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import argparse
import re
import os
from tqdm import tqdm
import pandas as pd
import torch
from natsort import index_natsorted
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
from utils.logger import logger
def extract_output(s, prefix='"rewritten description": '):
"""Customize the function according to the prompt."""
# Since some LLMs struggles to output strictly formatted JSON strings as specified by the prompt,
# thus manually parse the output string `{"rewritten description": "your rewritten description here"}`.
match = re.search(r"{(.+?)}", s, re.DOTALL)
if not match:
logger.warning(f"{s} is not in the json format. Return None.")
return None
output = match.group(1).strip()
if output.startswith(prefix):
output = output[len(prefix) :]
if output[0] == '"' and output[-1] == '"':
return output[1:-1]
else:
logger.warning(f"{output} does not start and end with the double quote. Return None.")
return None
else:
logger.warning(f"{output} does not start with {prefix}. Return None.")
return None
def parse_args():
parser = argparse.ArgumentParser(description="Rewrite the video caption by LLMs.")
parser.add_argument(
"--video_metadata_path", type=str, required=True, help="The path to the video dataset metadata (csv/jsonl)."
)
parser.add_argument(
"--video_path_column",
type=str,
default=None,
help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
)
parser.add_argument(
"--caption_column",
type=str,
default="caption",
help="The column contains the video caption.",
)
parser.add_argument(
"--batch_size",
type=int,
default=128,
required=False,
help="The batch size for vllm inference. Adjust according to the number of GPUs to maximize inference throughput.",
)
parser.add_argument(
"--model_name",
type=str,
default="NousResearch/Meta-Llama-3-8B-Instruct",
)
parser.add_argument(
"--prompt",
type=str,
required=True,
help="A string or a txt file contains the prompt.",
)
parser.add_argument(
"--prefix",
type=str,
required=True,
help="The prefix to extract the output from LLMs.",
)
parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
parser.add_argument("--saved_freq", type=int, default=1, help="The frequency to save the output results.")
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.video_metadata_path.endswith(".csv"):
video_metadata_df = pd.read_csv(args.video_metadata_path)
elif args.video_metadata_path.endswith(".jsonl"):
video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
elif args.video_metadata_path.endswith(".json"):
video_metadata_df = pd.read_json(args.video_metadata_path)
else:
raise ValueError(f"The {args.video_metadata_path} must end with .csv, .jsonl or .json.")
saved_suffix = os.path.splitext(args.saved_path)[1]
if saved_suffix not in set([".csv", ".jsonl", ".json"]):
raise ValueError(f"The saved_path must end with .csv, .jsonl or .json.")
if os.path.exists(args.saved_path) and args.video_path_column is not None:
if args.saved_path.endswith(".csv"):
saved_metadata_df = pd.read_csv(args.saved_path)
elif args.saved_path.endswith(".jsonl"):
saved_metadata_df = pd.read_json(args.saved_path, lines=True)
# Filter out the unprocessed video-caption pairs by setting the indicator=True.
merged_df = video_metadata_df.merge(saved_metadata_df, on=args.video_path_column, how="outer", indicator=True)
video_metadata_df = merged_df[merged_df["_merge"] == "left_only"]
# Sorting to guarantee the same result for each process.
video_metadata_df = video_metadata_df.iloc[index_natsorted(video_metadata_df[args.video_path_column])].reset_index(
drop=True
)
logger.info(
f"Resume from {args.saved_path}: {len(saved_metadata_df)} processed and {len(video_metadata_df)} to be processed."
