v8: multi-airport pretrain (comma-separated airports)

pretrain_v8.py

@@ -0,0 +1,616 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = ["torch>=2.1","numpy","pandas","scikit-learn","huggingface-hub","trackio"]
+# ///
+"""
+Flight-JEPA v7 — past-track masked JEPA pretraining.
+
+Adapted from Forecast-MAE (arxiv:2308.09882) and I-JEPA (arxiv:2301.08243):
+mask contiguous blocks of *past-track* patches and train an encoder +
+EMA target + predictor to reconstruct masked-patch latents from visible
+context. Encoder weights then transfer to v6 fine-tuning.
+
+Key differences from v6's JEPA aux:
+- Pretraining-only objective (no forecasting head, no Δ conditioning).
+- Masks past-track patches, not future segments.
+- Trains on the same RKSIa data — this is small-scale demo, not OpenSky-scale.
+- Output: a `pretrained_encoder.pt` checkpoint loadable by v6 fine-tune.
+
+Decision criterion at fine-tune time:
+- Significant FDE improvement at ≥30% past-track dropout (test-time).
+- No regression at 0% dropout.
+"""
+from __future__ import annotations
+import argparse
+import copy
+import json
+import math
+import os
+import shutil
+import time
+
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+
+try:
+    import trackio
+    HAS_TRACKIO = True
+except ImportError:
+    HAS_TRACKIO = False
+
+
+# ============================================================================
+# DATA UTILITIES (inlined from train_v2_prod.py for self-contained job)
+# ============================================================================
+
+def load_atfm(dset_name, mode, path):
+    variables = ["X", "Y", "Z"]
+    data, labels = [], None
+    for var in variables:
+        df = pd.read_csv(os.path.join(path, f"{dset_name}_{mode}_{var}.tsv"),
+                          sep="\t", header=None, na_values="NaN")
+        if labels is None:
+            labels = df.values[:, 0]
+        data.append(df.values[:, 1:])
+    return np.stack(data, axis=-1), labels.astype(int)
+
+
+def compute_features(traj_xyz: np.ndarray) -> np.ndarray:
+    if traj_xyz.shape[0] < 2:
+        T = traj_xyz.shape[0]
+        return np.concatenate([
+            traj_xyz, np.zeros((T, 3), dtype=traj_xyz.dtype),
+            np.zeros((T, 3), dtype=traj_xyz.dtype)
+        ], axis=1)
+    x, y, z = traj_xyz[:, 0], traj_xyz[:, 1], traj_xyz[:, 2]
+    diff = np.diff(traj_xyz, axis=0)
+    norms = np.maximum(np.linalg.norm(diff, axis=1, keepdims=True), 1e-8)
+    u = diff / norms
+    u = np.vstack([u, u[-1:]])
+    r = np.sqrt(x ** 2 + y ** 2)
+    theta = np.arctan2(y, x)
+    return np.column_stack([
+        traj_xyz, u,
+        r[:, None], np.sin(theta)[:, None], np.cos(theta)[:, None]
+    ]).astype(np.float32)
+
+
+def ensure_data(airport: str, data_dir: str = "data"):
+    target = os.path.join(data_dir, airport)
+    if os.path.isdir(target) and any(f.endswith(".tsv") for f in os.listdir(target)):
+        return target
+    print(f"[data] downloading {airport} from HF ...")
+    from huggingface_hub import snapshot_download
+    snap = snapshot_download(
+        "petchthwr/ATFMTraj",
+        repo_type="dataset",
+        allow_patterns=[f"{airport}/*"],
+    )
+    os.makedirs(data_dir, exist_ok=True)
+    src = os.path.join(snap, airport)
+    if not os.path.isdir(target):
+        shutil.copytree(src, target)
+    return target
+
+
+# ============================================================================
+# DATASET — full past-track windows (no Δ / target)
+# ============================================================================
+
+class PastTrackDataset(Dataset):
+    """
+    Yields fixed-length past-track windows for masked-prediction pretraining.
