app.py

# A mirror to gradio launch stream
# By Xuan Phi Nguyen at DAMO Academy, Alibaba Group

"""
Load FasterLlama with original VLLM codebase,

require changing config names to LlamaForCausalLM

tensor_parallel must == 1

"""


import os
import numpy as np
import argparse
import torch
import gradio as gr
from typing import Any, Iterator
from typing import Iterator, List, Optional, Tuple
import filelock
import glob
import json

from gradio_client.documentation import document, set_documentation_group

from typing import List, Optional, Union, Dict, Tuple
from tqdm.auto import tqdm
from huggingface_hub import snapshot_download

DEBUG = True

if not DEBUG:

    # vllm import
    from vllm import LLM, SamplingParams
    from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
    from vllm.engine.arg_utils import EngineArgs
    from vllm.engine.llm_engine import LLMEngine
    from vllm.outputs import RequestOutput
    from vllm.sampling_params import SamplingParams
    from vllm.utils import Counter
    from vllm.sequence import (Sequence, SequenceData, SequenceGroup,
                            SequenceGroupMetadata, SequenceOutputs,
                            SequenceStatus)
    # ! reconfigure vllm to faster llama
    from vllm.model_executor.model_loader import _MODEL_REGISTRY
    from vllm.model_executor.models import LlamaForCausalLM


    _MODEL_REGISTRY['FasterLlamaForCausalLM'] = LlamaForCausalLM


def hf_model_weights_iterator(
    model_name_or_path: str,
    cache_dir: Optional[str] = None,
    use_np_cache: bool = False,
) -> Iterator[Tuple[str, torch.Tensor]]:
    from vllm.model_executor.weight_utils import Disabledtqdm
    # Prepare file lock directory to prevent multiple processes from
    # downloading the same model weights at the same time.
    lock_dir = cache_dir if cache_dir is not None else "/tmp"
    lock_file_name = model_name_or_path.replace("/", "-") + ".lock"
    lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name))

    # Download model weights from huggingface.
    is_local = os.path.isdir(model_name_or_path)
    if not is_local:
        with lock:
            hf_folder = snapshot_download(model_name_or_path,
                                          allow_patterns="*.bin",
                                          cache_dir=cache_dir,
                                          local_files_only=True,
                                          tqdm_class=Disabledtqdm)
    else:
        hf_folder = model_name_or_path

    hf_bin_files = [
        # x for x in glob.glob(os.path.join(hf_folder, "*.bin"))
        x for x in glob.glob(os.path.join(hf_folder, "*model*.bin"))
        if not x.endswith("training_args.bin")
    ]
    hf_safetensors_files = [
        x for x in glob.glob(os.path.join(hf_folder, "*model*.safetensors"))
        if not x.endswith("training_args.bin")
    ]
    # print(F'Load bin files: {hf_bin_files} // safetensors: {hf_safetensors_files}')

    if use_np_cache:
        # Convert the model weights from torch tensors to numpy arrays for
        # faster loading.
        np_folder = os.path.join(hf_folder, "np")
        os.makedirs(np_folder, exist_ok=True)
        weight_names_file = os.path.join(np_folder, "weight_names.json")
        with lock:
            if not os.path.exists(weight_names_file):
                weight_names = []
                for bin_file in hf_bin_files:
                    state = torch.load(bin_file, map_location="cpu")
                    for name, param in state.items():
                        param_path = os.path.join(np_folder, name)
                        with open(param_path, "wb") as f:
                            np.save(f, param.cpu().detach().numpy())
                        weight_names.append(name)
                with open(weight_names_file, "w") as f:
                    json.dump(weight_names, f)

        with open(weight_names_file, "r") as f:
            weight_names = json.load(f)

