diff --git a/extra/onnx.py b/extra/onnx.py
index ae0d6a2e96..7c5515edbe 100644
--- a/extra/onnx.py
+++ b/extra/onnx.py
@@ -125,29 +125,29 @@ class OnnxRunner:
 
     self.onnx_ops = onnx_ops
 
-  def _is_valid(self, value: Tensor | list, spec: OnnxValue):
+  def _is_valid(self, name: str, value: Tensor | list, spec: OnnxValue):
     if isinstance(value, list):
       assert spec.is_sequence
-      if not all_same(tuple(v.shape for v in value)): raise RuntimeError
-      return value
-    for onnx_dim, user_dim_input in zip(spec.shape, value.shape, strict=True):
-      if isinstance(onnx_dim, str):
-        onnx_dim = self.variable_dims[onnx_dim] if onnx_dim in self.variable_dims else self.variable_dims.setdefault(onnx_dim, int(user_dim_input))
-      if user_dim_input != onnx_dim: raise RuntimeError
+      if not all_same(tuple(v.shape for v in value)): raise RuntimeError(f"sequence '{name}' must have homogenous shape. Received {value}.")
+    else:
+      for dim, (onnx_dim, value_dim) in enumerate(zip(spec.shape, value.shape, strict=True)):
+        if isinstance(onnx_dim, str):
+          onnx_dim = self.variable_dims[onnx_dim] if onnx_dim in self.variable_dims else self.variable_dims.setdefault(onnx_dim, int(value_dim))
+        if value_dim != onnx_dim: raise RuntimeError(f"tensor '{name}' has mismatch on {dim=}. Expected {onnx_dim}, received {value_dim}.")
     return value
 
   def _parse_input(self, name: str, value: Any, spec: OnnxValue):
     if spec.is_optional and value is None: return None
     elif spec.is_sequence:
-      if not isinstance(value, Sequence): raise RuntimeError(f"input '{name}' received {value}, expected a sequence type")
+      if not isinstance(value, Sequence): raise RuntimeError(f"'{name}' received {value}, expected a sequence type.")
       # sequence inputs and outputs are interpreted in OnnxRunner as list type
       value = [Tensor(v, dtype=spec.dtype, requires_grad=self.is_training) if not isinstance(v, Tensor) else v for v in value]
     elif not isinstance(value, Tensor): value = Tensor(value, dtype=spec.dtype, requires_grad=self.is_training)
-    return self._is_valid(value, spec)
+    return self._is_valid(name, value, spec)
 
   def _parse_output(self, name: str):
     value, spec = self.graph_values[name], self.graph_outputs[name]
-    if isinstance(value, (Tensor, list)): return self._is_valid(value, spec)
+    if isinstance(value, (Tensor, list)): return self._is_valid(name, value, spec)
     return value
 
   def _dispatch_op(self, op, inps, opts):