+
+    Per __getitem__:
+      - sample a random window of length past_len from a trajectory
+      - return its 9-dim features padded to past_max
+    """
+
+    def __init__(self, airport, mode, data_dir,
+                 past_max=256, past_min=128,
+                 seed=0, epoch_multiplier=4):
+        # `airport` may be a single string ("RKSIa") or comma-separated
+        # ("RKSId,ESSA,LSZH") for v8 multi-airport pretraining. We union
+        # the trajectories without preserving airport-id (pretraining only
+        # uses features, not labels or airport tokens).
+        airports = [a.strip() for a in airport.split(",")]
+        all_positions = []
+        for ap in airports:
+            ensure_data(ap, data_dir)
+            ap_dir = os.path.join(data_dir, ap)
+            raw, _ = load_atfm(ap, mode, ap_dir)
+            lengths = np.array(
+                [int(np.sum(~np.isnan(raw[i, :, 0]))) for i in range(raw.shape[0])],
+                dtype=np.int64,
+            )
+            keep = lengths >= past_min + 1
+            raw = raw[keep]
+            lengths = lengths[keep]
+            n_kept = 0
+            for i in range(raw.shape[0]):
+                L = int(lengths[i])
+                all_positions.append(
+                    np.nan_to_num(raw[i, :L], nan=0.0).astype(np.float32)
+                )
+                n_kept += 1
+            print(f"[data] {ap}/{mode}: {n_kept} trajectories")
+
+        self.past_max = past_max
+        self.past_min = past_min
+        self.epoch_multiplier = epoch_multiplier
+        self.rng_seed = seed
+        self.positions = all_positions
+        self.n_traj = len(self.positions)
+        if len(airports) > 1:
+            print(f"[data] union total: {self.n_traj} trajectories from "
+                  f"{airports}")
+
+    def __len__(self):
+        return self.n_traj * self.epoch_multiplier
+
+    def __getitem__(self, idx):
+        traj_idx = idx % self.n_traj
+        rng = np.random.default_rng(self.rng_seed + idx * 9173)
+        positions = self.positions[traj_idx]
+        L = positions.shape[0]
+        past_len = min(self.past_max, L)
+        start = int(rng.integers(0, max(1, L - past_len + 1)))
+        window = positions[start:start + past_len]
+        feats = compute_features(window)
+        T = feats.shape[0]
+        feat_pad = np.zeros((self.past_max, 9), dtype=np.float32)
+        feat_pad[:T] = feats
+        return {
+            "features": torch.from_numpy(feat_pad),
+            "length": torch.tensor(T, dtype=torch.long),
+        }
+
+
+# ============================================================================
+# MODEL — encoder + EMA target + predictor (no decoder, no Δ)
+# ============================================================================
+
+class LearnablePosEnc(nn.Module):
+    def __init__(self, max_len, d_model):
+        super().__init__()
+        self.pe = nn.Parameter(torch.randn(1, max_len, d_model) * 0.02)
+    def forward(self, x):
+        return x + self.pe[:, :x.size(1)]
+
+
+class PatchTokenizer(nn.Module):
+    def __init__(self, in_channels=9, d_model=256, patch_size=8, max_patches=64):
+        super().__init__()
+        self.patch_size = patch_size
+        self.d_model = d_model
+        self.embed = nn.Sequential(
+            nn.Conv1d(in_channels, d_model // 2, 5, padding=2),
+            nn.GELU(),
+            nn.Conv1d(d_model // 2, d_model, 3, padding=1),
+            nn.GELU(),
+        )
+        self.pos_enc = LearnablePosEnc(max_patches, d_model)
+        self.norm = nn.LayerNorm(d_model)
