add:xtuner

.ipynb_checkpoints/change_script-checkpoint.py

@@ -0,0 +1,47 @@
+import json
+import argparse
+from tqdm import tqdm
+
+def process_line(line, old_text, new_text):
+    # 解析 JSON 行
+    data = json.loads(line)
+    
+    # 递归函数来处理嵌套的字典和列表
+    def replace_text(obj):
+        if isinstance(obj, dict):
+            return {k: replace_text(v) for k, v in obj.items()}
+        elif isinstance(obj, list):
+            return [replace_text(item) for item in obj]
+        elif isinstance(obj, str):
+            return obj.replace(old_text, new_text)
+        else:
+            return obj
+    
+    # 处理整个 JSON 对象
+    processed_data = replace_text(data)
+    
+    # 将处理后的对象转回 JSON 字符串
+    return json.dumps(processed_data, ensure_ascii=False)
+
+def main(input_file, output_file, old_text, new_text):
+    with open(input_file, 'r', encoding='utf-8') as infile, \
+         open(output_file, 'w', encoding='utf-8') as outfile:
+        
+        # 计算总行数用于进度条
+        total_lines = sum(1 for _ in infile)
+        infile.seek(0)  # 重置文件指针到开头
+        
+        # 使用 tqdm 创建进度条
+        for line in tqdm(infile, total=total_lines, desc="Processing"):
+            processed_line = process_line(line.strip(), old_text, new_text)
+            outfile.write(processed_line + '\n')
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Replace text in a JSONL file.")
+    parser.add_argument("input_file", help="Input JSONL file to process")
+    parser.add_argument("output_file", help="Output file for processed JSONL")
+    parser.add_argument("--old_text", default="尖米", help="Text to be replaced")
+    parser.add_argument("--new_text", default="FAYO", help="Text to replace with")
+    args = parser.parse_args()
+
+    main(args.input_file, args.output_file, args.old_text, args.new_text)

.ipynb_checkpoints/xtuner_streamlit_demo-checkpoint.py

@@ -0,0 +1,292 @@
+"""This script refers to the dialogue example of streamlit, the interactive
+generation code of chatglm2 and transformers.
+
+We mainly modified part of the code logic to adapt to the
+generation of our model.
+Please refer to these links below for more information:
+    1. streamlit chat example:
+        https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
+    2. chatglm2:
+        https://github.com/THUDM/ChatGLM2-6B
+    3. transformers:
+        https://github.com/huggingface/transformers
+Please run with the command `streamlit run path/to/web_demo.py
+    --server.address=0.0.0.0 --server.port 7860`.
+Using `python path/to/web_demo.py` may cause unknown problems.
+"""
+# isort: skip_file
+import copy
+import warnings
+from dataclasses import asdict, dataclass
+from typing import Callable, List, Optional
+
+import streamlit as st
+import torch
+from torch import nn
+from transformers.generation.utils import (LogitsProcessorList,
+                                           StoppingCriteriaList)
+from transformers.utils import logging
+
+from transformers import AutoTokenizer, AutoModelForCausalLM  # isort: skip
+
+logger = logging.get_logger(__name__)
+model_name_or_path="/root/finetune/work_dirs/assistTuner/merged"
+
+@dataclass
+class GenerationConfig:
+    # this config is used for chat to provide more diversity
+    max_length: int = 32768
+    top_p: float = 0.8
+    temperature: float = 0.8
+    do_sample: bool = True
+    repetition_penalty: float = 1.005
+
+
+@torch.inference_mode()
+def generate_interactive(
+    model,
+    tokenizer,
+    prompt,
+    generation_config: Optional[GenerationConfig] = None,
+    logits_processor: Optional[LogitsProcessorList] = None,
+    stopping_criteria: Optional[StoppingCriteriaList] = None,
+    prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor],
+                                                List[int]]] = None,
