""" Evaluation script for RAG models.""" import argparse import ast import logging import os import sys import pandas as pd import torch from tqdm import tqdm from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration from transformers import logging as transformers_logging sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip from utils_rag import exact_match_score, f1_score # noqa: E402 # isort:skip logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) transformers_logging.set_verbosity_info() def infer_model_type(model_name_or_path): if "token" in model_name_or_path: return "rag_token" if "sequence" in model_name_or_path: return "rag_sequence" if "bart" in model_name_or_path: return "bart" return None def metric_max_over_ground_truths(metric_fn, prediction, ground_truths): return max(metric_fn(prediction, gt) for gt in ground_truths) def get_scores(args, preds_path, gold_data_path): hypos = [line.strip() for line in open(preds_path, "r").readlines()] answers = [] if args.gold_data_mode == "qa": data = pd.read_csv(gold_data_path, sep="\t", header=None) for answer_list in data[1]: ground_truths = ast.literal_eval(answer_list) answers.append(ground_truths) else: references = [line.strip() for line in open(gold_data_path, "r").readlines()] answers = [[reference] for reference in references] f1 = em = total = 0 for prediction, ground_truths in zip(hypos, answers): total += 1 em += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths) f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths) em = 100.0 * em / total f1 = 100.0 * f1 / total logger.info(f"F1: {f1:.2f}") logger.info(f"EM: {em:.2f}") def get_precision_at_k(args, preds_path, gold_data_path): k = args.k hypos = [line.strip() for line in open(preds_path, "r").readlines()] references = [line.strip() for line in open(gold_data_path, "r").readlines()] em = total = 0 for hypo, reference in zip(hypos, references): hypo_provenance = set(hypo.split("\t")[:k]) ref_provenance = set(reference.split("\t")) total += 1 em += len(hypo_provenance & ref_provenance) / k em = 100.0 * em / total logger.info(f"Precision@{k}: {em: .2f}") def evaluate_batch_retrieval(args, rag_model, questions): def strip_title(title): if title.startswith('"'): title = title[1:] if title.endswith('"'): title = title[:-1] return title retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus( questions, return_tensors="pt", padding=True, truncation=True, )["input_ids"].to(args.device) question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids) question_enc_pool_output = question_enc_outputs[0] result = rag_model.retriever( retriever_input_ids, question_enc_pool_output.cpu().detach().to(torch.float32).numpy(), prefix=rag_model.rag.generator.config.prefix, n_docs=rag_model.config.n_docs, return_tensors="pt", ) all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids) provenance_strings = [] for docs in all_docs: provenance = [strip_title(title) for title in docs["title"]] provenance_strings.append("\t".join(provenance)) return provenance_strings def evaluate_batch_e2e(args, rag_model, questions): with torch.no_grad(): inputs_dict = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus( questions, return_tensors="pt", padding=True, truncation=True ) input_ids = inputs_dict.input_ids.to(args.device) attention_mask = inputs_dict.attention_mask.to(args.device) outputs = rag_model.generate( # rag_model overwrites generate input_ids, attention_mask=attention_mask, num_beams=args.num_beams, min_length=args.min_length, max_length=args.max_length, early_stopping=False, num_return_sequences=1, bad_words_ids=[[0, 0]], # BART likes to repeat BOS tokens, dont allow it to generate more than one ) answers = rag_model.retriever.generator_tokenizer.batch_decode(outputs, skip_special_tokens=True) if args.print_predictions: for q, a in zip(questions, answers): logger.info("Q: {} - A: {}".format(q, a)) return answers def get_args(): parser = argparse.ArgumentParser() parser.add_argument( "--model_type", choices=["rag_sequence", "rag_token", "bart"], type=str, help=( "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the" " model_name_or_path" ), ) parser.add_argument( "--index_name", default=None, choices=["exact", "compressed", "legacy"], type=str, help="RAG model retriever type", ) parser.add_argument( "--index_path", default=None, type=str, help="Path to