)
if args.prompt.endswith(".txt") and os.path.exists(args.prompt):
with open(args.prompt, "r") as f:
args.prompt = "".join(f.readlines())
logger.info(f"Prompt: {args.prompt}")
if args.video_path_column is not None:
video_path_list = video_metadata_df[args.video_path_column].tolist()
if args.caption_column in video_metadata_df.columns:
sampled_frame_caption_list = video_metadata_df[args.caption_column].tolist()
else:
# When two columns with the same name, the dataframe merge operation on will distinguish them by adding 'x' and 'y'.
sampled_frame_caption_list = video_metadata_df[args.caption_column + "_x"].tolist()
CUDA_VISIBLE_DEVICES = os.getenv("CUDA_VISIBLE_DEVICES", None)
tensor_parallel_size = torch.cuda.device_count() if CUDA_VISIBLE_DEVICES is None else len(CUDA_VISIBLE_DEVICES.split(","))
logger.info(f"Automatically set tensor_parallel_size={tensor_parallel_size} based on the available devices.")
llm = LLM(model=args.model_name, trust_remote_code=True, tensor_parallel_size=tensor_parallel_size)
if "Meta-Llama-3" in args.model_name:
if "Meta-Llama-3-70B" in args.model_name:
# Llama-3-70B should use the tokenizer from Llama-3-8B
# https://github.com/vllm-project/vllm/issues/4180#issuecomment-2068292942
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
else:
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
stop_token_ids = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
sampling_params = SamplingParams(temperature=0.7, top_p=1, max_tokens=1024, stop_token_ids=stop_token_ids)
else:
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
sampling_params = SamplingParams(temperature=0.7, top_p=1, max_tokens=1024)
result_dict = {args.caption_column: []}
if args.video_path_column is not None:
result_dict = {args.video_path_column: [], args.caption_column: []}
for i in tqdm(range(0, len(sampled_frame_caption_list), args.batch_size)):
if args.video_path_column is not None:
batch_video_path = video_path_list[i : i + args.batch_size]
batch_caption = sampled_frame_caption_list[i : i + args.batch_size]
batch_prompt = []
for caption in batch_caption:
# batch_prompt.append("user:" + args.prompt + str(caption) + "\n assistant:")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": args.prompt + "\n" + str(caption)},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
batch_prompt.append(text)
batch_output = llm.generate(batch_prompt, sampling_params)
batch_output = [output.outputs[0].text.rstrip() for output in batch_output]
batch_output = [extract_output(output, prefix=args.prefix) for output in batch_output]
# Filter out data that does not meet the output format.
batch_result = []
if args.video_path_column is not None:
for video_path, output in zip(batch_video_path, batch_output):
if output is not None:
batch_result.append((video_path, output))
batch_video_path, batch_output = zip(*batch_result)
result_dict[args.video_path_column].extend(batch_video_path)
else:
for output in batch_output:
if output is not None:
batch_result.append(output)
result_dict[args.caption_column].extend(batch_result)
# Save the metadata every args.saved_freq.
if i != 0 and ((i // args.batch_size) % args.saved_freq) == 0:
if len(result_dict[args.caption_column]) > 0:
result_df = pd.DataFrame(result_dict)
if args.saved_path.endswith(".csv"):
header = True if not os.path.exists(args.saved_path) else False
result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
elif args.saved_path.endswith(".jsonl"):
result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
elif args.saved_path.endswith(".json"):
# Append is not supported.
if os.path.exists(args.saved_path):
saved_df = pd.read_json(args.saved_path, orient="records")
result_df = pd.concat([saved_df, result_df], ignore_index=True)
result_df.to_json(args.saved_path, orient="records", indent=4, force_ascii=False)
logger.info(f"Save result to {args.saved_path}.")
result_dict = {args.caption_column: []}
if args.video_path_column is not None:
result_dict = {args.video_path_column: [], args.caption_column: []}
if len(result_dict[args.caption_column]) > 0:
result_df = pd.DataFrame(result_dict)
if args.saved_path.endswith(".csv"):
header = True if not os.path.exists(args.saved_path) else False
result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
elif args.saved_path.endswith(".jsonl"):
result_df.to_json(args.saved_path, orient="records", lines=True, mode="a")
elif args.saved_path.endswith(".json"):
# Append is not supported.
if os.path.exists(args.saved_path):
saved_df = pd.read_json(args.saved_path, orient="records")
result_df = pd.concat([saved_df, result_df], ignore_index=True)
result_df.to_json(args.saved_path, orient="records", indent=4, force_ascii=False)
logger.info(f"Save the final result to {args.saved_path}.")
if __name__ == "__main__":
main()
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