feat: add grad_snr metric with EMA-based gradient SNR tracking

- add GradSNRTracker to metric_util.py computing SNR = E[g]^2 / Var(g) via per-parameter EMA moments
- add grad_snr_tracker field to TrainContext (instantiated by default)
- register grad_snr in MetricCallback, update tracker on each optimizer step before metrics are recorded
- add grad_snr to default --metrics in train.py CLI

scripts/tools/train.py

@@ -369,7 +369,7 @@ _START_METHODS = ["spawn", "fork", "forkserver"]
 @opt(
     "--metrics",
     multiple=True,
-    default=("loss", "lr", "grad_norm"),
+    default=("loss", "lr", "grad_norm", "grad_snr"),
     group="Validation",
     help="Metrics to log (repeatable).",
 )