diff --git a/python/paddle/fluid/tests/unittests/test_pool1d_api.py b/python/paddle/fluid/tests/unittests/test_pool1d_api.py
index 25216175d59935535a352b02afc3c8f371cedd63..c1169dfc5210ac80a709afa06d3bf9a470a785b0 100644
--- a/python/paddle/fluid/tests/unittests/test_pool1d_api.py
+++ b/python/paddle/fluid/tests/unittests/test_pool1d_api.py
@@ -195,6 +195,23 @@ class TestPool1d_API(unittest.TestCase):
             result = max_pool1d_dg(input)
             self.assertTrue(np.allclose(result.numpy(), result_np))
 
+    def check_max_dygraph_return_index_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result, index = F.max_pool1d(
+                input, kernel_size=2, stride=2, padding=0, return_indices=True)
+
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool1d_dg = paddle.nn.layer.MaxPool1d(
+                kernel_size=2, stride=None, padding=0)
+            result = max_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
     def check_max_dygraph_padding_same(self, place):
         with fluid.dygraph.guard(place):
             input_np = np.random.random([2, 3, 32]).astype("float32")
@@ -228,6 +245,7 @@ class TestPool1d_API(unittest.TestCase):
             self.check_avg_static_results(place)
             self.check_max_dygraph_padding_same(place)
             self.check_avg_dygraph_padding_same(place)
+            self.check_max_dygraph_return_index_results(place)
 
 
 class TestPool2dError_API(unittest.TestCase):
diff --git a/python/paddle/nn/functional/pooling.py b/python/paddle/nn/functional/pooling.py
index 1eb9167d0352f36bfcb87db79ba23dce14bac507..bed5df8fa78c753565c8391ba414135e63d335aa 100755
--- a/python/paddle/nn/functional/pooling.py
+++ b/python/paddle/nn/functional/pooling.py
@@ -571,15 +571,26 @@ def max_pool1d(x,
     padding = _expand_low_nd_padding(padding)
 
     if in_dygraph_mode():
-        pool_out = core.ops.max_pool2d_with_index(
-            x, 'ksize', kernel_size, 'global_pooling', False, 'strides', stride,
-            'paddings', padding, 'padding_algorithm', padding_algorithm,
-            'use_cudnn', True, 'ceil_mode', ceil_mode, 'use_mkldnn', False,
-            'exclusive', True, 'data_format', data_format)
-        return (squeeze(pool_out[0], [2]), squeeze(
-            pool_out[1], [2])) if return_indices else squeeze(pool_out[0], [2])
+        if return_indices:
+            pool_out = core.ops.max_pool2d_with_index(
+                x, 'ksize', kernel_size, 'global_pooling', False, 'strides',
+                stride, 'paddings', padding, 'padding_algorithm',
+                padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
+                'use_mkldnn', False, 'exclusive', True, 'data_format',
+                data_format)
+            return (squeeze(pool_out[0], [2]), squeeze(
+                pool_out[1],
+                [2])) if return_indices else squeeze(pool_out[0], [2])
+        else:
+            pool_out = core.ops.pool2d(
+                x, 'pooling_type', 'max', 'ksize', kernel_size,
+                'global_pooling', False, 'padding_algorithm', padding_algorithm,
+                'strides', stride, 'paddings', padding, 'use_cudnn', True,
+                'ceil_mode', ceil_mode, 'use_mkldnn', False, 'exclusive', True,
+                'data_format', data_format)
+            return squeeze(pool_out, [2])
 
-    op_type = 'max_pool2d_with_index'
+    op_type = 'max_pool2d_with_index' if return_indices else "pool2d"
     helper = LayerHelper(op_type, **locals())
     dtype = helper.input_dtype()
     pool_out = helper.create_variable_for_type_inference(dtype)
@@ -696,7 +707,7 @@ def max_pool2d(x,
         )
 
     if in_dygraph_mode():
-        if data_format == "NCHW":
+        if return_indices:
             output = core.ops.max_pool2d_with_index(
                 x, 'ksize', kernel_size, 'global_pooling', False, 'strides',
                 stride, 'paddings', padding, 'padding_algorithm',
@@ -704,7 +715,7 @@ def max_pool2d(x,
                 'use_mkldnn', False, 'exclusive', True, 'data_format',
                 data_format)
             return output if return_indices else output[0]
-        elif data_format == "NHWC" and not return_indices:
+        else:
             output = core.ops.pool2d(
                 x, 'pooling_type', 'max', 'ksize', kernel_size,
                 'global_pooling', False, 'padding_algorithm', padding_algorithm,
@@ -713,7 +724,7 @@ def max_pool2d(x,
                 'data_format', data_format)
             return output
 
-    op_type = 'max_pool2d_with_index' if data_format == "NCHW" else "pool2d"
+    op_type = 'max_pool2d_with_index' if return_indices else "pool2d"
     helper = LayerHelper(op_type, **locals())
     dtype = helper.input_dtype()
     pool_out = helper.create_variable_for_type_inference(dtype)
@@ -822,7 +833,7 @@ def max_pool3d(x,
         )
 
     if in_dygraph_mode():
-        if data_format == "NCDHW":
+        if return_indices:
             output = core.ops.max_pool3d_with_index(
                 x, 'pooling_type', 'max', 'ksize', kernel_size, 'strides',
                 stride, 'paddings', padding, 'global_pooling', False,
@@ -830,7 +841,7 @@ def max_pool3d(x,
                 'ceil_mode', ceil_mode, 'use_mkldnn', False, 'exclusive', True,
                 'data_format', data_format)
             return output if return_indices else output[0]
-        elif data_format == "NDHWC" and not return_indices:
+        else:
             output = core.ops.pool3d(
                 x, 'pooling_type', 'max', 'ksize', kernel_size,
                 'global_pooling', False, 'padding_algorithm', padding_algorithm,
@@ -839,7 +850,7 @@ def max_pool3d(x,
                 'data_format', data_format)
             return output
 
-    op_type = "max_pool3d_with_index" if data_format == "NCDHW" else "pool3d"
+    op_type = "max_pool3d_with_index" if return_indices else "pool3d"
     helper = LayerHelper(op_type, **locals())
     dtype = helper.input_dtype()
     pool_out = helper.create_variable_for_type_inference(dtype)