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import os
import argparse
import time
import json
import nltk
from rank_bm25 import BM25Okapi
import numpy as np
import torch
from vllm import LLM, SamplingParams
from datetime import datetime, timedelta
from itertools import islice
def download_nltk_data(package_name, download_dir='nltk_data'):
# Ensure the download directory exists
os.makedirs(download_dir, exist_ok=True)
# Set NLTK data path
nltk.data.path.append(download_dir)
try:
# Try to find the resource
nltk.data.find(f'tokenizers/{package_name}')
print(f"Package '{package_name}' is already downloaded")
except LookupError:
# If resource isn't found, download it
print(f"Downloading {package_name}...")
nltk.download(package_name, download_dir=download_dir)
print(f"Successfully downloaded {package_name}")
# def format_time(seconds):
# """Format time duration nicely."""
# return str(timedelta(seconds=round(seconds)))
def claim2prompts(example):
claim = example["claim"]
claim_str = "Example [NUMBER]:||Claim: " + claim + "||Evidence: "
for question in example["questions"]:
q_text = question["question"].strip()
if len(q_text) == 0:
continue
if not q_text[-1] == "?":
q_text += "?"
answer_strings = []
for a in question["answers"]:
if a["answer_type"] in ["Extractive", "Abstractive"]:
answer_strings.append(a["answer"])
if a["answer_type"] == "Boolean":
answer_strings.append(a["answer"] + ", because " + a["boolean_explanation"].lower().strip())
for a_text in answer_strings:
if not a_text[-1] in [".", "!", ":", "?"]:
a_text += "."
prompt_lookup_str = a_text
this_q_claim_str = claim_str + a_text.strip() + "||Question: " + q_text
yield (prompt_lookup_str, this_q_claim_str.replace("\n", " ").replace("||", "\n")[:1500])
def main(args):
# script_start = time.time()
# start_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
# print(f"Script started at: {start_time}")
# print(f"Loading model: {args.model}")
download_nltk_data('punkt')
download_nltk_data('punkt_tab')
# Load and prepare reference corpus
# corpus_start = time.time()
with open(args.reference_corpus, "r", encoding="utf-8") as json_file:
train_examples = json.load(json_file)
prompt_corpus, tokenized_corpus = [], []
for example in train_examples:
for lookup_str, prompt in claim2prompts(example):
entry = nltk.word_tokenize(lookup_str)
tokenized_corpus.append(entry)
prompt_corpus.append(prompt)
prompt_bm25 = BM25Okapi(tokenized_corpus)
# print(f"Reference corpus processed in: {format_time(time.time() - corpus_start)}")
# Initialize vLLM with optimized settings
gpu_count = torch.cuda.device_count()
print(f"Using {gpu_count} GPU{'s' if gpu_count > 1 else ''}")
# model_start = time.time()
llm = LLM(
model=args.model,
tensor_parallel_size=gpu_count,
max_model_len=4096,
gpu_memory_utilization=0.95,
enforce_eager=True,
trust_remote_code=True,
# dtype="half",
)
llm.get_tokenizer().pad_token = "<|end_of_text|>"
# print(f"Model loaded in: {format_time(time.time() - model_start)}")
sampling_params = SamplingParams(
temperature=0.6,
top_p=0.9,
top_k=1,
skip_special_tokens=False,
max_tokens=512,
stop=['<|end_of_text|>', '</s>', '<|im_end|>', '[INST]', '[/INST]','<|eot_id|>','<|end|>','<|endoftext|>']
)
# processing_start = time.time()
# Load target data
target_examples = []
with open(args.top_k_target_knowledge, "r", encoding="utf-8") as json_file:
for line in json_file:
target_examples.append(json.loads(line))
if args.end == -1:
args.end = len(target_examples)
print(f"Processing {args.end} examples")
# Process in batches
with torch.no_grad():
with open(args.output_questions, "w", encoding="utf-8") as output_file:
for idx in range(0, args.end, args.batch_size):
batch_end = min(idx + args.batch_size, args.end)
current_batch = target_examples[idx:batch_end]
print(f"\nProcessing batch {idx}-{batch_end}...")
