giant/giant/training/loop.py

"""The training loop.

`train()` owns the epoch structure and nothing else: the per-stage step is
`giant.training.trainers`' job, every number reported is
`giant.training.metrics`' job, and the on-disk checkpoint is
`giant.training.checkpoint`'s.
"""

import os
import signal
import time
from pathlib import Path
from types import FrameType
from typing import Callable

import numpy as np
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from giant.data.loader import TopNMap
from giant.data.setup_cache import topnmap_to_json
from giant.training.checkpoint import build_checkpoint, init_stages_from_checkpoints, load_checkpoint
from giant.training.metrics import MetricsCollector
from giant.training.trainers import (
    FlowDDPMStageTrainer,
    StageTrainer,
    build_stage_trainers,
)
from giant.validate import validate_marginals

_CATCHABLE_SIGNALS = (signal.SIGINT, signal.SIGTERM)


class _GracefulShutdown:
    """Turns SIGINT/SIGTERM into a flag check instead of an immediate crash.

    A second signal while already shutting down restores the default
    handler and re-sends the signal, so an unresponsive run can still be
    force-killed.
    """

    def __init__(self) -> None:
        self.requested = False
        self._previous: dict[
            int,
            Callable[[int, FrameType | None], object] | signal.Handlers | int | None,
        ] = {}

    def __enter__(self) -> "_GracefulShutdown":
        for sig in _CATCHABLE_SIGNALS:
            self._previous[sig] = signal.getsignal(sig)
            signal.signal(sig, self._handle)
        return self

    def __exit__(self, *exc_info) -> None:
        for sig, handler in self._previous.items():
            signal.signal(sig, handler)

    def _handle(self, signum: int, frame) -> None:
        if self.requested:
            signal.signal(signum, self._previous[signum])
            os.kill(os.getpid(), signum)
            return
        self.requested = True
        print(
            f"\nreceived {signal.Signals(signum).name} — finishing the current "
            "batch, then saving a checkpoint and exiting (send again to force-quit)"
        )


def _try_validate_marginals(trainer: StageTrainer, val_loader, device, **kwargs):
    """Runs `validate_marginals` on `trainer`'s sampling model (EMA model if
    present, else the raw model). `validate_marginals` itself dispatches
    through `giant.sample.sample_stage1`/`sample_stage2`/`resolve_n_sec`, so
    this is generator- and one-shot-vs-autoregressive-agnostic."""
    model = trainer.sampling_model()
    return validate_marginals(model, val_loader, device=device, **kwargs)


def _marginal_kl(trainers: dict[str, StageTrainer], val_loader, device, **kwargs) -> float:
    """Mean marginal KL over the stage-1 sampling chain, or NaN when stage 1
    is inactive or `validate_marginals` declined to produce a result."""
    stage1 = trainers.get("stage1")
    if stage1 is None:
        return float("nan")
    result = _try_validate_marginals(
        stage1,
        val_loader,
        device,
        sec_decoder=trainers["stage2"].sampling_model() if "stage2" in trainers else None,
        **kwargs,
    )
    if result is None:
        return float("nan")
    return float(np.mean(result["kl_divergence"]))


def train(
    cfg: dict,
    models: dict[str, torch.nn.Module | None],
    critics: dict[str, torch.nn.Module | None],
    train_loader: DataLoader,
    val_loader: DataLoader,
    device: torch.device,
    out_dir: str | Path,
    normalizer_dict: dict | None = None,
    pdg_map: dict | None = None,
    mat_map: dict | None = None,
    proc_map: dict | None = None,
    pdg_topn_map: TopNMap | None = None,
    sec_type_topn_map: TopNMap | None = None,
    mat_topn_map: TopNMap | None = None,
    model_config: dict | None = None,
    resume_path: str | Path | None = None,
    total_train_batches: int = 0,
    use_wandb: bool = False,
    wandb_project: str = "giant",
    wandb_run_name: str = "",
    wandb_log_every: int = 50,
) -> None:
    """Train whichever of stage1/stage2 are active, each through its own
    `StageTrainer`. `models`/`critics` are the dicts
    `giant.model.network.build_models`/`build_critics` return — a `None`
    entry means that stage is `active = false`.
    """
    out_dir = Path(out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)
    t = cfg["train"]
    epochs = t["epochs"]
    validate_every = t.get("validate_every", 0)
    validate_steps = t.get("validate_steps", 10)
    max_val_batches = t.get("max_val_batches", 0)

    sec_type_class_counts = sec_type_topn_map.class_counts if sec_type_topn_map is not None else None
    trainers = build_stage_trainers(cfg, models, critics, device, total_train_batches, sec_type_class_counts)
    if not trainers:
        raise ValueError("no active stage — stage1_model.active and stage2_model.active are both false")
    for line in init_stages_from_checkpoints(trainers):
        print(line)
    has_adversarial = any(not tr.supports_val_loss for tr in trainers.values())

    checkpoint_extras = {
        "normalizer": normalizer_dict,
        "pdg_map": pdg_map,
        "mat_map": mat_map,
        "proc_map": proc_map,
        "pdg_topn_map": topnmap_to_json(pdg_topn_map) if pdg_topn_map is not None else None,
        "sec_type_topn_map": topnmap_to_json(sec_type_topn_map) if sec_type_topn_map is not None else None,
        "mat_topn_map": topnmap_to_json(mat_topn_map) if mat_topn_map is not None else None,
        "model_config": model_config,
    }