+    additional_eos_token_id: Optional[int] = None,
+    **kwargs,
+):
+    inputs = tokenizer([prompt], padding=True, return_tensors='pt')
+    input_length = len(inputs['input_ids'][0])
+    for k, v in inputs.items():
+        inputs[k] = v.cuda()
+    input_ids = inputs['input_ids']
+    _, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
+    if generation_config is None:
+        generation_config = model.generation_config
+    generation_config = copy.deepcopy(generation_config)
+    model_kwargs = generation_config.update(**kwargs)
+    bos_token_id, eos_token_id = (  # noqa: F841  # pylint: disable=W0612
+        generation_config.bos_token_id,
+        generation_config.eos_token_id,
+    )
+    if isinstance(eos_token_id, int):
+        eos_token_id = [eos_token_id]
+    if additional_eos_token_id is not None:
+        eos_token_id.append(additional_eos_token_id)
+    has_default_max_length = kwargs.get(
+        'max_length') is None and generation_config.max_length is not None
+    if has_default_max_length and generation_config.max_new_tokens is None:
+        warnings.warn(
+            f"Using 'max_length''s default \
+                ({repr(generation_config.max_length)}) \
+                to control the generation length. "
+            'This behaviour is deprecated and will be removed from the \
+                config in v5 of Transformers -- we'
+            ' recommend using `max_new_tokens` to control the maximum \
+                length of the generation.',
+            UserWarning,
+        )
+    elif generation_config.max_new_tokens is not None:
+        generation_config.max_length = generation_config.max_new_tokens + \
+            input_ids_seq_length
+        if not has_default_max_length:
+            logger.warn(  # pylint: disable=W4902
+                f"Both 'max_new_tokens' (={generation_config.max_new_tokens}) "
+                f"and 'max_length'(={generation_config.max_length}) seem to "
+                "have been set. 'max_new_tokens' will take precedence. "
+                'Please refer to the documentation for more information. '
+                '(https://huggingface.co/docs/transformers/main/'
+                'en/main_classes/text_generation)',
+                UserWarning,
+            )
+
+    if input_ids_seq_length >= generation_config.max_length:
+        input_ids_string = 'input_ids'
+        logger.warning(
+            f'Input length of {input_ids_string} is {input_ids_seq_length}, '
+            f"but 'max_length' is set to {generation_config.max_length}. "
+            'This can lead to unexpected behavior. You should consider'
+            " increasing 'max_new_tokens'.")
+
+    # 2. Set generation parameters if not already defined
+    logits_processor = logits_processor if logits_processor is not None \
+        else LogitsProcessorList()
+    stopping_criteria = stopping_criteria if stopping_criteria is not None \
+        else StoppingCriteriaList()
+
+    logits_processor = model._get_logits_processor(
+        generation_config=generation_config,
+        input_ids_seq_length=input_ids_seq_length,
+        encoder_input_ids=input_ids,
+        prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+        logits_processor=logits_processor,
+    )
+
+    stopping_criteria = model._get_stopping_criteria(
+        generation_config=generation_config,
+        stopping_criteria=stopping_criteria)
+    logits_warper = model._get_logits_warper(generation_config)
+
+    unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+    scores = None
+    while True:
+        model_inputs = model.prepare_inputs_for_generation(
+            input_ids, **model_kwargs)
+        # forward pass to get next token
+        outputs = model(
+            **model_inputs,