+
+    def forward(self, features, lengths):
+        B, T, C = features.shape
+        h = self.embed(features.transpose(1, 2))
+        N = max(1, T // self.patch_size)
+        h = h[:, :, :N * self.patch_size]
+        h = h.reshape(B, self.d_model, N, self.patch_size).mean(-1)
+        h = h.transpose(1, 2)
+        h = self.norm(self.pos_enc(h))
+        patch_lengths = (lengths.float() / self.patch_size).clamp(min=1).long()
+        patch_lengths = patch_lengths.clamp(max=N)
+        return h, patch_lengths
+
+
+class CausalEncoder(nn.Module):
+    def __init__(self, d_model=256, n_heads=8, n_layers=4, d_ff=1024, dropout=0.1):
+        super().__init__()
+        layer = nn.TransformerEncoderLayer(
+            d_model=d_model, nhead=n_heads, dim_feedforward=d_ff,
+            dropout=dropout, activation="gelu", batch_first=True,
+            norm_first=True,
+        )
+        self.tf = nn.TransformerEncoder(layer, num_layers=n_layers)
+        self.norm = nn.LayerNorm(d_model)
+
+    def forward(self, x, key_padding_mask, attn_mask=None):
+        N = x.size(1)
+        if attn_mask is None:
+            # Default: causal. For pretraining we may pass full bidirectional.
+            attn_mask = torch.triu(
+                torch.ones(N, N, dtype=torch.bool, device=x.device), diagonal=1
+            )
+        return self.norm(
+            self.tf(x, mask=attn_mask, src_key_padding_mask=key_padding_mask)
+        )
+
+
+class JEPAPredictor(nn.Module):
+    """Predict target patch latents from context patch latents.
+    Adds a query token per masked position via positional embedding."""
+    def __init__(self, d_model=256, pred_dim=128, max_patches=64, dropout=0.1):
+        super().__init__()
+        self.proj_in = nn.Linear(d_model, pred_dim)
+        self.target_pe = nn.Parameter(torch.randn(1, max_patches, pred_dim) * 0.02)
+        layer = nn.TransformerEncoderLayer(
+            d_model=pred_dim, nhead=4, dim_feedforward=pred_dim * 2,
+            dropout=dropout, activation="gelu", batch_first=True, norm_first=True,
+        )
+        self.tf = nn.TransformerEncoder(layer, num_layers=2)
+        self.proj_out = nn.Linear(pred_dim, d_model)
+        self.norm = nn.LayerNorm(d_model)
+
+    def forward(self, ctx_latents, ctx_idx, tgt_idx):
+        """
+        ctx_latents: (B, N_ctx, d_model)
+        ctx_idx: (B, N_ctx) original positions of context patches
+        tgt_idx: (B, N_tgt) original positions of target patches
+        returns: (B, N_tgt, d_model) predicted target latents
+        """
+        B = ctx_latents.size(0)
+        d_pred = self.target_pe.size(-1)
+        # Project context to pred_dim and add target positional embeddings
+        ctx_p = self.proj_in(ctx_latents)  # (B, N_ctx, d_pred)
+        # Gather target PEs at the masked positions
+        tgt_pe = self.target_pe.expand(B, -1, -1)  # (B, max_patches, d_pred)
+        tgt_idx_expanded = tgt_idx.unsqueeze(-1).expand(-1, -1, d_pred)
+        tgt_q = torch.gather(tgt_pe, 1, tgt_idx_expanded)  # (B, N_tgt, d_pred)
+        # Concatenate and run transformer
+        h = torch.cat([ctx_p, tgt_q], dim=1)  # (B, N_ctx+N_tgt, d_pred)
+        h = self.tf(h)
+        # Take only the target-position outputs
+        N_ctx = ctx_p.size(1)
+        h_tgt = h[:, N_ctx:]
+        return self.norm(self.proj_out(h_tgt))
+
+
+def make_block_mask(B: int, N: int, mask_ratio: float, rng: np.random.Generator,
+                     device, min_visible: int = 4):
+    """
+    Sample a contiguous block mask per batch element.