    start_epoch = 1
    best_val_loss = float("inf")
    global_step = 0
    if resume_path is not None:
        ckpt = torch.load(resume_path, map_location=device, weights_only=False)
        load_checkpoint(trainers, ckpt, t["lr"])
        start_epoch = ckpt.get("epoch", 0) + 1
        best_val_loss = ckpt.get("best_val_loss", float("inf"))
        global_step = ckpt.get("global_step", 0)
        if start_epoch > epochs:
            print(f"checkpoint already completed epoch {start_epoch - 1} (>= --epochs {epochs}) — nothing to train")
            return

    collector = MetricsCollector.create(
        trainers,
        out_dir,
        cfg,
        model_config,
        resume=resume_path is not None,
        use_wandb=use_wandb,
        wandb_project=wandb_project,
        wandb_run_name=wandb_run_name,
        wandb_log_every=wandb_log_every,
    )

    epoch_w = len(str(epochs))
    last_completed_epoch = start_epoch - 1
    with _GracefulShutdown() as shutdown:
        for epoch in range(start_epoch, epochs + 1):
            epoch_start = time.monotonic()
            if device.type == "cuda":
                torch.cuda.reset_peak_memory_stats(device)
            collector.start_epoch(epoch)
            for trainer in trainers.values():
                trainer.train_mode()

            bar = tqdm(
                train_loader,
                desc=f"  epoch {epoch:{epoch_w}d}/{epochs}",
                total=total_train_batches or None,
                leave=False,
                unit="batch",
                dynamic_ncols=True,
            )
            for batch in bar:
                B = batch[0].size(0)
                collector.add_train_batch(
                    {name: trainer.step(batch, device, global_step) for name, trainer in trainers.items()},
                    B,
                )
                bar.set_postfix_str(collector.postfix(), refresh=False)
                global_step += 1
                collector.log_batch(global_step, batch, device)
                if shutdown.requested:
                    break
            bar.close()

            if shutdown.requested:
                ckpt = build_checkpoint(trainers, epoch - 1, global_step, best_val_loss, checkpoint_extras)
                torch.save(ckpt, out_dir / "last.pt")
                last_completed_epoch = epoch - 1
                print(
                    f"saved in-progress weights from partway through epoch "
                    f"{epoch} to {out_dir / 'last.pt'} "
                    f"(resume will restart epoch {epoch})"
                )
                break

            for trainer in trainers.values():
                trainer.eval_mode()

            # --- per-stage validation ---
            scored = {name: tr for name, tr in trainers.items() if tr.supports_val_loss}
            if scored:
                with torch.no_grad():
                    for val_batch_idx, batch in enumerate(val_loader):
                        if max_val_batches > 0 and val_batch_idx >= max_val_batches:
                            break
                        B = batch[0].size(0)
                        collector.add_val_batch(
                            {name: tr.val_loss(batch, device) for name, tr in scored.items()},
                            B,
                        )
                        collector.observe_routers(batch[0].to(device), batch[1].to(device), B)

            # An adversarial stage has no averageable validation loss, so it
            # needs the marginal-KL signal every epoch to pick a best
            # checkpoint at all; a purely non-adversarial run only pays for
            # it every `validate_every` epochs.
            marginal_kl = float("nan")
            if has_adversarial:
                marginal_kl = _marginal_kl(trainers, val_loader, device)
            elif validate_every > 0 and epoch % validate_every == 0:
                stage1 = trainers.get("stage1")
                ddpm_steps = 1000
                if isinstance(stage1, FlowDDPMStageTrainer) and stage1.ddpm_schedule is not None:
                    ddpm_steps = stage1.ddpm_schedule.T
                marginal_kl = _marginal_kl(
                    trainers,
                    val_loader,
                    device,
                    steps=validate_steps,
                    ddpm_steps=ddpm_steps,
                )

            val_loss = sum(
                trainer.val_objective(
                    collector.train_means(name),
                    collector.val_means(name),
                    marginal_kl,
                )
                for name, trainer in trainers.items()
            )

            epoch_time = time.monotonic() - epoch_start
            is_best = val_loss < best_val_loss
            collector.set("val/loss", val_loss)
            collector.set("val/marginal_kl", marginal_kl)
            collector.set(
                "gpu_mem_mb",
                torch.cuda.max_memory_allocated(device) / (1024 * 1024) if device.type == "cuda" else 0.0,
            )
            collector.set("samples_per_sec", collector.train_samples / max(epoch_time, 1e-8))
            collector.set("is_best", int(is_best))
            collector.set("epoch_time_s", epoch_time)

            print(collector.summary_line(val_loss, epoch_time, is_best))
            collector.write_epoch(global_step)

            ckpt = build_checkpoint(trainers, epoch, global_step, best_val_loss, checkpoint_extras)
            if is_best:
                best_val_loss = val_loss
                ckpt["best_val_loss"] = best_val_loss
                torch.save(ckpt, out_dir / "best.pt")
            torch.save(ckpt, out_dir / "last.pt")
            last_completed_epoch = epoch

            if shutdown.requested:
                break

    collector.close()

    if shutdown.requested:
        print(
            f"stopped after epoch {last_completed_epoch} due to shutdown signal — "
            f"resume with --resume {out_dir / 'last.pt'}"
        )