import functools import inspect import sys import os import traceback import typing from utils import set_seed, flatten_list, clear_torch_cache SEED = 1236 set_seed(SEED) os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' from typing import Union import numpy as np import pandas as pd import fire import torch from peft import PeftModel from transformers import GenerationConfig, StoppingCriteriaList, AutoModel from accelerate import init_empty_weights, infer_auto_device_map from prompter import Prompter from finetune import get_loaders, example_data_points, generate_prompt, get_githash, prompt_types_strings, \ human, bot, prompt_type_to_model_name, inv_prompt_type_to_model_lower from stopping import CallbackToGenerator, Stream, StoppingCriteriaSub def main( load_8bit: bool = False, load_half: bool = True, infer_devices: bool = True, base_model: str = '', tokenizer_base_model: str = '', lora_weights: str = "", force_1_gpu: bool = True, prompt_type: Union[int, str] = 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, llama_type: bool = None, debug: bool = False, share: bool = True, local_files_only: bool = False, resume_download: bool = True, use_auth_token: Union[str, bool] = False, # True requires CLI did huggingface-cli login before running src_lang: str = "English", tgt_lang: str = "Russian", gradio: bool = True, gradio_avoid_processing_markdown: bool = True, chat: bool = True, chat_history: int = 4096, # character length of chat context/history stream_output: bool = True, show_examples: bool = None, verbose: bool = False, h2ocolors: bool = True, height: int = 400, show_lora: bool = True, # set to True to load --base_model after client logs in, # to be able to free GPU memory when model is swapped login_mode_if_model0: bool = False, 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_sharegpt_prompts_only: int = 0, eval_sharegpt_prompts_only_seed: int = 1234, eval_sharegpt_as_output: bool = False, ): # allow set token directly use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token) # override share if in spaces if os.environ.get("HUGGINGFACE_SPACES"): share = False base_model = 'h2oai/h2ogpt-oasst1-512-12b' load_8bit = True # 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 placeholder_instruction, placeholder_input, \ stream_output, show_examples, \ prompt_type, 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, 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 not gradio: if eval_sharegpt_prompts_only > 0: # override default examples with shareGPT ones for human-level eval purposes only filename = 'ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json' if not os.path.isfile(filename): os.system('wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s' % filename) import json data = json.load(open(filename, 'rt')) # focus on data that starts with human, else likely chopped from other data turn_start = 0 # odd in general data = [x for x in data if len(x['conversations']) > turn_start + 1 and x['conversations'][turn_start]['from'] == 'human' and x['conversations'][turn_start + 1]['from'] == 'gpt'] np.random.seed(eval_sharegpt_prompts_only_seed) example1 = examples[-1] # pick reference example examples = [] responses = [] for i in list(np.random.randint(0, len(data), size=eval_sharegpt_prompts_only)): assert data[i]['conversations'][turn_start]['from'] == 'human' instruction = data[i]['conversations'][turn_start]['value'] assert data[i]['conversations'][turn_start + 1]['from'] == 'gpt' output = data[i]['conversations'][turn_start + 1]['value'] examplenew = example1.copy() examplenew[0] = instruction examplenew[1] = '' # no input examplenew[2] = '' # no context examples.append(examplenew) responses.append(output) with torch.device("cuda"): # ensure was set right above before examples generated assert not stream_output, "stream_output=True does not make sense with example loop" import time from functools import partial # get score model smodel, stokenizer, sdevice = get_score_model(**locals()) if not eval_sharegpt_as_output: model, tokenizer, device = get_model(**locals()) model_state = [model, tokenizer, device, base_model] fun = partial(evaluate, model_state, debug=debug, chat=chat) else: assert eval_sharegpt_prompts_only > 0 def get_response(*args, exi=0): # assumes same ordering of examples and responses yield responses[exi] fun = get_response t0 = time.time() score_dump = [] num_examples = len(examples) import matplotlib.pyplot as plt for exi, ex in enumerate(examples): clear_torch_cache() print("") print("START" + "=" * 100) print("Question: %s %s" % (ex[0], ('input=%s' % ex[1] if ex[1] else ''))) print("-" * 105) # fun yields as generator, so have to iterate over it # Also means likely do NOT want --stream_output=True, else would show all generations for res in fun(*tuple(ex), exi=exi): print(res) if smodel: score_with_prompt = False if