Update with h2oGPT hash ed82638ef67fed048ff93c8a1048c32dfd28ef84

app.py

@@ -1,1158 +0,0 @@
-import functools
-import sys
-import os
-import traceback
-import typing
-
-from utils import set_seed, clear_torch_cache, save_generate_output, NullContext, KThread, wrapped_partial
-
-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, TextIteratorStreamer
-from accelerate import init_empty_weights, infer_auto_device_map
-
-from prompter import Prompter
-
-from finetune import get_loaders, example_data_points, generate_prompt, human, bot, inv_prompt_type_to_model_lower
-from stopping import StoppingCriteriaSub
-
-eval_extra_columns = ['prompt', 'response', 'score']
-
-
-def main(
-        load_8bit: bool = False,
-        load_half: bool = True,
-        infer_devices: bool = True,  # really if to "control" devices now
-        base_model: str = '',
-        tokenizer_base_model: str = '',
-        lora_weights: str = "",
-        gpu_id: int = 0,  # if infer_devices = True and gpu_id != -1
-
-        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,
-
-        debug: bool = False,
-        save_dir: str = None,
-        share: bool = True,
-        local_files_only: bool = False,
-        resume_download: bool = True,
-        use_auth_token: Union[str, bool] = False,  # 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
-        chat_context: bool = False,  # use default context if human_bot
-        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,
-        block_gradio_exit: bool = True,
-        concurrency_count: int = 1,
-        api_open: bool = False,  # don't let API skip queue
-        allow_api: bool = True,
-        input_lines: int = 1,
-
-        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,
-
-        hard_stop_list: typing.List[str] = [],
-):
-    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")
-    # will sometimes appear in UI or sometimes actual generation, but maybe better than empty result
-    # but becomes unrecoverable sometimes if raise, so just be silent for now
-    raise_generate_gpu_exceptions = not is_public
-
-    # allow set token directly
-    use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token)
-
-    if is_public:
-        input_lines = 1  # ensure set, for ease of use
-        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_dir = os.getenv('SAVE_DIR', save_dir)
-    score_model = os.getenv('SCORE_MODEL', score_model)
-    if score_model == 'None':
-        score_model = ''
-    concurrency_count = int(os.getenv('CONCURRENCY_COUNT', concurrency_count))
-    api_open = bool(int(os.getenv('API_OPEN', api_open)))
-    allow_api = bool(int(os.getenv('ALLOW_API', allow_api)))
-
-    n_gpus = torch.cuda.device_count()
-
-    # 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
-            eval_filename = 'ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json'
-            if not os.path.isfile(eval_filename):
-                os.system(
-                    'wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s' % eval_filename)
-            import json
-            data = json.load(open(eval_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()
-                assert not chat, "No gradio must use chat=False, uses nochat instruct"
-                examplenew[eval_func_param_names.index('instruction_nochat')] = instruction
-                examplenew[eval_func_param_names.index('iinput_nochat')] = ''  # no input
-                examplenew[eval_func_param_names.index('context')] = get_context(chat_context, prompt_type)
-                examples.append(examplenew)
-                responses.append(output)
-
-        num_examples = len(examples)
-        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])
-        eval_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)
-        eval_filename = os.path.join(scoring_path, eval_filename)
-
-        # torch.device("cuda") leads to cuda:x cuda:y mismatches for multi-GPU consistently
-        context_class = NullContext() if n_gpus > 1 else torch.device("cuda")
-
-        with context_class:
-            # 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, save_dir=save_dir, is_low_mem=is_low_mem,
-                              raise_generate_gpu_exceptions=raise_generate_gpu_exceptions,
-                              chat_context=chat_context,
-                              concurrency_count=concurrency_count)
-            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 = []
-
-            import matplotlib.pyplot as plt
-
-            for exi, ex in enumerate(examples):
-                instruction = ex[eval_func_param_names.index('instruction_nochat')]
-                iinput = ex[eval_func_param_names.index('iinput_nochat')]
-                context = ex[eval_func_param_names.index('context')]
-                clear_torch_cache()
-                print("")
-                print("START" + "=" * 100)
-                print("Question: %s %s" % (instruction, ('input=%s' % iinput if iinput 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