+    Returns:
+        ctx_idx: list of LongTensors (variable length per sample)
+        tgt_idx: list of LongTensors
+    For batched processing we'll right-pad and provide separate masks.
+    """
+    ctx_idxs = []
+    tgt_idxs = []
+    for _ in range(B):
+        n_mask = max(1, int(round(N * mask_ratio)))
+        n_mask = min(n_mask, N - min_visible)
+        # Random contiguous block start
+        if N - n_mask <= 0:
+            start = 0
+            n_mask = N - min_visible
+        else:
+            start = int(rng.integers(0, N - n_mask + 1))
+        all_idx = np.arange(N)
+        tgt_mask = (all_idx >= start) & (all_idx < start + n_mask)
+        ctx_idxs.append(torch.tensor(all_idx[~tgt_mask], dtype=torch.long, device=device))
+        tgt_idxs.append(torch.tensor(all_idx[tgt_mask], dtype=torch.long, device=device))
+    return ctx_idxs, tgt_idxs
+
+
+def gather_by_indices(x: torch.Tensor, idx_list: list[torch.Tensor], pad_value=0.0):
+    """x: (B, N, D). idx_list: per-batch index tensors. Returns (B, N_max, D) padded
+    plus a (B, N_max) mask of which entries are real."""
+    B = x.size(0); D = x.size(-1)
+    N_max = max((idx.numel() for idx in idx_list), default=1)
+    out = torch.full((B, N_max, D), pad_value, device=x.device, dtype=x.dtype)
+    mask = torch.zeros((B, N_max), dtype=torch.bool, device=x.device)
+    for b in range(B):
+        idx = idx_list[b]
+        n = idx.numel()
+        if n > 0:
+            out[b, :n] = x[b, idx]
+            mask[b, :n] = True
+    return out, mask
+
+
+def gather_indices_only(idx_list: list[torch.Tensor], device):
+    """Pack a list of LongTensors into (B, N_max) padded with zeros."""
+    B = len(idx_list)
+    N_max = max((idx.numel() for idx in idx_list), default=1)
+    out = torch.zeros((B, N_max), dtype=torch.long, device=device)
+    mask = torch.zeros((B, N_max), dtype=torch.bool, device=device)
+    for b in range(B):
+        idx = idx_list[b]
+        n = idx.numel()
+        if n > 0:
+            out[b, :n] = idx
+            mask[b, :n] = True
+    return out, mask
+
+
+# ============================================================================
+# PRETRAIN MODULE
+# ============================================================================
+
+class FlightJEPAPretrain(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+        d = cfg.get("d_model", 256)
+        h_ = cfg.get("n_heads", 8)
+        n_l = cfg.get("n_layers", 4)
+        d_ff = cfg.get("d_ff", 1024)
+        dr = cfg.get("dropout", 0.1)
+        ps = cfg.get("patch_size", 8)
+        past_max = cfg.get("past_max", 256)
+        max_patches = past_max // ps
+        pred_dim = cfg.get("pred_dim", 128)
+        self.ema_decay = cfg.get("ema_decay", 0.998)
+        self.max_patches = max_patches
+
+        self.tokenizer = PatchTokenizer(9, d, ps, max_patches)
+        self.encoder = CausalEncoder(d, h_, n_l, d_ff, dr)
+        self.predictor = JEPAPredictor(d, pred_dim, max_patches, dr)
+
+        self.target_tokenizer = copy.deepcopy(self.tokenizer)
+        self.target_encoder = copy.deepcopy(self.encoder)
+        for p in self.target_tokenizer.parameters():
+            p.requires_grad = False
+        for p in self.target_encoder.parameters():
+            p.requires_grad = False
+
+    @torch.no_grad()
+    def update_ema(self):
+        m = self.ema_decay
+        for online, target in [(self.tokenizer, self.target_tokenizer),
+                                (self.encoder, self.target_encoder)]:
+            for po, pt in zip(online.parameters(), target.parameters()):
+                pt.data.mul_(m).add_(po.data, alpha=1.0 - m)
+
+    def forward(self, features, lengths, mask_ratio: float, rng: np.random.Generator):
+        """
+        Mask a contiguous block of patches per sample. Encode visible context.
