diff --git a/python/paddle/fluid/tests/unittests/test_pool3d_api.py b/python/paddle/fluid/tests/unittests/test_pool3d_api.py
index e1d7543e7bce0dd5374faa29cfdee4859dfdc598..2c069b9e844913718a92dee457eda8081d4a5ce4 100644
--- a/python/paddle/fluid/tests/unittests/test_pool3d_api.py
+++ b/python/paddle/fluid/tests/unittests/test_pool3d_api.py
@@ -366,6 +366,32 @@ class TestPool3D_API(unittest.TestCase):
             self.check_max_dygraph_ndhwc_results(place)
             self.check_max_dygraph_ceilmode_results(place)
 
+    def test_static_pf16_gpu(self):
+        if paddle.fluid.core.is_compiled_with_cuda():
+            place = paddle.CUDAPlace(0)
+            with paddle.static.program_guard(
+                paddle.static.Program(), paddle.static.Program()
+            ):
+                input = np.random.random([1, 2, 3, 32, 32]).astype("float16")
+
+                x = paddle.static.data(
+                    name="x", shape=[1, 2, 3, 32, 32], dtype="float16"
+                )
+
+                m = paddle.nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
+                y = m(x)
+
+                exe = paddle.static.Executor(place)
+                res = exe.run(
+                    paddle.static.default_main_program(),
+                    feed={
+                        "x": input,
+                    },
+                    fetch_list=[y],
+                )
+
+                assert np.array_equal(res[0].shape, [1, 2, 1, 16, 16])
+
 
 class TestPool3DError_API(unittest.TestCase):
     def test_error_api(self):
diff --git a/python/paddle/nn/functional/pooling.py b/python/paddle/nn/functional/pooling.py
index 03e7f202c531a6613543920ea07896b6a4e1d1ba..d4a1e3c1456b9b711ea1ba7c090fffa7b30e341b 100755
--- a/python/paddle/nn/functional/pooling.py
+++ b/python/paddle/nn/functional/pooling.py
@@ -511,7 +511,9 @@ def avg_pool3d(
     else:
         op_type = "pool3d"
         helper = LayerHelper(op_type, **locals())
-        check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool3d')
+        check_variable_and_dtype(
+            x, 'x', ['float16', 'float32', 'float64'], 'avg_pool3d'
+        )
         dtype = helper.input_dtype(input_param_name='x')
         pool_out = helper.create_variable_for_type_inference(dtype)
         outputs = {"Out": pool_out}