Persist global_step across --resume so W&B step stays monotonic

Previously global_step always reset to 0, even on --resume. Since the
W&B run reattaches to the same run id on resume, logging with
step=global_step after a restart passed step values below what was
already recorded, silently dropping the resumed portion's metrics.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

giant/train.py

@@ -483,6 +483,7 @@ def train(
 
     start_epoch = 1
     best_val_loss = float("inf")
+    resumed_global_step = 0
     if resume_path is not None:
         ckpt = torch.load(resume_path, map_location=device, weights_only=False)
         stage1_model.load_state_dict(ckpt["model"])
@@ -506,6 +507,7 @@ def train(
         lr_sched.load_state_dict(ckpt["lr_sched"])
         start_epoch = ckpt.get("epoch", 0) + 1
         best_val_loss = ckpt.get("best_val_loss", float("inf"))
+        resumed_global_step = ckpt.get("global_step", 0)
 
         # optimizer/lr_sched.load_state_dict() above restore the checkpoint's
         # own base LR, which would otherwise silently override an explicit
@@ -535,7 +537,11 @@ def train(
 
     epoch_w = len(str(epochs))
     last_completed_epoch = start_epoch - 1
-    global_step = 0
+    # Restored from the checkpoint on --resume so wandb_run.log(..., step=...)
+    # keeps advancing monotonically instead of restarting at 0 mid-run (a
+    # reattached wandb run — see wandb.init(id=..., resume="allow") below —
+    # would otherwise silently drop every post-resume point).
+    global_step = resumed_global_step
     with _GracefulShutdown() as shutdown:
         for epoch in range(start_epoch, epochs + 1):
             epoch_start = time.monotonic()
@@ -950,6 +956,7 @@ def train(
                 "lr_sched": lr_sched.state_dict(),
                 "epoch": epoch,
                 "best_val_loss": best_val_loss,
+                "global_step": global_step,
             }
             if mode == "wgan":
                 assert (