add type promotion (#27756)

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

@@ -115,5 +115,20 @@ def test_kl_loss_static_api(self):
         pred_loss = paddle.nn.functional.kl_div(input, label)
 
 
+class TestKLDivLossTypePromotion(unittest.TestCase):
+    def test_kl_div_promotion(self):
+
+        with paddle.fluid.dygraph.guard():
+            x1 = paddle.rand([5, 20], dtype='float32')
+            target1 = paddle.rand([5, 20], dtype='float64')
+
+            kldiv_criterion = paddle.nn.KLDivLoss()
+            pred_loss1 = kldiv_criterion(x1, target1)
+
+            x2 = paddle.rand([5, 20], dtype='float64')
+            target2 = paddle.rand([5, 20], dtype='float32')
+            pred_loss2 = paddle.nn.functional.kl_div(x2, target2)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/nn/functional/loss.py

@@ -800,6 +800,16 @@ def kl_div(input, label, reduction='mean', name=None):
             # shape=[5, 20]
 
     """
+    # ugly type promotion
+    if fluid.data_feeder.convert_dtype(
+            input.dtype) == 'float32' and fluid.data_feeder.convert_dtype(
+                label.dtype) == 'float64':
+        input = fluid.layers.cast(input, 'float64')
+    elif fluid.data_feeder.convert_dtype(
+            input.dtype) == 'float64' and fluid.data_feeder.convert_dtype(
+                label.dtype) == 'float32':
+        label = fluid.layers.cast(label, 'float64')
+
     if paddle.in_dynamic_mode():
         out = core.ops.kldiv_loss(input, label, 'reduction', reduction)
         return out