-                gener = fun(*tuple(ex), exi=exi) if eval_sharegpt_as_output else fun(*tuple(ex))
-                for res in gener:
-                    print(res)
-                    if smodel:
-                        score_with_prompt = False
-                        if score_with_prompt:
-                            data_point = dict(instruction=instruction, input=iinput, context=context)
-                            prompter = Prompter(prompt_type, debug=debug, chat=chat, stream_output=stream_output)
-                            prompt = prompter.generate_prompt(data_point)
-                        else:
-                            # just raw input and output
-                            if eval_sharegpt_prompts_only > 0:
-                                # only our own examples have this filled at moment
-                                assert iinput in [None, ''], iinput  # should be no iinput
-                            if not (chat_context and prompt_type == 'human_bot'):
-                                assert context in [None, ''], context  # should be no context
-                            prompt = instruction
-                        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 1: question: %s answer: %s exception: %s" % (prompt, res, str(e)), flush=True)
-                            traceback.print_exc()
-                            score = 0.0
-                            clear_torch_cache()
-                        except (Exception, RuntimeError) as e:
-                            if 'Expected all tensors to be on the same device' in str(e) or \
-                                    'expected scalar type Half but found Float' in str(e) or \
-                                    'probability tensor contains either' in str(e) or \
-                                    'cublasLt ran into an error!' in str(e):
-                                print("GPU error: question: %s answer: %s exception: %s" % (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
-                        df_scores = pd.DataFrame(score_dump,
-                                                 columns=eval_func_param_names + eval_extra_columns)
-                        df_scores.to_parquet(eval_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(eval_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 eval_filename
-
-    if gradio:
-        # imported here so don't require gradio to run generate
-        from gradio_runner import go_gradio
-
-        # get default model
-        all_kwargs = locals().copy()
-        if all_kwargs.get('base_model') and not all_kwargs['login_mode_if_model0']:
-            model0, tokenizer0, device = get_model(**all_kwargs)
-        else:
-            # if empty model, then don't load anything, just get gradio up
-            model0, tokenizer0, device = None, None, None
-        model_state0 = [model0, tokenizer0, device, all_kwargs['base_model']]
-
-        # get score model
-        smodel, stokenizer, sdevice = get_score_model(**all_kwargs)
-        score_model_state0 = [smodel, stokenizer, sdevice, score_model]
-
-        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,
-                       gpu_id=0,
-                       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:
-    :param gpu_id:
-    :param use_auth_token:
-    :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 gpu_id >= 0:
-        # FIXME: If really distributes model, tend to get things like: ValueError: gpt_neox.embed_in.weight doesn't have any device set.
-        # So avoid for now, just put on first GPU, unless score_model, put on last
-        n_gpus = torch.cuda.device_count()
-        if reward_type:
-            device_map = {'': n_gpus - 1}
-        else:
-            device_map = {'': min(n_gpus - 1, gpu_id)}
-    if gpu_id == -1:
-        device_map = {'': 'cuda'}
-
-    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 = "",
-        gpu_id: int = 0,
-
-        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 gpu_id: which GPU (0..n_gpus-1) or allow all GPUs if relevant (-1)
-    :param reward_type: reward type model for sequence classification
-    :param local_files_only: use local files instead of from HF
-    :param resume_download: resume downloads from HF
-    :param use_auth_token: assumes user did on CLI `huggingface-cli login` to access private repo
-    :param 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)"
-    )
-
-    from transformers import AutoConfig
-    config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token)
-    llama_type_from_config = 'llama' in str(config).lower()
-    llama_type_from_name = "llama" in base_model.lower()
-    llama_type = llama_type_from_config or llama_type_from_name
-    if llama_type:
-        print("Detected as llama type from"
-              " config (%s) or name (%s)" % (llama_type_from_config, llama_type_from_name), flush=True)
-
-    model_loader, tokenizer_loader = get_loaders(llama_type=llama_type, model_name=base_model, reward_type=reward_type)
-    if not tokenizer_base_model:
-        tokenizer_base_model = base_model
