convert.py

import datetime
import torch

from collections import defaultdict
from loguru import logger
from pathlib import Path
from safetensors.torch import save_file
from safetensors import safe_open
from typing import Dict, List


def check_file_size(source_file: Path, target_file: Path):
    """
    Check that two files are close in size
    """
    source_file_size = source_file.stat().st_size
    target_file_size = target_file.stat().st_size

    if (source_file_size - target_file_size) / source_file_size > 0.05:
        raise RuntimeError(
            f"""The file size different is more than 5%:
         - {source_file}: {source_file_size}
         - {target_file}: {target_file_size}
         """
        )


def remove_shared_pointers(tensors: Dict[str, torch.Tensor]):
    """
    For a Dict of tensors, check if two or more tensors point to the same underlying memory and
    remove them
    """
    ptrs = defaultdict(list)
    for k, v in tensors.items():
        ptrs[v.data_ptr()].append(k)

    # Iterate over all found memory addresses
    for ptr, names in ptrs.items():
        if len(names) > 1:
            # Multiple tensors are point to the same memory
            # Only keep the first tensor
            for name in names[1:]:
                tensors.pop(name)


def convert_file(pt_file: Path, sf_file: Path):
    """
    Convert a pytorch file to a safetensors file
    """
    logger.info(f"Convert {pt_file} to {sf_file}.")

    pt_state = torch.load(pt_file, map_location="cpu")
    if "state_dict" in pt_state:
        pt_state = pt_state["state_dict"]

    remove_shared_pointers(pt_state)

    # Tensors need to be contiguous
    pt_state = {k: v.contiguous() for k, v in pt_state.items()}

    sf_file.parent.mkdir(parents=True, exist_ok=True)
    save_file(pt_state, str(sf_file), metadata={"format": "pt"})

    # Check that both files are close in size
    check_file_size(pt_file, sf_file)

    # Load safetensors state
    for k in pt_state:
        pt_tensor = pt_state[k]
        with safe_open(sf_file, framework="pt") as f:
            sf_tensor = f.get_tensor(k)
            if not torch.equal(pt_tensor, sf_tensor):
                raise RuntimeError(f"The output tensors do not match for key {k}")


def convert_files(pt_files: List[Path], sf_files: List[Path]):
    assert len(pt_files) == len(sf_files)

    N = len(pt_files)
    # We do this instead of using tqdm because we want to parse the logs with the launcher

    for i, (pt_file, sf_file) in enumerate(zip(pt_files, sf_files)):
        start = datetime.datetime.now()
        convert_file(pt_file, sf_file)
        elapsed = datetime.datetime.now() - start
        logger.info(f"Convert: [{i + 1}/{N}] -- Took: {elapsed}")