        for name in weight_names:
            param_path = os.path.join(np_folder, name)
            with open(param_path, "rb") as f:
                param = np.load(f)
            yield name, torch.from_numpy(param)
    else:
        if len(hf_bin_files) > 0:
            print(F'Load bin files: {hf_bin_files}')
            for bin_file in hf_bin_files:
                state = torch.load(bin_file, map_location="cpu")
                for name, param in state.items():
                    yield name, param
                del state
                torch.cuda.empty_cache()
        elif len(hf_safetensors_files) > 0:
            print(F'Load safetensor files: {hf_safetensors_files}')
            from safetensors.torch import load_file
            for safe_file in hf_safetensors_files:
                # state = torch.load(bin_file, map_location="cpu")
                state = load_file(safe_file)
                for name, param in state.items():
                    yield name, param
                del state
                torch.cuda.empty_cache()
        else:
            raise ValueError(f'no files available either bin or safe')


def convert_pyslice_to_tensor(x: Any) -> torch.Tensor:
    """convert PySafeSlice object from safetensors to torch.Tensor

    PySafeSlice object supports indexing, which is done before loading the
    actual tensor and can reduce the amount of memory being read into the
    memory. However, it does not support more advanced functionalities
    like `.view()` or `.t()`. Therefore, if we need to modify the loaded
    tensor with these more complicated operators, we need to convert to
    tensor first.
    """
    if not isinstance(x, torch.Tensor):
        x = x[:]
    return x


def load_padded_tensor_parallel_vocab(
    param: torch.Tensor,
    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
    tensor_model_parallel_rank: int,
) -> None:
    shard_size = param.shape[0]
    start_idx = tensor_model_parallel_rank * shard_size
    end_idx = (tensor_model_parallel_rank + 1) * shard_size
    loaded_weight = loaded_weight[start_idx:end_idx]
    loaded_weight = convert_pyslice_to_tensor(loaded_weight)
    param[:loaded_weight.shape[0]].copy_(loaded_weight)


def llama_load_weights(
        self,
        model_name_or_path: str,
        cache_dir: Optional[str] = None,
        use_np_cache: bool = False,
        load_format: str = "auto",
        # load_format: str = "pt",
        revision: Optional[str] = None
):
    from vllm.model_executor.weight_utils import (
        load_tensor_parallel_weights
    )
    from vllm.model_executor.parallel_utils.parallel_state import (
        get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
    tp_size = get_tensor_model_parallel_world_size()
    tensor_model_parallel_rank = get_tensor_model_parallel_rank()

    q_proj_shard_size = (self.config.hidden_size // tp_size)
    kv_proj_shard_size = (self.config.hidden_size //
                            self.config.num_attention_heads *
                            getattr(self.config, "num_key_value_heads", self.config.num_attention_heads) // tp_size)
    attention_weight_specs = [
        # (weight_name, shard_size, offset)
        ("q_proj", q_proj_shard_size, 0),
        ("k_proj", kv_proj_shard_size, q_proj_shard_size),
        ("v_proj", kv_proj_shard_size,
            q_proj_shard_size + kv_proj_shard_size),
    ]
    state_dict = self.state_dict()
    need_to_load = len(state_dict)
    loaded = 0
    # try:
    #     iterator = hf_model_weights_iterator(model_name_or_path, cache_dir, use_np_cache)
    # except Exception as e:
    #     iterator = hf_model_weights_iterator(model_name_or_path, cache_dir, load_format, revision)
    iterator = hf_model_weights_iterator(model_name_or_path, cache_dir, use_np_cache)

    # for name, loaded_weight in hf_model_weights_iterator(
    #         model_name_or_path, cache_dir, load_format, revision):
            # model_name_or_path, cache_dir, use_np_cache):
    for name, loaded_weight in iterator:
        if "rotary_emb.inv_freq" in name:
            continue