+            return_dict=True,
+            output_attentions=False,
+            output_hidden_states=False,
+        )
+
+        next_token_logits = outputs.logits[:, -1, :]
+
+        # pre-process distribution
+        next_token_scores = logits_processor(input_ids, next_token_logits)
+        next_token_scores = logits_warper(input_ids, next_token_scores)
+
+        # sample
+        probs = nn.functional.softmax(next_token_scores, dim=-1)
+        if generation_config.do_sample:
+            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+        else:
+            next_tokens = torch.argmax(probs, dim=-1)
+
+        # update generated ids, model inputs, and length for next step
+        input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+        model_kwargs = model._update_model_kwargs_for_generation(
+            outputs, model_kwargs, is_encoder_decoder=False)
+        unfinished_sequences = unfinished_sequences.mul(
+            (min(next_tokens != i for i in eos_token_id)).long())
+
+        output_token_ids = input_ids[0].cpu().tolist()
+        output_token_ids = output_token_ids[input_length:]
+        for each_eos_token_id in eos_token_id:
+            if output_token_ids[-1] == each_eos_token_id:
+                output_token_ids = output_token_ids[:-1]
+        response = tokenizer.decode(output_token_ids)
+
+        yield response
+        # stop when each sentence is finished
+        # or if we exceed the maximum length
+        if unfinished_sequences.max() == 0 or stopping_criteria(
+                input_ids, scores):
+            break
+
+
+def on_btn_click():
+    del st.session_state.messages
+
+
+@st.cache_resource
+def load_model():
+    model = (AutoModelForCausalLM.from_pretrained(
+        model_name_or_path,
+        trust_remote_code=True).to(torch.bfloat16).cuda())
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,
+                                              trust_remote_code=True)
+    return model, tokenizer
+
+
+def prepare_generation_config():
+    with st.sidebar:
+        max_length = st.slider('Max Length',
+                               min_value=8,
+                               max_value=32768,
+                               value=32768)
+        top_p = st.slider('Top P', 0.0, 1.0, 0.8, step=0.01)
+        temperature = st.slider('Temperature', 0.0, 1.0, 0.7, step=0.01)
+        st.button('Clear Chat History', on_click=on_btn_click)
+
+    generation_config = GenerationConfig(max_length=max_length,
+                                         top_p=top_p,
+                                         temperature=temperature)
+
+    return generation_config
+
+
+user_prompt = '<|im_start|>user\n{user}<|im_end|>\n'
+robot_prompt = '<|im_start|>assistant\n{robot}<|im_end|>\n'
+cur_query_prompt = '<|im_start|>user\n{user}<|im_end|>\n\
+    <|im_start|>assistant\n'
+
+
+def combine_history(prompt):
+    messages = st.session_state.messages
+    meta_instruction = ('You are a helpful, honest, '
+                        'and harmless AI assistant.')
+    total_prompt = f'<s><|im_start|>system\n{meta_instruction}<|im_end|>\n'
+    for message in messages:
+        cur_content = message['content']
+        if message['role'] == 'user':
+            cur_prompt = user_prompt.format(user=cur_content)
+        elif message['role'] == 'robot':
+            cur_prompt = robot_prompt.format(robot=cur_content)
+        else:
+            raise RuntimeError
+        total_prompt += cur_prompt
+    total_prompt = total_prompt + cur_query_prompt.format(user=prompt)
+    return total_prompt
+
+
+def main():
+    st.title('internlm2_5-7b-chat-assistant')
+
+    # torch.cuda.empty_cache()
+    print('load model begin.')
+    model, tokenizer = load_model()
+    print('load model end.')