+        Predict masked-patch latents. Compare to EMA target encoder over the
+        full sequence at masked positions.
+
+        Pretraining uses *bidirectional* attention (no causal mask) — at
+        fine-tune time we restore the causal mask. This gives the encoder
+        more signal during pretraining; the encoder's transformer layers are
+        not architecturally causal, only the mask passed in changes the mode.
+        """
+        B = features.size(0)
+        device = features.device
+
+        # Tokenize for online (will be partially masked) and target (full).
+        patches_full, patch_lens = self.tokenizer(features, lengths)
+        N = patches_full.size(1)
+
+        # Padding mask (token absent because past sequence shorter than N*patch).
+        pad_mask = (torch.arange(N, device=device).unsqueeze(0)
+                    >= patch_lens.unsqueeze(1))  # True where padded
+
+        # Sample contiguous masks per sample (drawing only from valid patches).
+        ctx_idx_list, tgt_idx_list = [], []
+        for b in range(B):
+            n_valid = int(patch_lens[b].item())
+            if n_valid < 8:  # too short to mask meaningfully
+                ctx_idx_list.append(torch.arange(n_valid, device=device))
+                tgt_idx_list.append(torch.tensor([], dtype=torch.long, device=device))
+                continue
+            n_mask = max(2, int(round(n_valid * mask_ratio)))
+            n_mask = min(n_mask, n_valid - 4)  # keep at least 4 visible
+            start = int(rng.integers(0, n_valid - n_mask + 1))
+            all_idx = torch.arange(n_valid, device=device)
+            tgt_mask = (all_idx >= start) & (all_idx < start + n_mask)
+            ctx_idx_list.append(all_idx[~tgt_mask])
+            tgt_idx_list.append(all_idx[tgt_mask])
+
+        # Skip if any batch element produced no targets (e.g., very short sequences)
+        n_targets = sum(int(t.numel()) for t in tgt_idx_list)
+        if n_targets == 0:
+            return torch.tensor(0.0, device=device, requires_grad=True)
+
+        # Pack context indices and gather context tokens. We pass through
+        # the same encoder with a key_padding_mask that hides the masked
+        # positions plus the original padding.
+        # Easier: re-run encoder on a *new* tensor consisting only of context
+        # tokens with bidirectional attention.
+        N_ctx_max = max((idx.numel() for idx in ctx_idx_list), default=1)
+        ctx_tokens = torch.zeros((B, N_ctx_max, patches_full.size(-1)),
+                                  device=device, dtype=patches_full.dtype)
+        ctx_kpm = torch.ones((B, N_ctx_max), dtype=torch.bool, device=device)  # True=pad
+        for b in range(B):
+            idx = ctx_idx_list[b]
+            n = idx.numel()
+            if n > 0:
+                ctx_tokens[b, :n] = patches_full[b, idx]
+                ctx_kpm[b, :n] = False
+
+        # Bidirectional attention for pretraining (full mask).
+        bi_mask = torch.zeros((N_ctx_max, N_ctx_max), dtype=torch.bool, device=device)
+        ctx_encoded = self.encoder(ctx_tokens, key_padding_mask=ctx_kpm,
+                                    attn_mask=bi_mask)
+
+        # Build target-index packed tensors
+        tgt_idx_packed, tgt_idx_mask = gather_indices_only(tgt_idx_list, device)
+        # Build dummy "context indices in original layout" — we need to tell
+        # the predictor where the context tokens live (their original patch
+        # positions). Add positional info to ctx through a side embedding —
+        # we can use the same target_pe table for context too.
