs_from_hf() dbs = {} for langchain_mode1 in visible_langchain_modes: if langchain_mode1 in ['MyData']: # don't use what is on disk, remove it instead for gpath1 in glob.glob(os.path.join(scratch_base_dir, 'db_dir_%s*' % langchain_mode1)): if os.path.isdir(gpath1): print("Removing old MyData: %s" % gpath1, flush=True) remove(gpath1) continue if langchain_mode1 in ['All']: # FIXME: All should be avoided until scans over each db, shouldn't be separate db continue persist_directory1 = 'db_dir_%s' % langchain_mode1 # single place, no special names for each case try: db = prep_langchain(persist_directory1, load_db_if_exists, db_type, use_openai_embedding, langchain_mode1, user_path, hf_embedding_model, kwargs_make_db=locals()) finally: # in case updated embeddings or created new embeddings clear_torch_cache() dbs[langchain_mode1] = db # remove None db's so can just rely upon k in dbs for if hav db dbs = {k: v for k, v in dbs.items() if v is not None} else: dbs = {} # import control if os.environ.get("TEST_LANGCHAIN_IMPORT"): assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have" assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have" model_state_none = dict(model=None, tokenizer=None, device=None, base_model=None, tokenizer_base_model=None, lora_weights=None, inference_server=None, prompt_type=None, prompt_dict=None) if cli: from cli import run_cli return run_cli(**get_kwargs(run_cli, exclude_names=['model_state0'], **locals())) elif not gradio: from eval import run_eval return run_eval(**get_kwargs(run_eval, exclude_names=['model_state0'], **locals())) elif gradio: # imported here so don't require gradio to run generate from gradio_runner import go_gradio # get default model model_states = [] model_list = [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)] model_list0 = copy.deepcopy(model_list) # just strings, safe to deepcopy model_state0 = model_state_none.copy() assert len(model_state_none) == len(model_state0) if model_lock: model_list = model_lock for model_dict in reversed(model_list): # do reverse, so first is default base_model etc., so some logic works in go_gradio() more easily # handles defaults user didn't have to pass model_dict['base_model'] = base_model1 = model_dict.get('base_model', '') model_dict['tokenizer_base_model'] = tokenizer_base_model1 = model_dict.get('tokenizer_base_model', '') model_dict['lora_weights'] = lora_weights1 = model_dict.get('lora_weights', '') model_dict['inference_server'] = inference_server1 = model_dict.get('inference_server', '') prompt_type1 = model_dict.get('prompt_type', model_list0[0]['prompt_type']) # don't use mutated value # try to infer, ignore empty initial state leading to get_generate_params -> 'plain' if model_dict.get('prompt_type') is None: model_lower1 = base_model1.lower() if model_lower1 in inv_prompt_type_to_model_lower: prompt_type1 = inv_prompt_type_to_model_lower[model_lower1] prompt_dict1, error0 = get_prompt(prompt_type1, '', chat=False, context='', reduced=False, making_context=False, return_dict=True) else: prompt_dict1 = prompt_dict else: prompt_dict1 = prompt_dict model_dict['prompt_type'] = prompt_type1 model_dict['prompt_dict'] = prompt_dict1 = model_dict.get('prompt_dict', prompt_dict1) all_kwargs = locals().copy() all_kwargs.update(dict(base_model=base_model1, tokenizer_base_model=tokenizer_base_model1, lora_weights=lora_weights1, inference_server=inference_server1)) if base_model1 and not login_mode_if_model0: model0, tokenizer0, device = get_model(reward_type=False, **get_kwargs(get_model, exclude_names=['reward_type'], **all_kwargs)) else: # if empty model, then don't load anything, just get gradio up model0, tokenizer0, device = None, None, None if model0 is None: if fail_if_cannot_connect: raise RuntimeError("Could not connect, see logs") # skip if isinstance(model_lock, list): model_lock.remove(model_dict) continue model_state_trial = dict(model=model0, tokenizer=tokenizer0, device=device) model_state_trial.update(model_dict) assert len(model_state_none) == len(model_state_trial) print("Model %s" % model_dict, flush=True) if model_lock: # last in iteration will be first model_states.insert(0, model_state_trial) # fill model_state0 so go_gradio() easier, manage model_states separately model_state0 = model_state_trial.copy() else: model_state0 = model_state_trial.copy() assert len(model_state_none) == len(model_state0) # get score model all_kwargs = locals().copy() smodel, stokenizer, sdevice = get_score_model(reward_type=True, **get_kwargs(get_score_model, exclude_names=['reward_type'], **all_kwargs)) score_model_state0 = dict(model=smodel, tokenizer=stokenizer, device=sdevice, base_model=score_model, tokenizer_base_model='', lora_weights='', inference_server='', prompt_type='', prompt_dict='') if enable_captions: if pre_load_caption_model: from image_captions import H2OImageCaptionLoader caption_loader = H2OImageCaptionLoader(caption_gpu=caption_gpu).load_model() else: caption_loader = 'gpu' if caption_gpu else 'cpu' else: caption_loader = False # assume gradio needs everything go_gradio(**locals()) def get_config(base_model, use_auth_token=False, trust_remote_code=True, offload_folder=None, triton_attn=False, long_sequence=True, return_model=False, raise_exception=False, ): from accelerate import init_empty_weights with init_empty_weights(): from transformers import AutoConfig try: config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token, trust_remote_code=trust_remote_code, offload_folder=offload_folder) except OSError as e: if raise_exception: raise if 'not a local folder and is not a valid model identifier listed on' in str( e) or '404 Client Error' in str(e): # e.g. llama, gpjt, etc. # e.g. HF TGI but not model on HF or private etc. # HF TGI server only should really require prompt_type, not HF model state return None, None else: raise if triton_attn and 'mpt-' in base_model.lower(): config.attn_config['attn_impl'] = 'triton' if long_sequence: if 'mpt-7b-storywriter' in base_model.lower(): config.update({"max_seq_len": 83968}) if 'mosaicml/mpt-7b-chat' in base_model.lower(): config.update({"max_seq_len": 4096}) if 'mpt-30b' in base_model.lower(): config.update({"max_seq_len": 2 * 8192}) if return_model and \ issubclass(config.__class__, tuple(AutoModel._model_mapping.keys())): model = AutoModel.from_config( config, trust_remote_code=trust_remote_code, ) else: # can't infer model = None if 'falcon' in base_model.lower(): config.use_cache = False return config, model def get_non_lora_model(base_model, model_loader, load_half, model_kwargs, reward_type, config, model, gpu_id=0, ): """ Ensure model gets on correct device """ if model is not None: # NOTE: Can specify max_memory={0: max_mem, 1: max_mem}, to shard model # NOTE: Some models require avoiding sharding some layers, # then would pass no_split_module_classes and give list of those layers. from accelerate import infer_auto_device_map device_map = infer_auto_device_map( model, dtype=torch.float16 if load_half else torch.float32, ) if hasattr(model, 'model'): device_map_model = infer_auto_device_map( model.model, dtype=torch.float16 if load_half else torch.float32, ) device_map.update(device_map_model) else: device_map = "auto" n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0 if n_gpus > 0: if gpu_id >= 0: # FIXME: If really distributes model, tend to get things like: ValueError: gpt_neox.embed_in.weight doesn't have any device set. # So avoid for now, just put on first GPU, unless score_model, put on last if reward_type: device_map = {'': n_gpus - 1} else: device_map = {'': min(n_gpus - 1, gpu_id)} if gpu_id == -1: device_map = {'': 'cuda'} else: device_map = {'': 'cpu'} model_kwargs['load_in_8bit'] = False model_kwargs['load_in_4bit'] = False print('device_map: %s' % device_map, flush=True) load_in_8bit = model_kwargs.get('load_in_8bit', False) load_in_4bit = model_kwargs.get('load_in_4bit', False) model_kwargs['device_map'] = device_map pop_unused_model_kwargs(model_kwargs) if load_in_8bit or load_in_4bit or not load_half: model = model_loader.from_pretrained( base_model, config=config, **model_kwargs, ) else: model = model_loader.from_pretrained( base_model, config=config, **model_kwargs, ).half() return model def get_client_from_inference_server(inference_server, raise_connection_exception=False): inference_server, headers = get_hf_server(inference_server) # preload client since slow for gradio case especially from gradio_utils.grclient import GradioClient gr_client = None hf_client = None if headers is None: try: print("GR Client Begin: %s" % inference_server, flush=True) # first do sanity check if alive, else gradio client takes too long by default requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT', '30'))) gr_client = GradioClient(inference_server) print("GR Client End: %s" % inference_server, flush=True) except (OSError, ValueError) as e: # Occurs when wrong endpoint and should have been HF client, so don't hard raise, just move to HF gr_client = None print("GR Client Failed %s: %s" % (inference_server, str(e)), flush=True) except (ConnectTimeoutError, ConnectTimeout, MaxRetryError, ConnectionError, ConnectionError2, JSONDecodeError, ReadTimeout2, KeyError) as e: t, v, tb = sys.exc_info() ex = ''.join(traceback.format_exception(t, v, tb)) print("GR Client Failed %s: %s" % (inference_server, str(ex)), flush=True) if raise_connection_exception: raise if gr_client is None: res = None from text_generation import Client as HFClient print("HF Client Begin: %s" % inference_server) try: hf_client = HFClient(inference_server, headers=headers, timeout=int(os.getenv('REQUEST_TIMEOUT', '30'))) # quick check valid TGI endpoint res = hf_client.generate('What?', max_new_tokens=1) hf_client = HFClient(inference_server, headers=headers, timeout=300) except (ConnectTimeoutError, ConnectTimeout, MaxRetryError, ConnectionError, ConnectionError2, JSONDecodeError, ReadTimeout2, KeyError) as e: hf_client = None t, v, tb = sys.exc_info() ex = ''.join(traceback.format_exception(t, v, tb)) print("HF Client Failed %s: %s" % (inference_server, str(ex))) if raise_connection_exception: raise print("HF Client End: %s %s" % (inference_server, res)) return inference_server, gr_client, hf_client def get_model( load_8bit: bool = False, load_4bit: bool = False, load_half: bool = True, infer_devices: bool = True, base_model: str = '', inference_server: str = "", tokenizer_base_model: str = '', lora_weights: str = "", gpu_id: int = 0, reward_type: bool = None, local_files_only: bool = False, resume_download: bool = True, use_auth_token: Union[str, bool] = False, trust_remote_code: bool = True, offload_folder: str = None, compile_model: bool = True, verbose: bool = False, ): """ :param load_8bit: load model in 8-bit, not supported by all models :param load_4bit: load model in 4-bit, not supported by all models :param load_half: load model in 16-bit :param infer_devices: Use torch infer of optimal placement of layers on devices (for non-lora case) For non-LORA case, False will spread shards across multiple GPUs, but this can lead to cuda:x cuda:y mismatches So it is not the default :param base_model: name/path of base model :param inference_server: whether base_model is hosted locally ('') or via http (url) :param tokenizer_base_model: name/path of tokenizer :param lora_weights: name/path :param gpu_id: which GPU (0..n_gpus-1) or allow all GPUs if relevant (-1) :param reward_type: reward type model for sequence classification :param local_files_only: use local files instead of from HF :param resume_download: resume downloads from HF :param use_auth_token: assumes user did on CLI `huggingface-cli login` to access private repo :param trust_remote_code: trust code needed by model :param offload_folder: offload folder :param compile_model: whether to compile torch model :param verbose: :return: """ if verbose: print("Get %s model" % base_model, flush=True) triton_attn = False long_sequence = True config_kwargs = dict(use_auth_token=use_auth_token, trust_remote_code=trust_remote_code, offload_folder=offload_folder, triton_attn=triton_attn, long_sequence=long_sequence) config, _ = get_config(base_model, **config_kwargs, raise_exception=False) if base_model in non_hf_types: assert config is None, "Expected config None for %s" % base_model llama_type_from_config = 'llama' in str(config).lower() llama_type_from_name = "llama" in base_model.lower() llama_type = llama_type_from_config or llama_type_from_name if "xgen" in base_model.lower(): llama_type = False if llama_type: if verbose: print("Detected as llama type from" " config (%s) or name (%s)" % (llama_type_from_config, llama_type_from_name), flush=True) model_loader, tokenizer_loader = get_loaders(model_name=base_model, reward_type=reward_type, llama_type=llama_type) tokenizer_kwargs = dict(local_files_only=local_files_only, resume_download=resume_download, use_auth_token=use_auth_token, trust_remote_code=trust_remote_code, offload_folder=offload_folder, padding_side='left', config=config, ) if not tokenizer_base_model: tokenizer_base_model = base_model if config is not None and tokenizer_loader is not None and not isinstance(tokenizer_loader, str): tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model, **tokenizer_kwargs) # sets raw (no cushion) limit set_model_max_len(config, tokenizer, verbose=False) # if using fake tokenizer, not really accurate when lots of numbers, give a bit of buffer, else get: # Generation Failed: Input validation error: `inputs` must have less than 2048 tokens. Given: 2233 tokenizer.model_max_length = tokenizer.model_max_length - 50 else: tokenizer = FakeTokenizer() if isinstance(inference_server, str) and inference_server.startswith("http"): inference_server, gr_client, hf_client = get_client_from_inference_server(inference_server) client = gr_client or hf_client # Don't return None, None for model, tokenizer so triggers return client, tokenizer, 'http' if isinstance(inference_server, str) and inference_server.startswith('openai'): assert os.getenv('OPENAI_API_KEY'), "Set environment for OPENAI_API_KEY" # Don't return None, None for model, tokenizer so triggers # include small token cushion tokenizer = FakeTokenizer(model_max_length=model_token_mapping[base_model] - 50) return inference_server, tokenizer, inference_server assert not inference_server, "Malformed inference_server=%s" % inference_server if base_model in non_hf_types: from gpt4all_llm import get_model_tokenizer_gpt4all model, tokenizer, device = get_model_tokenizer_gpt4all(base_model) return model, tokenizer, device # get local torch-HF model return get_hf_model(load_8bit=load_8bit, load_4bit=load_4bit, load_half=load_half, infer_devices=infer_devices, base_model=base_model, tokenizer_base_model=tokenizer_base_model, lora_weights=lora_weights, gpu_id=gpu_id, reward_type=reward_type, local_files_only=local_files_only, resume_download=resume_download, use_auth_token=use_auth_token, trust_remote_code=trust_remote_code, offload_folder=offload_folder, compile_model=compile_model, llama_type=llama_type, config_kwargs=config_kwargs, tokenizer_kwargs=tokenizer_kwargs, verbose=verbose) def get_hf_model(load_8bit: bool = False, load_4bit: bool = False, load_half: bool = True, infer_devices: bool = True, base_model: str = '', tokenizer_base_model: str = '', lora_weights: str = "", gpu_id: int = 0, reward_type: bool = None, local_files_only: bool = False, resume_download: bool = True, use_auth_token: Union[str, bool] = False, trust_remote_code: bool = True, offload_folder: str = None, compile_model: bool = True, llama_type: bool = False, config_kwargs=None, tokenizer_kwargs=None, verbose: bool = False, ): assert config_kwargs is not None assert tokenizer_kwargs is not None if lora_weights is not None and lora_weights.strip(): if verbose: print("Get %s lora weights" % lora_weights, flush=True) device = get_device() if 'gpt2' in base_model.lower(): # RuntimeError: where expected condition to be a boolean tensor, but got a tensor with dtype Half load_8bit = False load_4bit = False assert base_model.strip(), ( "Please choose a base model with --base_model (CLI) or load one from Models Tab (gradio)" ) model_loader, tokenizer_loader = get_loaders(model_name=base_model, reward_type=reward_type, llama_type=llama_type) config, _ = get_config(base_model, return_model=False, raise_exception=True, **config_kwargs) if tokenizer_loader is not None and not isinstance(tokenizer_loader, str): tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model, **tokenizer_kwargs) else: tokenizer = tokenizer_loader if isinstance(tokenizer, str): # already a pipeline, tokenizer_loader is string for task model = model_loader(tokenizer, model=base_model, device=0 if device == "cuda" else -1, torch_dtype=torch.float16 if device == 'cuda' else torch.float32) else: assert device in ["cuda", "cpu"], "Unsupported device %s" % device model_kwargs = dict(local_files_only=local_files_only, torch_dtype=torch.float16 if device == 'cuda' else torch.float32, resume_download=resume_download, use_auth_token=use_auth_token, trust_remote_code=trust_remote_code, offload_folder=offload_folder, ) if 'mbart-' not in base_model.lower() and 'mpt-' not in base_model.lower(): model_kwargs.update(dict(load_in_8bit=load_8bit, load_in_4bit=load_4bit, device_map={"": 0} if (load_8bit or load_4bit) and device == 'cuda' else "auto", )) if 'mpt-' in base_model.lower() and gpu_id is not None and gpu_id >= 0: model_kwargs.update(dict(device_map={"": gpu_id} if device == 'cuda' else "cpu")) if 'OpenAssistant/reward-model'.lower() in base_model.lower(): # FIXME: could put on other GPUs model_kwargs['device_map'] = {"": 0} if device == 'cuda' else {"": 'cpu'} model_kwargs.pop('torch_dtype', None) pop_unused_model_kwargs(model_kwargs) if not lora_weights: with torch.device(device): if infer_devices: config, model = get_config(base_model, return_model=True, raise_exception=True, **config_kwargs) model = get_non_lora_model(base_model, model_loader, load_half, model_kwargs, reward_type, config, model, gpu_id=gpu_id, ) else: config, _ = get_config(base_model, **config_kwargs) if load_half and not (load_8bit or load_4bit): model = model_loader.from_pretrained( base_model, config=config, **model_kwargs).half() else: model = model_loader.from_pretrained( base_model, config=config, **model_kwargs) elif load_8bit or load_4bit: config, _ = get_config(base_model, **config_kwargs) model = model_loader.from_pretrained( base_model, config=config, **model_kwargs ) from peft import PeftModel # loads cuda, so avoid in global scope model = PeftModel.from_pretrained( model, lora_weights, torch_dtype=torch.float16 if device == 'cuda' else torch.float32, local_files_only=local_files_only, resume_download=resume_download, use_auth_token=use_auth_token, trust_remote_code=trust_remote_code, offload_folder=offload_folder, device_map={"": 0} if device == 'cuda' else {"": 'cpu'}, # seems to be required ) else: with torch.device(device): config, _ = get_config(base_model, raise_exception=True, **config_kwargs) model = model_loader.from_pretrained( base_model, config=config, **model_kwargs ) from peft import PeftModel # loads cuda, so avoid in global scope model = PeftModel.from_pretrained( model, lora_weights, torch_dtype=torch.float16 if device == 'cuda' else torch.float32, local_files_only=local_files_only, resume_download=resume_download, use_auth_token=use_auth_token, trust_remote_code=trust_remote_code, offload_folder=offload_folder, device_map="auto", ) if load_half: model.half() # unwind broken decapoda-research config if llama_type: model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk model.config.bos_token_id = 1 model.config.eos_token_id = 2 if 'gpt2' in base_model.lower(): # add special tokens that otherwise all share the same id tokenizer.add_special_tokens({'bos_token': '', 'eos_token': '', 'pad_token': ''}) if not isinstance(tokenizer, str): model.eval() if torch.__version__ >= "2" and sys.platform != "win32" and compile_model: model = torch.compile(model) set_model_max_len(config, tokenizer, verbose=False, reward_type=reward_type) return model, tokenizer, device def set_model_max_len(config, tokenizer, verbose=False, reward_type=False): if reward_type: # limit deberta, else uses too much memory and not worth response score tokenizer.model_max_length = 512 if hasattr(config, 'max_seq_len') and isinstance(config.max_seq_len, int): tokenizer.model_max_length = config.max_seq_len elif hasattr(config, 'max_position_embeddings') and isinstance(config.max_position_embeddings, int): # help automatically limit inputs to generate tokenizer.model_max_length = config.max_position_embeddings else: if verbose: print("Could not determine model_max_length, setting to 2048", flush=True) tokenizer.model_max_length = 2048 # for bug in HF transformers if tokenizer.model_max_length > 100000000: tokenizer.model_max_length = 2048 def pop_unused_model_kwargs(model_kwargs): """ in-place pop unused kwargs that are not dependency-upgrade friendly no point passing in False, is default, and helps avoid needing to update requirements for new deps :param model_kwargs: :return: """ check_list = ['load_in_8bit', 'load_in_4bit'] for k in check_list: if k in model_kwargs and not model_kwargs[k]: model_kwargs.pop(k) def get_score_model(score_model: str = None, load_8bit: bool = False, load_4bit: bool = False, load_half: bool = True, infer_devices: bool = True, base_model: str = '', inference_server: str = '', tokenizer_base_model: str = '', lora_weights: str = "", gpu_id: int = 0, reward_type: bool = None, local_files_only: bool = False, resume_download: bool = True, use_auth_token: Union[str, bool] = False, trust_remote_code: bool = True, offload_folder: str = None, compile_model: bool = True, verbose: bool = False, ): if score_model is not None and score_model.strip(): load_8bit = False load_4bit = False load_half = False base_model = score_model.strip() tokenizer_base_model = '' lora_weights = '' inference_server = '' llama_type = False compile_model = False smodel, stokenizer, sdevice = get_model(reward_type=True, **get_kwargs(get_model, exclude_names=['reward_type'], **locals())) else: smodel, stokenizer, sdevice = None, None, None return smodel, stokenizer, sdevice def evaluate( model_state, my_db_state, # START NOTE: Examples must have same order of parameters instruction, iinput, context, stream_output, prompt_type, prompt_dict, temperature, top_p, top_k, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample, chat, instruction_nochat, iinput_nochat, langchain_mode, langchain_action, top_k_docs, chunk, chunk_size, document_choice, # END NOTE: Examples must have same order of parameters src_lang=None, tgt_lang=None, debug=False, concurrency_count=None, save_dir=None, sanitize_bot_response=False, model_state0=None, memory_restriction_level=None, max_max_new_tokens=None, is_public=None, max_max_time=None, raise_generate_gpu_exceptions=None, chat_context=None, lora_weights=None, load_db_if_exists=True, dbs=None, user_path=None, detect_user_path_changes_every_query=None, use_openai_embedding=None, use_openai_model=None, hf_embedding_model=None, db_type=None, n_jobs=None, first_para=None, text_limit=None, verbose=False, cli=False, reverse_docs=True, use_cache=None, auto_reduce_chunks=None, max_chunks=None, model_lock=None, force_langchain_evaluate=None, model_state_none=None, ): # ensure passed these assert concurrency_count is not None assert memory_restriction_level is not None assert raise_generate_gpu_exceptions is not None assert chat_context is not None assert use_openai_embedding is not None assert use_openai_model is not None assert hf_embedding_model is not None assert db_type is not None assert top_k_docs is not None and isinstance(top_k_docs, int) assert chunk is not None and isinstance(chunk, bool) assert chunk_size is not None and isinstance(chunk_size, int) assert n_jobs is not None assert first_para is not None if debug: locals_dict = locals().copy() locals_dict.pop('model_state', None) locals_dict.pop('model_state0', None) locals_dict.pop('model_states', None) print(locals_dict) no_model_msg = "Please choose a base model with --base_model (CLI) or load in Models Tab (gradio).