Add application file

app.py

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+"""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 re
+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
+from transformers.utils import logging
+
+from transformers import AutoTokenizer, AutoModelForCausalLM  # isort: skip
+
+logger = logging.get_logger(__name__)
+st.set_page_config(layout='wide')
+
+
+@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,
+    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)
+    generation_config._eos_token_tensor = generation_config.eos_token_id
+    model_kwargs = generation_config.update(**kwargs)
+    if generation_config.temperature == 0.0:
+        generation_config.do_sample = False
+    eos_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()
+    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,
+    )
+    unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+    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)
+
+        # 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)
+        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:
+            break
+
+
+def on_btn_click():
+    del st.session_state.messages
+    del st.session_state.deepthink_messages
+
+
+def postprocess(text, add_prefix=True, deepthink=False):
+    text = re.sub(r'\\\(|\\\)', r'$', text)
+    text = re.sub(r'\\\[|\\\]', r'$$', text)
+    if add_prefix:
+        text = (':red[[Deep Thinking]]\n\n' if deepthink else ':blue[[Normal Response]]\n\n') + text
+    return text
+
+
+@st.cache_resource
+def load_model():
+    model_path = 'internlm/internlm3-8b-instruct'
+    model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(torch.bfloat16).cuda()
+    tokenizer = AutoTokenizer.from_pretrained(model_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)
+        radio = st.radio('Inference Mode', ['Normal Response', 'Deep Thinking'], key='mode')
+        st.button('Clear Chat History', on_click=on_btn_click)
+
+    st.session_state['inference_mode'] = radio
+    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, deepthink=False, start=0, stop=None):
+    if stop is None:
+        stop = len(st.session_state.messages)
+    elif stop < 0:
+        stop = len(st.session_state.messages) + stop
+    messages = []
+    for idx in range(start, stop):
+        message, deepthink_message = st.session_state.messages[idx], st.session_state.deepthink_messages[idx]
+        if deepthink:
+            if deepthink_message['content'] is not None:
+                messages.append(deepthink_message)
+            else:
+                messages.append(message)
+        else:
+            if message['content'] is not None:
+                messages.append(message)
+            else:
+                messages.append(deepthink_message)
+    meta_instruction = (
+        'You are InternLM (书生·浦语), a helpful, honest, '
+        'and harmless AI assistant developed by Shanghai '
+        'AI Laboratory (上海人工智能实验室).'
+    )
+    if deepthink:
+        meta_instruction += """You are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes:
+## Deep Understanding
+Take time to fully comprehend the problem before attempting a solution. Consider:
+- What is the real question being asked?
+- What are the given conditions and what do they tell us?
+- Are there any special restrictions or assumptions?
+- Which information is crucial and which is supplementary?
+## Multi-angle Analysis
+Before solving, conduct thorough analysis:
+- What mathematical concepts and properties are involved?
+- Can you recall similar classic problems or solution methods?
+- Would diagrams or tables help visualize the problem?
+- Are there special cases that need separate consideration?
+## Systematic Thinking
+Plan your solution path:
+- Propose multiple possible approaches
+- Analyze the feasibility and merits of each method
+- Choose the most appropriate method and explain why
+- Break complex problems into smaller, manageable steps
+## Rigorous Proof
+During the solution process:
+- Provide solid justification for each step
+- Include detailed proofs for key conclusions
+- Pay attention to logical connections
+- Be vigilant about potential oversights
+## Repeated Verification
+After completing your solution:
+- Verify your results satisfy all conditions
+- Check for overlooked special cases
+- Consider if the solution can be optimized or simplified
+- Review your reasoning process
+Remember:
+1. Take time to think thoroughly rather than rushing to an answer
+2. Rigorously prove each key conclusion
+3. Keep an open mind and try different approaches
+4. Summarize valuable problem-solving methods
+5. Maintain healthy skepticism and verify multiple times
+Your response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others.
+When you're ready, present your complete solution with:
+- Clear problem understanding
+- Detailed solution process
+- Key insights
+- Thorough verification
+Focus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer.
+"""  # noqa: E501
+    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():
+    # torch.cuda.empty_cache()
+    print('load model begin.')
+    model, tokenizer = load_model()
+    print('load model end.')
