Remove the labels range check under the dynamic graph

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

@@ -1465,34 +1465,6 @@ class TestCrossEntropyFAPIError(unittest.TestCase):
 
             self.assertRaises(ValueError, test_WeightLength_NotEqual)
 
-            def test_LabelValue_ExceedMax():
-                input_data = paddle.rand(shape=[20, 100])
-                label_data = paddle.randint(
-                    0, 100, shape=[20, 1], dtype="int64")
-                label_data[0] = 100
-                weight_data = paddle.rand([100])
-                paddle.nn.functional.cross_entropy(
-                    input=input_data,
-                    label=label_data,
-                    weight=weight_data,
-                    ignore_index=-100)
-
-            self.assertRaises(ValueError, test_LabelValue_ExceedMax)
-
-            def test_LabelValue_ExceedMin():
-                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=-100)
-
-            self.assertRaises(ValueError, test_LabelValue_ExceedMin)
-
             def static_test_WeightLength_NotEqual():
                 input_np = np.random.random([2, 4]).astype('float32')
                 label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)

python/paddle/nn/functional/loss.py

@@ -1665,26 +1665,6 @@ def cross_entropy(input,
     if input_dims - 1 == label_dims:
         label = paddle.unsqueeze(label, axis=axis)
     if in_dygraph_mode():
-        if soft_label == False:
-            valid_label = paddle.where(label == ignore_index,
-                                       paddle.zeros_like(label), label)
-            # TODO: Temporarily use paddle.nonzero instead of paddle.max 
-            # to detect and find out possible illegal label values
-            if len(paddle.nonzero(valid_label < 0)) > 0:
-                invalid_label = paddle.gather_nd(
-                    valid_label, paddle.nonzero(valid_label < 0))
-                raise ValueError(
-                    "Target({}) is out of class_dimension's lower bound({})".
-                    format(invalid_label[0], 0))
-            # TODO: Temporarily use paddle.nonzero instead of paddle.max 
-            # to detect and find out possible illegal label values
-            if len(paddle.nonzero(valid_label >= input.shape[axis])) > 0:
-                invalid_label = paddle.gather_nd(
-                    valid_label,
-                    paddle.nonzero(valid_label >= input.shape[axis]))
-                raise ValueError(
-                    "Target({}) is out of class_dimension's upper bound({})".
-                    format(invalid_label[0], input.shape[axis] - 1))
         if core.is_compiled_with_npu():
             _, _, out = _C_ops.softmax_with_cross_entropy(
                 input, label, 'soft_label', soft_label, 'ignore_index',
@@ -1716,6 +1696,25 @@ def cross_entropy(input,
                 out = _C_ops.elementwise_mul(out, weight_gather_reshape)
 
             else:
+                valid_label = paddle.where(label == ignore_index,
+                                        paddle.zeros_like(label), label)
+                # TODO: Temporarily use paddle.nonzero instead of paddle.max 
+                # to detect and find out possible illegal label values
+                if len(paddle.nonzero(valid_label < 0)) > 0:
+                    invalid_label = paddle.gather_nd(
+                        valid_label, paddle.nonzero(valid_label < 0))
+                    raise ValueError(
+                        "Target({}) is out of class_dimension's lower bound({})".
+                        format(invalid_label[0], 0))
+                # TODO: Temporarily use paddle.nonzero instead of paddle.max 
+                # to detect and find out possible illegal label values
+                if len(paddle.nonzero(valid_label >= input.shape[axis])) > 0:
+                    invalid_label = paddle.gather_nd(
+                        valid_label,
+                        paddle.nonzero(valid_label >= input.shape[axis]))
+                    raise ValueError(
+                        "Target({}) is out of class_dimension's upper bound({})".
+                        format(invalid_label[0], input.shape[axis] - 1))
                 if input.shape[axis] != weight.shape[-1]:
                     raise ValueError(
                         "input's class_dimension({}) must equal to "