[Bug fix] Fix fp16 dtype checking for AvgPool1D op

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

@@ -20,6 +20,7 @@
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.nn.functional as F
+import paddle.static as static
 
 
 def adaptive_start_index(index, input_size, output_size):
@@ -139,6 +140,32 @@ def check_avg_static_results(self, place):
             )
             np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
 
+    def check_avg_static_results_fp16(self, place):
+        with static.program_guard(static.Program()):
+            if core.is_compiled_with_cuda():
+                input = 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,
+                )
+
+                place = paddle.CUDAPlace(0)
+                exe = paddle.static.Executor(place)
+                fetches = exe.run(
+                    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 +299,7 @@ def test_pool1d(self):
             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')