score_with_prompt: data_point = dict(instruction=ex[0], input=ex[1]) prompter = Prompter(prompt_type, debug=debug, chat=chat, stream_output=stream_output) prompt = prompter.generate_prompt(data_point) else: # just raw input and output assert ex[1] in [None, ''] # should be no iinput assert ex[2] in [None, ''] # should be no context prompt = ex[0] cutoff_len = 768 if os.environ.get("HUGGINGFACE_SPACES") else 2048 inputs = stokenizer(prompt, res, return_tensors="pt", truncation=True, max_length=cutoff_len) try: score = torch.sigmoid(smodel(**inputs).logits[0]).cpu().detach().numpy()[0] except torch.cuda.OutOfMemoryError as e: print("GPU OOM: question: %s answer: %s exception: %s" % (prompt, res, str(e)), flush=True) traceback.print_exc() score = 0.0 clear_torch_cache() print("SCORE %s: %s" % (exi, score), flush=True) score_dump.append(ex + [prompt, res, score]) # dump every score in case abort scoring_path = 'scoring' os.makedirs(scoring_path, exist_ok=True) if eval_sharegpt_as_output: used_base_model = 'gpt35' used_lora_weights = '' else: used_base_model = str(base_model.split('/')[-1]) used_lora_weights = str(lora_weights.split('/')[-1]) df_scores = pd.DataFrame(score_dump, columns=eval_func_param_names + ['prompt', 'response', 'score']) filename = "df_scores_%s_%s_%s_%s_%s_%s.parquet" % (num_examples, eval_sharegpt_prompts_only, eval_sharegpt_prompts_only_seed, eval_sharegpt_as_output, used_base_model, used_lora_weights) filename = os.path.join(scoring_path, filename) df_scores.to_parquet(filename, index=False) # plot histogram so far plt.figure(figsize=(10, 10)) plt.hist(df_scores['score'], bins=20) score_avg = np.mean(df_scores['score']) score_median = np.median(df_scores['score']) plt.title("Score avg: %s median: %s" % (score_avg, score_median)) plt.savefig(filename.replace('.parquet', '.png')) plt.close() print("END" + "=" * 102) print("") t2 = time.time() print("Time taken so far: %.4f about %.4g per example" % (t2 - t0, (t2 - t0) / (1 + exi))) t1 = time.time() print("Total time taken: %.4f about %.4g per example" % (t1 - t0, (t1 - t0) / num_examples)) return if gradio: go_gradio(**locals()) def get_device(): if torch.cuda.is_available(): device = "cuda" else: raise RuntimeError("only cuda supported") return device def get_non_lora_model(base_model, model_loader, load_half, model_kwargs, reward_type, force_1_gpu=True, use_auth_token=False): """ Ensure model gets on correct device :param base_model: :param model_loader: :param load_half: :param model_kwargs: :param reward_type: :return: """ with init_empty_weights(): from transformers import AutoConfig config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token) model = AutoModel.from_config( config, ) # 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) print('device_map: %s' % device_map, flush=True) if force_1_gpu: # 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 n_gpus = torch.cuda.device_count() if reward_type: device_map = {'': n_gpus - 1} else: device_map = {'': 0} load_in_8bit = model_kwargs.get('load_in_8bit', False) model_kwargs['device_map'] = device_map if load_in_8bit or not load_half: model = model_loader.from_pretrained( base_model, **model_kwargs, ) else: model = model_loader.from_pretrained( base_model, **model_kwargs, ).half() return model def get_model( load_8bit: bool = False, load_half: bool = True, infer_devices: bool = True, base_model: str = '', tokenizer_base_model: str = '', lora_weights: str = "", force_1_gpu: bool = False, llama_type: bool = None, reward_type: bool = None, local_files_only: bool = False, resume_download: bool = True, use_auth_token: Union[str, bool] = False, compile: bool = True, **kwargs, ): """ :param load_8bit: load model in 8-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 force_1_gpu: :param llama_type: whether LLaMa type model :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 :parm compile: whether to compile torch model :param kwargs: :return: """ print("Get %s model" % base_model, flush=True) if lora_weights is not None and lora_weights.strip(): 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 assert base_model.strip(), ( "Please choose a base model with --base_model (CLI) or in Models Tab (gradio)" ) llama_type = llama_type or "llama" in base_model 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, ) 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) else: assert device == "cuda", "Unsupported device %s" % device model_kwargs = dict(local_files_only=local_files_only, torch_dtype=torch.float16, resume_download=resume_download, use_auth_token=use_auth_token) if 'mbart-' not in base_model.lower(): model_kwargs.update(dict(load_in_8bit=load_8bit, device_map={"": 0} if load_8bit else "auto", )) if 'OpenAssistant/reward-model'.lower() in base_model.lower(): # could put on other GPUs model_kwargs['device_map'] = {"": 0} model_kwargs.pop('torch_dtype', None) if