h2ogpt-chatbot2

Runtime error

App Files Files Community

h2ogpt-chatbot2 / generate.py

elitecode

Duplicate from h2oai/h2ogpt-chatbot2

ffb3860 11 months ago

raw history blame contribute delete

No virus

80.8 kB

	import ast
	import functools
	import glob
	import inspect
	import queue
	import shutil
	import sys
	import os
	import time
	import traceback
	import typing
	import warnings
	from datetime import datetime
	import filelock
	import psutil

	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
	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

	start_faulthandler()
	import_matplotlib()

	SEED = 1236
	set_seed(SEED)

	from typing import Union

	import fire
	import torch
	from transformers import GenerationConfig, AutoModel, TextIteratorStreamer
	from accelerate import init_empty_weights, infer_auto_device_map

	from prompter import Prompter, inv_prompt_type_to_model_lower, non_hf_types, PromptType, get_prompt
	from stopping import get_stopping

	eval_extra_columns = ['prompt', 'response', 'score']

	langchain_modes = [x.value for x in list(LangChainMode)]

	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,

	prompt_type: Union[int, str] = None,
	prompt_dict: typing.Dict = None,
	# 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_avoid_processing_markdown: bool = False,
	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 = False,
	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,
	auth: typing.List[typing.Tuple[str, str]] = None,

	sanitize_user_prompt: bool = True,
	sanitize_bot_response: bool = True,

	extra_model_options: typing.List[str] = [],
	extra_lora_options: typing.List[str] = [],

	score_model: str = 'OpenAssistant/reward-model-deberta-v3-large-v2',
	auto_score: bool = True,

	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',
	visible_langchain_modes: list = ['UserData', 'MyData'],
	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 = 3, # FIXME: Can go back to 4 once https://github.com/h2oai/h2ogpt/issues/192 fixed
	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 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 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_avoid_processing_markdown:
	: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.
	: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 from generate
	: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 auth: gradio auth for launcher in form [(user1, pass1), (user2, pass2), ...]
	e.g. --auth=[('jon','password')] with no spaces
	:param sanitize_user_prompt: whether to remove profanity from user input
	:param sanitize_bot_response: whether to remove profanity and repeat lines from bot output
	: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 score_model: which model to score responses (None means no scoring)
	:param auto_score: whether to automatically score responses
	: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 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 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 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: int = "Salesforce/blip-image-captioning-base", # continue capable
	captions_model: str = "Salesforce/blip2-flan-t5-xl", # question/answer capable, 16GB state
	captions_model: int = "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:
	"""
	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
	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]

	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
	else:
	# by default don't sample, too chatty
	do_sample = False if do_sample is None else do_sample

	if memory_restriction_level == 2:
	if not base_model:
	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
	else:
	base_model = 'h2oai/h2ogpt-oasst1-512-20b' if not base_model else base_model
	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"
	if is_hf:
	# must override share if in spaces
	share = False
	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:
	# 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
	model_lower = base_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)

	user_set_max_new_tokens = max_new_tokens is not None

	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,
	)

	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)), get_githash()), 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)
	shutil.rmtree(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"

	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
	all_kwargs = locals().copy()
	if all_kwargs.get('base_model') and not all_kwargs['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
	model_state0 = [model0, tokenizer0, device, all_kwargs['base_model']]

	# get score model
	smodel, stokenizer, sdevice = get_score_model(reward_type=True,
	**get_kwargs(get_score_model, exclude_names=['reward_type'],
	**all_kwargs))
	score_model_state0 = [smodel, stokenizer, sdevice, score_model]

	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_non_lora_model(base_model, model_loader, load_half, model_kwargs, reward_type,
	gpu_id=0,
	use_auth_token=False,
	trust_remote_code=True,
	offload_folder=None,
	triton_attn=False,
	long_sequence=True,
	):
	"""
	Ensure model gets on correct device
	:param base_model:
	:param model_loader:
	:param load_half:
	:param model_kwargs:
	:param reward_type:
	:param gpu_id:
	:param use_auth_token:
	:param trust_remote_code:
	:param offload_folder:
	:param triton_attn:
	:param long_sequence:
	:return:
	"""
	with init_empty_weights():
	from transformers import AutoConfig
	config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token,
	trust_remote_code=trust_remote_code,
	offload_folder=offload_folder)
	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 issubclass(config.__class__, tuple(AutoModel._model_mapping.keys())):
	model = AutoModel.from_config(
	config,
	)
	else:
	# can't infer
	model = None

	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.
	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_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,

	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 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)
	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