+        # Simpler: encode their original position as a query-like input by
+        # *pre-adding* a positional token to ctx encoded representation.
+        # The predictor only needs target PEs — context already carries pos
+        # info via the patch tokenizer's pos_enc, so we don't need to add
+        # context indices.
+
+        # Predict target latents.
+        pred = self.predictor(ctx_encoded, ctx_idx=None, tgt_idx=tgt_idx_packed)
+        # Pad target predictions to N_tgt_max already done by gather_indices_only
+
+        # Targets: run the EMA target encoder on the *full* sequence
+        # (causal mask, like fine-tune time) and gather at target positions.
+        with torch.no_grad():
+            tgt_patches, _ = self.target_tokenizer(features, lengths)
+            tgt_encoded = self.target_encoder(tgt_patches, key_padding_mask=pad_mask)
+            tgt_latents, _ = gather_by_indices(tgt_encoded, tgt_idx_list)
+
+        # L1 loss in latent space, masked over valid targets
+        loss_per = F.l1_loss(pred, tgt_latents, reduction="none").mean(-1)  # (B, N_tgt_max)
+        loss = (loss_per * tgt_idx_mask.float()).sum() / tgt_idx_mask.sum().clamp(min=1)
+        return loss
+
+
+# ============================================================================
+# TRAIN LOOP
+# ============================================================================
+
+def device_pick(arg=None):
+    if arg:
+        return arg
+    if torch.cuda.is_available():
+        return "cuda"
+    if torch.backends.mps.is_available():
+        return "mps"
+    return "cpu"
+
+
+def train_one_epoch(model, loader, optimizer, device, mask_ratio_lo, mask_ratio_hi,
+                     log_every=50, grad_clip=1.0, rng=None):
+    model.train()
+    sums = {"loss": 0.0, "n": 0}
+    t0 = time.time()
+    rng = rng or np.random.default_rng()
+    n_batches = len(loader) if hasattr(loader, "__len__") else 0
+    for bi, batch in enumerate(loader):
+        feats = batch["features"].to(device)
+        lens = batch["length"].to(device)
+        mr = float(rng.uniform(mask_ratio_lo, mask_ratio_hi))
+        loss = model(feats, lens, mask_ratio=mr, rng=rng)
+        optimizer.zero_grad()
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
+        optimizer.step()
+        model.update_ema()
+        bs = feats.size(0)
+        sums["loss"] += loss.item() * bs
+        sums["n"] += bs
+        if (bi + 1) % log_every == 0 or bi == 0:
+            dt = time.time() - t0
+            print(f"  [batch {bi+1}/{n_batches}] {dt:.1f}s elapsed, "
+                  f"mr={mr:.2f}, loss={loss.item():.4f}", flush=True)
+    n = max(sums["n"], 1)
+    return {"loss": sums["loss"] / n}
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--airport", default="RKSIa")
+    p.add_argument("--data-dir", default="data")
+    p.add_argument("--tag", default="v7-pretrain")
+    p.add_argument("--out-dir", default="runs")
+    p.add_argument("--epochs", type=int, default=60)
+    p.add_argument("--batch-size", type=int, default=64)
+    p.add_argument("--lr", type=float, default=1.5e-4)
+    p.add_argument("--weight-decay", type=float, default=1e-4)
+    p.add_argument("--past-max", type=int, default=256)
+    p.add_argument("--past-min", type=int, default=128)
+    p.add_argument("--epoch-multiplier", type=int, default=2)
+    p.add_argument("--ema-decay", type=float, default=0.998)
+    p.add_argument("--d-model", type=int, default=256)
+    p.add_argument("--n-layers", type=int, default=4)
+    p.add_argument("--n-heads", type=int, default=8)
+    p.add_argument("--patch-size", type=int, default=8)
+    p.add_argument("--pred-dim", type=int, default=128)
+    p.add_argument("--mask-ratio-lo", type=float, default=0.3)
