app.py

import os
# os.system('pip install bitsandbytes')
# os.system('pip install -q datasets loralib sentencepiece accelerate')
# os.system('pip install -q git+https://github.com/zphang/transformers@c3dc391')
# os.system('pip install -q git+https://github.com/huggingface/peft.git')
# os.system('pip install gradio')
os.system('pip install tenacity')

import re
import yaml
import gc
import copy
import time
from tenacity import RetryError
from tenacity import retry, stop_after_attempt, wait_fixed
import gradio as gr
import torch
from peft import PeftModel
from transformers import (
    LLaMATokenizer, 
    LLaMAForCausalLM, 
    GenerationConfig,
    AutoModelForCausalLM,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    LogitsProcessorList,
    MinNewTokensLengthLogitsProcessor,
    TemperatureLogitsWarper,
    TopPLogitsWarper,
    MinLengthLogitsProcessor
)

assert torch.cuda.is_available(), "Change the runtime type to GPU"

# constants
num_of_characters_to_keep = 1000

# regex
html_tag_pattern = re.compile(r"<.*?>")
multi_line_pattern = re.compile(r"\n+")
multi_space_pattern = re.compile(r"(  )")
multi_br_tag_pattern = re.compile(re.compile(r'<br>\s*(<br>\s*)*'))
    
# repl is short for replacement
repl_linebreak = "\n"
repl_empty_str = ""

TITLE = "🦌 Stambecco 🇮🇹"

ABSTRACT = """
Stambecco is a Italian Instruction-following model based on the [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) model. It comes in two versions: 7b and 13b parameters. It is trained on an Italian version of the [GPT-4-LLM](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) dataset, a dataset of `GPT-4` generated instruction-following data.
This demo is intended to show and evaluate the conversational capabilities of the model.
For more information, please visit [the project's website](https://github.com/mchl-labs/stambecco).
NOTE: Too long input (context, instruction) will not be allowed. Please keep context < 500 and instruction < 150
"""

BOTTOM_LINE = """
By default, this demo runs with streaming mode, but you can also run with dynamic batch generation model.
Stambecco is built on the same concept as Standford Alpaca project, but using LoRA it lets us train and inference on a smaller GPUs such as RTX4090 for 7B version. Also, we could build very small size of checkpoints on top of base models thanks to [🤗 transformers](https://huggingface.co/docs/transformers/index), [🤗 peft](https://github.com/huggingface/peft), and [bitsandbytes](https://github.com/TimDettmers/bitsandbytes/tree/main) libraries.
This demo currently runs 8Bit 7b version of the model.
"""

DEFAULT_EXAMPLES = {
    "Typical Questions": [
        {
            "title": "Parlami di Giulio Cesare.",
            "examples": [
                ["1", "Scrivi un articolo su Giulio Cesare"],
                ["2", "Davvero?"],
                ["3", "Quanto era ricco Giulio Cesare?"],
                ["4", "Chi è stato il suo successore?"],
            ]
        },
        {
            "title": "Parigi",
            "examples": [
                ["1", "Scrivi un tema sulla città di Parigi"],
                ["2", "Fai un elenco di 5 posti da visitare assolutamente"],
                ["3", "Quali eventi importanti della Storia sono avvenuti a Parigi?"],
                ["4", "Quale è il periodo migliore per visitare Parigi?"],
            ]
        },
        {
            "title": "Scrivi un programma in Python che stampi i primi 10 numeri di Fibonacci",
            "examples": [
                ["1", "Scrivi un programma in Python che stampi i primi 10 numeri di Fibonacci"],
                ["2", "Potresti spiegarmi come funziona il codice?"],
                ["3", "Cos'è la ricorsione?"],
            ]
        }
    ],
}

SPECIAL_STRS = {
    "continue": "continua",
    "summarize": "Di cosa abbiamo discusso finora? Descrivi nella user's view."
}