-
-    if tokenizer_loader is not None and not isinstance(tokenizer_loader, str):
-        tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model,
-                                                     local_files_only=local_files_only,
-                                                     resume_download=resume_download,
-                                                     use_auth_token=use_auth_token,
-                                                     )
-    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,
-                                               gpu_id=gpu_id, 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
-
-
-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',
-                         'chat',
-                         'instruction_nochat',
-                         'iinput_nochat',
-                         ]
-
-
-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,
-        chat,
-        instruction_nochat,
-        iinput_nochat,
-        # END NOTE: Examples must have same order of parameters
-        src_lang=None,
-        tgt_lang=None,
-        debug=False,
-        concurrency_count=None,
-        save_dir=None,
-        hard_stop_list=None,
-        sanitize_bot_response=True,
-        model_state0=None,
-        is_low_mem=None,
-        raise_generate_gpu_exceptions=None,
-        chat_context=None,
-):
-    # ensure passed these
-    assert concurrency_count is not None
-    assert is_low_mem is not None
-    assert raise_generate_gpu_exceptions is not None
-    assert chat_context is not None
-
-    if debug:
-        locals_dict = locals().copy()
-        locals_dict.pop('model_state', None)
-        locals_dict.pop('model_state0', None)
-        print(locals_dict)
-
-    no_model_msg = "Please choose a base model with --base_model (CLI) or in Models Tab (gradio).\nThen start New Conversation"
-
-    if model_state0 is None:
-        # e.g. for no gradio case, set dummy value, else should be set
-        model_state0 = [None, None, None, None]
-
-    if model_state is not None and len(model_state) == 4 and not isinstance(model_state[0], str):
-        # try to free-up original model (i.e. list was passed as reference)
-        if model_state0 is not None and model_state0[0] is not None:
-            model_state0[0].cpu()
-            model_state0[0] = None
-        # try to free-up original tokenizer (i.e. list was passed as reference)
-        if model_state0 is not None and model_state0[1] is not None:
-            model_state0[1] = None
-        clear_torch_cache()
-        model, tokenizer, device, base_model = model_state
-    elif model_state0 is not None and len(model_state0) == 4 and model_state0[0] is not None:
-        assert isinstance(model_state[0], str)
-        model, tokenizer, device, base_model = model_state0
-    else:
-        raise AssertionError(no_model_msg)
-
-    if base_model is None:
-        raise AssertionError(no_model_msg)
-
-    assert base_model.strip(), no_model_msg
-    assert model, "Model is missing"
-    assert tokenizer, "Tokenizer is missing"
-
-    # choose chat or non-chat mode
-    if not chat:
-        instruction = instruction_nochat
-        iinput = iinput_nochat
-
-    if not context:
-        # get hidden context if have one
-        context = get_context(chat_context, prompt_type)
-
-    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, '\n' + human, '\n' + 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]
-        # handle fake \n added
-        stop_words_ids = [x[1:] if y[0] == '\n' else x for x, y in zip(stop_words_ids, stop_words)]
-        # build stopper
-        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,
-    )
-
-    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:
-            #skip_prompt = prompt_type != 'plain'
-            skip_prompt = False
-            streamer = TextIteratorStreamer(tokenizer, skip_prompt=skip_prompt)
-            gen_kwargs.update(dict(streamer=streamer))
-            if debug:
-                KThread.show_threads()
-            target_func = generate_with_exceptions
-            if concurrency_count == 1:
-                # otherwise can't do this
-                KThread.kill_threads(target_func.__name__, debug=debug)
-            target = wrapped_partial(generate_with_exceptions, model.generate, prompt, inputs_decoded,
-                                     raise_generate_gpu_exceptions, **gen_kwargs)
-            thread = KThread(target=target)
-            thread.start()
-            outputs = ""
-            for new_text in streamer:
-                outputs += new_text
-                yield prompter.get_response(outputs, prompt=inputs_decoded,
-                                            sanitize_bot_response=sanitize_bot_response)
-        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)
-        if save_dir and outputs and len(outputs) >= 1:
-            decoded_output = prompt + outputs[0]
-            save_generate_output(output=decoded_output, base_model=base_model, save_dir=save_dir)
-
-
