task_forward/prediction.py

import argparse
import gc
import os
import sys
import warnings

import pandas as pd
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from generation_utils import (
    ReactionT5Dataset,
    decode_output,
    save_multiple_predictions,
)
from train import preprocess_df
from utils import seed_everything

warnings.filterwarnings("ignore")


def parse_args():
    parser = argparse.ArgumentParser(
        description="Script for reaction product prediction."
    )
    parser.add_argument(
        "--input_data",
        type=str,
        required=True,
        help="Path to the input data.",
    )
    parser.add_argument(
        "--input_max_length",
        type=int,
        default=400,
        help="Maximum token length of input.",
    )
    parser.add_argument(
        "--output_min_length",
        type=int,
        default=1,
        help="Minimum token length of output.",
    )
    parser.add_argument(
        "--output_max_length",
        type=int,
        default=300,
        help="Maximum token length of output.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        default="sagawa/ReactionT5v2-forward",
        help="Name or path of the finetuned model for prediction. Can be a local model or one from Hugging Face.",
    )
    parser.add_argument(
        "--num_beams", type=int, default=5, help="Number of beams used for beam search."
    )
    parser.add_argument(
        "--num_return_sequences",
        type=int,
        default=5,
        help="Number of predictions returned. Must be less than or equal to num_beams.",
    )
    parser.add_argument(
        "--batch_size", type=int, default=5, help="Batch size for prediction."
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="./",
        help="Directory where predictions are saved.",
    )
    parser.add_argument(
        "--debug", action="store_true", default=False, help="Use debug mode."
    )
    parser.add_argument(
        "--seed", type=int, default=42, help="Seed for reproducibility."
    )
    return parser.parse_args()


if __name__ == "__main__":
    CFG = parse_args()
    CFG.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    if not os.path.exists(CFG.output_dir):
        os.makedirs(CFG.output_dir)

    seed_everything(seed=CFG.seed)

    CFG.tokenizer = AutoTokenizer.from_pretrained(
        os.path.abspath(CFG.model_name_or_path)
        if os.path.exists(CFG.model_name_or_path)
        else CFG.model_name_or_path,
        return_tensors="pt",
    )
    model = AutoModelForSeq2SeqLM.from_pretrained(
        os.path.abspath(CFG.model_name_or_path)
        if os.path.exists(CFG.model_name_or_path)
        else CFG.model_name_or_path
    ).to(CFG.device)
    model.eval()

    input_data = pd.read_csv(CFG.input_data)
    input_data = preprocess_df(input_data, drop_duplicates=False)
    dataset = ReactionT5Dataset(CFG, input_data)
    dataloader = DataLoader(
        dataset,
        batch_size=CFG.batch_size,
        shuffle=False,
        num_workers=4,
        pin_memory=True,
        drop_last=False,
    )

    all_sequences, all_scores = [], []
    for inputs in tqdm(dataloader, total=len(dataloader)):
        inputs = {k: v.to(CFG.device) for k, v in inputs.items()}
        with torch.no_grad():
            output = model.generate(
                **inputs,
                min_length=CFG.output_min_length,
                max_length=CFG.output_max_length,
                num_beams=CFG.num_beams,
                num_return_sequences=CFG.num_return_sequences,
                return_dict_in_generate=True,
                output_scores=True,
            )
        sequences, scores = decode_output(output, CFG)
        all_sequences.extend(sequences)
        if scores:
            all_scores.extend(scores)
        del output
        torch.cuda.empty_cache()
        gc.collect()

    output_df = save_multiple_predictions(input_data, all_sequences, all_scores, CFG)

    output_df.to_csv(os.path.join(CFG.output_dir, "output.csv"), index=False)