inference.py

# !pip install -q transformers datasets sentencepiece
import argparse
import gc
import json
import os

import datasets
import pandas as pd
import torch
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer

TOTAL_NUM_FILES_C4_TRAIN = 1024


def parse_args():
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--start",
        type=int,
        required=True,
        help="Starting file number to download. Valid values: 0 - 1023",
    )
    parser.add_argument(
        "--end",
        type=int,
        required=True,
        help="Ending file number to download. Valid values: 0 - 1023",
    )
    parser.add_argument("--batch_size", type=int, default=64, help="Batch size")
    parser.add_argument(
        "--model_name",
        type=str,
        default="taskydata/deberta-v3-base_10xp3nirstbbflanseuni_10xc4",
        help="Model name",
    )
    parser.add_argument(
        "--local_cache_location",
        type=str,
        default="c4_download",
        help="local cache location from where the dataset will be loaded",
    )
    parser.add_argument(
        "--use_local_cache_location",
        type=bool,
        default=True,
        help="Set True if you want to load the dataset from local cache.",
    )
    parser.add_argument(
        "--clear_dataset_cache",
        type=bool,
        default=False,
        help="Set True if you want to delete the dataset files from the cache after inference.",
    )
    parser.add_argument(
        "--release_memory",
        type=bool,
        default=True,
        help="Set True if you want to release the memory of used variables.",
    )

    args = parser.parse_args()
    return args


def chunks(l, n):
    for i in range(0, len(l), n):
        yield l[i : i + n]


def batch_tokenize(data, batch_size):
    batches = list(chunks(data, batch_size))
    tokenized_batches = []
    for batch in batches:
        # max_length will automatically be set to the max length of the model (512 for deberta)
        tensor = tokenizer(
            batch,
            return_tensors="pt",
            padding="max_length",
            truncation=True,
            max_length=512,
        )
        tokenized_batches.append(tensor)
    return tokenized_batches, batches


def batch_inference(data, batch_size=32):
    preds = []
    tokenized_batches, batches = batch_tokenize(data, batch_size)
    for i in tqdm(range(len(batches))):
        with torch.no_grad():
            logits = model(**tokenized_batches[i].to(device)).logits.cpu()
        preds.extend(logits)
    return preds


if __name__ == "__main__":
    args = parse_args()

    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
    model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model.to(device)
    model.eval()

    path = "python_messages.jsonl"
    ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
    ds = ds[range(args.start, args.end)]
    df = pd.DataFrame(ds, index=None)
    texts = df["message"].to_list()
    commits = df["commit"].to_list()
    preds = batch_inference(texts, batch_size=args.batch_size)

    assert len(preds) == len(texts)

    # Write two jsonl files:
    # 1) Probas for all of C4
    # 2) Probas + texts for samples predicted as tasky
    tasky_commits_path = f"tasky_commits_python_{args.start}_{args.end}.jsonl"

    with open(tasky_commits_path, "w") as f:
        for i in range(len(preds)):
            predicted_class_id = preds[i].argmax().item()
            pred = model.config.id2label[predicted_class_id]
            tasky_proba = torch.softmax(preds[i], dim=-1)[-1].item()

            f.write(
                json.dumps(
                    {
                        "commit": commits[i],
                        "message": texts[i],
                        "proba": tasky_proba,
                    }
                )
                + "\n"
            )