bert/train.py

"""

bert/train.py

Fine-tuning loop for the DeBERTa-v3 Cross-Encoder entailment verifier.



Pipeline:

  1. Load DeBERTa-v3-base with 3-label classification head

  2. ANLI (R1+R2+R3) + TrueTeacher + MNLI combined dataset

  3. Weighted CrossEntropyLoss  (Contradiction=2.0, Neutral=1.5, Entailment=1.0)

  4. AdamW + linear warmup + cosine decay

  5. Checkpoint every epoch; early stop on validation loss

"""

from __future__ import annotations

import argparse
import math
import os
from pathlib import Path
from typing import Optional

import torch
import torch.nn as nn
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from tqdm import tqdm

from bert.dataset import build_combined_dataset, make_dataloader
from bert.model import BertCrossEncoderVerifier, CrossEncoderConfig, build_model, load_tokenizer


def get_linear_warmup_cosine_schedule(

    optimizer: AdamW,

    num_warmup_steps: int,

    num_training_steps: int,

) -> LambdaLR:
    def lr_lambda(current_step: int) -> float:
        if current_step < num_warmup_steps:
            return float(current_step) / float(max(1, num_warmup_steps))
        progress = float(current_step - num_warmup_steps) / float(
            max(1, num_training_steps - num_warmup_steps)
        )
        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))

    return LambdaLR(optimizer, lr_lambda)


def train_epoch(

    model: BertCrossEncoderVerifier,

    loader: torch.utils.data.DataLoader,

    optimizer: AdamW,

    scheduler: LambdaLR,

    device: torch.device,

    grad_accum_steps: int = 4,

    max_grad_norm: float = 1.0,

) -> float:
    model.train()
    total_loss = 0.0
    optimizer.zero_grad()

    for step, batch in enumerate(tqdm(loader, desc="train")):
        input_ids     = batch["input_ids"].to(device)
        attention_mask = batch["attention_mask"].to(device)
        token_type_ids = batch.get("token_type_ids")
        if token_type_ids is not None:
            token_type_ids = token_type_ids.to(device)
        labels = batch["label"].to(device)

        out = model(input_ids, attention_mask, token_type_ids, labels)
        loss = out["loss"] / grad_accum_steps
        loss.backward()
        total_loss += loss.item() * grad_accum_steps

        if (step + 1) % grad_accum_steps == 0:
            nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
            optimizer.step()
            scheduler.step()
            optimizer.zero_grad()

    return total_loss / len(loader)


@torch.no_grad()
def evaluate(

    model: BertCrossEncoderVerifier,

    loader: torch.utils.data.DataLoader,

    device: torch.device,

) -> dict:
    model.eval()
    total_loss = 0.0
    correct = 0
    total = 0
    # Per-class correct counts for precision analysis
    class_correct = [0, 0, 0]
    class_total   = [0, 0, 0]

    for batch in tqdm(loader, desc="eval"):
        input_ids      = batch["input_ids"].to(device)
        attention_mask = batch["attention_mask"].to(device)
        token_type_ids = batch.get("token_type_ids")
        if token_type_ids is not None:
            token_type_ids = token_type_ids.to(device)
        labels = batch["label"].to(device)

        out = model(input_ids, attention_mask, token_type_ids, labels)
        total_loss += out["loss"].item()

        preds = out["logits"].argmax(dim=-1)
        correct += (preds == labels).sum().item()
        total   += labels.size(0)
        for c in range(3):
            mask = labels == c
            class_correct[c] += (preds[mask] == labels[mask]).sum().item()
            class_total[c]   += mask.sum().item()

    acc = correct / total if total > 0 else 0.0
    per_class = {
        c: class_correct[c] / class_total[c] if class_total[c] > 0 else 0.0
        for c in range(3)
    }
    label_names = {0: "contradiction", 1: "neutral", 2: "entailment"}
    return {
        "loss": total_loss / len(loader),
        "accuracy": acc,
        "per_class_accuracy": {label_names[k]: v for k, v in per_class.items()},
    }


def train(

    data_dir: Path,

    output_dir: Path,

    backbone: str = "microsoft/deberta-v3-base",

    epochs: int = 5,

    batch_size: int = 32,

    lr: float = 2e-5,

    warmup_ratio: float = 0.06,

    max_length: int = 512,

    grad_accum: int = 4,

    seed: int = 42,

) -> None:
    torch.manual_seed(seed)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"[train] device={device}, backbone={backbone}")

    tokenizer = load_tokenizer(backbone)
    config    = CrossEncoderConfig(backbone=backbone, max_length=max_length)
    model     = build_model(config).to(device)

    train_ds  = build_combined_dataset(data_dir, tokenizer, "train", max_length, seed)
    val_ds    = build_combined_dataset(data_dir, tokenizer, "dev",   max_length, seed)
    train_loader = make_dataloader(train_ds, batch_size, shuffle=True)
    val_loader   = make_dataloader(val_ds,   batch_size, shuffle=False)

    num_training_steps = epochs * len(train_loader) // grad_accum
    num_warmup_steps   = int(warmup_ratio * num_training_steps)

    optimizer  = AdamW(model.parameters(), lr=lr, weight_decay=0.01, eps=1e-8)
    scheduler  = get_linear_warmup_cosine_schedule(optimizer, num_warmup_steps, num_training_steps)

    output_dir.mkdir(parents=True, exist_ok=True)
    best_val_loss = float("inf")

    for epoch in range(1, epochs + 1):
        print(f"\n── Epoch {epoch}/{epochs} ──────────────────────")
        train_loss = train_epoch(model, train_loader, optimizer, scheduler, device, grad_accum)
        val_metrics = evaluate(model, val_loader, device)

        print(f"  train_loss={train_loss:.4f}")
        print(f"  val_loss={val_metrics['loss']:.4f}  val_acc={val_metrics['accuracy']:.4f}")
        print(f"  per_class={val_metrics['per_class_accuracy']}")

        ckpt_path = output_dir / f"checkpoint_epoch{epoch}.pt"
        torch.save({
            "epoch": epoch,
            "model_state_dict": model.state_dict(),
            "optimizer_state_dict": optimizer.state_dict(),
            "val_loss": val_metrics["loss"],
            "config": config,
        }, ckpt_path)
        print(f"  saved → {ckpt_path}")

        if val_metrics["loss"] < best_val_loss:
            best_val_loss = val_metrics["loss"]
            best_path = output_dir / "best_model.pt"
            torch.save(model.state_dict(), best_path)
            print(f"  ★ new best → {best_path}")

    # Save tokenizer alongside model for export pipeline
    tokenizer.save_pretrained(output_dir / "tokenizer")
    print(f"\n[train] complete. Best val_loss={best_val_loss:.4f}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--data_dir",   type=Path,  required=True)
    parser.add_argument("--output_dir", type=Path,  default=Path("checkpoints"))
    parser.add_argument("--backbone",   type=str,   default="microsoft/deberta-v3-base")
    parser.add_argument("--epochs",     type=int,   default=5)
    parser.add_argument("--batch_size", type=int,   default=32)
    parser.add_argument("--lr",         type=float, default=2e-5)
    parser.add_argument("--max_length", type=int,   default=512)
    parser.add_argument("--grad_accum", type=int,   default=4)
    args = parser.parse_args()
    train(**vars(args))