convert-lora-to-ggml.py

#!/usr/bin/env python3
from __future__ import annotations

import logging
import json
import os
import struct
import sys
from pathlib import Path
from typing import Any, BinaryIO, Sequence

import numpy as np
import torch

if 'NO_LOCAL_GGUF' not in os.environ:
    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
import gguf

logger = logging.getLogger("lora-to-gguf")

NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}


def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
    fout.write(b"ggla"[::-1])  # magic (ggml lora)
    fout.write(struct.pack("i", 1))  # file version
    fout.write(struct.pack("i", params["r"]))
    # https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int
    # but some models ship a float value instead
    # let's convert to int, but fail if lossless conversion is not possible
    assert (
        int(params["lora_alpha"]) == params["lora_alpha"]
    ), "cannot convert float to int losslessly"
    fout.write(struct.pack("i", int(params["lora_alpha"])))


def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
    sname = name.encode("utf-8")
    fout.write(
        struct.pack(
            "iii",
            len(shape),
            len(sname),
            NUMPY_TYPE_TO_FTYPE[data_type.name],
        )
    )
    fout.write(struct.pack("i" * len(shape), *shape[::-1]))
    fout.write(sname)
    fout.seek((fout.tell() + 31) & -32)


if __name__ == '__main__':
    if len(sys.argv) < 2:
        logger.info(f"Usage: python {sys.argv[0]} <path> [arch]")
        logger.info("Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'")
        logger.info(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
        sys.exit(1)

    input_json = os.path.join(sys.argv[1], "adapter_config.json")
    input_model = os.path.join(sys.argv[1], "adapter_model.bin")
    output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")

    if os.path.exists(input_model):
        model = torch.load(input_model, map_location="cpu")
    else:
        input_model = os.path.join(sys.argv[1], "adapter_model.safetensors")
        # lazy import load_file only if lora is in safetensors format.
        from safetensors.torch import load_file
        model = load_file(input_model, device="cpu")

    arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"

    if arch_name not in gguf.MODEL_ARCH_NAMES.values():
        logger.error(f"Error: unsupported architecture {arch_name}")
        sys.exit(1)

    arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
    name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone

    with open(input_json, "r") as f:
        params = json.load(f)

    if params["peft_type"] != "LORA":
        logger.error(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
        sys.exit(1)

    if params["fan_in_fan_out"] is True:
        logger.error("Error: param fan_in_fan_out is not supported")
        sys.exit(1)

    if params["bias"] is not None and params["bias"] != "none":
        logger.error("Error: param bias is not supported")
        sys.exit(1)

    # TODO: these seem to be layers that have been trained but without lora.
    # doesn't seem widely used but eventually should be supported
    if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
        logger.error("Error: param modules_to_save is not supported")
        sys.exit(1)

    with open(output_path, "wb") as fout:
        fout.truncate()

        write_file_header(fout, params)
        for k, v in model.items():
            orig_k = k
            if k.endswith(".default.weight"):
                k = k.replace(".default.weight", ".weight")
            if k in ["llama_proj.weight", "llama_proj.bias"]:
                continue
            if k.endswith("lora_A.weight"):
                if v.dtype != torch.float16 and v.dtype != torch.float32:
                    v = v.float()
                v = v.T
            else:
                v = v.float()

            t = v.detach().numpy()

            prefix = "base_model.model."
            if k.startswith(prefix):
                k = k[len(prefix) :]

            lora_suffixes = (".lora_A.weight", ".lora_B.weight")
            if k.endswith(lora_suffixes):
                suffix = k[-len(lora_suffixes[0]):]
                k = k[: -len(lora_suffixes[0])]
            else:
                logger.error(f"Error: unrecognized tensor name {orig_k}")
                sys.exit(1)

            tname = name_map.get_name(k)
            if tname is None:
                logger.error(f"Error: could not map tensor name {orig_k}")
                logger.error(" Note: the arch parameter must be specified if the model is not llama")
                sys.exit(1)

            if suffix == ".lora_A.weight":
                tname += ".weight.loraA"
            elif suffix == ".lora_B.weight":
                tname += ".weight.loraB"
            else:
                assert False

            logger.info(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
            write_tensor_header(fout, tname, t.shape, t.dtype)
            t.tofile(fout)

    logger.info(f"Converted {input_json} and {input_model} to {output_path}")