not lora_weights: with torch.device("cuda"): if infer_devices: model = get_non_lora_model(base_model, model_loader, load_half, model_kwargs, reward_type, force_1_gpu=force_1_gpu, use_auth_token=use_auth_token) else: if load_half and not load_8bit: model = model_loader.from_pretrained( base_model, **model_kwargs).half() else: model = model_loader.from_pretrained( base_model, **model_kwargs) elif load_8bit: model = model_loader.from_pretrained( base_model, **model_kwargs ) model = PeftModel.from_pretrained( model, lora_weights, torch_dtype=torch.float16, local_files_only=local_files_only, resume_download=resume_download, use_auth_token=use_auth_token, device_map={"": 0}, # seems to be required ) else: with torch.device("cuda"): model = model_loader.from_pretrained( base_model, **model_kwargs ) model = PeftModel.from_pretrained( model, lora_weights, torch_dtype=torch.float16, local_files_only=local_files_only, resume_download=resume_download, use_auth_token=use_auth_token, 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 = torch.compile(model) return model, tokenizer, device def get_score_model(**kwargs): # score model if kwargs.get('score_model') is not None and kwargs.get('score_model').strip(): score_all_kwargs = kwargs.copy() score_all_kwargs['load_8bit'] = False score_all_kwargs['load_half'] = False score_all_kwargs['base_model'] = kwargs.get('score_model').strip() score_all_kwargs['tokenizer_base_model'] = '' score_all_kwargs['lora_weights'] = '' score_all_kwargs['llama_type'] = False score_all_kwargs['compile'] = False smodel, stokenizer, sdevice = get_model(**score_all_kwargs) else: smodel, stokenizer, sdevice = None, None, None return smodel, stokenizer, sdevice def go_gradio(**kwargs): # get default model all_kwargs = kwargs.copy() all_kwargs.update(locals()) if kwargs.get('base_model') and not kwargs['login_mode_if_model0']: model0, tokenizer0, device = get_model(**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, kwargs['base_model']] # get score model smodel, stokenizer, sdevice = get_score_model(**all_kwargs) if 'mbart-' in kwargs['model_lower']: instruction_label = "Text to translate" else: instruction_label = "Instruction" if kwargs['chat']: instruction_label = "You (Shift-Enter or push Submit to send message)" title = 'h2oGPT' if kwargs['verbose']: description = f"""Model {kwargs['base_model']} Instruct dataset. For more information, visit [the project's website](https://github.com/h2oai/h2ogpt). Command: {str(' '.join(sys.argv))} Hash: {get_githash()} """ else: description = "For more information, visit [the project's website](https://github.com/h2oai/h2ogpt).
" if os.environ.get("HUGGINGFACE_SPACES"): description += """

DISCLAIMERS:

  • The data used to train this model include The Pile and other sources. These may contain objectionable content, so the model may reproduce that material. Use application and responses at own risk.
    • """ if kwargs['load_8bit']: description += """
  • Model is loaded in 8-bit and 768 token context length to fit on HF GPUs, so model may perform worse than 16-bit with 2048 token limit.
    • """ description += """
  • Model loading and unloading disabled on HF SPACES to avoid GPU OOM for multi-user environment.

""" if kwargs['verbose']: task_info_md = f""" ### Task: {kwargs['task_info']}""" else: task_info_md = '' css_code = """footer {visibility: hidden} body{background-image:url("https://h2o.ai/content/experience-fragments/h2o/us/en/site/header/master/_jcr_content/root/container/header_copy/logo.coreimg.svg/1678976605175/h2o-logo.svg");}}""" from gradio.themes.utils import colors, fonts, sizes if kwargs['h2ocolors']: colors_dict = dict(primary_hue=colors.yellow, secondary_hue=colors.yellow, neutral_hue=colors.gray, spacing_size=sizes.spacing_md, radius_size=sizes.radius_md, text_size=sizes.text_md, ) else: colors_dict = dict(primary_hue=colors.indigo, secondary_hue=colors.indigo, neutral_hue=colors.gray, spacing_size=sizes.spacing_md, radius_size=sizes.radius_md, text_size=sizes.text_md, ) import gradio as gr if kwargs['gradio_avoid_processing_markdown']: from gradio_client import utils as client_utils from gradio.components import Chatbot # gradio has issue with taking too long to process input/output for markdown etc. # Avoid for now, allow raw html to render, good enough for chatbot. def _postprocess_chat_messages(self, chat_message: str): if chat_message is None: return None elif isinstance(chat_message, (tuple, list)): filepath = chat_message[0] mime_type = client_utils.get_mimetype(filepath) filepath = self.make_temp_copy_if_needed(filepath) return { "name": filepath, "mime_type": mime_type, "alt_text": chat_message[1] if len(chat_message) > 1 else None, "data": None, # These last two fields are filled in by the frontend "is_file": True, } elif isinstance(chat_message, str): return chat_message else: raise