-def generate_with_exceptions(func, prompt, inputs_decoded, raise_generate_gpu_exceptions, **kwargs):
-    try:
-        func(**kwargs)
-    except torch.cuda.OutOfMemoryError as e:
-        print("GPU OOM 2: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
-              flush=True)
-        if kwargs['input_ids'] is not None:
-            kwargs['input_ids'].cpu()
-        kwargs['input_ids'] = None
-        traceback.print_exc()
-        clear_torch_cache()
-        return
-    except (Exception, RuntimeError) as e:
-        if 'Expected all tensors to be on the same device' in str(e) or \
-                'expected scalar type Half but found Float' in str(e) or \
-                'probability tensor contains either' in str(e) or \
-                'cublasLt ran into an error!' in str(e) or \
-                'mat1 and mat2 shapes cannot be multiplied' in str(e):
-            print(
-                "GPU Error: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
-                flush=True)
-            traceback.print_exc()
-            clear_torch_cache()
-            if raise_generate_gpu_exceptions:
-                raise
-            return
-        else:
-            clear_torch_cache()
-            raise
-
-
-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]
-
-    # examples at first don't include chat, instruction_nochat, iinput_nochat, added at end
-    if show_examples is None:
-        if chat:
-            show_examples = False
-        else:
-            show_examples = True
-
-    summarize_example1 = """Jeff: Can I train a ? Transformers model on Amazon SageMaker? 
-Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
-Jeff: ok.
-Jeff: and how can I get started? 
-Jeff: where can I find documentation? 
-Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"""
-
-    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 = 0.4 if temperature is None else temperature
-        top_p = 0.85 if top_p is None else top_p
-        top_k = 70 if top_k is None else top_k
-        if 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 = True if do_sample is None else do_sample
-    # doesn't include chat, instruction_nochat, iinput_nochat, added later
-    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"
-
-    # move to correct position
-    for example in examples:
-        example += [chat, '', '']
-        # adjust examples if non-chat mode
-        if not chat:
-            example[eval_func_param_names.index('instruction_nochat')] = example[
-                eval_func_param_names.index('instruction')]
-            example[eval_func_param_names.index('instruction')] = ''
-
-            example[eval_func_param_names.index('iinput_nochat')] = example[eval_func_param_names.index('iinput')]
-            example[eval_func_param_names.index('iinput')] = ''
-
-    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 get_context(chat_context, prompt_type):
-    if chat_context and prompt_type == 'human_bot':
-        context0 = """<bot>: I am an intelligent, helpful, truthful, and fair assistant named h2oGPT, who will give accurate, balanced, and reliable responses.  I will not respond with I don't know or I don't understand.
-<human>: I am a human person seeking useful assistance and request all questions be answered completely, and typically expect detailed responses.  Give answers in numbered list format if several distinct but related items are being listed."""
-    else:
-        context0 = ''
-    return context0
-
-
-def 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)
-
-
-def score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len):
-    question = question[-cutoff_len:]
-    answer = answer[-cutoff_len:]
-
-    inputs = stokenizer(question, answer,
-                        return_tensors="pt",
-                        truncation=True,
-                        max_length=max_length_tokenize).to(smodel.device)
-    try:
-        score = torch.sigmoid(smodel(**inputs).logits[0]).cpu().detach().numpy()[0]
-    except torch.cuda.OutOfMemoryError as e:
-        print("GPU OOM 3: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True)
-        del inputs
-        traceback.print_exc()
-        clear_torch_cache()
-        return 'Response Score: GPU OOM'
-    except (Exception, RuntimeError) as e:
-        if 'Expected all tensors to be on the same device' in str(e) or \
-                'expected scalar type Half but found Float' in str(e) or \
-                'probability tensor contains either' in str(e) or \
-                'cublasLt ran into an error!' in str(e):
-            print("GPU Error: question: %s answer: %s exception: %s" % (question, answer, str(e)),
-                  flush=True)
-            traceback.print_exc()
-            clear_torch_cache()
-            return 'Response Score: GPU Error'
-        else:
-            raise
-    os.environ['TOKENIZERS_PARALLELISM'] = 'true'
-    return score
-
-
-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'
-
-    python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b
-
-    """, flush=True)
