From 5d970b586bd2a0b0c07de2033f6da5a782e22ce4 Mon Sep 17 00:00:00 2001
From: zhupengyang <zhu_py@qq.com>
Date: Fri, 10 Apr 2020 11:55:09 +0800
Subject: [PATCH] Op(leaky_relu) error message enhancement (#23627)

---
 python/paddle/fluid/layers/nn.py                   |  3 +++
 .../fluid/tests/unittests/test_activation_op.py    | 14 ++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index aece48f5a71..ca84ffe5f72 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -9238,6 +9238,9 @@ def leaky_relu(x, alpha=0.02, name=None):
     if in_dygraph_mode():
         return core.ops.leaky_relu(x, 'alpha', alpha)
 
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
+                             'leaky_relu')
+
     inputs = {'X': [x]}
     attrs = {'alpha': alpha}
     helper = LayerHelper('leaky_relu', **locals())
diff --git a/python/paddle/fluid/tests/unittests/test_activation_op.py b/python/paddle/fluid/tests/unittests/test_activation_op.py
index d37bb3e81cf..033e1dafc1e 100644
--- a/python/paddle/fluid/tests/unittests/test_activation_op.py
+++ b/python/paddle/fluid/tests/unittests/test_activation_op.py
@@ -450,6 +450,20 @@ class TestLeakyRelu(TestActivation):
         self.check_grad(['X'], 'Out')
 
 
+class TestLeakyReluOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program()):
+            # The input type must be Variable.
+            self.assertRaises(TypeError, fluid.layers.leaky_relu, 1)
+            # The input dtype must be float16, float32, float64.
+            x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
+            self.assertRaises(TypeError, fluid.layers.leaky_relu, x_int32)
+            # support the input dtype is float32
+            x_fp16 = fluid.layers.data(
+                name='x_fp16', shape=[12, 10], dtype='float32')
+            fluid.layers.leaky_relu(x_fp16)
+
+
 def gelu(x, approximate):
     if approximate:
         y_ref = 0.5 * x * (1.0 + np.tanh(
-- 
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