+
+    generation_config = prepare_generation_config()
+
+    # Initialize chat history
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message['role'], avatar=message.get('avatar')):
+            st.markdown(message['content'])
+
+    # Accept user input
+    if prompt := st.chat_input('What is up?'):
+        # Display user message in chat message container
+
+        with st.chat_message('user', avatar='user'):
+
+            st.markdown(prompt)
+        real_prompt = combine_history(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({
+            'role': 'user',
+            'content': prompt,
+            'avatar': 'user'
+        })
+
+        with st.chat_message('robot', avatar='assistant'):
+
+            message_placeholder = st.empty()
+            for cur_response in generate_interactive(
+                    model=model,
+                    tokenizer=tokenizer,
+                    prompt=real_prompt,
+                    additional_eos_token_id=92542,
+                    device='cuda:0',
+                    **asdict(generation_config),
+            ):
+                # Display robot response in chat message container
+                message_placeholder.markdown(cur_response + '▌')
+            message_placeholder.markdown(cur_response)
+        # Add robot response to chat history
+        st.session_state.messages.append({
+            'role': 'robot',
+            'content': cur_response,  # pylint: disable=undefined-loop-variable
+            'avatar': 'assistant',
+        })
+        torch.cuda.empty_cache()
+
+
+if __name__ == '__main__':
+    main()
+

change_script.py

@@ -0,0 +1,47 @@
+import json
+import argparse
+from tqdm import tqdm
+
+def process_line(line, old_text, new_text):
+    # 解析 JSON 行
+    data = json.loads(line)
+    
+    # 递归函数来处理嵌套的字典和列表
+    def replace_text(obj):
+        if isinstance(obj, dict):
+            return {k: replace_text(v) for k, v in obj.items()}
+        elif isinstance(obj, list):
+            return [replace_text(item) for item in obj]
+        elif isinstance(obj, str):
+            return obj.replace(old_text, new_text)
+        else:
+            return obj
+    
+    # 处理整个 JSON 对象
+    processed_data = replace_text(data)
+    
+    # 将处理后的对象转回 JSON 字符串
+    return json.dumps(processed_data, ensure_ascii=False)
+
+def main(input_file, output_file, old_text, new_text):
+    with open(input_file, 'r', encoding='utf-8') as infile, \
+         open(output_file, 'w', encoding='utf-8') as outfile:
+        
+        # 计算总行数用于进度条
+        total_lines = sum(1 for _ in infile)
+        infile.seek(0)  # 重置文件指针到开头
+        
+        # 使用 tqdm 创建进度条
+        for line in tqdm(infile, total=total_lines, desc="Processing"):
+            processed_line = process_line(line.strip(), old_text, new_text)
+            outfile.write(processed_line + '\n')
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Replace text in a JSONL file.")
+    parser.add_argument("input_file", help="Input JSONL file to process")
+    parser.add_argument("output_file", help="Output file for processed JSONL")
+    parser.add_argument("--old_text", default="尖米", help="Text to be replaced")
+    parser.add_argument("--new_text", default="FAYO", help="Text to replace with")
+    args = parser.parse_args()
+
+    main(args.input_file, args.output_file, args.old_text, args.new_text)

config.json

@@ -0,0 +1,37 @@
+{
+  "_name_or_path": "/root/finetune/models/internlm2_5-7b-chat",
+  "architectures": [
+    "InternLM2ForCausalLM"
+  ],
+  "attn_implementation": "eager",
+  "auto_map": {
+    "AutoConfig": "configuration_internlm2.InternLM2Config",
+    "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
+    "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
+  },
+  "bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "internlm2",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "dynamic"
+  },
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.39.0",
+  "use_cache": true,
+  "vocab_size": 92544
+}

internlm2_5_chat_7b_qlora_alpaca_e3_copy.py

@@ -0,0 +1,220 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from datasets import load_dataset
+from mmengine.dataset import DefaultSampler
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
+from peft import LoraConfig
+from torch.optim import AdamW
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          BitsAndBytesConfig)
+
+from xtuner.dataset import process_hf_dataset
+from xtuner.dataset.collate_fns import default_collate_fn
+from xtuner.dataset.map_fns import alpaca_map_fn, template_map_fn_factory
+from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook,
+                                 VarlenAttnArgsToMessageHubHook)
+from xtuner.engine.runner import TrainLoop
+from xtuner.model import SupervisedFinetune
+from xtuner.parallel.sequence import SequenceParallelSampler
+from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
+
+#######################################################################
+#                          PART 1  Settings                           #