        # if "embed_tokens" in name or "lm_head" in name:
        #     param = state_dict[name]
        #     # Consider padding in the vocab size.
        #     padded_vocab_size = (param.shape[0] * tp_size)
        #     # num_extra_rows = padded_vocab_size - self.config.vocab_size
        #     num_extra_rows = padded_vocab_size - loaded_weight.size(0)
        #     load_size = loaded_weight.size()
        #     extra_rows = torch.empty(num_extra_rows,
        #                                 loaded_weight.shape[1])
        #     extra_rows = extra_rows.to(loaded_weight)
        #     loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
        #     if num_extra_rows > 0:
        #         print(f'Add empty to {num_extra_rows} extra row for {name}')
        #     print(f'Load: {name} | {padded_vocab_size=} | {self.config.vocab_size=} | {num_extra_rows=} | {param.size()=} | {loaded_weight.size()=} | {load_size=}')
        
        if "embed_tokens" in name or "lm_head" in name:
            param = state_dict[name]
            load_padded_tensor_parallel_vocab(param, loaded_weight, tensor_model_parallel_rank)
            loaded += 1
            continue

        is_attention_weight = False
        for weight_name, shard_size, offset in attention_weight_specs:
            if weight_name not in name or "qkv_proj" in name:
                continue
            param = state_dict[name.replace(weight_name, "qkv_proj")]

            loaded_weight = loaded_weight[
                shard_size * tensor_model_parallel_rank:shard_size *
                (tensor_model_parallel_rank + 1)]
            param_slice = param.data[offset:offset + shard_size]
            assert param_slice.shape == loaded_weight.shape

            param_slice.copy_(loaded_weight)
            loaded += 1.0 / 3
            is_attention_weight = True
            break
        if is_attention_weight:
            continue
            
        # ! qkv_proj is sharded differently if concatenated into qkv
        # qkv:      qqqq kkkk vvvv
        # lweight:  qq0qq1 kk0kk1 vv0vv1
        # q_shard_size: hidden_size // tp_size = qq
        # qkv_s0:   qq0_kk0_vv0
        # qkv_s1:   qq1_kk1_vv1
        if "qkv_proj" in name:
            param = state_dict[name]
            # loaded_weight
            qsize = self.config.hidden_size
            kvsize = self.config.hidden_size // self.config.num_attention_heads * getattr(self.config, "num_key_value_heads", self.config.num_attention_heads)
            q_offsets = (
                q_proj_shard_size * tensor_model_parallel_rank, 
                q_proj_shard_size * (tensor_model_parallel_rank + 1)
            )
            k_offsets = (
                qsize + kv_proj_shard_size * tensor_model_parallel_rank, 
                qsize + kv_proj_shard_size * (tensor_model_parallel_rank + 1)
            )
            v_offsets = (
                qsize + kvsize + kv_proj_shard_size * tensor_model_parallel_rank, 
                qsize + kvsize + kv_proj_shard_size * (tensor_model_parallel_rank + 1)
            )
            _loaded_weight = torch.cat(
                [
                    loaded_weight[q_offsets[0]:q_offsets[1]],
                    loaded_weight[k_offsets[0]:k_offsets[1]],
                    loaded_weight[v_offsets[0]:v_offsets[1]],
                ], 0
            )
            # print(f'{name} | {q_offsets} | {k_offsets} | {v_offsets}')
            assert param.shape == _loaded_weight.shape, f'{param.shape=} != {_loaded_weight.shape=}'
            param.data.copy_(_loaded_weight)
            loaded += 1.0
            is_attention_weight = True
        if is_attention_weight:
            continue


        is_gate_up_weight = False
        for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
            if weight_name not in name or "gate_up_proj" in name:
                continue
            param = state_dict[name.replace(weight_name, "gate_up_proj")]
            shard_size = param.shape[0] // 2
            loaded_weight = loaded_weight[
                shard_size * tensor_model_parallel_rank:shard_size *
                (tensor_model_parallel_rank + 1)]
            param_slice = param.data[shard_size * stride_id:shard_size *
                                        (stride_id + 1)]
            assert param_slice.shape == loaded_weight.shape
            param_slice.copy_(loaded_weight)
            loaded += 1.0 / 2
            is_gate_up_weight = True
            break
        if is_gate_up_weight:
            continue
            
