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import argparse
import json
import os
import torch
from datasets import load_dataset
from tqdm.auto import tqdm
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DPRQuestionEncoder
from common import articles_to_paragraphs, kilt_wikipedia_columns
from common import kilt_wikipedia_paragraph_columns as columns
def eval_generate(args):
device = ("cuda" if torch.cuda.is_available() else "cpu")
question_tokenizer = AutoTokenizer.from_pretrained(args.question_encoder_name)
question_model = DPRQuestionEncoder.from_pretrained(args.question_encoder_name).to(device)
_ = question_model.eval()
eli5_tokenizer = AutoTokenizer.from_pretrained('vblagoje/bart_eli5')
eli5_model = AutoModelForSeq2SeqLM.from_pretrained('vblagoje/bart_eli5').to(device)
_ = eli5_model.eval()
min_snippet_length = 20
topk = 21
min_chars_per_passage = 200
kilt_wikipedia = load_dataset("kilt_wikipedia", split="full")
kilt_wikipedia_paragraphs = kilt_wikipedia.map(articles_to_paragraphs, batched=True,
remove_columns=kilt_wikipedia_columns,
batch_size=256,
cache_file_name=f"./data/wiki_kilt_paragraphs_full.arrow",
desc="Expanding wiki articles into paragraphs")
# use paragraphs that are not simple fragments or very short sentences
kilt_wikipedia_paragraphs = kilt_wikipedia_paragraphs.filter(
lambda x: (x["end_character"] - x["start_character"]) > min_chars_per_passage)
kilt_wikipedia_paragraphs.load_faiss_index("embeddings", args.index_file_name, device=0)
def embed_questions_for_retrieval(questions):
query = question_tokenizer(questions, max_length=128, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
q_reps = question_model(query["input_ids"].to(device),
query["attention_mask"].to(device)).pooler_output
return q_reps.cpu().numpy()
def query_index(question):
question_embedding = embed_questions_for_retrieval([question])
scores, wiki_passages = kilt_wikipedia_paragraphs.get_nearest_examples("embeddings", question_embedding, k=topk)
retrieved_examples = []
r = list(zip(wiki_passages[k] for k in columns))
for i in range(topk):
retrieved_examples.append({k: v for k, v in zip(columns, [r[j][0][i] for j in range(len(columns))])})
return retrieved_examples
def create_kilt_datapoint(q_id, query, answer, res_list):
# make a KILT data point
# see https://github.com/facebookresearch/KILT#kilt-data-format
provenance = [{
"wikipedia_id": r["wikipedia_id"], # *mandatory*
"title": r["title"],
"section": r["section"],
"start_paragraph_id": r["start_paragraph_id"],
"start_character": r["start_character"],
"end_paragraph_id": r["end_paragraph_id"],
"end_character": r["end_character"],
"text": r["text"],
"bleu_score": None, # wrt original evidence
"meta": None # dataset/task specific
} for r in res_list]
output = [{"answer": answer, "provenance": provenance}]
return {"id": q_id,
"input": query,
"output": output, # each element is an answer or provenance (can have multiple of each)
"meta": None # dataset/task specific
}
kilt_output = []
with open(args.kilt_input_file, "r") as f:
kilt_items = [json.loads(x) for x in f.read().strip().split("\n")]
progress_bar = tqdm(range(len(kilt_items)), desc="Creating KILT response document")
for idx, item in enumerate(kilt_items):
query = item["input"]
res_list = query_index(query)
res_list = [res for res in res_list if len(res["text"].split()) > min_snippet_length][:int(topk / 3)]
documents = [res["text"] for res in res_list]
conditioned_doc = "<P> " + " <P> ".join([d for d in documents])
query_and_docs = "question: {} context: {}".format(query, conditioned_doc)
model_input = eli5_tokenizer(query_and_docs, truncation=True, padding=True, return_tensors="pt")
generated_answers_encoded = eli5_model.generate(input_ids=model_input["input_ids"].to(device),
attention_mask=model_input["attention_mask"].to(device),
min_length=50,
max_length=250,
do_sample=False,
early_stopping=True,
num_beams=8,
temperature=1.0,
top_k=None,
top_p=None,
no_repeat_ngram_size=3,
num_return_sequences=1)
answer = eli5_tokenizer.batch_decode(generated_answers_encoded, skip_special_tokens=True,
clean_up_tokenization_spaces=True)
kilt_example = create_kilt_datapoint(item["id"], query, answer[0], res_list)
kilt_output.append(kilt_example)
progress_bar.update(1)
with open(args.kilt_output_file, "w") as fp:
for kilt_example in kilt_output:
json.dump(kilt_example, fp)
fp.write("\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--kilt_input_file', default="./eli5-dev-kilt.jsonl", type=str)
parser.add_argument('--kilt_output_file', default="./eli5-predicted_retrieval.jsonl", type=str)
parser.add_argument(
"--question_encoder_name",
default="vblagoje/dpr-question_encoder-single-lfqa-base",
help="Question encoder to use",
)
parser.add_argument(
"--index_file_name",
default="../data/kilt_dpr_wikipedia_first.faiss",
help="Faiss index with passage embeddings",
)
args = parser.parse_args()
assert os.path.isfile(args.kilt_input_file), f"Input file {args.kilt_input_file} couldn't be loaded"
eval_generate(args)
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