validate SampledSoftmaxLoss args

mindspore/nn/loss/loss.py

@@ -284,7 +284,7 @@ class SampledSoftmaxLoss(_Loss):
             where a sampled class equals one of the target classes.  Default is True.
         seed (int): Random seed for candidate sampling. Default: 0
         reduction (str): Type of reduction to be applied to loss. The optional values are "mean", "sum", and "none".
-            If "none", do not perform reduction. Default: "None".
+            If "none", do not perform reduction. Default: "none".
 
     Inputs:
         - **weights** (Tensor) - Tensor of shape (C, dim).
@@ -311,7 +311,22 @@ class SampledSoftmaxLoss(_Loss):
     def __init__(self, num_sampled, num_classes, num_true=1,
                  sampled_values=None, remove_accidental_hits=True, seed=0,
                  reduction='none'):
-        super(SampledSoftmaxLoss, self).__init__()
+        super(SampledSoftmaxLoss, self).__init__(reduction)
+
+        if num_true < 1:
+            raise ValueError(f"num_true {num_true} is less than 1.")
+        if seed < 0:
+            raise ValueError(f"seed {seed} is less than 0.")
+        if num_sampled > num_classes:
+            raise ValueError(f"num_sampled {num_sampled} is great than num_classes {num_classes}.")
+        if num_true > num_classes:
+            raise ValueError(f"num_true {num_true} is great than num_classes {num_classes}.")
+        if sampled_values is not None:
+            if not isinstance(sampled_values, (list, tuple)):
+                raise TypeError(f"sampled_values {sampled_values} is not a list.")
+            if len(sampled_values) != 3:
+                raise ValueError(f"sampled_values size {len(sampled_values)} is not 3.")
+
         self.num_sampled = num_sampled
         self.num_classes = num_classes
         self.num_true = num_true

tests/st/ops/gpu/test_sampled_softmax_loss_op.py

@@ -131,6 +131,36 @@ def test_sampled_softmax_loss_none_sampler():
     context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     case_no_sampler()
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sampledsoftmaxloss_reduction_invalid():
+    # Check 'reduction'
+    with pytest.raises(ValueError):
+        nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, reduction="")
+
+    with pytest.raises(ValueError):
+        nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, reduction="invalid")
+
+    # reduction can be None, as defined in _Loss
+    # with pytest.raises(ValueError):
+    #     nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, reduction=None)  #
+
+    # Check 'num_true'
+    with pytest.raises(ValueError):
+        nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, num_true=0)
+
+    # Check 'sampled_values'
+    with pytest.raises(ValueError):
+        sampled_values_more_para = (Tensor(np.array([1])), Tensor(np.array([1])),
+                                    Tensor(np.array([1])), Tensor(np.array([1])))
+        nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7,
+                              sampled_values=sampled_values_more_para)
+
+    with pytest.raises(TypeError):
+        sampled_values_wrong_type = Tensor(np.array([1]))
+        nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7,
+                              sampled_values=sampled_values_wrong_type)
 
 if __name__ == "__main__":
     test_sampled_softmax_loss_assigned_sampler()