+#######################################################################
+# Model
+pretrained_model_name_or_path = '/root/finetune/models/internlm2_5-7b-chat'
+use_varlen_attn = False
+
+# Data
+alpaca_en_path = '/root/finetune/data/assistant_Tuner_change.jsonl'
+prompt_template = PROMPT_TEMPLATE.internlm2_chat
+max_length = 2048
+pack_to_max_length = True
+
+# parallel
+sequence_parallel_size = 1
+
+# Scheduler & Optimizer
+batch_size = 1  # per_device
+accumulative_counts = 1
+accumulative_counts *= sequence_parallel_size
+dataloader_num_workers = 0
+max_epochs = 3
+optim_type = AdamW
+lr = 2e-4
+betas = (0.9, 0.999)
+weight_decay = 0
+max_norm = 1  # grad clip
+warmup_ratio = 0.03
+
+# Save
+save_steps = 500
+save_total_limit = 2  # Maximum checkpoints to keep (-1 means unlimited)
+
+# Evaluate the generation performance during the training
+evaluation_freq = 500
+SYSTEM = SYSTEM_TEMPLATE.alpaca
+evaluation_inputs = [
+    '请介绍一下你自己', 'Please introduce yourself'
+]
+
+#######################################################################
+#                      PART 2  Model & Tokenizer                      #
+#######################################################################
+tokenizer = dict(
+    type=AutoTokenizer.from_pretrained,
+    pretrained_model_name_or_path=pretrained_model_name_or_path,
+    trust_remote_code=True,
+    padding_side='right')
+
+model = dict(
+    type=SupervisedFinetune,
+    use_varlen_attn=use_varlen_attn,
+    llm=dict(
+        type=AutoModelForCausalLM.from_pretrained,
+        pretrained_model_name_or_path=pretrained_model_name_or_path,
+        trust_remote_code=True,
+        torch_dtype=torch.float16,
+        quantization_config=dict(
+            type=BitsAndBytesConfig,
+            load_in_4bit=True,
+            load_in_8bit=False,
+            llm_int8_threshold=6.0,
+            llm_int8_has_fp16_weight=False,
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type='nf4')),
+    lora=dict(
+        type=LoraConfig,
+        r=64,
+        lora_alpha=16,
+        lora_dropout=0.1,
+        bias='none',
+        task_type='CAUSAL_LM'))
+
+#######################################################################
+#                      PART 3  Dataset & Dataloader                   #
+#######################################################################
+alpaca_en = dict(
+    type=process_hf_dataset,
+    dataset=dict(type=load_dataset, path='json', data_files=dict(train=alpaca_en_path)),
+    tokenizer=tokenizer,
+    max_length=max_length,
+    dataset_map_fn=None,
+    template_map_fn=dict(
+        type=template_map_fn_factory, template=prompt_template),
+    remove_unused_columns=True,
+    shuffle_before_pack=True,
+    pack_to_max_length=pack_to_max_length,
+    use_varlen_attn=use_varlen_attn)
+
+sampler = SequenceParallelSampler \
+    if sequence_parallel_size > 1 else DefaultSampler
+train_dataloader = dict(
+    batch_size=batch_size,
+    num_workers=dataloader_num_workers,
+    dataset=alpaca_en,
+    sampler=dict(type=sampler, shuffle=True),
+    collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))
+
+#######################################################################
+#                    PART 4  Scheduler & Optimizer                    #
+#######################################################################
+# optimizer
+optim_wrapper = dict(
+    type=AmpOptimWrapper,
+    optimizer=dict(
+        type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
+    clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
+    accumulative_counts=accumulative_counts,
+    loss_scale='dynamic',
+    dtype='float16')
+
+# learning policy
+# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md  # noqa: E501
+param_scheduler = [
+    dict(
+        type=LinearLR,
+        start_factor=1e-5,
+        by_epoch=True,
+        begin=0,
+        end=warmup_ratio * max_epochs,
+        convert_to_iter_based=True),
+    dict(
+        type=CosineAnnealingLR,
+        eta_min=0.0,
+        by_epoch=True,
+        begin=warmup_ratio * max_epochs,
+        end=max_epochs,
+        convert_to_iter_based=True)
+]
+
+# train, val, test setting
+train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
+
+#######################################################################
+#                           PART 5  Runtime                           #
+#######################################################################
+# Log the dialogue periodically during the training process, optional
+custom_hooks = [
+    dict(type=DatasetInfoHook, tokenizer=tokenizer),
+    dict(
+        type=EvaluateChatHook,
+        tokenizer=tokenizer,
+        every_n_iters=evaluation_freq,
+        evaluation_inputs=evaluation_inputs,
+        system=SYSTEM,
+        prompt_template=prompt_template)
+]
+
+if use_varlen_attn:
+    custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]
+
+# configure default hooks
+default_hooks = dict(
+    # record the time of every iteration.
