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| # 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 copy | |
| import json | |
| import multiprocessing | |
| import os | |
| import time | |
| from create_dummy_models import COMPOSITE_MODELS, create_tiny_models | |
| from huggingface_hub import ModelFilter, hf_api | |
| import transformers | |
| from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer | |
| from transformers.image_processing_utils import BaseImageProcessor | |
| def get_all_model_names(): | |
| model_names = set() | |
| # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way. | |
| for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]: | |
| module = getattr(transformers.models.auto, module_name, None) | |
| if module is None: | |
| continue | |
| # 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_") or x.startswith("TF_MODEL_") or x.startswith("FLAX_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(): | |
| # All model names defined in auto mappings | |
| model_names = set(get_all_model_names()) | |
| 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"]) | |
| # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub. | |
| not_on_hub = model_names.difference(tiny_models_names) | |
| for model_name in copy.copy(tiny_models_names): | |
| if not model_name.startswith("TF") and f"TF{model_name}" in not_on_hub: | |
| tiny_models_names.remove(model_name) | |
| elif model_name.startswith("TF") and model_name[2:] in not_on_hub: | |
| tiny_models_names.remove(model_name) | |
| return sorted(tiny_models_names) | |
| def get_tiny_model_summary_from_hub(output_path): | |
| special_models = COMPOSITE_MODELS.values() | |
| # All tiny model base names on Hub | |
| model_names = get_all_model_names() | |
| models = hf_api.list_models( | |
| filter=ModelFilter( | |
| author="hf-internal-testing", | |
| ) | |
| ) | |
| _models = set() | |
| for x in models: | |
| model = x.modelId | |
| 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 = hf_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 | |
| try: | |
| time.sleep(1) | |
| model_class = getattr(transformers, f"TF{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, | |
| ) | |