PARENT_BLOCK_CSS = """
#col_container {
    width: 95%; 
    margin-left: auto; 
    margin-right: auto;
}
#chatbot {
    height: 500px; 
    overflow: auto;
}
"""

def load_model(
    base="decapoda-research/llama-7b-hf",
    finetuned="mchl-labs/stambecco-7b-plus",
):
    tokenizer = LLaMATokenizer.from_pretrained(base)
    tokenizer.pad_token_id = 0
    tokenizer.padding_side = "left"

    model = LLaMAForCausalLM.from_pretrained(
        base,
        load_in_8bit=True,
        device_map="auto",
    )
#    model = PeftModel.from_pretrained(model, finetuned, device_map={'': 0})
    
    model = PeftModel.from_pretrained(model, finetuned)
    return model, tokenizer

def get_generation_config(path):
    with open(path, 'rb') as f:
        generation_config = yaml.safe_load(f.read())

    return GenerationConfig(**generation_config["generation_config"])

def generate_prompt(prompt, histories, ctx=None, partial=False):
    convs = f"""Di seguito è riportata una cronologia delle istruzioni che descrivono le tasks, abbinate a un input che fornisce ulteriore contesto. Scrivi una risposta che completi adeguatamente la richiesta ricordando la cronologia della conversazione.

"""

    if ctx is not None:
        convs = f"""### Input: {ctx}
"""

    sub_convs = ""
    start_idx = 0
    
    for idx, history in enumerate(histories):
        history_prompt = history[0]
        history_response = history[1]
        if history_response == "✅ Riepilogo della conversazione effettuato e impostato come contesto" or history_prompt == SPECIAL_STRS["summarize"]:
            start_idx = idx

    # drop the previous conversations if user has summarized
    for history in histories[start_idx if start_idx == 0 else start_idx+1:]:
        history_prompt = history[0]
        history_response = history[1]
        
        history_response = history_response.replace("<br>", "\n")
        history_response = re.sub(
            html_tag_pattern, repl_empty_str, history_response
        )

        sub_convs = sub_convs + f"""### Istruzione: {history_prompt}
### Risposta: {history_response}
"""

    sub_convs = sub_convs + f"""### Istruzione: {prompt}
### Risposta:"""

    convs = convs + sub_convs
    return sub_convs if partial else convs, len(sub_convs)

def common_post_process(original_str):
    original_str = re.sub(
        multi_line_pattern, repl_linebreak, original_str
    )
    return original_str

def post_process_stream(bot_response):
    # sometimes model spits out text containing 
    # "### Risposta:" and "### Istruzione: -> in this case, we want to stop generating
    if "### Risposta:" in bot_response or "### Input:" in bot_response:
        bot_response = bot_response.replace("### Risposta:", '').replace("### Input:", '').strip()
        return bot_response, True
    
    return common_post_process(bot_response), False

def post_process_batch(bot_response):
    bot_response = bot_response.split("### Risposta:")[-1].strip()
    return common_post_process(bot_response)

def post_processes_batch(bot_responses):
    return [post_process_batch(r) for r in bot_responses]

def get_output_batch(
    model, tokenizer, prompts, generation_config
):
    if len(prompts) == 1:
        encoding = tokenizer(prompts, return_tensors="pt")
        input_ids = encoding["input_ids"].cuda()
        generated_id = model.generate(
            input_ids=input_ids,
            generation_config=generation_config,
            max_new_tokens=256
        )

        decoded = tokenizer.batch_decode(generated_id)
        del input_ids, generated_id
        torch.cuda.empty_cache()
        return decoded
    else:
        encodings = tokenizer(prompts, padding=True, return_tensors="pt").to('cuda')
        generated_ids = model.generate(
            **encodings,
            generation_config=generation_config,
            max_new_tokens=256
        )

        decoded = tokenizer.batch_decode(generated_ids)
        del encodings, generated_ids
        torch.cuda.empty_cache()
        return decoded


# StreamModel is borrowed from basaran project
# please find more info about it -> https://github.com/hyperonym/basaran
class StreamModel:
    """StreamModel wraps around a language model to provide stream decoding."""

    def __init__(self, model, tokenizer):
        super().__init__()
        self.model = model
        self.tokenizer = tokenizer
        self.device = "cuda" if torch.cuda.is_available() else "cpu"

        self.processor = LogitsProcessorList()
        self.processor.append(TemperatureLogitsWarper(0.9))
        self.processor.append(TopPLogitsWarper(0.75))


    def __call__(
        self,
        prompt,
        min_tokens=0,
        max_tokens=16,
        temperature=1.0,
        top_p=1.0,
        n=1,
        logprobs=0,
    ):
        """Create a completion stream for the provided prompt."""
        input_ids = self.tokenize(prompt)
        logprobs = max(logprobs, 0)