\n" \ "Then start New Conversation" if model_state is None: model_state = model_state_none.copy() if model_state0 is None: # e.g. for no gradio case, set dummy value, else should be set model_state0 = model_state_none.copy() # model_state['model] is only 'model' if should use model_state0 # model could also be None have_model_lock = model_lock is not None have_fresh_model = model_state['model'] not in [None, 'model', no_model_str] # for gradio UI control, expect model_state and model_state0 to match, so if have_model_lock=True, then should have_fresh_model=True # but gradio API control will only use nochat api etc. and won't use fresh model, so can't assert in general # if have_model_lock: # assert have_fresh_model, "Expected model_state and model_state0 to match if have_model_lock" have_cli_model = model_state0['model'] not in [None, 'model', no_model_str] if have_fresh_model: # USE FRESH MODEL if not have_model_lock: # model_state0 is just one of model_state if model_lock, so don't nuke # try to free-up original model (i.e. list was passed as reference) if model_state0['model'] and hasattr(model_state0['model'], 'cpu'): model_state0['model'].cpu() model_state0['model'] = None # try to free-up original tokenizer (i.e. list was passed as reference) if model_state0['tokenizer']: model_state0['tokenizer'] = None clear_torch_cache() chosen_model_state = model_state elif have_cli_model: # USE MODEL SETUP AT CLI assert isinstance(model_state['model'], str) # expect no fresh model chosen_model_state = model_state0 else: raise AssertionError(no_model_msg) # get variables model = chosen_model_state['model'] tokenizer = chosen_model_state['tokenizer'] device = chosen_model_state['device'] base_model = chosen_model_state['base_model'] tokenizer_base_model = chosen_model_state['tokenizer_base_model'] lora_weights = chosen_model_state['lora_weights'] inference_server = chosen_model_state['inference_server'] # prefer use input from API over model state prompt_type = prompt_type or chosen_model_state['prompt_type'] prompt_dict = prompt_dict or chosen_model_state['prompt_dict'] if base_model is None: raise AssertionError(no_model_msg) assert base_model.strip(), no_model_msg assert model, "Model is missing" assert tokenizer, "Tokenizer is missing" # choose chat or non-chat mode if not chat: instruction = instruction_nochat iinput = iinput_nochat # in some cases, like lean nochat API, don't want to force sending prompt_type, allow default choice model_lower = base_model.lower() if not prompt_type and model_lower in inv_prompt_type_to_model_lower and prompt_type != 'custom': prompt_type = inv_prompt_type_to_model_lower[model_lower] if verbose: print("Auto-selecting prompt_type=%s for %s" % (prompt_type, model_lower), flush=True) assert prompt_type is not None, "prompt_type was None" # Control generation hyperparameters # adjust for bad inputs, e.g. in case also come from API that doesn't get constrained by gradio sliders # below is for TGI server, not required for HF transformers # limits are chosen similar to gradio_runner.py sliders/numbers top_p = min(max(1e-3, top_p), 1.0 - 1e-3) top_k = min(max(1, int(top_k)), 100) temperature = min(max(0.01, temperature), 2.0) # FIXME: https://github.com/h2oai/h2ogpt/issues/106 num_beams = 1 if stream_output else num_beams # See max_beams in gradio_runner max_max_new_tokens = get_max_max_new_tokens(chosen_model_state, memory_restriction_level=memory_restriction_level, max_new_tokens=max_new_tokens, max_max_new_tokens=max_max_new_tokens) model_max_length = get_model_max_length(chosen_model_state) max_new_tokens = min(max(1, int(max_new_tokens)), max_max_new_tokens) min_new_tokens = min(max(0, int(min_new_tokens)), max_new_tokens) max_time = min(max(0, max_time), max_max_time) repetition_penalty = min(max(0.01, repetition_penalty), 3.0) num_return_sequences = 1 if chat else min(max(1, int(num_return_sequences)), 10) min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public) top_k_docs = min(max(min_top_k_docs, int(top_k_docs)), max_top_k_docs) chunk_size = min(max(128, int(chunk_size)), 2048) if not context: # get hidden context if have one context = get_context(chat_context, prompt_type) # restrict instruction, typically what has large input from h2oai_pipeline import H2OTextGenerationPipeline instruction, num_prompt_tokens1 = H2OTextGenerationPipeline.limit_prompt(instruction, tokenizer) context, num_prompt_tokens2 = H2OTextGenerationPipeline.limit_prompt(context, tokenizer) iinput, num_prompt_tokens3 = H2OTextGenerationPipeline.limit_prompt(iinput, tokenizer) num_prompt_tokens = (num_prompt_tokens1 or 0) + (num_prompt_tokens2 or 0) + (num_prompt_tokens3 or 0) # get prompt prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output) data_point = dict(context=context, instruction=instruction, input=iinput) prompt = prompter.generate_prompt(data_point) # THIRD PLACE where LangChain referenced, but imports only occur if enabled and have db to use assert langchain_mode in langchain_modes, "Invalid langchain_mode %s" % langchain_mode assert langchain_action in langchain_actions, "Invalid langchain_action %s" % langchain_action if langchain_mode in ['MyData'] and my_db_state is not None and len(my_db_state) > 0 and my_db_state[0] is not None: db1 = my_db_state[0] elif dbs is not None and langchain_mode in dbs: db1 = dbs[langchain_mode] else: db1 = None do_langchain_path = langchain_mode not in [False, 'Disabled', 'ChatLLM', 'LLM'] or \ base_model in non_hf_types or \ force_langchain_evaluate if do_langchain_path: outr = "" # use smaller cut_distanct for wiki_full since so many matches could be obtained, and often irrelevant unless close from gpt_langchain import run_qa_db gen_hyper_langchain = dict(do_sample=do_sample, temperature=temperature, repetition_penalty=repetition_penalty, top_k=top_k, top_p=top_p, num_beams=num_beams, min_new_tokens=min_new_tokens, max_new_tokens=max_new_tokens, early_stopping=early_stopping, max_time=max_time, num_return_sequences=num_return_sequences, ) for r in run_qa_db(query=instruction, iinput=iinput, context=context, model_name=base_model, model=model, tokenizer=tokenizer, inference_server=inference_server, stream_output=stream_output, prompter=prompter, load_db_if_exists=load_db_if_exists, db=db1, user_path=user_path, detect_user_path_changes_every_query=detect_user_path_changes_every_query, cut_distanct=1.1 if langchain_mode in ['wiki_full'] else 1.64, # FIXME, too arbitrary use_openai_embedding=use_openai_embedding, use_openai_model=use_openai_model, hf_embedding_model=hf_embedding_model, first_para=first_para, text_limit=text_limit, chunk=chunk, chunk_size=chunk_size, langchain_mode=langchain_mode, langchain_action=langchain_action, document_choice=document_choice, db_type=db_type, top_k_docs=top_k_docs, **gen_hyper_langchain, prompt_type=prompt_type, prompt_dict=prompt_dict, n_jobs=n_jobs, verbose=verbose, cli=cli, sanitize_bot_response=sanitize_bot_response, reverse_docs=reverse_docs, lora_weights=lora_weights, auto_reduce_chunks=auto_reduce_chunks, max_chunks=max_chunks, ): outr, extra = r # doesn't accumulate, new answer every yield, so only save that full answer yield dict(response=outr, sources=extra) if save_dir: extra_dict = gen_hyper_langchain.copy() extra_dict.update(prompt_type=prompt_type, inference_server=inference_server, langchain_mode=langchain_mode, langchain_action=langchain_action, document_choice=document_choice, num_prompt_tokens=num_prompt_tokens, instruction=instruction, iinput=iinput, context=context, ) save_generate_output(prompt=prompt, output=outr, base_model=base_model, save_dir=save_dir, where_from='run_qa_db', extra_dict=extra_dict) if verbose: print( 'Post-Generate Langchain: %s decoded_output: %s' % (str(datetime.now()), len(outr) if outr else -1), flush=True) if outr or base_model in non_hf_types: # if got no response (e.g. not showing sources and got no sources, # so nothing to give to LLM), then slip through and ask LLM # Or if llama/gptj, then just return since they had no response and can't go down below code path # clear before return, since .then() never done if from API clear_torch_cache() return if inference_server.startswith('openai') or inference_server.startswith('http'): if inference_server.startswith('openai'): import openai where_from = "openai_client" openai.api_key = os.getenv("OPENAI_API_KEY") stop_sequences = list(set(prompter.terminate_response + [prompter.PreResponse])) stop_sequences = [x for x in stop_sequences if x] # OpenAI will complain if ask for too many new tokens, takes it as min in some sense, wrongly so. max_new_tokens_openai = min(max_new_tokens, model_max_length - num_prompt_tokens) gen_server_kwargs = dict(temperature=temperature if do_sample else 0, max_tokens=max_new_tokens_openai, top_p=top_p if do_sample else 1, frequency_penalty=0, n=num_return_sequences, presence_penalty=1.07 - repetition_penalty + 0.6, # so good default ) if inference_server == 'openai': response = openai.Completion.create( model=base_model, prompt=prompt, **gen_server_kwargs, stop=stop_sequences, stream=stream_output, ) if not stream_output: text = response['choices'][0]['text'] yield dict(response=prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources='') else: collected_events = [] text = '' for event in response: collected_events.append(event) # save the event response event_text = event['choices'][0]['text'] # extract the text text += event_text # append the text yield dict(response=prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources='') elif inference_server == 'openai_chat': response = openai.ChatCompletion.create( model=base_model, messages=[ {"role": "system", "content": "You are a helpful assistant."