ValueError(f"Invalid message for Chatbot component: {chat_message}") Chatbot._postprocess_chat_messages = _postprocess_chat_messages demo = gr.Blocks(theme=gr.themes.Soft(**colors_dict), css=css_code, title="h2oGPT", analytics_enabled=False) callback = gr.CSVLogger() # css_code = 'body{background-image:url("https://h2o.ai/content/experience-fragments/h2o/us/en/site/header/master/_jcr_content/root/container/header_copy/logo.coreimg.svg/1678976605175/h2o-logo.svg");}' # demo = gr.Blocks(theme='gstaff/xkcd', css=css_code) model_options = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options'] if kwargs['base_model'].strip() not in model_options: lora_options = [kwargs['base_model'].strip()] + model_options lora_options = kwargs['extra_lora_options'] if kwargs['lora_weights'].strip() not in lora_options: lora_options = [kwargs['lora_weights'].strip()] + lora_options # always add in no lora case # add fake space so doesn't go away in gradio dropdown lora_options = [' '] + kwargs['extra_lora_options'] output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get('base_model') else 'h2oGPT [ !!! Please Load Model in Models Tab !!! ]' with demo: # avoid actual model/tokenizer here or anything that would be bad to deepcopy # https://github.com/gradio-app/gradio/issues/3558 model_state = gr.State(['model', 'tokenizer', device, kwargs['base_model']]) model_options_state = gr.State([model_options]) lora_options_state = gr.State([lora_options]) gr.Markdown( f"""

{title}

{description} {task_info_md} """) # go button visible if base_wanted = bool(kwargs['base_model']) and kwargs['login_mode_if_model0'] go_btn = gr.Button(value="LOGIN", visible=base_wanted, variant="primary") normal_block = gr.Row(visible=not base_wanted) with normal_block: with gr.Tabs(): with gr.Row(): if not kwargs['chat']: with gr.Column(): instruction = gr.Textbox( lines=4, label=instruction_label, placeholder=kwargs['placeholder_instruction'], ) iinput = gr.Textbox(lines=4, label="Input", placeholder=kwargs['placeholder_input']) flag_btn = gr.Button("Flag") if kwargs['score_model']: if not kwargs['auto_score']: with gr.Column(): score_btn = gr.Button("Score last prompt & response") score_text = gr.Textbox("Response Score: NA", show_label=False) else: score_text = gr.Textbox("Response Score: NA", show_label=False) with gr.Column(): if kwargs['chat']: text_output = gr.Chatbot(label=output_label0).style(height=kwargs['height'] or 400) with gr.Row(): with gr.Column(scale=50): instruction = gr.Textbox( lines=4, label=instruction_label, placeholder=kwargs['placeholder_instruction'], ) with gr.Row(): # .style(equal_height=False, equal_width=False): submit = gr.Button(value='Submit').style(full_width=False, size='sm') stop_btn = gr.Button(value="Stop").style(full_width=False, size='sm') with gr.Row(): clear = gr.Button("New Conversation") flag_btn = gr.Button("Flag") if kwargs['score_model']: if not kwargs['auto_score']: with gr.Column(): score_btn = gr.Button("Score last prompt & response").style(full_width=False, size='sm') score_text = gr.Textbox("Response Score: NA", show_label=False) else: score_text = gr.Textbox("Response Score: NA", show_label=False) retry = gr.Button("Regenerate") undo = gr.Button("Undo") else: text_output = gr.Textbox(lines=5, label=output_label0) with gr.TabItem("Input/Output"): with gr.Row(): if 'mbart-' in kwargs['model_lower']: src_lang = gr.Dropdown(list(languages_covered().keys()), value=kwargs['src_lang'], label="Input Language") tgt_lang = gr.Dropdown(list(languages_covered().keys()), value=kwargs['tgt_lang'], label="Output Language") with gr.TabItem("Expert"): with gr.Row(): with gr.Column(): stream_output = gr.components.Checkbox(label="Stream output", value=kwargs['stream_output']) prompt_type = gr.Dropdown(prompt_types_strings, value=kwargs['prompt_type'], label="Prompt Type") temperature = gr.Slider(minimum=0, maximum=3, value=kwargs['temperature'], label="Temperature", info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)") top_p = gr.Slider(minimum=0, maximum=1, value=kwargs['top_p'], label="Top p", info="Cumulative probability of tokens to sample from") top_k = gr.Slider( minimum=0, maximum=100, step=1, value=kwargs['top_k'], label="Top k", info='Num. tokens to sample from' ) num_beams = gr.Slider(minimum=1, maximum=8, step=1, value=kwargs['num_beams'], label="Beams", info="Number of searches for optimal overall probability. Uses more GPU memory/compute") max_new_tokens = gr.Slider( minimum=1, maximum=2048, step=1, value=kwargs['max_new_tokens'], label="Max output length" ) min_new_tokens = gr.Slider( minimum=0, maximum=2048, step=1, value=kwargs['min_new_tokens'], label="Min output length" ) early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search", value=kwargs['early_stopping']) max_time = gr.Slider(minimum=0, maximum=60 * 5, step=1, value=kwargs['max_time'], label="Max. time", info="Max. time