app.py

import functools
import inspect
import sys
import os
import time
import traceback
import typing
import filelock
from utils import set_seed, flatten_list, clear_torch_cache, system_info_print, zip_data, save_generate_output

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

is_hf = bool(os.getenv("HUGGINGFACE_SPACES"))
is_gpth2oai = bool(os.getenv("GPT_H2O_AI"))
is_public = is_hf or is_gpth2oai  # multi-user case with fixed model and disclaimer
is_low_mem = is_hf  # assumes run on 24GB consumer GPU
admin_pass = os.getenv("ADMIN_PASS")


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,
        save_path: str = None,
        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 = False,
        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)

    if is_public:
        temperature = 0.4
        top_p = 0.85
        top_k = 70
        do_sample = True
        if is_low_mem:
            base_model = 'h2oai/h2ogpt-oasst1-512-12b'
            load_8bit = True
        else:
            base_model = 'h2oai/h2ogpt-oasst1-512-20b'
    if is_low_mem:
        load_8bit = True
    if is_hf:
        # must override share if in spaces
        share = False
    save_path = os.getenv('SAVE_PATH')

    # 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, save_path=save_path)
            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 is_low_mem 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()
                        except 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):
                                print("GPU error: question: %s answer: %s exception: %s" % (prompt, res, str(e)),
                                      flush=True)
                                traceback.print_exc()
                                score = 0.0
                                clear_torch_cache()
                            else:
                                raise
                        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': '<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 = 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).<br>"
    if is_public:
        description += """<p><b> DISCLAIMERS: </b><ul><i><li>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.</i></li>"""
        if kwargs['load_8bit']:
            description += """<i><li> Model is loaded in 8-bit, model loading-unloading is disabled, and other limitations exist in order to fit on GPUs with lower amounts of VRAM, so UX can be worse than non-hosted version.</i></li>"""
        description += """<i><li>Conversations may be used to improve h2oGPT.  Do not share sensitive information.</i></li>"""
        description += """<i><li>By using h2oGPT, you accept our [Terms of Service](https://github.com/h2oai/h2ogpt/blob/main/tos.md).</i></li></ul></p>"""

    if kwargs['verbose']:
        task_info_md = f"""
        ### Task: {kwargs['task_info']}"""
    else:
        task_info_md = ''

    css_code = """footer {visibility: hidden;}
body{background:linear-gradient(#f5f5f5,#e5e5e5);}
body.dark{background:linear-gradient(#0d0d0d,#333333);}"""

    from gradio.themes.utils import Color, colors, fonts, sizes
    if kwargs['h2ocolors']:
        h2o_yellow = Color(
            name="yellow",
            c50="#fffef2",
            c100="#fff9e6",
            c200="#ffecb3",
            c300="#ffe28c",
            c400="#ffd659",
            c500="#fec925",
            c600="#e6ac00",
            c700="#bf8f00",
            c800="#a67c00",
            c900="#664d00",
            c950="#403000",
        )
        h2o_gray = Color(
            name="gray",
            c50="#f2f2f2",
            c100="#e5e5e5",
            c200="#cccccc",
            c300="#b2b2b2",
            c400="#999999",
            c500="#7f7f7f",
            c600="#666666",
            c700="#4c4c4c",
            c800="#333333",
            c900="#191919",
            c950="#0d0d0d",
        )
        colors_dict = dict(primary_hue=h2o_yellow,
                           secondary_hue=h2o_yellow,
                           neutral_hue=h2o_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"""
            <h1 align="center"> {title}</h1>

            {description}
            {task_info_md}
            """)
        if is_hf:
            gr.HTML('''<center><a href="https://huggingface.co/spaces/h2oai/h2ogpt-chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate this Space to skip the queue and run in a private space</center>''')

        # go button visible if
        base_wanted = bool(kwargs['base_model']) and kwargs['login_mode_if_model0']
        go_btn = gr.Button(value="ENTER", 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",
                                                      visible=not is_public)
                            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'
                            )
                            max_beams = 8 if not is_low_mem else 2
                            num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1,
                                                  value=min(max_beams, kwargs['num_beams']), label="Beams",
                                                  info="Number of searches for optimal overall probability.  "
                                                       "Uses more GPU memory/compute")
                            max_max_new_tokens = 2048 if not is_low_mem else kwargs['max_new_tokens']
                            max_new_tokens = gr.Slider(
                                minimum=1, maximum=max_max_new_tokens, step=1,
                                value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length",
                            )
                            min_new_tokens = gr.Slider(
                                minimum=0, maximum=max_max_new_tokens, step=1,
                                value=min(max_max_new_tokens, 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_max_time = 60 * 5 if not is_low_mem else 60
                            max_time = gr.Slider(minimum=0, maximum=max_max_time, step=1,
                                                 value=min(max_max_time, 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",
                                                             visible=not is_public)
                            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'],
                                                    visible=not is_public)
                            # nominally empty for chat mode
                            context = gr.Textbox(lines=1, label="Context",
                                                 info="Ignored in chat mode.",
                                                 visible=not is_public)

                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 is_public \
                                        else "LOAD DISABLED FOR HOSTED DEMO"
                                    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'])
                with gr.TabItem("System"):
                    system_row = gr.Row(visible=not is_public)
                    admin_pass_textbox = gr.Textbox(label="Admin Password", type='password', visible=is_public)
                    admin_btn = gr.Button(value="admin", visible=is_public)
                    with system_row:
                        with gr.Column():
                            system_text = gr.Textbox(label='System Info')
                            system_btn = gr.Button(value='Get System Info')

                            zip_btn = gr.Button("Zip")
                            file_output = gr.File()

        # Get flagged data
        zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['save_path']])
        zip_btn.click(zip_data1, inputs=None, outputs=file_output)

        def check_admin_pass(x):
            return gr.update(visible=x == admin_pass)

        admin_btn.click(check_admin_pass, inputs=admin_pass_textbox, outputs=system_row)

        # Get inputs to evaluate()
        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 is_low_mem 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'
                except 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):
                        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 '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(
                            '<br>', '\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 is_public:
            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')

        def get_system_info():
            return gr.Textbox.update(value=system_info_print())

        system_event = system_btn.click(get_system_info, outputs=system_text, api_name='system_info')

        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', 'save_path', '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,
        save_path=None,
        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 is_low_mem 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
                except 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):
                        print(
                            "GPU Error: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
                            flush=True)
                        traceback.print_exc()
                        clear_torch_cache()
                        return
                    else:
                        raise

            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)
            if save_path:
                save_generate_output(output=decoded_output, base_model=base_model, json_file_path=save_path)
            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'

    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'

    """, flush=True)
    fire.Fire(main)