        if "gate_up_proj" in name:
            param = state_dict[name]
            shard_size = param.shape[0] // 2
            intermediate_size = self.config.intermediate_size
            g_offsets = (
                shard_size * tensor_model_parallel_rank, 
                shard_size * (tensor_model_parallel_rank + 1)
            )
            u_offsets = (
                intermediate_size + shard_size * tensor_model_parallel_rank, 
                intermediate_size + shard_size * (tensor_model_parallel_rank + 1)
            )
            # print(f'{name} {param.size()} | {g_offsets} | {u_offsets}')
            _loaded_weight = torch.cat(
                [
                    loaded_weight[g_offsets[0]:g_offsets[1]],
                    loaded_weight[u_offsets[0]:u_offsets[1]],
                ], 0
            )
            assert param.shape == _loaded_weight.shape
            param.data.copy_(_loaded_weight)
            loaded += 1.0
            is_gate_up_weight = True
        if is_gate_up_weight:
            continue


        param = state_dict[name]
        load_tensor_parallel_weights(param, loaded_weight, name,
                                        self._column_parallel_weights,
                                        self._row_parallel_weights,
                                        tensor_model_parallel_rank)
        loaded += 1

    if np.abs(loaded - need_to_load) < 0.01:
        print(f'WARNING: only {loaded} params loaded out of {need_to_load}')
    else:
        print(f'Loaded all {loaded} params loaded out of {need_to_load}')


# Reassign LlamaForCausalLM.load_weights with llama_load_weights
if not DEBUG:
    LlamaForCausalLM.load_weights = llama_load_weights

# ! ==================================================================

set_documentation_group("component")

DATA_ROOT = os.environ.get("dataroot", "/mnt/workspace/workgroup/phi")
MODEL_CACHE_DIR = os.path.join(DATA_ROOT, "pret_models")


DTYPES = {
    'float16': torch.float16,
    'bfloat16': torch.bfloat16
}

llm = None
demo = None

RELOAD_SIGNAL = '<<<reload:'

BOS_TOKEN = '<s>'
EOS_TOKEN = '</s>'

B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"

SYSTEM_PROMPT_1 = """You are a multilingual, helpful, respectful and honest assistant. Your name is SeaL and you are built by DAMO Academy, Alibaba Group. Always answer as helpfully as possible, while being safe. Your \
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
 that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
correct. If you don't know the answer to a question, please don't share false information.

As a multilingual assistant, you must respond and follow instructions in the native language of the user by default, unless told otherwise. \
Your response should adapt to the norms and customs of the respective language and culture.
"""

RES_PRINTED = False

def llama_chat_sys_input_seq_constructor(text, sys_prompt=SYSTEM_PROMPT_1, bos_token=BOS_TOKEN, eos_token=EOS_TOKEN):
    return f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {text} {E_INST}"


def llama_chat_multiturn_sys_input_seq_constructor(
    message: str,
    history: List[Tuple[str, str]], 
    sys_prompt=SYSTEM_PROMPT_1, 
    bos_token=BOS_TOKEN, 
    eos_token=EOS_TOKEN,
):
    """
    ```
        <bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos>
        <bos>[INST] Prompt [/INST] Answer <eos>
        <bos>[INST] Prompt [/INST]
    ```
    """
    text = ''
    for i, (prompt, res) in enumerate(history):
        if i == 0:
            text += f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {prompt} {E_INST}"
        else:
            text += f"{bos_token}{B_INST} {prompt} {E_INST}"

        if res is not None:
            text += f" {res} {eos_token} "
    if len(history) == 0 or text.strip() == '':
        text = f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {message} {E_INST}"
    else:
        text += f"{bos_token}{B_INST} {message} {E_INST}"
    return text


