2024-06-21 03:56:09 -06:00
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import huggingface_hub
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import argparse
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import shutil
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import time
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REQUIRED_MODELS = {
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"bigscience/bloom-560m": "main",
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"TinyLlama/TinyLlama-1.1B-Chat-v1.0": "main",
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"abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq": "main",
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"tiiuae/falcon-7b": "main",
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"TechxGenus/gemma-2b-GPTQ": "main",
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"google/gemma-2b": "main",
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"openai-community/gpt2": "main",
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"turboderp/Llama-3-8B-Instruct-exl2": "2.5bpw",
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"huggingface/llama-7b-gptq": "main",
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"neuralmagic/llama-2-7b-chat-marlin": "main",
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"huggingface/llama-7b": "main",
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"FasterDecoding/medusa-vicuna-7b-v1.3": "refs/pr/1",
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"mistralai/Mistral-7B-Instruct-v0.1": "main",
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"OpenAssistant/oasst-sft-1-pythia-12b": "main",
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"stabilityai/stablelm-tuned-alpha-3b": "main",
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"google/paligemma-3b-pt-224": "main",
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"microsoft/phi-2": "main",
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"Qwen/Qwen1.5-0.5B": "main",
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"bigcode/starcoder": "main",
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"Narsil/starcoder-gptq": "main",
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"bigcode/starcoder2-3b": "main",
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"HuggingFaceM4/idefics-9b-instruct": "main",
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"HuggingFaceM4/idefics2-8b": "main",
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"llava-hf/llava-v1.6-mistral-7b-hf": "main",
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"state-spaces/mamba-130m": "main",
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"mosaicml/mpt-7b": "main",
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"bigscience/mt0-base": "main",
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"google/flan-t5-xxl": "main",
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}
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def cleanup_cache(token: str):
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# Retrieve the size per model for all models used in the CI.
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size_per_model = {}
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extension_per_model = {}
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for model_id, revision in REQUIRED_MODELS.items():
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2024-06-21 10:11:38 -06:00
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print(f"Crawling {model_id}...")
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2024-06-21 03:56:09 -06:00
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model_size = 0
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all_files = huggingface_hub.list_repo_files(
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model_id,
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repo_type="model",
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revision=revision,
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token=token,
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)
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extension = None
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if any(".safetensors" in filename for filename in all_files):
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extension = ".safetensors"
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elif any(".pt" in filename for filename in all_files):
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extension = ".pt"
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elif any(".bin" in filename for filename in all_files):
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extension = ".bin"
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extension_per_model[model_id] = extension
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for filename in all_files:
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if filename.endswith(extension):
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file_url = huggingface_hub.hf_hub_url(
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model_id, filename, revision=revision
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)
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file_metadata = huggingface_hub.get_hf_file_metadata(
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file_url, token=token
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)
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model_size += file_metadata.size * 1e-9 # in GB
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size_per_model[model_id] = model_size
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total_required_size = sum(size_per_model.values())
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print(f"Total required disk: {total_required_size:.2f} GB")
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cached_dir = huggingface_hub.scan_cache_dir()
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cache_size_per_model = {}
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cached_required_size_per_model = {}
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cached_shas_per_model = {}
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# Retrieve the SHAs and model ids of other non-necessary models in the cache.
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for repo in cached_dir.repos:
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if repo.repo_id in REQUIRED_MODELS:
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cached_required_size_per_model[repo.repo_id] = (
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repo.size_on_disk * 1e-9
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) # in GB
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elif repo.repo_type == "model":
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cache_size_per_model[repo.repo_id] = repo.size_on_disk * 1e-9 # in GB
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shas = []
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for rev in repo.revisions:
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shas.append(rev.commit_hash)
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cached_shas_per_model[repo.repo_id] = shas
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total_required_cached_size = sum(cached_required_size_per_model.values())
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total_other_cached_size = sum(cache_size_per_model.values())
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total_non_cached_required_size = total_required_size - total_required_cached_size
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print(
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f"Total HF cached models size: {total_other_cached_size + total_required_cached_size:.2f} GB"
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)
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print(
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f"Total non-necessary HF cached models size: {total_other_cached_size:.2f} GB"
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)
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free_memory = shutil.disk_usage("/data").free * 1e-9
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print(f"Free memory: {free_memory:.2f} GB")
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if free_memory + total_other_cached_size < total_non_cached_required_size * 1.05:
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raise ValueError(
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"Not enough space on device to execute the complete CI, please clean up the CI machine"
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)
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while free_memory < total_non_cached_required_size * 1.05:
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if len(cache_size_per_model) == 0:
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raise ValueError("This should not happen.")
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largest_model_id = max(cache_size_per_model, key=cache_size_per_model.get)
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print("Removing", largest_model_id)
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for sha in cached_shas_per_model[largest_model_id]:
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huggingface_hub.scan_cache_dir().delete_revisions(sha).execute()
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del cache_size_per_model[largest_model_id]
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free_memory = shutil.disk_usage("/data").free * 1e-9
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return extension_per_model
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Cache cleaner")
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parser.add_argument(
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"--token", help="Hugging Face Hub token.", required=True, type=str
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)
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args = parser.parse_args()
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start = time.time()
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extension_per_model = cleanup_cache(args.token)
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end = time.time()
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print(f"Cache cleanup done in {end - start:.2f} s")
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print("Downloading required models")
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start = time.time()
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for model_id, revision in REQUIRED_MODELS.items():
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print(f"Downloading {model_id}'s *{extension_per_model[model_id]}...")
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huggingface_hub.snapshot_download(
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model_id,
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repo_type="model",
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revision=revision,
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token=args.token,
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allow_patterns=f"*{extension_per_model[model_id]}",
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)
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end = time.time()
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print(f"Models download done in {end - start:.2f} s")
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