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import os
import json
import logging
import glob
from shutil import rmtree
from huggingface_hub import snapshot_download
from utils import timer_decorator
BASE_DIR = "/"
TOKENIZER_PATTERNS = [["*.json", "tokenizer*"]]
MODEL_PATTERNS = [["*.safetensors"], ["*.bin"], ["*.pt"]]
def setup_env():
if os.getenv("TESTING_DOWNLOAD") == "1":
BASE_DIR = "tmp"
os.makedirs(BASE_DIR, exist_ok=True)
os.environ.update({
"HF_HOME": f"{BASE_DIR}/hf_cache",
"MODEL_NAME": "openchat/openchat-3.5-0106",
"HF_HUB_ENABLE_HF_TRANSFER": "1",
"TENSORIZE": "1",
"TENSORIZER_NUM_GPUS": "1",
"DTYPE": "auto"
})
@timer_decorator
def download(name, revision, type, cache_dir):
if type == "model":
pattern_sets = [model_pattern + TOKENIZER_PATTERNS[0] for model_pattern in MODEL_PATTERNS]
elif type == "tokenizer":
pattern_sets = TOKENIZER_PATTERNS
else:
raise ValueError(f"Invalid type: {type}")
try:
for pattern_set in pattern_sets:
path = snapshot_download(name, revision=revision, cache_dir=cache_dir,
allow_patterns=pattern_set)
for pattern in pattern_set:
if glob.glob(os.path.join(path, pattern)):
logging.info(f"Successfully downloaded {pattern} model files.")
return path
except ValueError:
raise ValueError(f"No patterns matching {pattern_sets} found for download.")
# @timer_decorator
# def tensorize_model(model_path): TODO: Add back once tensorizer is ready
# from vllm.engine.arg_utils import EngineArgs
# from vllm.model_executor.model_loader.tensorizer import TensorizerConfig, tensorize_vllm_model
# from torch.cuda import device_count
# tensorizer_num_gpus = int(os.getenv("TENSORIZER_NUM_GPUS", "1"))
# if tensorizer_num_gpus > device_count():
# raise ValueError(f"TENSORIZER_NUM_GPUS ({tensorizer_num_gpus}) exceeds available GPUs ({device_count()})")
# dtype = os.getenv("DTYPE", "auto")
# serialized_dir = f"{BASE_DIR}/serialized_model"
# os.makedirs(serialized_dir, exist_ok=True)
# serialized_uri = f"{serialized_dir}/model{'-%03d' if tensorizer_num_gpus > 1 else ''}.tensors"
# tensorize_vllm_model(
# EngineArgs(model=model_path, tensor_parallel_size=tensorizer_num_gpus, dtype=dtype),
# TensorizerConfig(tensorizer_uri=serialized_uri)
# )
# logging.info("Successfully serialized model to %s", str(serialized_uri))
# logging.info("Removing HF Model files after serialization")
# rmtree("/".join(model_path.split("/")[:-2]))
# return serialized_uri, tensorizer_num_gpus, dtype
if __name__ == "__main__":
setup_env()
cache_dir = os.getenv("HF_HOME")
model_name, model_revision = os.getenv("MODEL_NAME"), os.getenv("MODEL_REVISION") or None
tokenizer_name, tokenizer_revision = os.getenv("TOKENIZER_NAME") or model_name, os.getenv("TOKENIZER_REVISION") or model_revision
model_path = download(model_name, model_revision, "model", cache_dir)
metadata = {
"MODEL_NAME": model_path,
"MODEL_REVISION": os.getenv("MODEL_REVISION"),
"QUANTIZATION": os.getenv("QUANTIZATION"),
}
# if os.getenv("TENSORIZE") == "1": TODO: Add back once tensorizer is ready
# serialized_uri, tensorizer_num_gpus, dtype = tensorize_model(model_path)
# metadata.update({
# "MODEL_NAME": serialized_uri,
# "TENSORIZER_URI": serialized_uri,
# "TENSOR_PARALLEL_SIZE": tensorizer_num_gpus,
# "DTYPE": dtype
# })
tokenizer_path = download(tokenizer_name, tokenizer_revision, "tokenizer", cache_dir)
metadata.update({
"TOKENIZER_NAME": tokenizer_path,
"TOKENIZER_REVISION": tokenizer_revision
})
with open(f"{BASE_DIR}/local_model_args.json", "w") as f:
json.dump({k: v for k, v in metadata.items() if v not in (None, "")}, f) |