-    fire.Fire(main)

app.py

@@ -0,0 +1 @@
+generate.py

generate.py

@@ -0,0 +1,1158 @@
+import functools
+import sys
+import os
+import traceback
+import typing
+
+from utils import set_seed, clear_torch_cache, save_generate_output, NullContext, KThread, wrapped_partial
+
+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, TextIteratorStreamer
+from accelerate import init_empty_weights, infer_auto_device_map
+
+from prompter import Prompter
+
+from finetune import get_loaders, example_data_points, generate_prompt, human, bot, inv_prompt_type_to_model_lower
+from stopping import StoppingCriteriaSub
+
+eval_extra_columns = ['prompt', 'response', 'score']
+
+
+def main(
+        load_8bit: bool = False,
+        load_half: bool = True,
+        infer_devices: bool = True,  # really if to "control" devices now
+        base_model: str = '',
+        tokenizer_base_model: str = '',
+        lora_weights: str = "",
+        gpu_id: int = 0,  # if infer_devices = True and gpu_id != -1
+
+        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,
+
+        debug: bool = False,
+        save_dir: str = None,
+        share: bool = True,
+        local_files_only: bool = False,
+        resume_download: bool = True,
+        use_auth_token: Union[str, bool] = False,  # 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
+        chat_context: bool = False,  # use default context if human_bot
+        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,
+        block_gradio_exit: bool = True,
+        concurrency_count: int = 1,
+        api_open: bool = False,  # don't let API skip queue
+        allow_api: bool = True,
+        input_lines: int = 1,
+
+        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,
+
+        hard_stop_list: typing.List[str] = [],
+):
+    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")
+    # will sometimes appear in UI or sometimes actual generation, but maybe better than empty result
+    # but becomes unrecoverable sometimes if raise, so just be silent for now
+    raise_generate_gpu_exceptions = not is_public
+
+    # allow set token directly
+    use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token)
+
+    if is_public:
+        input_lines = 1  # ensure set, for ease of use
+        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_dir = os.getenv('SAVE_DIR', save_dir)
+    score_model = os.getenv('SCORE_MODEL', score_model)
+    if score_model == 'None':
+        score_model = ''
+    concurrency_count = int(os.getenv('CONCURRENCY_COUNT', concurrency_count))
+    api_open = bool(int(os.getenv('API_OPEN', api_open)))
+    allow_api = bool(int(os.getenv('ALLOW_API', allow_api)))
+
+    n_gpus = torch.cuda.device_count()
+
+    # 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
+            eval_filename = 'ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json'
+            if not os.path.isfile(eval_filename):
+                os.system(
+                    'wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s' % eval_filename)
+            import json
+            data = json.load(open(eval_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()
+                assert not chat, "No gradio must use chat=False, uses nochat instruct"
+                examplenew[eval_func_param_names.index('instruction_nochat')] = instruction
+                examplenew[eval_func_param_names.index('iinput_nochat')] = ''  # no input
+                examplenew[eval_func_param_names.index('context')] = get_context(chat_context, prompt_type)
+                examples.append(examplenew)
+                responses.append(output)
+
+        num_examples = len(examples)
+        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])
+        eval_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)
+        eval_filename = os.path.join(scoring_path, eval_filename)
+
+        # torch.device("cuda") leads to cuda:x cuda:y mismatches for multi-GPU consistently
+        context_class = NullContext() if n_gpus > 1 else torch.device("cuda")
+
+        with context_class:
+            # 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, save_dir=save_dir, is_low_mem=is_low_mem,
+                              raise_generate_gpu_exceptions=raise_generate_gpu_exceptions,
+                              chat_context=chat_context,
+                              concurrency_count=concurrency_count)
+            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 = []
+
+            import matplotlib.pyplot as plt
+
+            for exi, ex in enumerate(examples):
+                instruction = ex[eval_func_param_names.index('instruction_nochat')]
+                iinput = ex[eval_func_param_names.index('iinput_nochat')]
+                context = ex[eval_func_param_names.index('context')]
+                clear_torch_cache()
+                print("")
+                print("START" + "=" * 100)
+                print("Question: %s %s" % (instruction, ('input=%s' % iinput if iinput 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