	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)"
	)

	from transformers import AutoConfig
	config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token,
	trust_remote_code=trust_remote_code,
	offload_folder=offload_folder)
	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 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(llama_type=llama_type, model_name=base_model, reward_type=reward_type)
	if not tokenizer_base_model:
	tokenizer_base_model = base_model

	if tokenizer_loader is not None and not isinstance(tokenizer_loader, str):
	tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model,
	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,
	)
	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 >= 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:
	model = get_non_lora_model(base_model, model_loader, load_half, model_kwargs, reward_type,
	gpu_id=gpu_id,
	use_auth_token=use_auth_token,
	trust_remote_code=trust_remote_code,
	offload_folder=offload_folder,
	)
	else:
	if load_half and not (load_8bit or load_4bit):
	model = model_loader.from_pretrained(
	base_model,
	**model_kwargs).half()
	else:
	model = model_loader.from_pretrained(
	base_model,
	**model_kwargs)
	elif load_8bit or load_4bit:
	model = model_loader.from_pretrained(
	base_model,
	**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):
	model = model_loader.from_pretrained(
	base_model,
	**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': '<bos>',
	'eos_token': '<eos>',
	'pad_token': '<pad>'})

	if not isinstance(tokenizer, str):
	model.eval()
	if torch.__version__ >= "2" and sys.platform != "win32" and compile_model:
	model = torch.compile(model)

	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

	return model, tokenizer, device


	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 = '',
	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 = ''
	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


	no_default_param_names = [
	'instruction',
	'iinput',
	'context',
	'instruction_nochat',
	'iinput_nochat',
	]

	gen_hyper = ['temperature',
	'top_p',
	'top_k',
	'num_beams',
	'max_new_tokens',
	'min_new_tokens',
	'early_stopping',
	'max_time',
	'repetition_penalty',
	'num_return_sequences',
	'do_sample',
	]

	eval_func_param_names = ['instruction',
	'iinput',
	'context',
	'stream_output',
	'prompt_type',
	'prompt_dict'] + \
	gen_hyper + \
	['chat',
	'instruction_nochat',
	'iinput_nochat',
	'langchain_mode',
	'top_k_docs',
	'chunk',
	'chunk_size',
	'document_choice',
	]

	# form evaluate defaults for submit_nochat_api
	eval_func_param_names_defaults = eval_func_param_names.copy()
	for k in no_default_param_names:
	if k in eval_func_param_names_defaults:
	eval_func_param_names_defaults.remove(k)


	def evaluate_from_str(
	model_state,
	my_db_state,
	# START NOTE: Examples must have same order of parameters
	user_kwargs,
	# END NOTE: Examples must have same order of parameters
	default_kwargs=None,
	src_lang=None,
	tgt_lang=None,
	debug=False,
	concurrency_count=None,
	save_dir=None,
	sanitize_bot_response=True,
	model_state0=None,
	memory_restriction_level=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,
	chunk=None,
	chunk_size=None,
	db_type=None,
	n_jobs=None,
	first_para=None,
	text_limit=None,
	verbose=False,
	cli=False,
	):
	if isinstance(user_kwargs, str):
	user_kwargs = ast.literal_eval(user_kwargs)
	# only used for submit_nochat_api
	user_kwargs['chat'] = False
	user_kwargs['stream_output'] = False
	if 'langchain_mode' not in user_kwargs:
	# if user doesn't specify, then assume disabled, not use default
	user_kwargs['langchain_mode'] = 'Disabled'

	assert set(list(default_kwargs.keys())) == set(eval_func_param_names)
	# correct ordering. Note some things may not be in default_kwargs, so can't be default of user_kwargs.get()
	args_list = [user_kwargs[k] if k in user_kwargs else default_kwargs[k] for k in eval_func_param_names]

	ret = evaluate(
	model_state,
	my_db_state,
	# START NOTE: Examples must have same order of parameters
	*tuple(args_list),
	# END NOTE: Examples must have same order of parameters
	src_lang=src_lang,
	tgt_lang=tgt_lang,
	debug=debug,
	concurrency_count=concurrency_count,
	save_dir=save_dir,
	sanitize_bot_response=sanitize_bot_response,
	model_state0=model_state0,
	memory_restriction_level=memory_restriction_level,
	raise_generate_gpu_exceptions=raise_generate_gpu_exceptions,
	chat_context=chat_context,
	lora_weights=lora_weights,
	load_db_if_exists=load_db_if_exists,
	dbs=dbs,
	user_path=user_path,
	detect_user_path_changes_every_query=detect_user_path_changes_every_query,
	use_openai_embedding=use_openai_embedding,
	use_openai_model=use_openai_model,
	hf_embedding_model=hf_embedding_model,
	db_type=db_type,
	n_jobs=n_jobs,
	first_para=first_para,
	text_limit=text_limit,
	verbose=verbose,
	cli=cli,
	)
	try:
	for ret1 in ret:
	yield ret1
	finally:
	# clear before return, in finally in case GPU OOM exception
	clear_torch_cache()