}, {'role': 'user', 'content': prompt, } ], stream=stream_output, **gen_server_kwargs, ) if not stream_output: text = response["choices"][0]["message"]["content"] yield dict(response=prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources='') else: text = "" for chunk in response: delta = chunk["choices"][0]["delta"] if 'content' in delta: text += delta['content'] yield dict(response=prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources='') else: raise RuntimeError("No such OpenAI mode: %s" % inference_server) elif inference_server.startswith('http'): inference_server, headers = get_hf_server(inference_server) from gradio_utils.grclient import GradioClient from text_generation import Client as HFClient if isinstance(model, GradioClient): gr_client = model hf_client = None elif isinstance(model, HFClient): gr_client = None hf_client = model else: inference_server, gr_client, hf_client = get_client_from_inference_server(inference_server) # quick sanity check to avoid long timeouts, just see if can reach server requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT_FAST', '10'))) if gr_client is not None: # Note: h2oGPT gradio server could handle input token size issues for prompt, # but best to handle here so send less data to server chat_client = False where_from = "gr_client" client_langchain_mode = 'Disabled' client_langchain_action = LangChainAction.QUERY.value gen_server_kwargs = dict(temperature=temperature, top_p=top_p, top_k=top_k, num_beams=num_beams, max_new_tokens=max_new_tokens, min_new_tokens=min_new_tokens, early_stopping=early_stopping, max_time=max_time, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences, do_sample=do_sample, chat=chat_client, ) # account for gradio into gradio that handles prompting, avoid duplicating prompter prompt injection if prompt_type in [None, '', PromptType.plain.name, PromptType.plain.value, str(PromptType.plain.value)]: # if our prompt is plain, assume either correct or gradio server knows different prompt type, # so pass empty prompt_Type gr_prompt_type = '' gr_prompt_dict = '' gr_prompt = prompt # already prepared prompt gr_context = '' gr_iinput = '' else: # if already have prompt_type that is not plain, None, or '', then already applied some prompting # But assume server can handle prompting, and need to avoid double-up. # Also assume server can do better job of using stopping.py to stop early, so avoid local prompting, let server handle # So avoid "prompt" and let gradio server reconstruct from prompt_type we passed # Note it's ok that prompter.get_response() has prompt+text, prompt=prompt passed, # because just means extra processing and removal of prompt, but that has no human-bot prompting doesn't matter # since those won't appear gr_context = context gr_prompt = instruction gr_iinput = iinput gr_prompt_type = prompt_type gr_prompt_dict = prompt_dict client_kwargs = dict(instruction=gr_prompt if chat_client else '', # only for chat=True iinput=gr_iinput, # only for chat=True context=gr_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, **gen_server_kwargs, prompt_type=gr_prompt_type, prompt_dict=gr_prompt_dict, instruction_nochat=gr_prompt if not chat_client else '', iinput_nochat=gr_iinput, # only for chat=False langchain_mode=client_langchain_mode, langchain_action=client_langchain_action, top_k_docs=top_k_docs, chunk=chunk, chunk_size=chunk_size, document_choice=[DocumentChoices.All_Relevant.name], ) api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing if not stream_output: res = gr_client.predict(str(dict(client_kwargs)), api_name=api_name) res_dict = ast.literal_eval(res) text = res_dict['response'] sources = res_dict['sources'] yield dict(response=prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources=sources) else: job = gr_client.submit(str(dict(client_kwargs)), api_name=api_name) text = '' sources = '' res_dict = dict(response=text, sources=sources) 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) text = res_dict['response'] sources = res_dict['sources'] if gr_prompt_type == 'plain': # then gradio server passes back full prompt + text prompt_and_text = text else: prompt_and_text = prompt + text yield dict(response=prompter.get_response(prompt_and_text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources=sources) time.sleep(0.01) # ensure get last output to avoid race res_all = job.outputs() if len(res_all) > 0: res = res_all[-1] res_dict = ast.literal_eval(res) text = res_dict['response'] sources = res_dict['sources'] else: # go with old text if last call didn't work e = job.future._exception if e is not None: stre = str(e) strex = ''.join(traceback.format_tb(e.__traceback__)) else: stre = '' strex = '' print("Bad final response: %s %s %s %s %s: %s %s" % (base_model, inference_server, res_all, prompt, text, stre, strex), flush=True) if gr_prompt_type == 'plain': # then gradio server passes back full prompt + text prompt_and_text = text else: prompt_and_text = prompt + text yield dict(response=prompter.get_response(prompt_and_text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources=sources) elif hf_client: # HF inference server needs control over input tokens where_from = "hf_client" # prompt must include all human-bot like tokens, already added by prompt # https://github.com/huggingface/text-generation-inference/tree/main/clients/python#types stop_sequences = list(set(prompter.terminate_response + [prompter.PreResponse])) stop_sequences = [x for x in stop_sequences if x] gen_server_kwargs = dict(do_sample=do_sample, max_new_tokens=max_new_tokens, # best_of=None, repetition_penalty=repetition_penalty, return_full_text=True, seed=SEED, stop_sequences=stop_sequences, temperature=temperature, top_k=top_k, top_p=top_p, # truncate=False, # behaves oddly # typical_p=top_p, # watermark=False, # decoder_input_details=False, ) # work-around for timeout at constructor time, will be issue if multi-threading, # so just do something reasonable or max_time if larger # lower bound because client is re-used if multi-threading hf_client.timeout = max(300, max_time) if not stream_output: text = hf_client.generate(prompt, **gen_server_kwargs).generated_text yield dict(response=prompter.get_response(text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources='') else: text = "" for response in hf_client.generate_stream(prompt, **gen_server_kwargs): if not response.token.special: # stop_sequences text_chunk = response.token.text text += text_chunk yield dict(response=prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response), sources='') else: raise RuntimeError("Failed to get client: %s" % inference_server) else: raise RuntimeError("No such inference_server %s" % inference_server) if save_dir and text: # save prompt + new text extra_dict = gen_server_kwargs.copy() extra_dict.update(dict(inference_server=inference_server, num_prompt_tokens=num_prompt_tokens)) save_generate_output(prompt=prompt, output=text, base_model=base_model, save_dir=save_dir, where_from=where_from, extra_dict=extra_dict) return else: assert not inference_server, "inferene_server=%s not supported" % inference_server if isinstance(tokenizer, str): # pipeline if tokenizer == "summarization": key = 'summary_text' else: raise RuntimeError("No such task type %s" % tokenizer) # NOTE: uses max_length only yield dict(response=model(prompt, max_length=max_new_tokens)[0][key], sources='') if 'mbart-' in base_model.lower(): assert src_lang is not None tokenizer.src_lang = languages_covered()[src_lang] stopping_criteria = get_stopping(prompt_type, prompt_dict, tokenizer, device, model_max_length=tokenizer.model_max_length) inputs = tokenizer(prompt, return_tensors="pt") if debug and len(inputs["input_ids"]) > 0: print('input_ids