+    timer=dict(type=IterTimerHook),
+    # print log every 10 iterations.
+    logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
+    # enable the parameter scheduler.
+    param_scheduler=dict(type=ParamSchedulerHook),
+    # save checkpoint per `save_steps`.
+    checkpoint=dict(
+        type=CheckpointHook,
+        by_epoch=False,
+        interval=save_steps,
+        max_keep_ckpts=save_total_limit),
+    # set sampler seed in distributed evrionment.
+    sampler_seed=dict(type=DistSamplerSeedHook),
+)
+
+# configure environment
+env_cfg = dict(
+    # whether to enable cudnn benchmark
+    cudnn_benchmark=False,
+    # set multi process parameters
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    # set distributed parameters
+    dist_cfg=dict(backend='nccl'),
+)
+
+# set visualizer
+visualizer = None
+
+# set log level
+log_level = 'INFO'
+
+# load from which checkpoint
+load_from = None
+
+# whether to resume training from the loaded checkpoint
+resume = False
+
+# Defaults to use random seed and disable `deterministic`
+randomness = dict(seed=None, deterministic=False)
+
+# set log processor
+log_processor = dict(by_epoch=False)
+

xtuner_streamlit_demo.py

@@ -0,0 +1,292 @@
+"""This script refers to the dialogue example of streamlit, the interactive
+generation code of chatglm2 and transformers.
+
+We mainly modified part of the code logic to adapt to the
+generation of our model.
+Please refer to these links below for more information:
+    1. streamlit chat example:
+        https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
+    2. chatglm2:
+        https://github.com/THUDM/ChatGLM2-6B
+    3. transformers:
+        https://github.com/huggingface/transformers
+Please run with the command `streamlit run path/to/web_demo.py
+    --server.address=0.0.0.0 --server.port 7860`.
+Using `python path/to/web_demo.py` may cause unknown problems.
+"""
+# isort: skip_file
+import copy
+import warnings
+from dataclasses import asdict, dataclass
+from typing import Callable, List, Optional
+
+import streamlit as st
+import torch
+from torch import nn
+from transformers.generation.utils import (LogitsProcessorList,
+                                           StoppingCriteriaList)
+from transformers.utils import logging
+
+from transformers import AutoTokenizer, AutoModelForCausalLM  # isort: skip
+
+logger = logging.get_logger(__name__)
+model_name_or_path="/root/finetune/work_dirs/assistTuner/merged"
+
+@dataclass
+class GenerationConfig:
+    # this config is used for chat to provide more diversity
+    max_length: int = 32768
+    top_p: float = 0.8
+    temperature: float = 0.8
+    do_sample: bool = True
+    repetition_penalty: float = 1.005
+
+
+@torch.inference_mode()
+def generate_interactive(
+    model,
+    tokenizer,
+    prompt,
+    generation_config: Optional[GenerationConfig] = None,
+    logits_processor: Optional[LogitsProcessorList] = None,
+    stopping_criteria: Optional[StoppingCriteriaList] = None,
+    prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor],
+                                                List[int]]] = None,
+    additional_eos_token_id: Optional[int] = None,
+    **kwargs,
+):
+    inputs = tokenizer([prompt], padding=True, return_tensors='pt')
+    input_length = len(inputs['input_ids'][0])
+    for k, v in inputs.items():
+        inputs[k] = v.cuda()
+    input_ids = inputs['input_ids']
+    _, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
+    if generation_config is None:
+        generation_config = model.generation_config
+    generation_config = copy.deepcopy(generation_config)
+    model_kwargs = generation_config.update(**kwargs)
+    bos_token_id, eos_token_id = (  # noqa: F841  # pylint: disable=W0612
+        generation_config.bos_token_id,
+        generation_config.eos_token_id,
+    )
+    if isinstance(eos_token_id, int):
+        eos_token_id = [eos_token_id]
+    if additional_eos_token_id is not None:
+        eos_token_id.append(additional_eos_token_id)
+    has_default_max_length = kwargs.get(
+        'max_length') is None and generation_config.max_length is not None