        # bigger than 1
        chunk_size = 2
        chunk_count = 0
        
        # Generate completion tokens.
        final_tokens = torch.empty(0)
        
        for tokens in self.generate(
            input_ids[None, :].repeat(n, 1),
            logprobs=logprobs,
            min_new_tokens=min_tokens,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
        ):
            if chunk_count < chunk_size:
                chunk_count = chunk_count + 1        
            
            final_tokens = torch.cat((final_tokens, tokens.to("cpu")))

            if chunk_count == chunk_size-1:
                chunk_count = 0
                yield self.tokenizer.decode(final_tokens, skip_special_tokens=True)

        if chunk_count > 0:
            yield self.tokenizer.decode(final_tokens, skip_special_tokens=True)
                
        del final_tokens, input_ids
        if self.device == "cuda": 
            torch.cuda.empty_cache()

    def _infer(self, model_fn, **kwargs):
        with torch.inference_mode():
            return model_fn(**kwargs)

    def tokenize(self, text):
        """Tokenize a string into a tensor of token IDs."""
        batch = self.tokenizer.encode(text, return_tensors="pt")
        return batch[0].to(self.device)

    def generate(self, input_ids, logprobs=0, **kwargs):
        """Generate a stream of predicted tokens using the language model."""

        # Store the original batch size and input length.
        batch_size = input_ids.shape[0]
        input_length = input_ids.shape[-1]

        # Separate model arguments from generation config.
        config = self.model.generation_config
        config = copy.deepcopy(config)
        kwargs = config.update(**kwargs)
        kwargs["output_attentions"] = False
        kwargs["output_hidden_states"] = False
        kwargs["use_cache"] = True

        # Collect special token IDs.
        pad_token_id = config.pad_token_id
        bos_token_id = config.bos_token_id
        eos_token_id = config.eos_token_id
        if isinstance(eos_token_id, int):
            eos_token_id = [eos_token_id]
        if pad_token_id is None and eos_token_id is not None:
            pad_token_id = eos_token_id[0]

        # Generate from eos if no input is specified.
        if input_length == 0:
            input_ids = input_ids.new_ones((batch_size, 1)).long()
            if eos_token_id is not None:
                input_ids = input_ids * eos_token_id[0]
            input_length = 1

        # Keep track of which sequences are already finished.
        unfinished = input_ids.new_ones(batch_size)

        # Start auto-regressive generation.
        while True:
            inputs = self.model.prepare_inputs_for_generation(
                input_ids, **kwargs
            )  # noqa: E501

            outputs = self._infer(
                self.model,
                **inputs,
                # return_dict=True,
                output_attentions=False,
                output_hidden_states=False,
            )

            # Pre-process the probability distribution of the next tokens.
            logits = outputs.logits[:, -1, :]
            with torch.inference_mode():
                logits = self.processor(input_ids, logits)
            probs = torch.nn.functional.softmax(logits, dim=-1)

            # Select deterministic or stochastic decoding strategy.
            if (config.top_p is not None and config.top_p <= 0) or (
                config.temperature is not None and config.temperature <= 0
            ):
                tokens = torch.argmax(probs, dim=-1)[:, None]
            else:
                tokens = torch.multinomial(probs, num_samples=1)

            tokens = tokens.squeeze(1)

            # Finished sequences should have their next token be a padding.
            if pad_token_id is not None:
                tokens = tokens * unfinished + pad_token_id * (1 - unfinished)

            # Append selected tokens to the inputs.
            input_ids = torch.cat([input_ids, tokens[:, None]], dim=-1)

            # Mark sequences with eos tokens as finished.
            if eos_token_id is not None:
                not_eos = sum(tokens != i for i in eos_token_id)
                unfinished = unfinished.mul(not_eos.long())

            # Set status to -1 if exceeded the max length.
            status = unfinished.clone()
            if input_ids.shape[-1] - input_length >= config.max_new_tokens:
                status = 0 - status

            # Yield predictions and status.
            yield tokens

            # Stop when finished or exceeded the max length.
            if status.max() <= 0:
                break

generation_config = get_generation_config(
    "./generation_config_default.yaml"
)

model, tokenizer = load_model(
    # base="decapoda-research/llama-13b-hf",
    # finetuned="mchl-labs/stambecco-13b-plus",
)    

stream_model = StreamModel(model, tokenizer)

def chat_stream(
    context,
    instruction,
    state_chatbot,
):
    if len(context) > 1000 or len(instruction) > 300:
        raise gr.Error("Context or prompt is too long!")
        
    bot_summarized_response = ''
    # user input should be appropriately formatted (don't be confused by the function name)
    instruction_display = instruction
    instruction_prompt, conv_length = generate_prompt(instruction, state_chatbot, context)
    
    if conv_length > num_of_characters_to_keep:
        instruction_prompt = generate_prompt(SPECIAL_STRS["summarize"], state_chatbot, context, partial=True)[0]
        
        state_chatbot = state_chatbot + [
            (
                None, 
                "![](https://s2.gifyu.com/images/icons8-loading-circle.gif) Conversazione troppo lunga, sto riassumendo..."
            )
        ]
        yield (state_chatbot, state_chatbot, context)
        
        bot_summarized_response = get_output_batch(
            model, tokenizer, [instruction_prompt], generation_config
        )[0]
        bot_summarized_response = bot_summarized_response.split("### Risposta:")[-1].strip()
        
        state_chatbot[-1] = (
            None, 
            "✅ Riepilogo della conversazione effettuato e impostato come contesto"
        )
        print(f"bot_summarized_response: {bot_summarized_response}")
        yield (state_chatbot, state_chatbot, f"{context}. {bot_summarized_response}".strip())
        
    instruction_prompt = generate_prompt(instruction, state_chatbot, f"{context} {bot_summarized_response}")[0]
    
    bot_response = stream_model(
        instruction_prompt,
        max_tokens=256,
        temperature=1,
        top_p=0.9
    )
    
    instruction_display = None if instruction_display == SPECIAL_STRS["continue"] else instruction_display
    state_chatbot = state_chatbot + [(instruction_display, None)]
    yield (state_chatbot, state_chatbot, f"{context}. {bot_summarized_response}".strip())
    
    prev_index = 0
    agg_tokens = ""
    cutoff_idx = 0
    for tokens in bot_response:
        tokens = tokens.strip()
        cur_token = tokens[prev_index:]
        
        if "#" in cur_token and agg_tokens == "":
            cutoff_idx = tokens.find("#")
            agg_tokens = tokens[cutoff_idx:]

        if agg_tokens != "":
            if len(agg_tokens) < len("### Istruzione:") :
                agg_tokens = agg_tokens + cur_token
            elif len(agg_tokens) >= len("### Istruzione:"):
                if tokens.find("### Istruzione:") > -1:
                    processed_response, _ = post_process_stream(tokens[:tokens.find("### Istruzione:")].strip())

                    state_chatbot[-1] = (
                        instruction_display, 
                        processed_response
                    )
                    yield (state_chatbot, state_chatbot, f"{context} {bot_summarized_response}".strip())
                    break
                else:
                    agg_tokens = ""
                    cutoff_idx = 0

        if agg_tokens == "":
            processed_response, to_exit = post_process_stream(tokens)
            state_chatbot[-1] = (instruction_display, processed_response)
            yield (state_chatbot, state_chatbot, f"{context} {bot_summarized_response}".strip())

            if to_exit:
                break

        prev_index = len(tokens)

    yield (
        state_chatbot,
        state_chatbot,
        f"{context} {bot_summarized_response}".strip()
    )


def chat_batch(
    contexts,
    instructions, 
    state_chatbots,
):
    state_results = []
    ctx_results = []

    instruct_prompts = [
        generate_prompt(instruct, histories, ctx) 
        for ctx, instruct, histories in zip(contexts, instructions, state_chatbots)
    ]
        
    bot_responses = get_output_batch(
        model, tokenizer, instruct_prompts, generation_config
    )
    bot_responses = post_processes_batch(bot_responses)