for example in current_batch:
# batch_start = time.time()
claim = example["claim"]
claim_id = example["claim_id"]
top_k_sentences_urls = example[f"top_{args.top_k}"]
batch_prompts = []
batch_metadata = []
# Prepare all prompts for current example
for sentences_urls in top_k_sentences_urls:
prompt_lookup_str = sentences_urls["sentence"]
url = sentences_urls["url"]
prompt_s = prompt_bm25.get_scores(nltk.word_tokenize(prompt_lookup_str))
prompt_n = 10
prompt_top_n = np.argsort(prompt_s)[::-1][:prompt_n]
prompt_docs = [prompt_corpus[i] for i in prompt_top_n]
temp_prompt = "\n\n".join(prompt_docs)
for k in range(1, temp_prompt.count("[NUMBER]")+1):
temp_prompt = temp_prompt.replace("[NUMBER]", f"{k}", 1)
claim_prompt = "Your task is to generate a question based on the given claim and evidence. The question should clarify the relationship between the evidence and the claim\n\n"
evidence = prompt_lookup_str.replace("\n", " ")
full_prompt = claim_prompt + temp_prompt + "\n\nNow, generate a question that links the following claim and evidence:" + f"\n\nClaim: {claim}" + f"\nEvidence: {evidence}"
if "OLMo" in args.model:
inputs = [full_prompt]
else:
messages = [{"role":"user", "content":full_prompt}]
inputs = llm.get_tokenizer().apply_chat_template(messages, tokenize=False)
inputs += "<|start_header_id|>assistant<|end_header_id|>\n\nQuestion: "
batch_prompts.append(inputs)
batch_metadata.append((url, prompt_lookup_str))
# Process batch
outputs = llm.generate(batch_prompts, sampling_params)
# Process outputs
evidence = []
for output, (url, sent) in zip(outputs, batch_metadata):
question = output.outputs[0].text.strip().split("?")[0].replace("\n", " ") + "?"
evidence.append({
"question": question,
"answer": sent,
"url": url
})
# Write results
json_data = {
"claim_id": claim_id,
"claim": claim,
"evidence": evidence
}
output_file.write(json.dumps(json_data, ensure_ascii=False) + "\n")
output_file.flush()
# batch_time = time.time() - batch_start
# print(f"Processed example {claim_id}. Time elapsed: {batch_time:.2f}s")
# Calculate and display timing information
# total_time = time.time() - script_start
# processing_time = time.time() - processing_start
# end_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
# print("\nTiming Summary:")
# print(f"Start time: {start_time}")
# print(f"End time: {end_time}")
# print(f"Total runtime: {format_time(total_time)}")
# print(f"Setup time: {format_time(processing_start - script_start)}")
# print(f"Processing time: {format_time(processing_time)}")
# print(f"Results written to: {args.output_questions}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Use a prompt to generate questions that could be answered by top-k retrieved evidence. Output generated questions.")
parser.add_argument("--model", type=str, default="meta-llama/Meta-Llama-3-8B-Instruct")
parser.add_argument("--reference_corpus", default="baseline/train.json")
parser.add_argument(
"-i",
"--top_k_target_knowledge",
default="data_store/dev_reranking_top_k.json",
help="Directory where the sentences for the scraped data is saved.",
)
parser.add_argument(
"-o",
"--output_questions",
default="data_store/dev_top_k_qa.json",
help="Directory where the sentences for the scraped data is saved.",
)
parser.add_argument(
"--top_k",
default=10,
type=int
)
parser.add_argument(
"--batch_size",
type=int,
default=4,
help="Number of examples to process in each batch"
)
parser.add_argument(
"-e",
"--end",
type=int,
default=-1
)
args = parser.parse_args()
main(args) |