+    if has_default_max_length and generation_config.max_new_tokens is None:
+        warnings.warn(
+            f"Using 'max_length''s default \
+                ({repr(generation_config.max_length)}) \
+                to control the generation length. "
+            'This behaviour is deprecated and will be removed from the \
+                config in v5 of Transformers -- we'
+            ' recommend using `max_new_tokens` to control the maximum \
+                length of the generation.',
+            UserWarning,
+        )
+    elif generation_config.max_new_tokens is not None:
+        generation_config.max_length = generation_config.max_new_tokens + \
+            input_ids_seq_length
+        if not has_default_max_length:
+            logger.warn(  # pylint: disable=W4902
+                f"Both 'max_new_tokens' (={generation_config.max_new_tokens}) "
+                f"and 'max_length'(={generation_config.max_length}) seem to "
+                "have been set. 'max_new_tokens' will take precedence. "
+                'Please refer to the documentation for more information. '
+                '(https://huggingface.co/docs/transformers/main/'
+                'en/main_classes/text_generation)',
+                UserWarning,
+            )
+
+    if input_ids_seq_length >= generation_config.max_length:
+        input_ids_string = 'input_ids'
+        logger.warning(
+            f'Input length of {input_ids_string} is {input_ids_seq_length}, '
+            f"but 'max_length' is set to {generation_config.max_length}. "
+            'This can lead to unexpected behavior. You should consider'
+            " increasing 'max_new_tokens'.")
+
+    # 2. Set generation parameters if not already defined
+    logits_processor = logits_processor if logits_processor is not None \
+        else LogitsProcessorList()
+    stopping_criteria = stopping_criteria if stopping_criteria is not None \
+        else StoppingCriteriaList()
+
+    logits_processor = model._get_logits_processor(
+        generation_config=generation_config,
+        input_ids_seq_length=input_ids_seq_length,
+        encoder_input_ids=input_ids,
+        prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+        logits_processor=logits_processor,
+    )
+
+    stopping_criteria = model._get_stopping_criteria(
+        generation_config=generation_config,
+        stopping_criteria=stopping_criteria)
+    logits_warper = model._get_logits_warper(generation_config)
+
+    unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+    scores = None
+    while True:
+        model_inputs = model.prepare_inputs_for_generation(
+            input_ids, **model_kwargs)
+        # forward pass to get next token
+        outputs = model(
+            **model_inputs,
+            return_dict=True,
+            output_attentions=False,
+            output_hidden_states=False,
+        )
+
+        next_token_logits = outputs.logits[:, -1, :]
+
+        # pre-process distribution
+        next_token_scores = logits_processor(input_ids, next_token_logits)
+        next_token_scores = logits_warper(input_ids, next_token_scores)
+
+        # sample
+        probs = nn.functional.softmax(next_token_scores, dim=-1)
+        if generation_config.do_sample:
+            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+        else:
+            next_tokens = torch.argmax(probs, dim=-1)
+
+        # update generated ids, model inputs, and length for next step
+        input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+        model_kwargs = model._update_model_kwargs_for_generation(
+            outputs, model_kwargs, is_encoder_decoder=False)
+        unfinished_sequences = unfinished_sequences.mul(
+            (min(next_tokens != i for i in eos_token_id)).long())
+
+        output_token_ids = input_ids[0].cpu().tolist()
+        output_token_ids = output_token_ids[input_length:]
+        for each_eos_token_id in eos_token_id:
+            if output_token_ids[-1] == each_eos_token_id:
+                output_token_ids = output_token_ids[:-1]