	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,
	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=True,
	model_state0=None,
	memory_restriction_level=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,
	):
	# 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)
	print(locals_dict)

	no_model_msg = "Please choose a base model with --base_model (CLI) or load in Models Tab (gradio).\nThen start New Conversation"

	if model_state0 is None:
	# e.g. for no gradio case, set dummy value, else should be set
	model_state0 = [None, None, None, None]

	if model_state is not None and len(model_state) == 4 and not isinstance(model_state[0], str):
	# try to free-up original model (i.e. list was passed as reference)
	if model_state0 is not None and model_state0[0] is not None:
	model_state0[0].cpu()
	model_state0[0] = None
	# try to free-up original tokenizer (i.e. list was passed as reference)
	if model_state0 is not None and model_state0[1] is not None:
	model_state0[1] = None
	clear_torch_cache()
	model, tokenizer, device, base_model = model_state
	elif model_state0 is not None and len(model_state0) == 4 and model_state0[0] is not None:
	assert isinstance(model_state[0], str)
	model, tokenizer, device, base_model = model_state0
	else:
	raise AssertionError(no_model_msg)

	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

	if not context:
	# get hidden context if have one
	context = get_context(chat_context, prompt_type)

	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
	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
	if langchain_mode not in [False, 'Disabled', 'ChatLLM', 'LLM'] and db1 is not None or base_model in non_hf_types:
	query = instruction if not iinput else "%s\n%s" % (instruction, iinput)
	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
	for r in run_qa_db(query=query,
	model_name=base_model, model=model, tokenizer=tokenizer,
	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,
	document_choice=document_choice,
	db_type=db_type,
	top_k_docs=top_k_docs,

	# gen_hyper:
	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,

	prompt_type=prompt_type,
	prompt_dict=prompt_dict,
	n_jobs=n_jobs,
	verbose=verbose,
	cli=cli,
	):
	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:
	save_generate_output(output=outr, base_model=base_model, save_dir=save_dir)
	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 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]

	if chat:
	# override, ignore user change
	num_return_sequences = 1
	stopping_criteria = get_stopping(prompt_type, prompt_dict, tokenizer, device)
	_, _, max_length_tokenize, max_prompt_length = get_cutoffs(memory_restriction_level,
	model_max_length=tokenizer.model_max_length)
	prompt = prompt[-max_prompt_length:]
	inputs = tokenizer(prompt,
	return_tensors="pt",
	truncation=True,
	max_length=max_length_tokenize)
	if inputs['input_ids'].shape[1] >= max_length_tokenize - 1:
	print("Cutting off input: %s %s" % (inputs['input_ids'].shape[1], max_length_tokenize), flush=True)
	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 - max_new_tokens
	input_ids = input_ids[:, -max_input_tokens:]
	generation_config = GenerationConfig(
	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,
	)

	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:
	gen_kwargs.update(dict(pad_token_id=tokenizer.eos_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():
	context_class_cast = NullContext if device == 'cpu' or 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("<unk> ", "").replace("<unk>", "").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:
	save_generate_output(output=decoded_output, base_model=base_model, save_dir=save_dir)
	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:
	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
	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:
	raise StopIteration()
	else:
	return value


	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:
	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:
	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 = "Enter a question or imperative."
	else:
	placeholder_instruction = "Give detailed answer for whether Einstein or Newton is smarter."
	placeholder_input = ""
	if model_lower:
	# default is plain, because might relly 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', 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, 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 = """<bot>: 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.
	<human>: 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):
	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_max_max_new_tokens(model_state, **kwargs):
	if kwargs['max_new_tokens'] and kwargs['user_set_max_new_tokens']:
	max_max_new_tokens = kwargs['max_new_tokens']
	elif kwargs['memory_restriction_level'] == 1:
	max_max_new_tokens = 768
	elif kwargs['memory_restriction_level'] == 2:
	max_max_new_tokens = 512
	elif kwargs['memory_restriction_level'] >= 3:
	max_max_new_tokens = 256
	else:
	if not isinstance(model_state[1], str):
	max_max_new_tokens = model_state[1].model_max_length
	else:
	# FIXME: Need to update after new model loaded, so user can control with slider
	max_max_new_tokens = 2048
	return max_max_new_tokens


	if __name__ == "__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)