llm_toolkit/llm_utils.py

import os
import re
import sys
import torch
from llamafactory.chat import ChatModel
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    TextStreamer,
)


def load_model(
    model_name,
    max_seq_length=2048,
    dtype=torch.bfloat16,
    load_in_4bit=False,
    adapter_name_or_path=None,
):
    print(f"loading model: {model_name}")

    if adapter_name_or_path:
        template = "llama3" if "llama-3" in model_name.lower() else "chatml"

        args = dict(
            model_name_or_path=model_name,
            adapter_name_or_path=adapter_name_or_path,  # load the saved LoRA adapters
            template=template,  # same to the one in training
            finetuning_type="lora",  # same to the one in training
            quantization_bit=4 if load_in_4bit else None,  # load 4-bit quantized model
        )
        chat_model = ChatModel(args)
        return chat_model.engine.model, chat_model.engine.tokenizer

    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=load_in_4bit,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_use_double_quant=False,
        bnb_4bit_compute_dtype=dtype,
    )

    model = (
        AutoModelForCausalLM.from_pretrained(
            model_name,
            quantization_config=bnb_config,
            torch_dtype=dtype,
            trust_remote_code=True,
            device_map="auto",
        )
        if load_in_4bit
        else AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=dtype,
            trust_remote_code=True,
            device_map="auto",
        )
    )

    return model, tokenizer


def test_model(model, tokenizer, prompt):
    inputs = tokenizer(
        [prompt],
        return_tensors="pt",
    ).to("cuda")

    text_streamer = TextStreamer(tokenizer)

    _ = model.generate(
        **inputs, max_new_tokens=2048, streamer=text_streamer, use_cache=True
    )


def extract_answer(text, debug=False):
    if text:
        # Remove the begin and end tokens
        text = re.sub(
            r".*?(assistant|\[/INST\]).+?\b",
            "",
            text,
            flags=re.DOTALL | re.MULTILINE,
        )
        if debug:
            print("--------\nstep 1:", text)

        text = re.sub(r"<.+?>.*", "", text, flags=re.DOTALL | re.MULTILINE)
        if debug:
            print("--------\nstep 2:", text)

        text = re.sub(
            r".*?end_header_id\|>\n\n", "", text, flags=re.DOTALL | re.MULTILINE
        )
        if debug:
            print("--------\nstep 3:", text)

        text = text.split(".")[0].strip()
        if debug:
            print("--------\nstep 4:", text)

        text = re.sub(
            r"^Response:.+?\b",
            "",
            text,
            flags=re.DOTALL | re.MULTILINE,
        )
        if debug:
            print("--------\nstep 5:", text)

    return text


def eval_model(model, tokenizer, eval_dataset):
    total = len(eval_dataset)
    predictions = []
    for i in tqdm(range(total)):
        inputs = tokenizer(
            eval_dataset["prompt"][i : i + 1],
            return_tensors="pt",
        ).to("cuda")

        outputs = model.generate(**inputs, max_new_tokens=4096, use_cache=False)
        decoded_output = tokenizer.batch_decode(outputs)
        debug = i == 0
        decoded_output = [
            extract_answer(output, debug=debug) for output in decoded_output
        ]
        predictions.extend(decoded_output)

    return predictions


def save_model(
    model,
    tokenizer,
    include_gguf=True,
    include_merged=True,
    publish=True,
):
    try:
        token = os.getenv("HF_TOKEN") or None
        model_name = os.getenv("MODEL_NAME")

        save_method = "lora"
        quantization_method = "q5_k_m"

        model_names = get_model_names(
            model_name, save_method=save_method, quantization_method=quantization_method
        )

        model.save_pretrained(model_names["local"])
        tokenizer.save_pretrained(model_names["local"])

        if publish:
            model.push_to_hub(
                model_names["hub"],
                token=token,
            )
            tokenizer.push_to_hub(
                model_names["hub"],
                token=token,
            )

        if include_merged:
            model.save_pretrained_merged(
                model_names["local"] + "-merged", tokenizer, save_method=save_method
            )
            if publish:
                model.push_to_hub_merged(
                    model_names["hub"] + "-merged",
                    tokenizer,
                    save_method="lora",
                    token="",
                )

        if include_gguf:
            model.save_pretrained_gguf(
                model_names["local-gguf"],
                tokenizer,
                quantization_method=quantization_method,
            )

            if publish:
                model.push_to_hub_gguf(
                    model_names["hub-gguf"],
                    tokenizer,
                    quantization_method=quantization_method,
                    token=token,
                )
    except Exception as e:
        print(e)


def print_row_details(df, indices=[0]):
    for index in indices:
        for col in df.columns:
            print("-" * 50)
            print(f"{col}: {df[col].iloc[index]}")