+                gener = fun(*tuple(ex), exi=exi) if eval_sharegpt_as_output else fun(*tuple(ex))
+                for res in gener:
+                    print(res)
+                    if smodel:
+                        score_with_prompt = False
+                        if score_with_prompt:
+                            data_point = dict(instruction=instruction, input=iinput, context=context)
+                            prompter = Prompter(prompt_type, debug=debug, chat=chat, stream_output=stream_output)
+                            prompt = prompter.generate_prompt(data_point)
+                        else:
+                            # just raw input and output
+                            if eval_sharegpt_prompts_only > 0:
+                                # only our own examples have this filled at moment
+                                assert iinput in [None, ''], iinput  # should be no iinput
+                            if not (chat_context and prompt_type == 'human_bot'):
+                                assert context in [None, ''], context  # should be no context
+                            prompt = instruction
+                        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 1: question: %s answer: %s exception: %s" % (prompt, res, str(e)), flush=True)
+                            traceback.print_exc()
+                            score = 0.0
+                            clear_torch_cache()
+                        except (Exception, RuntimeError) as e:
+                            if 'Expected all tensors to be on the same device' in str(e) or \
+                                    'expected scalar type Half but found Float' in str(e) or \
+                                    'probability tensor contains either' in str(e) or \
+                                    'cublasLt ran into an error!' in str(e):
+                                print("GPU error: question: %s answer: %s exception: %s" % (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
+                        df_scores = pd.DataFrame(score_dump,
+                                                 columns=eval_func_param_names + eval_extra_columns)
+                        df_scores.to_parquet(eval_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(eval_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 eval_filename
+
+    if gradio:
+        # imported here so don't require gradio to run generate
+        from gradio_runner import go_gradio
+
+        # get default model
+        all_kwargs = locals().copy()
+        if all_kwargs.get('base_model') and not all_kwargs['login_mode_if_model0']:
+            model0, tokenizer0, device = get_model(**all_kwargs)
+        else:
+            # if empty model, then don't load anything, just get gradio up
+            model0, tokenizer0, device = None, None, None
+        model_state0 = [model0, tokenizer0, device, all_kwargs['base_model']]
+
+        # get score model
+        smodel, stokenizer, sdevice = get_score_model(**all_kwargs)
+        score_model_state0 = [smodel, stokenizer, sdevice, score_model]
+
+        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,
+                       gpu_id=0,
+                       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:
+    :param gpu_id:
+    :param use_auth_token:
+    :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 gpu_id >= 0:
+        # FIXME: If really distributes model, tend to get things like: ValueError: gpt_neox.embed_in.weight doesn't have any device set.
+        # So avoid for now, just put on first GPU, unless score_model, put on last
+        n_gpus = torch.cuda.device_count()
+        if reward_type:
+            device_map = {'': n_gpus - 1}
+        else:
+            device_map = {'': min(n_gpus - 1, gpu_id)}
+    if gpu_id == -1:
+        device_map = {'': 'cuda'}
+
+    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 = "",
+        gpu_id: int = 0,
+
+        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 gpu_id: which GPU (0..n_gpus-1) or allow all GPUs if relevant (-1)
+    :param reward_type: reward type model for sequence classification
+    :param local_files_only: use local files instead of from HF
+    :param resume_download: resume downloads from HF
+    :param use_auth_token: assumes user did on CLI `huggingface-cli login` to access private repo
+    :param 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)"
+    )
+
+    from transformers import AutoConfig
+    config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token)
+    llama_type_from_config = 'llama' in str(config).lower()
+    llama_type_from_name = "llama" in base_model.lower()
+    llama_type = llama_type_from_config or llama_type_from_name
+    if llama_type:
+        print("Detected as llama type from"
+              " config (%s) or name (%s)" % (llama_type_from_config, llama_type_from_name), flush=True)
+
+    model_loader, tokenizer_loader = get_loaders(llama_type=llama_type, model_name=base_model, reward_type=reward_type)
+    if not tokenizer_base_model:
+        tokenizer_base_model = base_model