    for ctx, instruction, bot_response, state_chatbot in zip(contexts, instructions, bot_responses, state_chatbots):
        new_state_chatbot = state_chatbot + [('' if instruction == SPECIAL_STRS["continue"] else instruction, bot_response)]
        ctx_results.append(gr.Textbox.update(value=bot_response) if instruction == SPECIAL_STRS["summarize"] else ctx)
        state_results.append(new_state_chatbot)

    return (state_results, state_results, ctx_results)

def reset_textbox():
    return gr.Textbox.update(value='')

def reset_everything(
    context_txtbox, 
    instruction_txtbox, 
    state_chatbot):

    state_chatbot = []
    
    return (
        state_chatbot,
        state_chatbot,
        gr.Textbox.update(value=''),
        gr.Textbox.update(value=''),
    )

with gr.Blocks(css=PARENT_BLOCK_CSS) as demo:
    state_chatbot = gr.State([])

    with gr.Column(elem_id='col_container'):
        gr.Markdown(f"## {TITLE}\n\n\n{ABSTRACT}")

        with gr.Accordion("Context Setting", open=False):
            context_txtbox = gr.Textbox(placeholder="Surrounding information to AI", label="Enter Context")
            hidden_txtbox = gr.Textbox(placeholder="", label="Order", visible=False)

        chatbot = gr.Chatbot(elem_id='chatbot', label="Stambecco")
        instruction_txtbox = gr.Textbox(placeholder="What do you want to say to AI?", label="Instruction")
        with gr.Row():
            cancel_btn = gr.Button(value="Cancel")
            reset_btn = gr.Button(value="Reset")

        with gr.Accordion("Helper Buttons", open=False):
            gr.Markdown(f"`Continue` lets AI to complete the previous incomplete answers. `Summarize` lets AI to summarize the conversations so far.")
            continue_txtbox = gr.Textbox(value=SPECIAL_STRS["continue"], visible=False)
            summrize_txtbox = gr.Textbox(value=SPECIAL_STRS["summarize"], visible=False)

            continue_btn = gr.Button(value="Continue")
            summarize_btn = gr.Button(value="Summarize")

        gr.Markdown("#### Examples")
        for _, (category, examples) in enumerate(DEFAULT_EXAMPLES.items()):
            with gr.Accordion(category, open=False):
                if category == "Identity":
                    for item in examples:
                        with gr.Accordion(item["title"], open=False):
                            gr.Examples(
                                examples=item["examples"],
                                inputs=[
                                    hidden_txtbox, context_txtbox, instruction_txtbox
                                ],
                                label=None
                            )
                else:
                    for item in examples:
                        with gr.Accordion(item["title"], open=False):
                            gr.Examples(
                                examples=item["examples"],
                                inputs=[
                                    hidden_txtbox, instruction_txtbox
                                ],
                                label=None
                            )

        gr.Markdown(f"{BOTTOM_LINE}")


    send_event = instruction_txtbox.submit(
        chat_stream, 
        [context_txtbox, instruction_txtbox, state_chatbot],
        [state_chatbot, chatbot, context_txtbox],
    )
    reset_event = instruction_txtbox.submit(
        reset_textbox, 
        [], 
        [instruction_txtbox],
    )
    
    continue_event = continue_btn.click(
        chat_stream, 
        [context_txtbox, continue_txtbox, state_chatbot],
        [state_chatbot, chatbot, context_txtbox],
    )
    reset_continue_event = continue_btn.click(
        reset_textbox, 
        [], 
        [instruction_txtbox],
    )
    
    summarize_event = summarize_btn.click(
        chat_stream, 
        [context_txtbox, summrize_txtbox, state_chatbot],
        [state_chatbot, chatbot, context_txtbox],
    )
    summarize_reset_event = summarize_btn.click(
        reset_textbox, 
        [], 
        [instruction_txtbox],
    )
    
    cancel_btn.click(
        None, None, None, 
        cancels=[
            send_event, continue_event, summarize_event
        ]
    )

    reset_btn.click(
        reset_everything,
        [context_txtbox, instruction_txtbox, state_chatbot],
        [state_chatbot, chatbot, context_txtbox, instruction_txtbox],
        cancels=[
            send_event, continue_event, summarize_event
        ]            
    )    

demo.queue(
    concurrency_count=1,
    max_size=100,
).launch(
    max_threads=5,
    server_name="0.0.0.0",
    share=True
)