diff --git a/icefall/diagnostics.py b/icefall/diagnostics.py
index 37872f2331..e5eaba619e 100644
--- a/icefall/diagnostics.py
+++ b/icefall/diagnostics.py
@@ -63,12 +63,22 @@ def get_tensor_stats(
         "rms" -> square before summing, we'll take sqrt later
         "value"  -> just sum x itself
         "max", "min" -> take the maximum or minimum [over all other dims but dim] instead of summing
+        "rms-sort" -> this is a bit different than the others, it's based on computing the
+             rms over the specified dim and returning percentiles of the result (11 of them).
     Returns:
       stats: a Tensor of shape (x.shape[dim],).
       count: an integer saying how many items were counted in each element
       of stats.
     """
 
+    if stats_type == "rms-sort":
+        rms = (x**2).mean(dim=dim).sqrt()
+        rms = rms.flatten()
+        rms = rms.sort()[0]
+        rms = rms[(torch.arange(11) * rms.numel() // 10).clamp(max=rms.numel() - 1)]
+        count = 1.0
+        return rms, count
+
     count = x.numel() // x.shape[dim]
 
     if stats_type == "eigs":
@@ -164,7 +174,17 @@ def accumulate(self, x, class_name: Optional[str] = None):
         for dim in range(ndim):
             this_dim_stats = self.stats[dim]
             if ndim > 1:
-                stats_types = ["abs", "max", "min", "positive", "value", "rms"]
+                # rms-sort is different from the others, it's based on summing over just this
+                # dim, then sorting and returning the percentiles.
+                stats_types = [
+                    "abs",
+                    "max",
+                    "min",
+                    "positive",
+                    "value",
+                    "rms",
+                    "rms-sort",
+                ]
                 if x.shape[dim] <= self.opts.max_eig_dim:
                     stats_types.append("eigs")
             else: