ms_adapter/pytorch/nn/functional.py

@@ -2144,7 +2144,10 @@ def layer_norm(inputs, normalized_shape, weight=None, bias=None, eps=1e-05):
         bias = cast_to_ms_tensor(bias)
     else:
         bias = ms.Tensor(np.zeros(normalized_shape), inputs.dtype)
-    assert inputs.shape[-len(normalized_shape):] == normalized_shape
+
+    if inputs.shape[-len(normalized_shape):] != normalized_shape:
+        raise ValueError("For layer_norm, normalized_shape should fit inputs' shape"
+                         f"but got input_shape: {inputs.shape}, normalized_shape: {normalized_shape}")
     _layer_norm = ms.ops.LayerNorm(epsilon=eps)
     out = _layer_norm(inputs, weight, bias)
     return cast_to_adapter_tensor(out[0])

ms_adapter/pytorch/nn/modules/loss.py

@@ -203,7 +203,9 @@ class MultiMarginLoss(_WeightedLoss):
         super(MultiMarginLoss, self).__init__(weight, size_average, reduce, reduction)
         if p not in (1, 2):
             raise ValueError("only p == 1 and p == 2 supported")
-        assert weight is None or weight.dim() == 1
+        if weight is not None and weight.dim() != 1:
+            raise ValueError(f"For MultiMarginLoss, `weight` must be 1-D, but got {weight.dim()}-D.")
+
         self.p = p
         self.margin = margin