sampling_score.py

import argparse
import json

import model_training.models.reward_model  # noqa: F401 (registers reward model for AutoModel loading)
import numpy as np
import pandas as pd
import torch
from eval_datasets import get_sampling_dataloader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from utils import load_sampling_data


def batch_inference(model, dataloader):
    """
    Batch inference
    """

    scores, sampling = [], []
    device = model.device
    for i, data in enumerate(dataloader):
        sampling.append(data.pop("sampling").cpu().detach().numpy())
        data = {k: v.squeeze().to(device) for k, v in data.items()}
        pred = model(**data).logits[:, 0].cpu().detach().numpy()
        scores.append(pred)

    return np.hstack(sampling), np.hstack(scores)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="")
    parser.add_argument("--data_path", type=str, help="Path of the sampling data file")
    parser.add_argument("--model", type=str, help="Path or url of the model file")
    parser.add_argument("--max_length", type=int, help="max length of input")
    parser.add_argument("--batch_size", type=int, help="device", default=4)
    parser.add_argument("--device", type=str, help="device", default="cpu")
    parser.add_argument("--save", type=bool, help="whether to save the results", default=True)

    args = parser.parse_args().__dict__
    if args.get("device") != "cpu":
        device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    else:
        device = torch.device("cpu")

    data = load_sampling_data(args.get("data_path"))

    model_name = args.get("model")

    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    model.eval()
    model.to(device)
    max_length = args.get("max_length") or model.config.max_position_embeddings
    dataloader = get_sampling_dataloader(data, tokenizer, max_length, args.get("batch_size"))
    sampling, scores = batch_inference(model, dataloader)

    df = pd.DataFrame({"sampling": sampling, "score": scores})
    id2label = {v: k for k, v in dataloader.dataset.label2id.items()}
    df["sampling"] = df["sampling"].map(id2label)
    results = df.groupby("sampling")["score"].mean().to_dict()
    results["mean_reward"] = str(df["score"].mean())
    print("RESULTS: ", results)

    results = {"model_name": data["model_name"], "results": results, "reward_model": args.get("model")}
    name = "-".join(data["model_name"].split("/"))

    if args.get("save"):
        with open(f"{name}.json", "w") as file:
            json.dump(results, file, indent=4)