fix NoRepeatNGram example

mindspore/ops/operations/inner_ops.py

@@ -155,19 +155,28 @@ class NoRepeatNGram(PrimitiveWithInfer):
     Examples:
         >>> no_repeat_ngram = ops.NoRepeatNGram(ngram_size=3)
         >>> state_seq = Tensor([[[1, 2, 1, 2, 5, 1, 2],
-                                 [9, 3, 9, 5, 4, 1, 5]],
-                                [[4, 8, 6, 4, 5, 6, 4],
-                                 [4, 8, 8, 4, 3, 4, 8]]], dtype=mindspore.int32)
-        >>> log_probs = Tensor([[[0.75858542, 0.8437121 , 0.69025469, 0.79379992, 0.27400691,
-                                  0.84709179, 0.78771346, 0.68587179, 0.22943851, 0.17682976]],
-                                [[0.99401879, 0.77239773, 0.81973878, 0.32085208, 0.59944118,
-                                  0.3125177, 0.52604189, 0.77111461, 0.98443699, 0.71532898]]], dtype=mindspore.float32)
+        ...                      [9, 3, 9, 5, 4, 1, 5]],
+        ...                     [[4, 8, 6, 4, 5, 6, 4],
+        ...                      [4, 8, 8, 4, 3, 4, 8]]], dtype=mindspore.int32)
+        >>> log_probs = Tensor([[[0.7, 0.8, 0.6, 0.9, 0.2, 0.8, 0.4, 0.6, 0.2, 0.7],
+        ...                      [0.4, 0.5, 0.6, 0.7, 0.8, 0.1, 0.9, 0.8, 0.7, 0.1]],
+        ...                     [[0.9, 0.7, 0.6, 0.3, 0.5, 0.3, 0.5, 0.4, 0.8, 0.6],
+        ...                      [0.5, 0.8, 0.8, 0.7, 0.7, 0.8, 0.2, 0.7, 0.9, 0.7]]], dtype=mindspore.float32)
         >>> output = no_repeat_ngram(state_seq, log_probs)
         >>> print(output)
-        [[[0.75858542 -3.4028235e+38 0.69025469 0.79379992 0.27400691
-           -3.4028235e+38 0.78771346 0.68587179 0.22943851 0.17682976]]
-         [[0.99401879 0.77239773 0.81973878 0.32085208 0.59944118
-           -3.4028235e+38 0.52604189 0.77111461 0.98443699 0.71532898]]]
+        [[[ 6.9999999e-01 -3.4028235e+38  6.0000002e-01  8.9999998e-01
+            2.0000000e-01 -3.4028235e+38  4.0000001e-01  6.0000002e-01
+            2.0000000e-01  6.9999999e-01]
+          [ 4.0000001e-01  5.0000000e-01  6.0000002e-01  6.9999999e-01
+            8.0000001e-01  1.0000000e-01  8.9999998e-01  8.0000001e-01
+            6.9999999e-01  1.0000000e-01]]
+
+         [[ 8.9999998e-01  6.9999999e-01  6.0000002e-01  3.0000001e-01
+            5.0000000e-01 -3.4028235e+38  5.0000000e-01  4.0000001e-01
+            8.0000001e-01  6.0000002e-01]
+          [ 5.0000000e-01  8.0000001e-01  8.0000001e-01  6.9999999e-01
+            6.9999999e-01  8.0000001e-01  2.0000000e-01  6.9999999e-01
+           -3.4028235e+38  6.9999999e-01]]]
     """
 
     @prim_attr_register
@@ -179,11 +188,11 @@ class NoRepeatNGram(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['state_seq', 'log_probs'], outputs=['log_probs'])
 
     def infer_shape(self, seq_shape, log_shape):
-        validator.check_int(len(seq_shape), 3, Rel.EQ, "rank_of_seq", self.name)
-        validator.check_int(len(log_shape), 3, Rel.EQ, "rank_of_log", self.name)
-        validator.check_int(seq_shape[0], log_shape[0], Rel.EQ, "seq_shape shape[0]", self.name)
-        validator.check_int(seq_shape[1], log_shape[1], Rel.EQ, "seq_shape shape[1]", self.name)
-        validator.check_int(self.ngram_size, seq_shape[2] + 1, Rel.LE, "ngram_size", self.name)
+        validator.check_int(len(seq_shape), 3, Rel.EQ, "rank of state_seq", self.name)
+        validator.check_int(len(log_shape), 3, Rel.EQ, "rank of log_probs", self.name)
+        validator.check("state_seq shape[0]", seq_shape[0], "log_probs shape[0]", log_shape[0], Rel.EQ, self.name)
+        validator.check("state_seq shape[1]", seq_shape[1], "log_probs shape[1]", log_shape[1], Rel.EQ, self.name)
+        validator.check("ngram_size", self.ngram_size, "state_seq shape[2] + 1", seq_shape[2] + 1, Rel.LE, self.name)
         return log_shape
 
     def infer_dtype(self, seq_type, log_type):