diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py
index aee0366ab3093bb2baed4ff402e9a1d1239987e0..f571f5d30285f3d08a0f1525197aae449b50a46c 100755
--- a/python/paddle/nn/functional/loss.py
+++ b/python/paddle/nn/functional/loss.py
@@ -1665,6 +1665,27 @@ def cross_entropy(input,
     if input_dims - 1 == label_dims:
         label = paddle.unsqueeze(label, axis=axis)
     if in_dygraph_mode():
+        if not soft_label:
+            valid_label = paddle.cast(
+                label != ignore_index, dtype=label.dtype) * 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',
@@ -1681,7 +1702,7 @@ def cross_entropy(input,
             # trans weight from class to sample, shape:N or [N,H,W] for 1d and 2d cases.
             if soft_label == True:
                 # chajchaj:
-                # weight's shape is C, where C is class num. 
+                # weight's shape is C, where C is class num.
                 # for 1d case: label's shape is [N,C], weight_gather's shape is N.
                 # for 2d case: label's shape is [N,H,W,C], weight_gather's shape is [N,H,W].
                 weight_gather = paddle.matmul(
@@ -1703,32 +1724,8 @@ def cross_entropy(input,
                         "when weight is provided" \
                             .format(input.shape[axis], weight.shape[-1]))
 
-                ignore_weight_mask = (
-                    label != ignore_index)  # ignored position will be False
-
-                valid_label = paddle.cast(
-                    ignore_weight_mask,
-                    dtype=label.dtype) * label  # ignored position will be 0
-
-                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))
-
-                ignore_weight_mask = paddle.cast(
-                    ignore_weight_mask, out.dtype)  # convert from 0 to 0.0
-
+                ignore_weight_mask = paddle.cast((label != ignore_index),
+                                                 out.dtype)
                 if ignore_weight_mask.ndim > 1 and ignore_weight_mask.shape[
                         axis] == 1:
                     # TODO: Temporarily use squeeze instead of squeeze_
@@ -1842,32 +1839,12 @@ def cross_entropy(input,
                                  "when weight is provided" \
                                  .format(input.shape[axis], weight.shape[-1]))
 
-            ignore_weight_mask = (
-                label != ignore_index)  # ignored position will be False
-
-            valid_label = paddle.cast(
-                ignore_weight_mask,
-                dtype=label.dtype) * label  # ignored position will be 0
-
-            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))
-
-            ignore_weight_mask = paddle.cast(ignore_weight_mask,
-                                             out.dtype)  # convert from 0 to 0.0
+            valid_label = paddle.multiply(
+                paddle.cast(
+                    label != ignore_index, dtype=label.dtype), label)
 
+            ignore_weight_mask = paddle.cast((label != ignore_index),
+                                             input.dtype)
             if ignore_weight_mask.ndim > 1 and ignore_weight_mask.shape[
                     axis] == 1:
                 ignore_weight_mask = paddle.squeeze(ignore_weight_mask, axis)