length', len(inputs["input_ids"][0]), flush=True) input_ids = inputs["input_ids"].to(device) # CRITICAL LIMIT else will fail max_max_tokens = tokenizer.model_max_length max_input_tokens = max_max_tokens - min_new_tokens # NOTE: Don't limit up front due to max_new_tokens, let go up to max or reach max_max_tokens in stopping.py input_ids = input_ids[:, -max_input_tokens:] # required for falcon if multiple threads or asyncio accesses to model during generation if use_cache is None: use_cache = False if 'falcon' in base_model else True gen_config_kwargs = dict(temperature=float(temperature), top_p=float(top_p), top_k=top_k, num_beams=num_beams, do_sample=do_sample, repetition_penalty=float(repetition_penalty), num_return_sequences=num_return_sequences, renormalize_logits=True, remove_invalid_values=True, use_cache=use_cache, ) token_ids = ['eos_token_id', 'pad_token_id', 'bos_token_id', 'cls_token_id', 'sep_token_id'] for token_id in token_ids: if hasattr(tokenizer, token_id) and getattr(tokenizer, token_id) is not None: gen_config_kwargs.update({token_id: getattr(tokenizer, token_id)}) generation_config = GenerationConfig(**gen_config_kwargs) gen_kwargs = dict(input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=max_new_tokens, # prompt + new min_new_tokens=min_new_tokens, # prompt + new early_stopping=early_stopping, # False, True, "never" max_time=max_time, stopping_criteria=stopping_criteria, ) if 'gpt2' in base_model.lower(): gen_kwargs.update(dict(bos_token_id=tokenizer.bos_token_id, pad_token_id=tokenizer.eos_token_id)) elif 'mbart-' in base_model.lower(): assert tgt_lang is not None tgt_lang = languages_covered()[tgt_lang] gen_kwargs.update(dict(forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang])) else: token_ids = ['eos_token_id', 'bos_token_id', 'pad_token_id'] for token_id in token_ids: if hasattr(tokenizer, token_id) and getattr(tokenizer, token_id) is not None: gen_kwargs.update({token_id: getattr(tokenizer, token_id)}) decoder_kwargs = dict(skip_special_tokens=True, clean_up_tokenization_spaces=True) decoder = functools.partial(tokenizer.decode, **decoder_kwargs ) decoder_raw_kwargs = dict(skip_special_tokens=False, clean_up_tokenization_spaces=True) decoder_raw = functools.partial(tokenizer.decode, **decoder_raw_kwargs ) with torch.no_grad(): have_lora_weights = lora_weights not in [no_lora_str, '', None] context_class_cast = NullContext if device == 'cpu' or have_lora_weights else torch.autocast with context_class_cast(device): # protection for gradio not keeping track of closed users, # else hit bitsandbytes lack of thread safety: # https://github.com/h2oai/h2ogpt/issues/104 # but only makes sense if concurrency_count == 1 context_class = NullContext # if concurrency_count > 1 else filelock.FileLock if verbose: print('Pre-Generate: %s' % str(datetime.now()), flush=True) decoded_output = None with context_class("generate.lock"): if verbose: print('Generate: %s' % str(datetime.now()), flush=True) # decoded tokenized prompt can deviate from prompt due to special characters inputs_decoded = decoder(input_ids[0]) inputs_decoded_raw = decoder_raw(input_ids[0]) if inputs_decoded == prompt: # normal pass elif inputs_decoded.lstrip() == prompt.lstrip(): # sometimes extra space in front, make prompt same for prompt removal prompt = inputs_decoded elif inputs_decoded_raw == prompt: # some models specify special tokens that are part of normal prompt, so can't skip them inputs_decoded = prompt = inputs_decoded_raw decoder = decoder_raw decoder_kwargs = decoder_raw_kwargs elif inputs_decoded_raw.replace(" ", "").replace("", "").replace('\n', ' ').replace(' ', '') == prompt.replace( '\n', ' ').replace(' ', ''): inputs_decoded = prompt = inputs_decoded_raw decoder = decoder_raw decoder_kwargs = decoder_raw_kwargs else: if verbose: print("WARNING: Special characters in prompt", flush=True) if stream_output: skip_prompt = False streamer = H2OTextIteratorStreamer(tokenizer, skip_prompt=skip_prompt, block=False, **decoder_kwargs) gen_kwargs.update(dict(streamer=streamer)) target = wrapped_partial(generate_with_exceptions, model.generate, prompt=prompt, inputs_decoded=inputs_decoded, raise_generate_gpu_exceptions=raise_generate_gpu_exceptions, **gen_kwargs) bucket = queue.Queue() thread = EThread(target=target, streamer=streamer, bucket=bucket) thread.start() outputs = "" try: for new_text in streamer: if bucket.qsize() > 0 or thread.exc: thread.join() outputs += new_text yield dict(response=prompter.get_response(outputs, prompt=inputs_decoded, sanitize_bot_response=sanitize_bot_response), sources='') except BaseException: # if any exception, raise that exception if was from thread, first if thread.exc: raise thread.exc raise finally: # clear before return, since .then() never done if from API clear_torch_cache() # in case no exception and didn't join with thread yet, then join if not thread.exc: thread.join() # in case raise StopIteration or broke queue loop in streamer, but still have exception if thread.exc: raise thread.exc decoded_output = outputs else: try: outputs = model.generate(**gen_kwargs) finally: clear_torch_cache() # has to be here for API submit_nochat_api since.then() not called outputs = [decoder(s) for s in outputs.sequences] yield dict(response=prompter.get_response(outputs, prompt=inputs_decoded, sanitize_bot_response=sanitize_bot_response), sources='') if outputs and len(outputs) >= 1: decoded_output = prompt + outputs[0] if save_dir and decoded_output: extra_dict = gen_config_kwargs.copy() extra_dict.update(dict(num_prompt_tokens=num_prompt_tokens)) save_generate_output(prompt=prompt, output=decoded_output, base_model=base_model, save_dir=save_dir, where_from="evaluate_%s" % str(stream_output), extra_dict=gen_config_kwargs) if verbose: print('Post-Generate: %s decoded_output: %s' % ( str(datetime.now()), len(decoded_output) if decoded_output else -1), flush=True) inputs_list_names = list(inspect.signature(evaluate).parameters) state_names = ['model_state', 'my_db_state'] inputs_kwargs_list = [x for x in inputs_list_names if x not in eval_func_param_names + state_names] def get_cutoffs(memory_restriction_level, for_context=False, model_max_length=2048): # help to avoid errors like: # RuntimeError: The size of tensor a (2048) must match the size of tensor b (2049) at non-singleton dimension 3 # RuntimeError: expected scalar type Half but found Float # with - 256 if memory_restriction_level > 0: max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256 else: # at least give room for 1 paragraph output max_length_tokenize = model_max_length - 256 cutoff_len = max_length_tokenize * 4 # if reaches limit, then can't generate new tokens output_smallest = 30 * 4 max_prompt_length = cutoff_len - output_smallest if for_context: # then lower even more to avoid later chop, since just estimate tokens in context bot max_prompt_length = max(64, int(max_prompt_length * 0.8)) return cutoff_len, output_smallest, max_length_tokenize, max_prompt_length class H2OTextIteratorStreamer(TextIteratorStreamer): """ normally, timeout required for now to handle exceptions, else get() but with H2O version of TextIteratorStreamer, loop over block to handle """ def __init__(self, tokenizer, skip_prompt: bool = False, timeout: typing.Optional[float] = None, block=True, **decode_kwargs): super().