+        response = tokenizer.decode(output_token_ids)
+
+        yield response
+        # stop when each sentence is finished
+        # or if we exceed the maximum length
+        if unfinished_sequences.max() == 0 or stopping_criteria(
+                input_ids, scores):
+            break
+
+
+def on_btn_click():
+    del st.session_state.messages
+
+
+@st.cache_resource
+def load_model():
+    model = (AutoModelForCausalLM.from_pretrained(
+        model_name_or_path,
+        trust_remote_code=True).to(torch.bfloat16).cuda())
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,
+                                              trust_remote_code=True)
+    return model, tokenizer
+
+
+def prepare_generation_config():
+    with st.sidebar:
+        max_length = st.slider('Max Length',
+                               min_value=8,
+                               max_value=32768,
+                               value=32768)
+        top_p = st.slider('Top P', 0.0, 1.0, 0.8, step=0.01)
+        temperature = st.slider('Temperature', 0.0, 1.0, 0.7, step=0.01)
+        st.button('Clear Chat History', on_click=on_btn_click)
+
+    generation_config = GenerationConfig(max_length=max_length,
+                                         top_p=top_p,
+                                         temperature=temperature)
+
+    return generation_config
+
+
+user_prompt = '<|im_start|>user\n{user}<|im_end|>\n'
+robot_prompt = '<|im_start|>assistant\n{robot}<|im_end|>\n'
+cur_query_prompt = '<|im_start|>user\n{user}<|im_end|>\n\
+    <|im_start|>assistant\n'
+
+
+def combine_history(prompt):
+    messages = st.session_state.messages
+    meta_instruction = ('You are a helpful, honest, '
+                        'and harmless AI assistant.')
+    total_prompt = f'<s><|im_start|>system\n{meta_instruction}<|im_end|>\n'
+    for message in messages:
+        cur_content = message['content']
+        if message['role'] == 'user':
+            cur_prompt = user_prompt.format(user=cur_content)
+        elif message['role'] == 'robot':
+            cur_prompt = robot_prompt.format(robot=cur_content)
+        else:
+            raise RuntimeError
+        total_prompt += cur_prompt
+    total_prompt = total_prompt + cur_query_prompt.format(user=prompt)
+    return total_prompt
+
+
+def main():
+    st.title('internlm2_5-7b-chat-assistant')
+
+    # torch.cuda.empty_cache()
+    print('load model begin.')
+    model, tokenizer = load_model()
+    print('load model end.')
+
+    generation_config = prepare_generation_config()
+
+    # Initialize chat history
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message['role'], avatar=message.get('avatar')):
+            st.markdown(message['content'])
+
+    # Accept user input
+    if prompt := st.chat_input('What is up?'):
+        # Display user message in chat message container
+
+        with st.chat_message('user', avatar='user'):
+
+            st.markdown(prompt)
+        real_prompt = combine_history(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({
+            'role': 'user',
+            'content': prompt,
+            'avatar': 'user'
+        })
+
+        with st.chat_message('robot', avatar='assistant'):
+
+            message_placeholder = st.empty()
+            for cur_response in generate_interactive(
+                    model=model,
+                    tokenizer=tokenizer,
+                    prompt=real_prompt,
+                    additional_eos_token_id=92542,
+                    device='cuda:0',
+                    **asdict(generation_config),
+            ):
+                # Display robot response in chat message container
+                message_placeholder.markdown(cur_response + '▌')
+            message_placeholder.markdown(cur_response)
+        # Add robot response to chat history
+        st.session_state.messages.append({
+            'role': 'robot',
+            'content': cur_response,  # pylint: disable=undefined-loop-variable
+            'avatar': 'assistant',
+        })
+        torch.cuda.empty_cache()
+
+
+if __name__ == '__main__':
+    main()
+