to search optimal output.") repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0, value=kwargs['repetition_penalty'], label="Repetition Penalty") num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1, value=kwargs['num_return_sequences'], label="Number Returns", info="Must be <= num_beams") do_sample = gr.Checkbox(label="Sample", info="Sample, for diverse output(s)", value=kwargs['do_sample']) if kwargs['chat']: iinput = gr.Textbox(lines=4, label="Input", placeholder=kwargs['placeholder_input']) context = gr.Textbox(lines=1, label="Context", info="Ignored in chat mode.") # nominally empty for chat mode with gr.TabItem("Models"): with gr.Row(): with gr.Column(): with gr.Row(scale=1): with gr.Column(scale=50): model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model", value=kwargs['base_model']) lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA", value=kwargs['lora_weights'], visible=kwargs['show_lora']) with gr.Column(scale=1): load_msg = "Load Model/LORA" if not os.environ.get("HUGGINGFACE_SPACES") \ else "LOAD DISABLED ON HF SPACES" load_model_button = gr.Button(load_msg) model_used = gr.Textbox(label="Current Model", value=kwargs['base_model']) lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'], visible=kwargs['show_lora']) with gr.Row(scale=1): with gr.Column(scale=50): new_model = gr.Textbox(label="New Model HF name/path") new_lora = gr.Textbox(label="New LORA HF name/path", visible=kwargs['show_lora']) with gr.Column(scale=1): add_model_button = gr.Button("Add new model name") add_lora_button = gr.Button("Add new LORA name", visible=kwargs['show_lora']) inputs_list = get_inputs_list(locals(), kwargs['model_lower']) from functools import partial all_kwargs = kwargs.copy() all_kwargs.update(locals()) kwargs_evaluate = {k: v for k, v in all_kwargs.items() if k in inputs_kwargs_list} fun = partial(evaluate, **kwargs_evaluate) dark_mode_btn = gr.Button("Dark Mode", variant="primary").style( size="sm", ) dark_mode_btn.click( None, None, None, _js="""() => { if (document.querySelectorAll('.dark').length) { document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark')); } else { document.querySelector('body').classList.add('dark'); } }""", api_name="dark", ) if not kwargs['chat']: submit = gr.Button("Submit") submit_event = submit.click(fun, inputs=[model_state] + inputs_list, outputs=text_output, api_name='submit') # examples after submit or any other buttons for chat or no chat if kwargs['examples'] is not None and kwargs['show_examples']: gr.Examples(examples=kwargs['examples'], inputs=inputs_list) # Score def score_last_response(*args): """ Similar to user() """ args_list = list(args) history = args_list[-1] if history is None: print("Bad history in scoring last response, fix for now", flush=True) history = [] if smodel is not None and \ stokenizer is not None and \ sdevice is not None and \ history is not None and len(history) > 0 and \ history[-1] is not None and \ len(history[-1]) >= 2: os.environ['TOKENIZERS_PARALLELISM'] = 'false' max_length_tokenize = 512 if os.environ.get("HUGGINGFACE_SPACES") else 2048 cutoff_len = max_length_tokenize*4 # restrict deberta related to max for LLM question = history[-1][0] question = question[-cutoff_len:] answer = history[-1][1] 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: 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' os.environ['TOKENIZERS_PARALLELISM'] = 'true' return 'Response Score: {:.1%}'.format(score) else: return 'Response Score: NA' if kwargs['score_model']: score_args = dict(fn=score_last_response, inputs=inputs_list + [text_output], outputs=[score_text], ) if not kwargs['auto_score']: score_event = score_btn.click(**score_args, queue=stream_output, api_name='score') if kwargs['chat']: def user(*args, undo=False, sanitize_user_prompt=True): args_list = list(args) user_message = args_list[0] input1 = args_list[1] context1 = args_list[2] if input1 and not user_message.endswith(':'): user_message1 = user_message + ":" + input1 elif input1: user_message1 = user_message + input1 else: user_message1 = user_message if sanitize_user_prompt: from better_profanity import profanity user_message1 = profanity.censor(user_message1) history = args_list[-1] if undo and history: history.pop() args_list = args_list[:-1] if history is None: print("Bad history, fix for now", flush=True) history = [] if undo: return "", history else: return "", history + [[user_message1, None]] def bot(*args, retry=False): args_list = list(args) history = args_list[-1] if retry and history: history.pop() if not history: print("No history", flush=True) return instruction1 = history[-1][0] context1 = '' if kwargs['chat_history'] > 0: prompt_type1 = args_list[prompt_type_arg_id] context1 = '' for histi in range(len(history) - 1): data_point = dict(instruction=history[histi][0], input='', output=history[histi][1]) context1 += generate_prompt(data_point, prompt_type1, kwargs['chat'], reduced=True)[0].replace( '