__init__(tokenizer, skip_prompt, **decode_kwargs) self.text_queue = queue.Queue() self.stop_signal = None self.do_stop = False self.timeout = timeout self.block = block def on_finalized_text(self, text: str, stream_end: bool = False): """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue.""" self.text_queue.put(text, timeout=self.timeout) if stream_end: self.text_queue.put(self.stop_signal, timeout=self.timeout) def __iter__(self): return self def __next__(self): while True: try: value = self.stop_signal # value looks unused in pycharm, not true if self.do_stop: print("hit stop", flush=True) # could raise or break, maybe best to raise and make parent see if any exception in thread self.clear_queue() self.do_stop = False raise StopIteration() # break value = self.text_queue.get(block=self.block, timeout=self.timeout) break except queue.Empty: time.sleep(0.01) if value == self.stop_signal: self.clear_queue() self.do_stop = False raise StopIteration() else: return value def clear_queue(self): # make sure streamer is reusable after stop hit with self.text_queue.mutex: self.text_queue.queue.clear() def generate_with_exceptions(func, *args, prompt='', inputs_decoded='', raise_generate_gpu_exceptions=True, **kwargs): try: func(*args, **kwargs) except torch.cuda.OutOfMemoryError as e: print("GPU OOM 2: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)), flush=True) if 'input_ids' in kwargs: if kwargs['input_ids'] is not None: kwargs['input_ids'].cpu() kwargs['input_ids'] = None traceback.print_exc() clear_torch_cache() return except (Exception, RuntimeError) as e: if 'Expected all tensors to be on the same device' in str(e) or \ 'expected scalar type Half but found Float' in str(e) or \ 'probability tensor contains either' in str(e) or \ 'cublasLt ran into an error!' in str(e) or \ 'mat1 and mat2 shapes cannot be multiplied' in str(e): print( "GPU Error: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)), flush=True) traceback.print_exc() clear_torch_cache() if raise_generate_gpu_exceptions: raise return else: clear_torch_cache() if raise_generate_gpu_exceptions: raise def get_generate_params(model_lower, chat, stream_output, show_examples, prompt_type, prompt_dict, temperature, top_p, top_k, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample, top_k_docs, chunk, chunk_size, verbose): use_defaults = False use_default_examples = True examples = [] task_info = 'LLM' if model_lower: print(f"Using Model {model_lower}", flush=True) else: if verbose: print("No model defined yet", flush=True) min_new_tokens = min_new_tokens if min_new_tokens is not None else 0 early_stopping = early_stopping if early_stopping is not None else False max_time_defaults = 60 * 3 max_time = max_time if max_time is not None else max_time_defaults if not prompt_type and model_lower in inv_prompt_type_to_model_lower and prompt_type != 'custom': prompt_type = inv_prompt_type_to_model_lower[model_lower] if verbose: print("Auto-selecting prompt_type=%s for %s" % (prompt_type, model_lower), flush=True) # examples at first don't include chat, instruction_nochat, iinput_nochat, added at end if show_examples is None: if chat: show_examples = False else: show_examples = True summarize_example1 = """Jeff: Can I train a ? Transformers model on Amazon SageMaker? Philipp: Sure you can use the new Hugging Face Deep Learning Container. Jeff: ok. Jeff: and how can I get started? Jeff: where can I find documentation? Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face""" use_placeholder_instruction_as_example = False if 'bart-large-cnn-samsum' in model_lower or 'flan-t5-base-samsum' in model_lower: placeholder_instruction = summarize_example1 placeholder_input = "" use_defaults = True use_default_examples = False use_placeholder_instruction_as_example = True task_info = "Summarization" elif 't5-' in model_lower or 't5' == model_lower or 'flan-' in model_lower: placeholder_instruction = "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" placeholder_input = "" use_defaults = True use_default_examples = True task_info = "Multi-Task: Q/A, translation, Chain-of-Thought, Logical Reasoning, Summarization, etc. Best to use task prefix as trained on, e.g. `translate English to German: ` (space after colon)" elif 'mbart-' in model_lower: placeholder_instruction = "The girl has long hair." placeholder_input = "" use_defaults = True use_default_examples = False use_placeholder_instruction_as_example = True elif 'gpt2' in model_lower: placeholder_instruction = "The sky is" placeholder_input = "" prompt_type = prompt_type or 'plain' use_default_examples = True # some will be odd "continuations" but can be ok use_placeholder_instruction_as_example = True task_info = "Auto-complete phrase, code, etc." use_defaults = True else: if chat: placeholder_instruction = "" else: placeholder_instruction = "Give detailed answer for whether Einstein or Newton is smarter." placeholder_input = "" if model_lower in inv_prompt_type_to_model_lower: if prompt_type != 'custom': prompt_type = inv_prompt_type_to_model_lower[model_lower] elif model_lower: # default is plain, because might rely upon trust_remote_code to handle prompting prompt_type = prompt_type or 'plain' else: prompt_type = '' task_info = "No task" if prompt_type == 'instruct': task_info = "Answer question or follow imperative as instruction with optionally input." elif prompt_type == 'plain': task_info = "Auto-complete phrase, code, etc." elif prompt_type == 'human_bot': if chat: task_info = "Chat (Shift-Enter to give question/imperative, input concatenated with instruction)" else: task_info = "Ask question/imperative (input concatenated with instruction)" # revert to plain if still nothing prompt_type = prompt_type or 'plain' if use_defaults: temperature = 1.0 if temperature is None else temperature top_p = 1.0 if top_p is None else top_p top_k = 40 if top_k is None else top_k num_beams = num_beams or 1 max_new_tokens = max_new_tokens or 128 repetition_penalty = repetition_penalty or 1.07 num_return_sequences = min(num_beams, num_return_sequences or 1) do_sample = False if do_sample is None else do_sample else: temperature = 0.1 if temperature is None else temperature top_p = 0.75 if top_p is None else top_p top_k = 40 if top_k is None else top_k num_beams = num_beams or 1 max_new_tokens = max_new_tokens or 256 repetition_penalty = repetition_penalty or 1.07 num_return_sequences = min(num_beams, num_return_sequences or 1) do_sample = False if do_sample is None else do_sample # doesn't include chat, instruction_nochat, iinput_nochat, added later params_list = ["", stream_output, prompt_type, prompt_dict, temperature, top_p, top_k, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample] if use_placeholder_instruction_as_example: examples += [[placeholder_instruction, ''] + params_list] if use_default_examples: examples += [ ["Translate English to French", "Good morning"] + params_list, ["Give detailed answer for whether Einstein or Newton is smarter.", ''] + params_list, ["Explain in detailed list, all the best practices for coding in python.", ''] + params_list, [ "Create a markdown table with 3 rows for the primary colors, and 2 columns, with color name and hex codes.", ''] + params_list, ['Translate to German: My name is Arthur', ''] + params_list, ["Please answer to the following question. Who is going to be the next Ballon d'or?", ''] + params_list, ['Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.', ''] + params_list, ['Please answer the following question. What is the boiling point of Nitrogen?', ''] + params_list, ['Answer the following yes/no question. Can you write a whole Haiku in a single tweet?', ''] + params_list, ["Simplify the following expression: (False or False and True). Explain your answer.", ''] + params_list, [ "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?", ''] + params_list, ['The square root of x is the cube root of y. What is y to the power of 2, if x = 4?', ''] + params_list, [ 'Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?', ''] + params_list, ["""def area_of_rectangle(a: float, b: float): \"\"\"Return the area of the rectangle.