@document()
class ChatBot(gr.Chatbot):
    def _postprocess_chat_messages(
        self, chat_message
    ):
        x = super()._postprocess_chat_messages(chat_message)
        if isinstance(x, str):
            x = x.replace("\n", "<br>")
        return x


def load_ckpt(ckpt_file: str) -> str:
    global llm
    status = "Failed"
    if not os.path.exists(ckpt_file):
        status = f"Failed - file not found: {ckpt_file}"
    elif not ckpt_file.endswith(".bin"):
        status = f"Failed - file not .bin: {ckpt_file}"
    else:
        try:
            state_dict = torch.load(ckpt_file, map_location='cpu')
            print(f'loaded state_dict: {ckpt_file}')
            llm.llm_engine.workers[0].model.load_state_dict(state_dict)
            status = f'Success. Loaded {ckpt_file}'
        except Exception as e:
            status = f'Failed - {str(e)}'
    return status



def chat_response(message, history, temperature: float, max_tokens: int, system_prompt: str = '') -> str:
    global llm
    assert llm is not None
    temperature = float(temperature)
    max_tokens = int(max_tokens)
    if system_prompt.strip() != '':
        # chat version, add system prompt
        message = llama_chat_sys_input_seq_constructor(
            message.strip(),
            sys_prompt=system_prompt
        )

    sampling_params = SamplingParams(temperature=temperature, max_tokens=max_tokens)
    gen = llm.generate(message, sampling_params)
    out = gen[0].outputs[0].text
    # print(f'{message}<<<{out}>>>')
    return f'{out}'


def vllm_abort(self: Any):
    scheduler = self.llm_engine.scheduler
    for state_queue in [scheduler.waiting, scheduler.running, scheduler.swapped]:
        for seq_group in state_queue:
            # if seq_group.request_id == request_id:
            # Remove the sequence group from the state queue.
            state_queue.remove(seq_group)
            for seq in seq_group.seqs:
                if seq.is_finished():
                    continue
                scheduler.free_seq(seq, SequenceStatus.FINISHED_ABORTED)

# def _vllm_run_engine(self: LLM, use_tqdm: bool = False) -> Dict[str, RequestOutput]:
def _vllm_run_engine(self: Any, use_tqdm: bool = False) -> Dict[str, Any]:
    # Initialize tqdm.
    if use_tqdm:
        num_requests = self.llm_engine.get_num_unfinished_requests()
        pbar = tqdm(total=num_requests, desc="Processed prompts")
    # Run the engine.
    outputs: Dict[str, RequestOutput] = {}
    while self.llm_engine.has_unfinished_requests():
        step_outputs = self.llm_engine.step()
        for output in step_outputs:
            # if output.finished:
            #     outputs.append(output)
                # if use_tqdm:
                #     pbar.update(1)
            outputs[output.request_id] = output
        # outputs = sorted(outputs, key=lambda x: int(x.request_id))
        if len(outputs) > 0:
            yield outputs
    # if use_tqdm:
    #     pbar.close()
    # Sort the outputs by request ID.
    # This is necessary because some requests may be finished earlier than
    # its previous requests.
    # outputs = sorted(outputs, key=lambda x: int(x.request_id))
    # return outputs


def vllm_generate_stream(
    self: Any,
    prompts: Optional[Union[str, List[str]]] = None,
    sampling_params: Optional[Any] = None,
    prompt_token_ids: Optional[List[List[int]]] = None,
    use_tqdm: bool = False,
) -> Dict[str, Any]:
    """Generates the completions for the input prompts.

    NOTE: This class automatically batches the given prompts, considering
    the memory constraint. For the best performance, put all of your prompts
    into a single list and pass it to this method.

    Args:
        prompts: A list of prompts to generate completions for.
        sampling_params: The sampling parameters for text generation. If
            None, we use the default sampling parameters.
        prompt_token_ids: A list of token IDs for the prompts. If None, we
            use the tokenizer to convert the prompts to token IDs.
        use_tqdm: Whether to use tqdm to display the progress bar.

