[Bug fix] Fix fp16 dtype checking for AvgPool1D op (#50929)

* Fix fp16 dtype checking for AvgPool1D op

* Update code style for PR-CI-Static-Check

python/paddle/fluid/tests/unittests/test_pool1d_api.py

@@ -139,6 +139,32 @@ class TestPool1D_API(unittest.TestCase):
             )
             np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
 
+    def check_avg_static_results_fp16(self, place):
+        with paddle.static.program_guard(paddle.static.Program()):
+            input = paddle.static.data(
+                name="input", shape=[2, 3, 32], dtype="float16"
+            )
+            result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0)
+
+            input_np = np.random.random([2, 3, 32]).astype("float16")
+            result_np = avg_pool1D_forward_naive(
+                input_np,
+                ksize=[2],
+                strides=[2],
+                paddings=[0],
+                ceil_mode=False,
+            )
+
+            if core.is_compiled_with_cuda():
+                place = paddle.CUDAPlace(0)
+                exe = paddle.static.Executor(place)
+                fetches = exe.run(
+                    paddle.static.default_main_program(),
+                    feed={"input": input_np},
+                    fetch_list=[result],
+                )
+                np.testing.assert_allclose(fetches[0], result_np, rtol=1e-03)
+
     def check_avg_dygraph_results(self, place):
         with fluid.dygraph.guard(place):
             input_np = np.random.random([2, 3, 32]).astype("float32")
@@ -272,6 +298,7 @@ class TestPool1D_API(unittest.TestCase):
             self.check_max_dygraph_padding_same(place)
             self.check_avg_dygraph_padding_same(place)
             self.check_max_dygraph_return_index_results(place)
+            self.check_avg_static_results_fp16(place)
 
 
 class TestPool1DError_API(unittest.TestCase):

python/paddle/nn/functional/pooling.py

@@ -187,7 +187,7 @@ def avg_pool1d(
     Args:
         x (Tensor): The input tensor of pooling operator which is a 3-D tensor with
                           shape [N, C, L]. where `N` is batch size, `C` is the number of channels,
-                          `L` is the length of the feature. The data type is float32 or float64.
+                          `L` is the length of the feature. The data type is float16, float32 or float64.
         kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
             it must contain an integer.
         stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
@@ -223,7 +223,9 @@ def avg_pool1d(
     """NCL to NCHW"""
     data_format = "NCHW"
     if not in_dynamic_mode():
-        check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'avg_pool1d')
+        check_variable_and_dtype(
+            x, 'x', ['float16', 'float32', 'float64'], 'avg_pool1d'
+        )
     _check_input(x, 3)
     x = unsqueeze(x, [2])
     kernel_size = utils.convert_to_list(kernel_size, 1, 'kernel_size')