+
+    if tokenizer_loader is not None and not isinstance(tokenizer_loader, str):
+        tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model,
+                                                     local_files_only=local_files_only,
+                                                     resume_download=resume_download,
+                                                     use_auth_token=use_auth_token,
+                                                     )
+    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,
+                                               gpu_id=gpu_id, 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
+
+
+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',
+                         'chat',
+                         'instruction_nochat',
+                         'iinput_nochat',
+                         ]
+
+
+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,
+        chat,
+        instruction_nochat,
+        iinput_nochat,
+        # END NOTE: Examples must have same order of parameters
+        src_lang=None,
+        tgt_lang=None,
+        debug=False,
+        concurrency_count=None,
+        save_dir=None,
+        hard_stop_list=None,
+        sanitize_bot_response=True,
+        model_state0=None,
+        is_low_mem=None,
+        raise_generate_gpu_exceptions=None,
+        chat_context=None,
+):
+    # ensure passed these
+    assert concurrency_count is not None
+    assert is_low_mem is not None
+    assert raise_generate_gpu_exceptions is not None
+    assert chat_context is not None
+
+    if debug:
+        locals_dict = locals().copy()
+        locals_dict.pop('model_state', None)
+        locals_dict.pop('model_state0', None)
+        print(locals_dict)
+
+    no_model_msg = "Please choose a base model with --base_model (CLI) or in Models Tab (gradio).\nThen start New Conversation"
+
+    if model_state0 is None:
+        # e.g. for no gradio case, set dummy value, else should be set
+        model_state0 = [None, None, None, None]
+
+    if model_state is not None and len(model_state) == 4 and not isinstance(model_state[0], str):
+        # try to free-up original model (i.e. list was passed as reference)
+        if model_state0 is not None and model_state0[0] is not None:
+            model_state0[0].cpu()
+            model_state0[0] = None
+        # try to free-up original tokenizer (i.e. list was passed as reference)
+        if model_state0 is not None and model_state0[1] is not None:
+            model_state0[1] = None
+        clear_torch_cache()
+        model, tokenizer, device, base_model = model_state
+    elif model_state0 is not None and len(model_state0) == 4 and model_state0[0] is not None:
+        assert isinstance(model_state[0], str)
+        model, tokenizer, device, base_model = model_state0
+    else:
+        raise AssertionError(no_model_msg)
+
+    if base_model is None:
+        raise AssertionError(no_model_msg)
+
+    assert base_model.strip(), no_model_msg
+    assert model, "Model is missing"
+    assert tokenizer, "Tokenizer is missing"
+
+    # choose chat or non-chat mode
+    if not chat:
+        instruction = instruction_nochat
+        iinput = iinput_nochat
+
+    if not context:
+        # get hidden context if have one
+        context = get_context(chat_context, prompt_type)
+
+    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, '\n' + human, '\n' + 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]
+        # handle fake \n added
+        stop_words_ids = [x[1:] if y[0] == '\n' else x for x, y in zip(stop_words_ids, stop_words)]
+        # build stopper
+        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,
+    )
+
+    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:
+            #skip_prompt = prompt_type != 'plain'
+            skip_prompt = False
+            streamer = TextIteratorStreamer(tokenizer, skip_prompt=skip_prompt)
+            gen_kwargs.update(dict(streamer=streamer))
+            if debug:
+                KThread.show_threads()
+            target_func = generate_with_exceptions
+            if concurrency_count == 1:
+                # otherwise can't do this
+                KThread.kill_threads(target_func.__name__, debug=debug)
+            target = wrapped_partial(generate_with_exceptions, model.generate, prompt, inputs_decoded,
+                                     raise_generate_gpu_exceptions, **gen_kwargs)
+            thread = KThread(target=target)
+            thread.start()
+            outputs = ""
+            for new_text in streamer:
+                outputs += new_text
+                yield prompter.get_response(outputs, prompt=inputs_decoded,
+                                            sanitize_bot_response=sanitize_bot_response)
+        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)
+        if save_dir and outputs and len(outputs) >= 1:
+            decoded_output = prompt + outputs[0]
+            save_generate_output(output=decoded_output, base_model=base_model, save_dir=save_dir)
+
+