    Returns:
        A list of `RequestOutput` objects containing the generated
        completions in the same order as the input prompts.
    """
    if prompts is None and prompt_token_ids is None:
        raise ValueError("Either prompts or prompt_token_ids must be "
                            "provided.")
    if isinstance(prompts, str):
        # Convert a single prompt to a list.
        prompts = [prompts]
    if prompts is not None and prompt_token_ids is not None:
        if len(prompts) != len(prompt_token_ids):
            raise ValueError("The lengths of prompts and prompt_token_ids "
                                "must be the same.")
    if sampling_params is None:
        # Use default sampling params.
        sampling_params = SamplingParams()

    # Add requests to the engine.
    if prompts is not None:
        num_requests = len(prompts)
    else:
        num_requests = len(prompt_token_ids)
    for i in range(num_requests):
        prompt = prompts[i] if prompts is not None else None
        if prompt_token_ids is None:
            token_ids = None
        else:
            token_ids = prompt_token_ids[i]
        self._add_request(prompt, sampling_params, token_ids)
    # return self._run_engine(use_tqdm)
    yield from _vllm_run_engine(self, use_tqdm)


def chat_response_stream(
    message: str, 
    history: List[Tuple[str, str]], 
    temperature: float, 
    max_tokens: int, 
    frequency_penalty: float,
    system_prompt: str
) -> str:
    global llm, RES_PRINTED
    assert llm is not None
    # force removing all 
    vllm_abort(llm)

    temperature = float(temperature)
    frequency_penalty = float(frequency_penalty)
    max_tokens = int(max_tokens)
    if system_prompt.strip() != '':
        # chat version, add system prompt
        message = llama_chat_sys_input_seq_constructor(
            message.strip(),
            sys_prompt=system_prompt
        )
    sampling_params = SamplingParams(
        temperature=temperature, max_tokens=max_tokens,
        frequency_penalty=frequency_penalty,
    )
    cur_out = None
    for gen in vllm_generate_stream(llm, message, sampling_params):
        if cur_out is not None:
            yield cur_out
        assert len(gen) == 1, f'{gen}'
        item = next(iter(gen.values()))
        cur_out = item.outputs[0].text
    if not RES_PRINTED:
        print(f'{message}<<<{cur_out}>>>')
        RES_PRINTED = True
    if cur_out is not None:
        yield cur_out


def chat_response_stream_multiturn(
    message: str, 
    history: List[Tuple[str, str]], 
    temperature: float, 
    max_tokens: int, 
    frequency_penalty: float,
    system_prompt: str
) -> str:
    """Build multi turn
    <bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos>
    <bos>[INST] Prompt [/INST] Answer <eos>
    <bos>[INST] Prompt [/INST]

    message is incoming prompt
    history don't have the current messauge
    """
    global llm, RES_PRINTED
    assert llm is not None
    assert system_prompt.strip() != '', f'system prompt is empty'
    # force removing all 
    vllm_abort(llm)

    temperature = float(temperature)
    frequency_penalty = float(frequency_penalty)
    max_tokens = int(max_tokens)

    # history.append([message, None])
    # history will be appended with message later on
    full_prompt = llama_chat_multiturn_sys_input_seq_constructor(
        message, history, sys_prompt=system_prompt
    )
    sampling_params = SamplingParams(
        temperature=temperature, max_tokens=max_tokens,
        frequency_penalty=frequency_penalty,
    )
    cur_out = None
    for gen in vllm_generate_stream(llm, full_prompt, sampling_params):
        if cur_out is not None:
            yield cur_out
        assert len(gen) == 1, f'{gen}'
        item = next(iter(gen.values()))
        cur_out = item.outputs[0].text
    if not RES_PRINTED:
        print(f'{full_prompt}<<<{cur_out}>>>')
        RES_PRINTED = True
    if cur_out is not None:
        yield cur_out


def debug_chat_response_echo(
    message: str, 
    history: List[Tuple[str, str]], 
    temperature: float = 0.0, 
    max_tokens: int = 4096, 
    frequency_penalty: float = 0.4,
    system_prompt: str = SYSTEM_PROMPT_1,
) -> str:
    yield f"repeat: {message}"


