Fix summary api for rnn gru lstm (#28566)

* fix summary for rnn gru lstm

python/paddle/hapi/model_summary.py

@@ -244,6 +244,9 @@ def summary_string(model, input_size, dtypes=None):
             (not (layer == model) or depth < 1)):
 
             hooks.append(layer.register_forward_post_hook(hook))
+        # For rnn, gru and lstm layer
+        elif hasattr(layer, 'could_use_cudnn') and layer.could_use_cudnn:
+            hooks.append(layer.register_forward_post_hook(hook))
 
     if isinstance(input_size, tuple):
         input_size = [input_size]

python/paddle/tests/test_model.py

@@ -295,6 +295,12 @@ class TestModel(unittest.TestCase):
         np.testing.assert_equal(output[0].shape[0], len(self.test_dataset))
         fluid.disable_dygraph()
 
+    def test_summary_gpu(self):
+        paddle.disable_static(self.device)
+        rnn = paddle.nn.LSTM(16, 32, 2)
+        params_info = paddle.summary(
+            rnn, [(-1, 23, 16), ((2, None, 32), (2, -1, 32))])
+
 
 class MyModel(paddle.nn.Layer):
     def __init__(self):
@@ -512,14 +518,33 @@ class TestModelFunction(unittest.TestCase):
             model.summary(input_size=(20), dtype='float32')
 
     def test_summary_nlp(self):
-        paddle.enable_static()
+        def _get_param_from_state_dict(state_dict):
+            params = 0
+            for k, v in state_dict.items():
+                params += np.prod(v.numpy().shape)
+            return params
+
         nlp_net = paddle.nn.GRU(input_size=2,
                                 hidden_size=3,
                                 num_layers=3,
                                 direction="bidirectional")
         paddle.summary(nlp_net, (1, 1, 2))
+
         rnn = paddle.nn.LSTM(16, 32, 2)
-        paddle.summary(rnn, [(-1, 23, 16), ((2, None, 32), (2, -1, 32))])
+        params_info = paddle.summary(
+            rnn, [(-1, 23, 16), ((2, None, 32), (2, -1, 32))])
+        gt_params = _get_param_from_state_dict(rnn.state_dict())
+        np.testing.assert_allclose(params_info['total_params'], gt_params / 2.0)
+
+        rnn = paddle.nn.GRU(16, 32, 2, direction='bidirectional')
+        params_info = paddle.summary(rnn, (4, 23, 16))
+        gt_params = _get_param_from_state_dict(rnn.state_dict())
+        np.testing.assert_allclose(params_info['total_params'], gt_params / 2.0)
+
+        rnn = paddle.nn.SimpleRNN(16, 32, 2, direction='bidirectional')
+        params_info = paddle.summary(rnn, (4, 23, 16))
+        gt_params = _get_param_from_state_dict(rnn.state_dict())
+        np.testing.assert_allclose(params_info['total_params'], gt_params / 2.0)
 
     def test_summary_dtype(self):
         input_shape = (3, 1)