#!/usr/bin/env python3 from __future__ import annotations import logging import argparse import os import sys from tqdm import tqdm from pathlib import Path import numpy as np # Necessary to load the local gguf package if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists(): sys.path.insert(0, str(Path(__file__).parent.parent.parent)) import gguf logger = logging.getLogger("gguf-convert-endian") def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None: file_endian = reader.endianess.name if reader.byte_order == 'S': host_endian = 'BIG' if file_endian == 'LITTLE' else 'LITTLE' else: host_endian = file_endian order = host_endian if args.order == "native" else args.order.upper() logger.info(f"* Host is {host_endian} endian, GGUF file seems to be {file_endian} endian") if file_endian == order: logger.info(f"* File is already {order} endian. Nothing to do.") sys.exit(0) logger.info("* Checking tensors for conversion compatibility") for tensor in reader.tensors: if tensor.tensor_type not in ( gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16, gguf.GGMLQuantizationType.Q8_0, gguf.GGMLQuantizationType.Q4_K, gguf.GGMLQuantizationType.Q6_K, ): raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}") logger.info(f"* Preparing to convert from {file_endian} to {order}") if args.dry_run: return logger.warning("*** Warning *** Warning *** Warning **") logger.warning("* This conversion process may damage the file. Ensure you have a backup.") if order != host_endian: logger.warning("* Requested endian differs from host, you will not be able to load the model on this machine.") logger.warning("* The file will be modified immediately, so if conversion fails or is interrupted") logger.warning("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:") response = input("YES, I am sure> ") if response != "YES": logger.warning("You didn't enter YES. Okay then, see ya!") sys.exit(0) logger.info(f"* Converting fields ({len(reader.fields)})") for idx, field in enumerate(reader.fields.values()): logger.info(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}") for part in field.parts: part.byteswap(inplace=True) logger.info(f"* Converting tensors ({len(reader.tensors)})") for idx, tensor in enumerate(pbar := tqdm(reader.tensors, desc="Converting tensor")): log_message = ( f"Converting tensor {repr(tensor.name)}, " f"type={tensor.tensor_type.name}, " f"elements={tensor.n_elements} " ) # Byte-swap each part of the tensor's field for part in tensor.field.parts: part.byteswap(inplace=True) # Byte-swap tensor data if necessary if tensor.tensor_type == gguf.GGMLQuantizationType.Q8_0: # Handle Q8_0 tensor blocks (block_q8_0) # Specific handling of block_q8_0 is required. # Each block_q8_0 consists of an f16 delta (scaling factor) followed by 32 int8 quantizations. block_size = 34 # 34 bytes = + 32 * n_blocks = len(tensor.data) // block_size for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)): block_offs = block_num * block_size # Byte-Swap f16 sized delta field delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) delta.byteswap(inplace=True) # Byte-Swap Q8 weights if block_num % 100000 == 0: inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]") elif tensor.tensor_type == gguf.GGMLQuantizationType.Q4_K: # Handle Q4_K tensor blocks (block_q4_k) # Specific handling of block_q4_k is required. # Each block_q4_k consists of 2 f16 values followed by 140 int8 values. # first flatten structure newshape = 1 for i in tensor.data.shape: newshape *= i tensor.data.resize(newshape) block_size = 144 n_blocks = len(tensor.data) // block_size for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)): block_offs = block_num * block_size # Byte-Swap f16 sized fields delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) delta.byteswap(inplace=True) delta = tensor.data[block_offs + 2:block_offs + 4].view(dtype=np.uint16) delta.byteswap(inplace=True) # Byte-Swap if block_num % 100000 == 0: inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]") elif tensor.tensor_type == gguf.GGMLQuantizationType.Q6_K: # Handle Q6_K tensor blocks (block_q6_k) # Specific handling of block_q6_k is required. # Each block_q6_k consists of 208 int8 values followed by 1 f16 value. # first flatten structure newshape = 1 for i in tensor.data.shape: newshape *= i tensor.data.resize(newshape) block_size = 210 n_blocks = len(tensor.data) // block_size for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)): block_offs = block_num * block_size # Byte-Swap f16 sized field delta = tensor.data[block_offs + 208:block_offs + 210].view(dtype=np.uint16) delta.byteswap(inplace=True) # Byte-Swap if block_num % 100000 == 0: inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]") else: # Handle other tensor types tensor.data.byteswap(inplace=True) pbar.set_description(log_message) logger.info("* Completion") def main() -> None: parser = argparse.ArgumentParser(description="Convert GGUF file byte order") parser.add_argument( "model", type=str, help="GGUF format model filename", ) parser.add_argument( "order", type=str, choices=['big', 'little', 'native'], help="Requested byte order", ) parser.add_argument( "--dry-run", action="store_true", help="Don't actually change anything", ) parser.add_argument("--verbose", action="store_true", help="increase output verbosity") args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) logger.info(f'* Loading: {args.model}') reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+') convert_byteorder(reader, args) if __name__ == "__main__": main()