!10307 add type check support to sampledsoftmaxloss

From: @TFbunny
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/nn/loss/loss.py

@@ -281,6 +281,10 @@ class SoftmaxCrossEntropyWithLogits(_Loss):
         x = self.softmax_cross_entropy(logits, labels)[0]
         return self.get_loss(x)
 
+@constexpr
+def _check_label_dtype(labels_dtype, cls_name):
+    validator.check_type_name("labels", labels_dtype, [mstype.int32, mstype.int64], cls_name)
+
 
 class SampledSoftmaxLoss(_Loss):
     r"""
@@ -373,8 +377,11 @@ class SampledSoftmaxLoss(_Loss):
         self.zeros_like = P.ZerosLike()
         self.mul = P.Mul()
         self.expand_dims = P.ExpandDims()
+        self.dtype = P.DType()
 
     def construct(self, weights, biases, labels, inputs):
+        _check_label_dtype(self.dtype(labels), self.cls_name)
+
         logits, labels = self._compute_sampled_logits(
             weights=weights,
             biases=biases,
@@ -424,6 +431,7 @@ class SampledSoftmaxLoss(_Loss):
                 `[batch_size, num_true + num_sampled]`
             out_labels: A Tensor object with the same shape as `out_logits`.
         """
+
         if not labels.dtype == mstype.int32:
             labels = self.cast(labels, mstype.int32)
         labels = self.reshape(labels, (-1, num_true))