', '\n') if not context1.endswith('\n'): context1 += '\n' if context1 and not context1.endswith('\n'): context1 += '\n' # ensure if terminates abruptly, then human continues on next line args_list[0] = instruction1 # only include desired chat history args_list[2] = context1[-kwargs['chat_history']:] model_state1 = args_list[-2] args_list = args_list[:-2] fun1 = partial(evaluate, model_state1, **kwargs_evaluate) try: for output in fun1(*tuple(args_list)): bot_message = output history[-1][1] = bot_message yield history except StopIteration: yield history except RuntimeError as e: if "generator raised StopIteration" in str(e): # assume last entry was bad, undo history.pop() yield history raise except Exception as e: # put error into user input history[-1][0] = "Exception: %s" % str(e) yield history raise return user_args = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']), inputs=inputs_list + [text_output], outputs=[instruction, text_output], ) bot_args = dict(fn=bot, inputs=inputs_list + [model_state] + [text_output], outputs=[text_output], ) retry_bot_args = dict(fn=functools.partial(bot, retry=True), inputs=inputs_list + [model_state] + [text_output], outputs=[text_output], ) undo_user_args = dict(fn=functools.partial(user, undo=True), inputs=inputs_list + [text_output], outputs=[instruction, text_output], ) if kwargs['auto_score']: submit_event = instruction.submit(**user_args, queue=stream_output, api_name='instruction').then( **bot_args, api_name='instruction_bot', ).then(**score_args, api_name='instruction_bot_score').then(clear_torch_cache) submit_event2 = submit.click(**user_args, queue=stream_output, api_name='submit').then( **bot_args, api_name='submit_bot', ).then(**score_args, api_name='submit_bot_score').then(clear_torch_cache) submit_event3 = retry.click(**user_args, queue=stream_output, api_name='retry').then( **retry_bot_args, api_name='retry_bot', ).then(**score_args, api_name='retry_bot_score').then(clear_torch_cache) submit_event4 = undo.click(**undo_user_args, queue=stream_output, api_name='undo').then(**score_args, api_name='undo_score') else: submit_event = instruction.submit(**user_args, queue=stream_output, api_name='instruction').then( **bot_args, api_name='instruction_bot', ).then(clear_torch_cache) submit_event2 = submit.click(**user_args, queue=stream_output, api_name='submit').then( **bot_args, api_name='submit_bot', ).then(clear_torch_cache) submit_event3 = retry.click(**user_args, queue=stream_output, api_name='retry').then( **retry_bot_args, api_name='retry_bot', ).then(clear_torch_cache) submit_event4 = undo.click(**undo_user_args, queue=stream_output, api_name='undo') clear.click(lambda: None, None, text_output, queue=False, api_name='clear') def load_model(model_name, lora_weights, model_state_old, prompt_type_old): # ensure old model removed from GPU memory if kwargs['debug']: print("Pre-switch pre-del GPU memory: %s" % torch.cuda.memory_allocated(), flush=True) if isinstance(model_state_old[0], str) and model0 is not None: # best can do, move model loaded at first to CPU model0.cpu() if model_state_old[0] is not None and not isinstance(model_state_old[0], str): try: model_state_old[0].cpu() except Exception as e: # sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data! print("Unable to put model on CPU: %s" % str(e), flush=True) del model_state_old[0] model_state_old[0] = None if model_state_old[1] is not None and not isinstance(model_state_old[1], str): del model_state_old[1] model_state_old[1] = None clear_torch_cache() if kwargs['debug']: print("Pre-switch post-del GPU memory: %s" % torch.cuda.memory_allocated(), flush=True) all_kwargs['base_model'] = model_name.strip() model_lower = model_name.strip().lower() if model_lower in inv_prompt_type_to_model_lower: prompt_type1 = inv_prompt_type_to_model_lower[model_lower] else: prompt_type1 = prompt_type_old all_kwargs['lora_weights'] = lora_weights.strip() model1, tokenizer1, device1 = get_model(**all_kwargs) clear_torch_cache() if kwargs['debug']: print("Post-switch GPU memory: %s" % torch.cuda.memory_allocated(), flush=True) return {model_state: [model1, tokenizer1, device1, model_name], model_used: model_name, lora_used: lora_weights, prompt_type: prompt_type1} def dropdown_prompt_type_list(x): return gr.Dropdown.update(value=x) def chatbot_list(x, model_used_in): return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]') load_model_args = dict(fn=load_model, inputs=[model_choice, lora_choice, model_state, prompt_type], outputs=[model_state, model_used, lora_used, prompt_type]) prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type) chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output) if not os.environ.get("HUGGINGFACE_SPACES"): load_model_event = load_model_button.click(**load_model_args).then(**prompt_update_args).then(**chatbot_update_args).then(clear_torch_cache) def dropdown_model_list(list0, x): new_state = [list0[0] + [x]] new_options = [*new_state[0]] return gr.Dropdown.update(value=x, choices=new_options), '', new_state add_model_event = add_model_button.click(fn=dropdown_model_list, inputs=[model_options_state, new_model], outputs=[model_choice, new_model, model_options_state]) def dropdown_lora_list(list0, x): new_state = [list0[0] + [x]] new_options = [*new_state[0]] return gr.Dropdown.update(value=x, choices=new_options), '', new_state add_lora_event = add_lora_button.click(fn=dropdown_lora_list, inputs=[lora_options_state, new_lora], outputs=[lora_choice, new_lora, lora_options_state]) go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go") \ .then(lambda: gr.update(visible=True), None, normal_block) \ .then(**load_model_args).then(**prompt_update_args) # callback for logging flagged input/output callback.setup(inputs_list + [text_output], "flagged_data_points") flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output], None, preprocess=False, api_name='flag') if kwargs['chat']: # don't pass text_output, don't want to clear output, just stop it # FIXME: have to click once to stop output and second time to stop GPUs going stop_btn.click(lambda: None, None, None, cancels=[submit_event, submit_event2, submit_event3], queue=False, api_name='stop').then(clear_torch_cache) demo.queue(concurrency_count=1) favicon_path = "h2o-logo.svg" demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True, favicon_path=favicon_path, prevent_thread_lock=True) # , enable_queue=True) print("Started GUI", flush=True) demo.block_thread() input_args_list = ['model_state'] inputs_kwargs_list = ['debug', 'chat', 'hard_stop_list', 'sanitize_bot_response', 'model_state0'] def get_inputs_list(inputs_dict, model_lower): inputs_list_names = list(inspect.signature(evaluate).parameters) inputs_list = [] for k in inputs_list_names: if k == 'kwargs': continue if k in input_args_list + inputs_kwargs_list: # these are added via partial, not taken as input continue if 'mbart-' not in model_lower and k in ['src_lang', 'tgt_lang']: continue inputs_list.append(inputs_dict[k]) return inputs_list # index of prompt_type in evaluate function, after model_state prompt_type_arg_id = 4 eval_func_param_names = ['instruction', 'iinput', 'context', 'stream_output', 'prompt_type', 'temperature', 'top_p', 'top_k', 'num_beams', 'max_new_tokens', 'min_new_tokens', 'early_stopping', 'max_time', 'repetition_penalty', 'num_return_sequences', 'do_sample', ] def evaluate( model_state, # START NOTE: Examples must have same order of parameters instruction, iinput, context, stream_output, prompt_type, temperature, top_p, top_k, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample, # END NOTE: Examples must have same order of parameters src_lang=None, tgt_lang=None, debug=False, chat=False, hard_stop_list=None, sanitize_bot_response=True, model_state0=None, **kwargs, ): if debug: locals_dict = locals().copy() locals_dict.pop('model_state', None) print(locals_dict) no_model_msg = "Please choose a base model with --base_model (CLI) or in Models Tab (gradio).\nThen start New Conversation" 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) assert base_model.strip(), no_model_msg assert model, "Model is missing" assert tokenizer, "Tokenizer is missing" data_point = dict(context=context, instruction=instruction, input=iinput) prompter = Prompter(prompt_type, debug=debug, chat=chat, stream_output=stream_output) prompt = prompter.generate_prompt(data_point) if hard_stop_list is None: # acts like undo on user entry and bot response hard_stop_list = [] 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 model(prompt, max_length=max_new_tokens)[0][key] 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 if prompt_type in ['human_bot', 'instruct_vicuna', 'instruct_with_end']: if prompt_type == 'human_bot': # encounters = [prompt.count(human) + 1, prompt.count(bot) + 1] # stopping only starts once output is beyond prompt # 1 human is enough to trigger, but need 2 bots, because very first view back will be bot we added stop_words = [human, bot] encounters = [1, 2] elif prompt_type == 'instruct_vicuna': # even below is not enough, generic strings and many ways to encode stop_words = [ '### Human:', """ ### Human:""", """ ### Human: """, '### Assistant:', """ ### Assistant:""", """ ### Assistant: """, ] encounters = [1, 2] else: # some instruct prompts have this as end, doesn't hurt to stop on it since not common otherwise stop_words = ['### End'] encounters = [1] stop_words_ids = [ tokenizer(stop_word, return_tensors='pt')['input_ids'].squeeze() for stop_word in stop_words] # handle single token case stop_words_ids = [x if len(x.shape) > 0 else torch.tensor([x]) for x in