the retrieval index", ) parser.add_argument("--n_docs", default=5, type=int, help="Number of retrieved docs") parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained checkpoints or model identifier from huggingface.co/models", ) parser.add_argument( "--eval_mode", choices=["e2e", "retrieval"], default="e2e", type=str, help=( "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates" " precision@k." ), ) parser.add_argument("--k", default=1, type=int, help="k for the precision@k calculation") parser.add_argument( "--evaluation_set", default=None, type=str, required=True, help="Path to a file containing evaluation samples", ) parser.add_argument( "--gold_data_path", default=None, type=str, required=True, help="Path to a tab-separated file with gold samples", ) parser.add_argument( "--gold_data_mode", default="qa", type=str, choices=["qa", "ans"], help=( "Format of the gold data file" "qa - a single line in the following format: question [tab] answer_list" "ans - a single line of the gold file contains the expected answer string" ), ) parser.add_argument( "--predictions_path", type=str, default="predictions.txt", help="Name of the predictions file, to be stored in the checkpoints directory", ) parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument( "--eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.", ) parser.add_argument( "--recalculate", help="Recalculate predictions even if the prediction file exists", action="store_true", ) parser.add_argument( "--num_beams", default=4, type=int, help="Number of beams to be used when generating answers", ) parser.add_argument("--min_length", default=1, type=int, help="Min length of the generated answers") parser.add_argument("--max_length", default=50, type=int, help="Max length of the generated answers") parser.add_argument( "--print_predictions", action="store_true", help="If True, prints predictions while evaluating.", ) parser.add_argument( "--print_docs", action="store_true", help="If True, prints docs retried while generating.", ) args = parser.parse_args() args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") return args def main(args): model_kwargs = {} if args.model_type is None: args.model_type = infer_model_type(args.model_name_or_path) assert args.model_type is not None if args.model_type.startswith("rag"): model_class = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration model_kwargs["n_docs"] = args.n_docs if args.index_name is not None: model_kwargs["index_name"] = args.index_name if args.index_path is not None: model_kwargs["index_path"] = args.index_path else: model_class = BartForConditionalGeneration checkpoints = ( [f.path for f in os.scandir(args.model_name_or_path) if f.is_dir()] if args.eval_all_checkpoints else [args.model_name_or_path] ) logger.info("Evaluate the following checkpoints: %s", checkpoints) score_fn = get_scores if args.eval_mode == "e2e" else get_precision_at_k evaluate_batch_fn = evaluate_batch_e2e if args.eval_mode == "e2e" else evaluate_batch_retrieval for checkpoint in checkpoints: if os.path.exists(args.predictions_path) and (not args.recalculate): logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path)) score_fn(args, args.predictions_path, args.gold_data_path) continue logger.info("***** Running evaluation for {} *****".format(checkpoint)) logger.info(" Batch size = %d", args.eval_batch_size) logger.info(" Predictions will be stored under {}".format(args.predictions_path)) if args.model_type.startswith("rag"): retriever = RagRetriever.from_pretrained(checkpoint, **model_kwargs) model = model_class.from_pretrained(checkpoint, retriever=retriever, **model_kwargs) model.retriever.init_retrieval() else: model = model_class.from_pretrained(checkpoint, **model_kwargs) model.to(args.device) with open(args.evaluation_set, "r") as eval_file, open(args.predictions_path, "w") as preds_file: questions = [] for line in tqdm(eval_file): questions.append(line.strip()) if len(questions) == args.eval_batch_size: answers = evaluate_batch_fn(args, model, questions) preds_file.write("\n".join(answers) + "\n") preds_file.flush() questions = [] if len(questions) > 0: answers = evaluate_batch_fn(args, model, questions) preds_file.write("\n".join(answers)) preds_file.flush() score_fn(args, args.predictions_path, args.gold_data_path) if __name__ == "__main__": args = get_args() main(args)