# ============ CONSTANT ============
MODEL_TITLE = "DAMO-SeaL-13B - An Assistant for South East Asian Languages"
MODEL_DESC = """
This is a 13B DAMO-SeaL-Chat assistant model built by DAMO Academy, Alibaba Group. It can produce helpful responses in English, Vietnamese, Indonesian and Thai.
""".strip()


cite_markdown = """
## Citation
If you find our project useful, hope you can star our repo and cite our paper as follows:
```
@article{damonlpsg2023seallm,
  author = {???},
  title = {SeaL: A language model for South East Asian Languages},
  year = 2023,
}
"""
#   journal = {arXiv preprint arXiv:2306.02858}
#   url = {https://arxiv.org/abs/2306.02858}


TENSOR_PARALLEL = int(os.environ.get("TENSOR_PARALLEL", "1"))
DTYPE = 'bfloat16'
DTYPE = 'float16'

MODEL_PATH = os.environ.get("MODEL_PATH", "notfound, please set `export MODEL_PATH=`")




def launch():
    global demo, llm, DEBUG
    model_desc = MODEL_DESC
    model_path = MODEL_PATH
    assert os.path.exists(model_path), f'{model_path} not found'
    model_title = MODEL_TITLE
    tensor_parallel = TENSOR_PARALLEL
    assert tensor_parallel > 0 , f'{tensor_parallel} invalid'
    dtype = DTYPE
    sys_prompt = SYSTEM_PROMPT_1
    max_tokens = 4096

    if DEBUG:
        model_desc += "<br>!!!!! This is in debug mode, responses will be copy original"
        response_fn = debug_chat_response_echo
    else:
        # ! load the model
        llm = LLM(model=model_path, dtype=dtype, tensor_parallel_size=tensor_parallel)

        print(f'Use system prompt:\n{sys_prompt}')

        # response_fn = chat_response_stream_multiturn if args.multiturn else chat_response_stream
        response_fn = chat_response_stream_multiturn
        print(F'respond: {response_fn}')

    demo = gr.ChatInterface(
        response_fn,
        chatbot=ChatBot(
            bubble_full_width=False,
            latex_delimiters=[
                { "left": "$", "right": "$", "display": False},
                { "left": "$$", "right": "$$", "display": True},
            ]
        ),
        textbox=gr.Textbox(placeholder='Type message', lines=8, max_lines=128, min_width=200),
        submit_btn=gr.Button(value='Submit', variant="primary", scale=0),
        # stop_btn=None,
        title=f"{model_title}",
        description=f"{model_desc}",
        # ! decide if can change the system prompt.
        additional_inputs=[
            gr.Number(value=0, label='Temperature (higher -> more random)'), 
            gr.Number(value=max_tokens, label='Max generated tokens (increase if want more generation)'), 
            gr.Number(value=0.4, label='Frequency penalty (> 0 encourage new tokens)'), 
            gr.Textbox(value=sys_prompt, label='System prompt', lines=8)], 
    )

    gr.Markdown(cite_markdown)
    demo.queue()
    # demo.launch(server_port=args.port)
    demo.launch()


def main():

    # launch(parser.parse_args())
    launch()


if __name__ == "__main__":
    main()


"""

export CUDA_VISIBLE_DEVICES=0
export MODEL_PATH=${dataroot}/hf_train/pretrain_lm/swpn/merlion13s108Hi8kPretFlCW8k.LMFromHf.a.gc.t5k0.vizhthid.mean_std.TrainTask.NLNL.Multi.Vi.FSePlCq13M.FSePlCq13M.m4k.b8.lr1e5.linear.wa0k.ms858k.grac1.se1.8g.v4c.zfsdp/step_4000
export MODEL_PATH=${dataroot}/llama-2-7b-lxxp-faster
export MODEL_PATH=${dataroot}/llama-2-7b-chat-xp
python app.py 


"""