hf_text-generation-inference/integration-tests/clean_cache_and_download.py

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