+
+    user_avator = 'assets/user.png'
+    robot_avator = 'assets/robot.png'
+
+    st.title('InternLM3-8B-Instruct')
+
+    generation_config = prepare_generation_config()
+
+    def render_message(msg, msg_idx, deepthink):
+        if msg['content'] is None:
+            real_prompt = combine_history(
+                st.session_state.messages[msg_idx - 1]['content'], deepthink=deepthink, stop=msg_idx - 1
+            )
+            placeholder = st.empty()
+            for cur_response in generate_interactive(
+                model=model,
+                tokenizer=tokenizer,
+                prompt=real_prompt,
+                additional_eos_token_id=92542,
+                **asdict(generation_config),
+            ):
+                placeholder.markdown(postprocess(cur_response, deepthink=deepthink) + '▌')
+            placeholder.markdown(postprocess(cur_response, deepthink=deepthink))
+            msg['content'] = cur_response
+            torch.cuda.empty_cache()
+        else:
+            st.markdown(postprocess(msg['content'], deepthink=deepthink))
+
+    # Initialize chat history
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+    if 'deepthink_messages' not in st.session_state:
+        st.session_state.deepthink_messages = []
+
+    # Display chat messages from history on app rerun
+    for idx, (message, deepthink_message) in enumerate(
+        zip(st.session_state.messages, st.session_state.deepthink_messages)
+    ):
+        with st.chat_message(message['role'], avatar=message.get('avatar')):
+            if message['role'] == 'user':
+                st.markdown(postprocess(message['content'], add_prefix=False))
+            else:
+                if st.toggle('compare', key=f'compare_{idx}'):
+                    cols = st.columns(2)
+                    if st.session_state['inference_mode'] == 'Deep Thinking':
+                        with cols[1]:
+                            render_message(deepthink_message, idx, True)
+                        with cols[0]:
+                            render_message(message, idx, False)
+                    else:
+                        with cols[0]:
+                            render_message(message, idx, False)
+                        with cols[1]:
+                            render_message(deepthink_message, idx, True)
+                else:
+                    if st.session_state['inference_mode'] == 'Deep Thinking':
+                        if deepthink_message['content'] is not None:
+                            st.markdown(postprocess(deepthink_message['content'], deepthink=True))
+                        else:
+                            st.markdown(postprocess(message['content']))
+                    else:
+                        if message['content'] is not None:
+                            st.markdown(postprocess(message['content']))
+                        else:
+                            st.markdown(postprocess(deepthink_message['content'], deepthink=True))
+
+    # 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_avator):
+            st.markdown(postprocess(prompt, add_prefix=False))
+        real_prompt = combine_history(prompt, deepthink=st.session_state['inference_mode'] == 'Deep Thinking')
+        # Add user message to chat history
+        st.session_state.messages.append({'role': 'user', 'content': prompt, 'avatar': user_avator})
+        st.session_state.deepthink_messages.append({'role': 'user', 'content': prompt, 'avatar': user_avator})
+
+        with st.chat_message('robot', avatar=robot_avator):
+            st.toggle('compare', key=f'compare_{len(st.session_state.messages)}')
+            message_placeholder = st.empty()
+            for cur_response in generate_interactive(
+                model=model,
+                tokenizer=tokenizer,
+                prompt=real_prompt,
+                additional_eos_token_id=92542,
+                **asdict(generation_config),
+            ):
+                # Display robot response in chat message container
+                message_placeholder.markdown(
+                    postprocess(cur_response, deepthink=st.session_state['inference_mode'] == 'Deep Thinking') + '▌'
+                )
+            message_placeholder.markdown(
+                postprocess(cur_response, deepthink=st.session_state['inference_mode'] == 'Deep Thinking')
+            )
+        # Add robot response to chat history
+        response, deepthink_response = (
+            (None, cur_response) if st.session_state['inference_mode'] == 'Deep Thinking' else (cur_response, None)
+        )
+        st.session_state.messages.append(
+            {
+                'role': 'robot',
+                'content': response,  # pylint: disable=undefined-loop-variable
+                'avatar': robot_avator,
+            }
+        )
+        st.session_state.deepthink_messages.append(
+            {
+                'role': 'robot',
+                'content': deepthink_response,
+                'avatar': robot_avator,
+            }
+        )
+        torch.cuda.empty_cache()
+
+
+main()

requirements.txt

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+transformers>=4.48
+streamlit>=1.41.1