stop_words_ids] stop_words_ids = [x for x in stop_words_ids if x.shape[0] > 0] # avoid padding in front of tokens if tokenizer.pad_token: stop_words_ids = [x[1:] if x[0] == tokenizer.pad_token_id and len(x) > 1 else x for x in stop_words_ids] stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids, encounters=encounters)]) else: stopping_criteria = StoppingCriteriaList() # 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 max_length_tokenize = 768 - 256 if os.environ.get("HUGGINGFACE_SPACES") else 2048 - 256 cutoff_len = max_length_tokenize * 4 # if reaches limit, then can't generate new tokens output_smallest = 30 * 4 prompt = prompt[-cutoff_len - output_smallest:] inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=max_length_tokenize) 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) 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, **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: gen_kwargs.update(dict(pad_token_id=tokenizer.eos_token_id)) decoder = functools.partial(tokenizer.decode, skip_special_tokens=True, clean_up_tokenization_spaces=True, ) decoder_raw = functools.partial(tokenizer.decode, skip_special_tokens=False, clean_up_tokenization_spaces=True, ) with torch.no_grad(): # 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_raw = inputs_decoded decoder = decoder_raw else: print("WARNING: Special characters in prompt", flush=True) if stream_output: def generate(callback=None, **kwargs): # re-order stopping so Stream first and get out all chunks before stop for other reasons stopping_criteria0 = kwargs.get('stopping_criteria', StoppingCriteriaList()).copy() kwargs['stopping_criteria'] = StoppingCriteriaList() kwargs['stopping_criteria'].append(Stream(func=callback)) for stopping_criteria1 in stopping_criteria0: kwargs['stopping_criteria'].append(stopping_criteria1) try: model.generate(**kwargs) except torch.cuda.OutOfMemoryError as e: print("GPU OOM: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)), flush=True) if kwargs['input_ids'] is not None: kwargs['input_ids'].cpu() kwargs['input_ids'] = None traceback.print_exc() clear_torch_cache() return for output in CallbackToGenerator(generate, callback=None, **gen_kwargs): decoded_output = decoder(output) if output[-1] in [tokenizer.eos_token_id]: if debug: print("HIT EOS", flush=True) break if any(ele in decoded_output for ele in hard_stop_list): raise StopIteration yield prompter.get_response(decoded_output, prompt=inputs_decoded, sanitize_bot_response=sanitize_bot_response) return else: outputs = model.generate(**gen_kwargs) outputs = [decoder(s) for s in outputs.sequences] yield prompter.get_response(outputs, prompt=inputs_decoded, sanitize_bot_response=sanitize_bot_response) def get_generate_params(model_lower, chat, stream_output, show_examples, prompt_type, temperature, top_p, top_k, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample): use_defaults = False use_default_examples = True examples = [] task_info = f"{prompt_type}" 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 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""" 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 examples += [ [placeholder_instruction, "", "", stream_output, 'plain', 1.0, 1.0, 50, 1, 128, 0, False, max_time_defaults, 1.0, 1, False]] 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 examples += [ [placeholder_instruction, "", "", stream_output, 'plain', 1.0, 1.0, 50, 1, 128, 0, False, max_time_defaults, 1.0, 1, False]] 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 examples += [ [placeholder_instruction, "", "", stream_output, 'plain', 1.0, 1.0, 50, 1, 128, 0, False, max_time_defaults, 1.0, 1, False]] 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: prompt_type = prompt_type or 'human_bot' else: prompt_type = '' examples += [[summarize_example1, 'Summarize' if prompt_type not in ['plain', 'instruct_simple'] else '', "", stream_output, prompt_type or 'plain', 0.1, 0.75, 40, 4, 256, 0, False, max_time_defaults, 1.0, 1, False]] 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 if chat: num_beams = num_beams or 1 else: num_beams = num_beams or 4 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 params_list = ["", stream_output, prompt_type, 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_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, ] src_lang = "English" tgt_lang = "Russian" return placeholder_instruction, placeholder_input, \ stream_output, show_examples, \ prompt_type, 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 test_test_prompt(prompt_type='instruct', data_point=0): example_data_point = example_data_points[data_point] example_data_point.pop('output', None) return generate_prompt(example_data_point, prompt_type, False, False) if __name__ == "__main__": print(""" 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' """, flush=True) fire.Fire(main)