fix ce error message, test=release/2.1 (#32758)

python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py

@@ -20,6 +20,7 @@ import numpy as np
 import unittest
 from test_softmax_op import stable_softmax
 from test_softmax_with_cross_entropy_op import cross_entropy
+from paddle.fluid import Program, program_guard
 
 
 def stable_softmax(x):
@@ -1363,5 +1364,37 @@ class CrossEntropyLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_ret_value, expected))
 
 
+class TestCrossEntropyFAPIError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+
+            def test_LabelValue():
+                input_data = paddle.rand(shape=[20, 100])
+                label_data = paddle.randint(
+                    0, 100, shape=[20, 1], dtype="int64")
+                label_data[0] = 255
+                weight_data = paddle.rand([100])
+                paddle.nn.functional.cross_entropy(
+                    input=input_data,
+                    label=label_data,
+                    weight=weight_data,
+                    ignore_index=255)
+
+            self.assertRaises(ValueError, test_LabelValue)
+
+            def test_LabelValueNeg():
+                input_data = paddle.rand(shape=[20, 100])
+                label_data = paddle.randint(
+                    0, 100, shape=[20, 1], dtype="int64")
+                label_data[0] = -1
+                weight_data = paddle.rand([100])
+                paddle.nn.functional.cross_entropy(
+                    input=input_data,
+                    label=label_data,
+                    weight=weight_data,
+                    ignore_index=-1)
+
+            self.assertRaises(ValueError, test_LabelValueNeg)
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/nn/functional/loss.py

@@ -1411,6 +1411,13 @@ def cross_entropy(input,
                 out = core.ops.elementwise_mul(out, weight_gather_reshape)
 
             else:
+                label_min = paddle.min(label)
+                label_max = paddle.max(label)
+                if label_min < 0 or label_max >= input.shape[-1]:
+                    raise ValueError(
+                        'Expected 0 <= label_value < class_dimension({}), but got {} <= label_value <= {} '.
+                        format(input.shape[-1],
+                               label_min.numpy(), label_max.numpy()))
                 weight_gather = core.ops.gather_nd(weight, label)
                 input_shape = list(label.shape)
                 weight_gather_reshape = reshape(