convert.py

import argparse
import json
import os
import shutil
from tempfile import TemporaryDirectory
from collections import defaultdict
from inspect import signature
from typing import Optional, List

import torch

from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download, get_repo_discussions
from huggingface_hub.file_download import repo_folder_name
from transformers import AutoConfig
from transformers.pipelines.base import infer_framework_load_model
from safetensors.torch import save_file


class AlreadyExists(Exception):
    pass


def shared_pointers(tensors):
    ptrs = defaultdict(list)
    for k, v in tensors.items():
        ptrs[v.data_ptr()].append(k)
    failing = []
    for ptr, names in ptrs.items():
        if len(names) > 1:
            failing.append(names)
    return failing

def check_file_size(sf_filename: str, pt_filename: str):
    sf_size = os.stat(sf_filename).st_size
    pt_size = os.stat(pt_filename).st_size

    if (sf_size - pt_size) / pt_size > 0.01:
        raise RuntimeError(f"""The file size different is more than 1%:
         - {sf_filename}: {sf_size}
         - {pt_filename}: {pt_size}
         """)


def rename(pt_filename: str) -> str:
    local = pt_filename.replace(".bin", ".safetensors")
    local = local.replace("pytorch_model", "model")
    return local


def convert_multi(model_id: str) -> List["CommitOperationAdd"]:
    filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin.index.json")
    with open(filename, "r") as f:
        data = json.load(f)

    filenames = set(data["weight_map"].values())
    for filename in filenames:
        cached_filename = hf_hub_download(repo_id=model_id, filename=filename)
        loaded = torch.load(cached_filename)
        sf_filename = rename(filename)

        local = os.path.join(folder, sf_filename)
        save_file(loaded, local, metadata={"format": "pt"})
        check_file_size(local, cached_filename)
        local_filenames.append(local)

    index = os.path.join(folder, "model.safetensors.index.json")
    with open(index, "w") as f:
        newdata = {k: v for k, v in data.items()}
        newmap = {k: rename(v) for k, v in data["weight_map"].items()}
        newdata["weight_map"] = newmap
        json.dump(newdata, f)
    local_filenames.append(index)

    operations = [CommitOperationAdd(path_in_repo=local.split("/")[-1], path_or_fileobj=local) for local in local_filenames]

    return operations


def convert_single(model_id: str, folder: str) -> List["CommitOperationAdd"]:
    sf_filename = "model.safetensors"
    filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin")
    loaded = torch.load(filename)

    local = os.path.join(folder, sf_filename)
    shared = shared_pointers(loaded)
    for shared_weights in shared:
        for name in shared_weights[1:]:
            loaded.pop(name)

    # For tensors to be contiguous
    loaded = {k: v.contiguous() for k, v in loaded.items()}

    save_file(loaded, local, metadata={"format": "pt"})

    check_file_size(local, filename)

    operations = [CommitOperationAdd(path_in_repo=sf_filename, path_or_fileobj=local)]
    return operations

def check_final_model(model_id: str, folder: str):
    config = hf_hub_download(repo_id=model_id, filename="config.json")
    shutil.copy(config, os.path.join(folder, "config.json"))
    config = AutoConfig.from_pretrained(folder)

    _, pt_model = infer_framework_load_model(model_id, config)
    _, sf_model = infer_framework_load_model(folder, config)

    pt_model = pt_model
    sf_model = sf_model

    pt_params = pt_model.state_dict()
    sf_params = sf_model.state_dict()

    pt_shared = shared_pointers(pt_params)
    sf_shared = shared_pointers(sf_params)
    if pt_shared != sf_shared:
        raise RuntimeError("The reconstructed model is wrong, shared tensors are different {shared_pt} != {shared_tf}")

    sig = signature(pt_model.forward)
    input_ids = torch.arange(10).unsqueeze(0)
    pixel_values = torch.randn(1, 3, 224, 224)
    input_values = torch.arange(1000).float().unsqueeze(0)
    kwargs = {}
    if "input_ids" in sig.parameters:
        kwargs["input_ids"] = input_ids
    if "decoder_input_ids" in sig.parameters:
        kwargs["decoder_input_ids"] = input_ids
    if "pixel_values" in sig.parameters:
        kwargs["pixel_values"] = pixel_values
    if "input_values" in sig.parameters:
        kwargs["input_values"] = input_values
    if "bbox" in sig.parameters:
        kwargs["bbox"] = torch.zeros((1, 10, 4)).long()
    if "image" in sig.parameters:
        kwargs["image"] = pixel_values


    if torch.cuda.is_available():
        pt_model = pt_model.cuda()
        sf_model = sf_model.cuda()
        kwargs = {k: v.cuda() for k, v in kwargs.items()}

    pt_logits = pt_model(**kwargs)[0]
    sf_logits = sf_model(**kwargs)[0]

    torch.testing.assert_close(sf_logits, pt_logits)
    print(f"Model {model_id} is ok !")

def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
    try:
        discussions = api.get_repo_discussions(repo_id=model_id)
    except Exception:
        return None
    for discussion in discussions:
        if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
            return discussion


def convert(api: "HfApi", model_id: str, force: bool=False) -> Optional["CommitInfo"]:
    pr_title = "Adding `safetensors` variant of this model"
    info = api.model_info(model_id)
    filenames = set(s.rfilename for s in info.siblings)

    with TemporaryDirectory() as d:
        folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
        os.makedirs(folder)
        new_pr = None
        try:
            operations = None
            pr = previous_pr(api, model_id, pr_title)
            if ("model.safetensors" in filenames or "model_index.safetensors.index.json" in filenames) and not force:
                raise AlreadyExists(f"Model {model_id} is already converted, skipping..")
            elif pr is not None and not force:
                url = f"https://huggingface.co/{model_id}/discussions/{pr.num}"
                new_pr = pr
                raise AlreadyExists(f"Model {model_id} already has an open PR check out {url}")
            elif "pytorch_model.bin" in filenames:
                operations = convert_single(model_id, folder)
            elif "pytorch_model.bin.index.json" in filenames:
                operations = convert_multi(model_id, folder)
            else:
                raise RuntimeError(f"Model {model_id} doesn't seem to be a valid pytorch model. Cannot convert")

            if operations:
                check_final_model(model_id, folder)
                new_pr = api.create_commit(
                    repo_id=model_id,
                    operations=operations,
                    commit_message=pr_title,
                    create_pr=True,
                )
        finally:
            shutil.rmtree(folder)
        return new_pr


if __name__ == "__main__":
    DESCRIPTION = """
    Simple utility tool to convert automatically some weights on the hub to `safetensors` format.
    It is PyTorch exclusive for now.
    It works by downloading the weights (PT), converting them locally, and uploading them back
    as a PR on the hub.
    """
    parser = argparse.ArgumentParser(description=DESCRIPTION)
    parser.add_argument(
        "model_id",
        type=str,
        help="The name of the model on the hub to convert. E.g. `gpt2` or `facebook/wav2vec2-base-960h`",
    )
    parser.add_argument(
        "--force",
        action="store_true",
        help="Create the PR even if it already exists of if the model was already converted.",
    )
    args = parser.parse_args()
    model_id = args.model_id
    api = HfApi()
    convert(api, model_id, force=args.force)