gguf-imat.py

import os
import requests
import zipfile
import subprocess
import shutil
from huggingface_hub import snapshot_download

# Clone or update the llama.cpp repository with shallow cloning
def clone_or_update_llama_cpp():
    print("Preparing...")
    base_dir = os.path.dirname(os.path.abspath(__file__))
    os.chdir(base_dir)
    if not os.path.exists("llama.cpp"):
        subprocess.run(["git", "clone", "--depth", "1", "https://github.com/ggerganov/llama.cpp"])
    else:
        os.chdir("llama.cpp")
        subprocess.run(["git", "pull"])
    os.chdir(base_dir)
    print("The 'llama.cpp' repository is ready.")

# Cownload and extract the latest release of llama.cpp
def download_llama_release():
    base_dir = os.path.dirname(os.path.abspath(__file__))
    dl_dir = os.path.join(base_dir, "bin", "dl")
    if not os.path.exists(dl_dir):
        os.makedirs(dl_dir)

    os.chdir(dl_dir)
    latest_release_url = "https://github.com/ggerganov/llama.cpp/releases/latest"
    response = requests.get(latest_release_url)
    if response.status_code == 200:
        latest_release_tag = response.url.split("/")[-1]
        download_url = f"https://github.com/ggerganov/llama.cpp/releases/download/{latest_release_tag}/llama-{latest_release_tag}-bin-win-cuda-cu12.2.0-x64.zip"
        response = requests.get(download_url)
        if response.status_code == 200:
            with open(f"llama-{latest_release_tag}-bin-win-cuda-cu12.2.0-x64.zip", "wb") as f:
                f.write(response.content)
            with zipfile.ZipFile(f"llama-{latest_release_tag}-bin-win-cuda-cu12.2.0-x64.zip", "r") as zip_ref:
                zip_ref.extractall(os.path.join(base_dir, "bin"))
            print("Downloading latest 'llama.cpp' prebuilt Windows binaries...")
            print("Download and extraction completed successfully.")
            return latest_release_tag
        else:
            print("Failed to download the release file.")
    else:
        print("Failed to fetch the latest release information.")

# Download and extract cudart if necessary
def download_cudart_if_necessary(latest_release_tag):
    base_dir = os.path.dirname(os.path.abspath(__file__))
    cudart_dl_dir = os.path.join(base_dir, "bin", "dl")
    if not os.path.exists(cudart_dl_dir):
        os.makedirs(cudart_dl_dir)

    cudart_zip_file = os.path.join(cudart_dl_dir, "cudart-llama-bin-win-cu12.2.0-x64.zip")
    cudart_extracted_files = ["cublas64_12.dll", "cublasLt64_12.dll", "cudart64_12.dll"]

    # Check if all required files exist
    if all(os.path.exists(os.path.join(base_dir, "bin", file)) for file in cudart_extracted_files):
        print("Cuda resources already exist. Skipping download.")
    else:
        cudart_download_url = f"https://github.com/ggerganov/llama.cpp/releases/download/{latest_release_tag}/cudart-llama-bin-win-cu12.2.0-x64.zip"
        response = requests.get(cudart_download_url)
        if response.status_code == 200:
            with open(cudart_zip_file, "wb") as f:
                f.write(response.content)
            with zipfile.ZipFile(cudart_zip_file, "r") as zip_ref:
                zip_ref.extractall(os.path.join(base_dir, "bin"))
            print("Preparing 'cuda' resources...")
            print("Download and extraction of cudart completed successfully.")
        else:
            print("Failed to download the cudart release file.")

# Collect user input and download the specified model repository
def download_model_repo():
    base_dir = os.path.dirname(os.path.abspath(__file__))
    models_dir = os.path.join(base_dir, "models")
    if not os.path.exists(models_dir):
        os.makedirs(models_dir)

    model_id = input("Enter the model ID to download (e.g., huggingface/transformers): ")
    model_name = model_id.split("/")[-1]
    model_dir = os.path.join(models_dir, model_name)

    # Download the model repository if it doesn't exist
    if not os.path.exists(model_dir):
        revision = input("Enter the revision (branch, tag, or commit) to download (default: main): ") or "main"

        print("Downloading model repository...")
        snapshot_download(repo_id=model_id, local_dir=model_dir, revision=revision)
        print("Model repository downloaded successfully.")
    else:
        print("Model already exists.")

    # Convert the downloaded model to GGUF F16 format and generate imatrix.dat
    convert_model_to_gguf_f16(base_dir, model_dir, model_name)

# Convert the downloaded model to GGUF F16 format
def convert_model_to_gguf_f16(base_dir, model_dir, model_name):
    convert_script = os.path.join(base_dir, "llama.cpp", "convert.py")
    gguf_dir = os.path.join(base_dir, "models", f"{model_name}-GGUF")
    gguf_model_path = os.path.join(gguf_dir, f"{model_name}-F16.gguf")

    if not os.path.exists(gguf_dir):
        os.makedirs(gguf_dir)

    # Execute the conversion command if F16 file doesn't exist
    if not os.path.exists(gguf_model_path):
        subprocess.run(["python", convert_script, model_dir, "--outfile", gguf_model_path, "--outtype", "f16"])

        # Delete the original model directory
        shutil.rmtree(model_dir)
        print(f"Original model directory '{model_dir}' deleted.")

        # Execute the imatrix command if imatrix.dat doesn't exist
        imatrix_exe = os.path.join(base_dir, "bin", "imatrix.exe")
        imatrix_output = os.path.join(gguf_dir, "imatrix.dat")
        imatrix_txt = os.path.join(base_dir, "imatrix", "imatrix.txt")
        if not os.path.exists(imatrix_output):
            subprocess.run([imatrix_exe, "-m", gguf_model_path, "-f", imatrix_txt, "-ngl", "13"])
            # Move the imatrix.dat file to the GGUF folder
            shutil.move("imatrix.dat", gguf_dir)
            print("imatrix.dat generated successfully.")

    # Quantize the models
    quantize_models(base_dir, model_name)

# Qantize models with different options
def quantize_models(base_dir, model_name):
    gguf_dir = os.path.join(base_dir, "models", f"{model_name}-GGUF")
    f16_gguf_path = os.path.join(gguf_dir, f"{model_name}-F16.gguf")

    quantization_options = [
        "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", 
        "Q5_K_S", "Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XS", "IQ3_XXS"
    ]

    for quant_option in quantization_options:
        quantized_gguf_name = f"{model_name}-{quant_option}-imat.gguf"
        quantized_gguf_path = os.path.join(gguf_dir, quantized_gguf_name)
        quantize_command = os.path.join(base_dir, "bin", "quantize.exe")
        imatrix_path = os.path.join(gguf_dir, "imatrix.dat")

        subprocess.run([quantize_command, "--imatrix", imatrix_path, 
                        f16_gguf_path, quantized_gguf_path, quant_option], cwd=gguf_dir)
        print(f"Model quantized with {quant_option} option.")

# Main function - Steps
def main():
    clone_or_update_llama_cpp()
    latest_release_tag = download_llama_release()
    download_cudart_if_necessary(latest_release_tag)
    download_model_repo()
    print("Finished preparing resources.")

if __name__ == "__main__":
    main()