Print indexes of largest grad

egs/librispeech/ASR/zipformer/optim.py

@@ -633,18 +633,37 @@ def _show_param_with_unusual_grad(
         """
         largest_ratio = 0.0
         largest_name = ""
+        # ratios_names is a list of 3-tuples: (grad_ratio, param_name, tensor)
+        ratios_names = []
         for (p, state, batch_param_names) in tuples:
             dims = list(range(1, p.ndim))
-            grad_ratio = (p.grad**2).mean(dim=dims) / state["exp_avg_sq"].mean(
-                dim=dims
+
+            def mean(x):
+                # workaround for bad interface of torch's "mean" for when dims is the empty list.
+                if len(dims) > 0:
+                    return x.mean(dim=dims)
+                else:
+                    return x
+
+            grad_ratio = (
+                (mean(p.grad**2) / state["exp_avg_sq"].mean(dim=dims))
+                .sqrt()
+                .to("cpu")
+            )
+
+            ratios_names += zip(
+                grad_ratio.tolist(), batch_param_names, p.grad.unbind(dim=0)
             )
-            max_grad_ratio, max_index = grad_ratio.to("cpu").max(dim=0)
-            if max_grad_ratio.item() > largest_ratio:
-                largest_ratio = max_grad_ratio.item()
-                largest_name = batch_param_names[max_index.item()]
+
+        ratios_names = sorted(ratios_names, reverse=True)
+        ratios_names = ratios_names[:10]
+        ratios_names = [
+            (ratio, name, largest_index(tensor))
+            for (ratio, name, tensor) in ratios_names
+        ]
+
         logging.warning(
-            f"Parameter with most larger-than-usual grad is {largest_name}, with ratio (cur_grad / normal_grad) of "
-            f"{largest_ratio ** 0.5}"
+            f"Parameters with most larger-than-usual grads, with ratios, are: {ratios_names}"
         )
 
     def _show_gradient_dominating_parameter(
@@ -714,6 +733,12 @@ def _show_gradient_dominating_parameter(
         )
 
 
+def largest_index(x: Tensor):
+    x = x.contiguous()
+    argmax = x.abs().argmax().item()
+    return [(argmax // x.stride(i)) % x.size(i) for i in range(x.ndim)]
+
+
 class LRScheduler(object):
     """
     Base-class for learning rate schedulers where the learning-rate depends on both the