transformers/utils/update_tiny_models.py

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A script running `create_dummy_models.py` with a pre-defined set of arguments.

This file is intended to be used in a CI workflow file without the need of specifying arguments. It creates and uploads
tiny models for all model classes (if their tiny versions are not on the Hub yet), as well as produces an updated
version of `tests/utils/tiny_model_summary.json`. That updated file should be merged into the `main` branch of
`transformers` so the pipeline testing will use the latest created/updated tiny models.
"""

import argparse
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import HfApi

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor


def get_all_model_names():
    model_names = set()

    module_name = "modeling_auto"
    module = getattr(transformers.models.auto, module_name, None)
    if module is not None:
        # all mappings in a single auto modeling file
        mapping_names = [x for x in dir(module) if x.endswith("_MAPPING_NAMES") and x.startswith("MODEL_")]
        for name in mapping_names:
            mapping = getattr(module, name)
            if mapping is not None:
                for v in mapping.values():
                    if isinstance(v, (list, tuple)):
                        model_names.update(v)
                    elif isinstance(v, str):
                        model_names.add(v)

    return sorted(model_names)


def get_tiny_model_names_from_repo():
    with open("tests/utils/tiny_model_summary.json") as fp:
        tiny_model_info = json.load(fp)
    tiny_models_names = set()
    for model_base_name in tiny_model_info:
        tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"])

    return sorted(tiny_models_names)


def get_tiny_model_summary_from_hub(output_path):
    api = HfApi()
    special_models = COMPOSITE_MODELS.values()

    # All tiny model base names on Hub
    model_names = get_all_model_names()
    models = api.list_models(author="hf-internal-testing")
    _models = set()
    for x in models:
        model = x.id
        org, model = model.split("/")
        if not model.startswith("tiny-random-"):
            continue
        model = model.replace("tiny-random-", "")
        if not model[0].isupper():
            continue
        if model not in model_names and model not in special_models:
            continue
        _models.add(model)

    models = sorted(_models)
    # All tiny model names on Hub
    summary = {}
    for model in models:
        repo_id = f"hf-internal-testing/tiny-random-{model}"
        model = model.split("-")[0]
        try:
            repo_info = api.repo_info(repo_id)
            content = {
                "tokenizer_classes": set(),
                "processor_classes": set(),
                "model_classes": set(),
                "sha": repo_info.sha,
            }
        except Exception:
            continue
        try:
            time.sleep(1)
            tokenizer_fast = AutoTokenizer.from_pretrained(repo_id)
            content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            tokenizer_slow = AutoTokenizer.from_pretrained(repo_id, use_fast=False)
            content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            img_p = AutoImageProcessor.from_pretrained(repo_id)
            content["processor_classes"].add(img_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            feat_p = AutoFeatureExtractor.from_pretrained(repo_id)
            if not isinstance(feat_p, BaseImageProcessor):
                content["processor_classes"].add(feat_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            model_class = getattr(transformers, model)
            m = model_class.from_pretrained(repo_id)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass

        content["tokenizer_classes"] = sorted(content["tokenizer_classes"])
        content["processor_classes"] = sorted(content["processor_classes"])
        content["model_classes"] = sorted(content["model_classes"])

        summary[model] = content
        with open(os.path.join(output_path, "hub_tiny_model_summary.json"), "w") as fp:
            json.dump(summary, fp, ensure_ascii=False, indent=4)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
    args = parser.parse_args()

    # This has to be `spawn` to avoid hanging forever!
    multiprocessing.set_start_method("spawn")

    output_path = "tiny_models"
    all = True
    model_types = None
    models_to_skip = get_tiny_model_names_from_repo()
    no_check = True
    upload = True
    organization = "hf-internal-testing"

    create_tiny_models(
        output_path,
        all,
        model_types,
        models_to_skip,
        no_check,
        upload,
        organization,
        token=os.environ.get("TOKEN", None),
        num_workers=args.num_workers,
    )