+    p.add_argument("--mask-ratio-hi", type=float, default=0.7)
+    p.add_argument("--seed", type=int, default=0)
+    p.add_argument("--num-workers", type=int, default=2)
+    p.add_argument("--push-to-hub", action="store_true")
+    p.add_argument("--hub-model-id", default=None)
+    p.add_argument("--trackio-name", default=None)
+    args = p.parse_args()
+
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+    rng = np.random.default_rng(args.seed)
+
+    device = device_pick()
+    print(f"[v7-pretrain] device={device} tag={args.tag}", flush=True)
+    if device == "cuda":
+        print(f"[v7-pretrain] cuda: {torch.cuda.get_device_name(0)} "
+              f"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.1f}GB",
+              flush=True)
+    if HAS_TRACKIO and args.trackio_name:
+        trackio.init(project="flight-jepa-v7-pretrain",
+                      name=args.trackio_name, config=vars(args))
+
+    train_ds = PastTrackDataset(
+        airport=args.airport, mode="TRAIN", data_dir=args.data_dir,
+        past_max=args.past_max, past_min=args.past_min,
+        seed=args.seed, epoch_multiplier=args.epoch_multiplier,
+    )
+    train_dl = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True,
+                           num_workers=args.num_workers, pin_memory=True,
+                           drop_last=True)
+
+    cfg = {
+        "d_model": args.d_model, "n_heads": args.n_heads,
+        "n_layers": args.n_layers, "d_ff": args.d_model * 4,
+        "dropout": 0.1, "patch_size": args.patch_size,
+        "past_max": args.past_max, "ema_decay": args.ema_decay,
+        "pred_dim": args.pred_dim,
+    }
+    model = FlightJEPAPretrain(cfg).to(device)
+    n_params = sum(p.numel() for p in model.parameters())
+    print(f"[v7-pretrain] params={n_params/1e6:.2f}M")
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr,
+                                   weight_decay=args.weight_decay)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
+
+    os.makedirs(args.out_dir, exist_ok=True)
+    history = []
+    for epoch in range(args.epochs):
+        t0 = time.time()
+        stats = train_one_epoch(
+            model, train_dl, optimizer, device,
+            mask_ratio_lo=args.mask_ratio_lo,
+            mask_ratio_hi=args.mask_ratio_hi,
+            rng=rng,
+        )
+        scheduler.step()
+        elapsed = time.time() - t0
+        print(f"[v7-pretrain] ep {epoch+1:03d} loss={stats['loss']:.4f} | {elapsed:.0f}s",
+              flush=True)
+        history.append({"epoch": epoch + 1, "loss": stats["loss"], "elapsed_s": elapsed})
+        if HAS_TRACKIO and args.trackio_name:
+            trackio.log({"pretrain/loss": stats["loss"]}, step=epoch + 1)
+
+    out_path = os.path.join(args.out_dir, f"{args.tag}.pt")
+    torch.save({
+        "encoder_state_dict": model.encoder.state_dict(),
+        "tokenizer_state_dict": model.tokenizer.state_dict(),
+        "config": cfg, "args": vars(args),
+        "history": history,
+    }, out_path)
+    print(f"[v7-pretrain] saved {out_path}")
+
+    if args.push_to_hub and args.hub_model_id:
+        try:
+            from huggingface_hub import HfApi
+            api = HfApi()
+            api.create_repo(args.hub_model_id, exist_ok=True)
+            api.upload_file(path_or_fileobj=out_path,
+                             path_in_repo=f"{args.tag}.pt",
+                             repo_id=args.hub_model_id)
+            print(f"[v7-pretrain] uploaded to {args.hub_model_id}")
+        except Exception as e:
+            print(f"[v7-pretrain] hub upload failed: {e}")
+
+    if HAS_TRACKIO and args.trackio_name:
+        trackio.finish()
+
+
+if __name__ == "__main__":
+    main()