\"\"\"""", ''] + params_list, ["""# a function in native python: def mean(a): return sum(a)/len(a) # the same function using numpy: import numpy as np def mean(a):""", ''] + params_list, ["""X = np.random.randn(100, 100) y = np.random.randint(0, 1, 100) # fit random forest classifier with 20 estimators""", ''] + params_list, ] # add summary example examples += [ [summarize_example1, 'Summarize' if prompt_type not in ['plain', 'instruct_simple'] else ''] + params_list] src_lang = "English" tgt_lang = "Russian" # move to correct position for example in examples: example += [chat, '', '', 'Disabled', LangChainAction.QUERY.value, top_k_docs, chunk, chunk_size, [DocumentChoices.All_Relevant.name] ] # adjust examples if non-chat mode if not chat: example[eval_func_param_names.index('instruction_nochat')] = example[ eval_func_param_names.index('instruction')] example[eval_func_param_names.index('instruction')] = '' example[eval_func_param_names.index('iinput_nochat')] = example[eval_func_param_names.index('iinput')] example[eval_func_param_names.index('iinput')] = '' assert len(example) == len(eval_func_param_names), "Wrong example: %s %s" % ( len(example), len(eval_func_param_names)) if prompt_type == PromptType.custom.name and not prompt_dict: raise ValueError("Unexpected to get non-empty prompt_dict=%s for prompt_type=%s" % (prompt_dict, prompt_type)) # get prompt_dict from prompt_type, so user can see in UI etc., or for custom do nothing except check format prompt_dict, error0 = get_prompt(prompt_type, prompt_dict, chat=False, context='', reduced=False, making_context=False, return_dict=True) if error0: raise RuntimeError("Prompt wrong: %s" % error0) return placeholder_instruction, placeholder_input, \ stream_output, show_examples, \ prompt_type, prompt_dict, \ temperature, top_p, top_k, num_beams, \ max_new_tokens, min_new_tokens, early_stopping, max_time, \ repetition_penalty, num_return_sequences, \ do_sample, \ src_lang, tgt_lang, \ examples, \ task_info def languages_covered(): # https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered covered = """Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)""" covered = covered.split(', ') covered = {x.split(' ')[0]: x.split(' ')[1].replace(')', '').replace('(', '') for x in covered} return covered def get_context(chat_context, prompt_type): if chat_context and prompt_type == 'human_bot': context0 = """: I am an intelligent, helpful, truthful, and fair assistant named h2oGPT, who will give accurate, balanced, and reliable responses. I will not respond with I don't know or I don't understand. : I am a human person seeking useful assistance and request all questions be answered completely, and typically expect detailed responses. Give answers in numbered list format if several distinct but related items are being listed.""" else: context0 = '' return context0 def score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len): question = question[-cutoff_len:] answer = answer[-cutoff_len:] inputs = stokenizer(question, answer, return_tensors="pt", truncation=True, max_length=max_length_tokenize).to(smodel.device) try: score = torch.sigmoid(smodel(**inputs).logits[0]).cpu().detach().numpy()[0] except torch.cuda.OutOfMemoryError as e: print("GPU OOM 3: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True) del inputs traceback.print_exc() clear_torch_cache() return 'Response Score: GPU OOM' except (Exception, RuntimeError) as e: if 'Expected all tensors to be on the same device' in str(e) or \ 'expected scalar type Half but found Float' in str(e) or \ 'probability tensor contains either' in str(e) or \ 'cublasLt ran into an error!' in str(e) or \ 'device-side assert triggered' in str(e): print("GPU Error: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True) traceback.print_exc() clear_torch_cache() return 'Response Score: GPU Error' else: raise os.environ['TOKENIZERS_PARALLELISM'] = 'true' return score def check_locals(**kwargs): # ensure everything in evaluate is here can_skip_because_locally_generated = no_default_param_names + [ # get_model: 'reward_type' ] for k in eval_func_param_names: if k in can_skip_because_locally_generated: continue assert k in kwargs, "Missing %s" % k for k in inputs_kwargs_list: if k in can_skip_because_locally_generated: continue assert k in kwargs, "Missing %s" % k for k in list(inspect.signature(get_model).parameters): if k in can_skip_because_locally_generated: continue assert k in kwargs, "Missing %s" % k def get_model_max_length(model_state): if not isinstance(model_state['tokenizer'], (str, types.NoneType)): return model_state['tokenizer'].model_max_length else: return 2048 def get_max_max_new_tokens(model_state, **kwargs): if not isinstance(model_state['tokenizer'], (str, types.NoneType)): max_max_new_tokens = model_state['tokenizer'].model_max_length else: max_max_new_tokens = None if kwargs['max_max_new_tokens'] is not None and max_max_new_tokens is not None: return min(max_max_new_tokens, kwargs['max_max_new_tokens']) elif kwargs['max_max_new_tokens'] is not None: return kwargs['max_max_new_tokens'] elif kwargs['memory_restriction_level'] == 1: return 768 elif kwargs['memory_restriction_level'] == 2: return 512 elif kwargs['memory_restriction_level'] >= 3: return 256 else: # FIXME: Need to update after new model loaded, so user can control with slider return 2048 def get_minmax_top_k_docs(is_public): if is_public: min_top_k_docs = 1 max_top_k_docs = 3 label_top_k_docs = "Number of document chunks" else: min_top_k_docs = -1 max_top_k_docs = 100 label_top_k_docs = "Number of document chunks (-1 = auto fill model context)" return min_top_k_docs, max_top_k_docs, label_top_k_docs def history_to_context(history, langchain_mode1, prompt_type1, prompt_dict1, chat1, model_max_length1, memory_restriction_level1, keep_sources_in_context1): """ consumes all history up to (but not including) latest history item that is presumed to be an [instruction, None] pair :param history: :param langchain_mode1: :param prompt_type1: :param prompt_dict1: :param chat1: :param model_max_length1: :param memory_restriction_level1: :param keep_sources_in_context1: :return: """ # ensure output will be unique to models _, _, _, max_prompt_length = get_cutoffs(memory_restriction_level1, for_context=True, model_max_length=model_max_length1) context1 = '' if max_prompt_length is not None and langchain_mode1 not in ['LLM']: context1 = '' # - 1 below because current instruction already in history from user() for histi in range(0, len(history) - 1): data_point = dict(instruction=history[histi][0], input='', output=history[histi][1]) prompt, pre_response, terminate_response, chat_sep, chat_turn_sep = generate_prompt(data_point, prompt_type1, prompt_dict1, chat1, reduced=True, making_context=True) # md -> back to text, maybe not super important if model trained enough if not keep_sources_in_context1 and langchain_mode1 != 'Disabled' and prompt.find(source_prefix) >= 0: # FIXME: This is relatively slow even for small amount of text, like 0.3s each history item import re prompt = re.sub(f'{re.escape(source_prefix)}.*?{re.escape(source_postfix)}', '', prompt, flags=re.DOTALL) if prompt.endswith('\n

'): prompt = prompt[:-4] prompt = prompt.replace('
', chat_turn_sep) if not prompt.endswith(chat_turn_sep): prompt += chat_turn_sep # most recent first, add older if can # only include desired chat history if len(prompt + context1) > max_prompt_length: break context1 += prompt _, pre_response, terminate_response, chat_sep, chat_turn_sep = generate_prompt({}, prompt_type1, prompt_dict1, chat1, reduced=True, making_context=True) if context1 and not context1.endswith(chat_turn_sep): context1 += chat_turn_sep # ensure if terminates abruptly, then human continues on next line return context1 def entrypoint_main(): """ Examples: WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 --master_port=1234 generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights=lora-alpaca_6B python generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights='lora-alpaca_6B' python generate.py --base_model='EleutherAI/gpt-neox-20b' --lora_weights='lora-alpaca_20B' # generate without lora weights, no prompt python generate.py --base_model='EleutherAI/gpt-neox-20b' --prompt_type='plain' python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq' python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq' --lora_weights='lora_20B_daifaq' # OpenChatKit settings: python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0 python generate.py --base_model='distilgpt2' --prompt_type='plain' --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0 --share=False python generate.py --base_model='t5-large' --prompt_type='simple_instruct' python generate.py --base_model='philschmid/bart-large-cnn-samsum' python generate.py --base_model='philschmid/flan-t5-base-samsum' python generate.py --base_model='facebook/mbart-large-50-many-to-many-mmt' python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot' --lora_weights='GPT-NeoXT-Chat-Base-20B.merged.json.8_epochs.57b2892c53df5b8cefac45f84d019cace803ef26.28' must have 4*48GB GPU and run without 8bit in order for sharding to work with infer_devices=False can also pass --prompt_type='human_bot' and model can somewhat handle instructions without being instruct tuned python generate.py --base_model=decapoda-research/llama-65b-hf --load_8bit=False --infer_devices=False --prompt_type='human_bot' python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b """ fire.Fire(main) if __name__ == "__main__": entrypoint_main()