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get_datastore_code.py
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54 lines (45 loc) · 1.56 KB
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from datasets import load_dataset
from transformers import AutoTokenizer
import draftretriever
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--model-path",
type=str,
default="codellama/CodeLlama-7b-instruct-hf",
help="The path to the weights. This can be a local folder or a Hugging Face repo ID.",
)
parser.add_argument(
"--large-datastore",
type=bool,
default=False,
help="Whether to use a large datastore",
)
args = parser.parse_args()
print(args)
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
segment = 30 if args.large_datastore else 1 # Maximum number of segment: 144
data_files = []
for i in range(segment):
if i>=100:
data_files.append(f"data-00{i}-of-00144.parquet")
elif i >=10:
data_files.append(f"data-000{i}-of-00144.parquet")
else:
data_files.append(f"data-0000{i}-of-00144.parquet")
print("data_files:", data_files)
dataset = load_dataset('bigcode/the-stack-dedup', \
data_dir='data/python', split='train', data_files=data_files)
datastore_path = './datastore_stack_large.idx' if args.large_datastore else './datastore_stack_small.idx'
writer = draftretriever.Writer(
index_file_path=datastore_path,
max_chunk_len=512 * 1024 * 1024,
vocab_size=tokenizer.vocab_size + len(tokenizer.get_added_vocab()),
)
total_length = len(dataset)
print("number of samples: ", total_length)
for sample in tqdm(dataset, total=len(dataset)):
token_list = tokenizer.encode(sample['content'])
writer.add_entry(token_list)
writer.finalize()