+def generate_with_exceptions(func, prompt, inputs_decoded, raise_generate_gpu_exceptions, **kwargs):
+    try:
+        func(**kwargs)
+    except torch.cuda.OutOfMemoryError as e:
+        print("GPU OOM 2: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
+              flush=True)
+        if kwargs['input_ids'] is not None:
+            kwargs['input_ids'].cpu()
+        kwargs['input_ids'] = None
+        traceback.print_exc()
+        clear_torch_cache()
+        return
+    except (Exception, RuntimeError) as e:
+        if 'Expected all tensors to be on the same device' in str(e) or \
+                'expected scalar type Half but found Float' in str(e) or \
+                'probability tensor contains either' in str(e) or \
+                'cublasLt ran into an error!' in str(e) or \
+                'mat1 and mat2 shapes cannot be multiplied' in str(e):
+            print(
+                "GPU Error: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
+                flush=True)
+            traceback.print_exc()
+            clear_torch_cache()
+            if raise_generate_gpu_exceptions:
+                raise
+            return
+        else:
+            clear_torch_cache()
+            raise
+
+
+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]
+
+    # examples at first don't include chat, instruction_nochat, iinput_nochat, added at end
+    if show_examples is None:
+        if chat:
+            show_examples = False
+        else:
+            show_examples = True
+
+    summarize_example1 = """Jeff: Can I train a ? Transformers model on Amazon SageMaker? 
+Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
+Jeff: ok.
+Jeff: and how can I get started? 
+Jeff: where can I find documentation? 
+Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"""
+
+    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 = 0.4 if temperature is None else temperature
+        top_p = 0.85 if top_p is None else top_p
+        top_k = 70 if top_k is None else top_k
+        if 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 = True if do_sample is None else do_sample
+    # doesn't include chat, instruction_nochat, iinput_nochat, added later
+    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"
+
+    # move to correct position
+    for example in examples:
+        example += [chat, '', '']
+        # adjust examples if non-chat mode
+        if not chat:
+            example[eval_func_param_names.index('instruction_nochat')] = example[
+                eval_func_param_names.index('instruction')]
+            example[eval_func_param_names.index('instruction')] = ''
+
+            example[eval_func_param_names.index('iinput_nochat')] = example[eval_func_param_names.index('iinput')]
+            example[eval_func_param_names.index('iinput')] = ''
+
+    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 get_context(chat_context, prompt_type):
+    if chat_context and prompt_type == 'human_bot':
+        context0 = """<bot>: I am an intelligent, helpful, truthful, and fair assistant named h2oGPT, who will give accurate, balanced, and reliable responses.  I will not respond with I don't know or I don't understand.
+<human>: I am a human person seeking useful assistance and request all questions be answered completely, and typically expect detailed responses.  Give answers in numbered list format if several distinct but related items are being listed."""
+    else:
+        context0 = ''
+    return context0
+
+
+def 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)
+
+
+def score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len):
+    question = question[-cutoff_len:]
+    answer = answer[-cutoff_len:]
+
+    inputs = stokenizer(question, answer,
+                        return_tensors="pt",
+                        truncation=True,
+                        max_length=max_length_tokenize).to(smodel.device)
+    try:
+        score = torch.sigmoid(smodel(**inputs).logits[0]).cpu().detach().numpy()[0]
+    except torch.cuda.OutOfMemoryError as e:
+        print("GPU OOM 3: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True)
+        del inputs
+        traceback.print_exc()
+        clear_torch_cache()
+        return 'Response Score: GPU OOM'
+    except (Exception, RuntimeError) as e:
+        if 'Expected all tensors to be on the same device' in str(e) or \
+                'expected scalar type Half but found Float' in str(e) or \
+                'probability tensor contains either' in str(e) or \
+                'cublasLt ran into an error!' in str(e):
+            print("GPU Error: question: %s answer: %s exception: %s" % (question, answer, str(e)),
+                  flush=True)
+            traceback.print_exc()
+            clear_torch_cache()
+            return 'Response Score: GPU Error'
+        else:
+            raise
+    os.environ['TOKENIZERS_PARALLELISM'] = 'true'
+    return score
+
+
+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'
+
+    python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b
+
+    """, flush=True)
+    fire.Fire(main)