fix wmt14 doc, remove backward, add bidirect direction in rnn api (#29633) (#29695)

* fix wmt14 doc, remove backward, add bidirect direction in rnn api

* fix rnn unittest

* fix test_rnn_nets_static.py bug

python/paddle/fluid/tests/unittests/rnn/rnn_numpy.py

@@ -414,9 +414,9 @@ class SimpleRNN(RNNMixin):
                  time_major=False,
                  dtype="float64"):
         super(SimpleRNN, self).__init__()
-
-        if direction in ["forward", "backward"]:
-            is_reverse = direction == "backward"
+        bidirectional_list = ["bidirectional", "bidirect"]
+        if direction in ["forward"]:
+            is_reverse = False
             cell = SimpleRNNCell(
                 input_size, hidden_size, nonlinearity=nonlinearity, dtype=dtype)
             self.append(RNN(cell, is_reverse, time_major))
@@ -427,7 +427,7 @@ class SimpleRNN(RNNMixin):
                     nonlinearity=nonlinearity,
                     dtype=dtype)
                 self.append(RNN(cell, is_reverse, time_major))
-        elif direction == "bidirectional":
+        elif direction in bidirectional_list:
             cell_fw = SimpleRNNCell(
                 input_size, hidden_size, nonlinearity=nonlinearity, dtype=dtype)
             cell_bw = SimpleRNNCell(
@@ -447,7 +447,7 @@ class SimpleRNN(RNNMixin):
         self.input_size = input_size
         self.hidden_size = hidden_size
         self.dropout = dropout
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.time_major = time_major
         self.num_layers = num_layers
         self.state_components = 1
@@ -464,14 +464,15 @@ class LSTM(RNNMixin):
                  dtype="float64"):
         super(LSTM, self).__init__()
 
-        if direction in ["forward", "backward"]:
-            is_reverse = direction == "backward"
+        bidirectional_list = ["bidirectional", "bidirect"]
+        if direction in ["forward"]:
+            is_reverse = False
             cell = LSTMCell(input_size, hidden_size, dtype=dtype)
             self.append(RNN(cell, is_reverse, time_major))
             for i in range(1, num_layers):
                 cell = LSTMCell(hidden_size, hidden_size, dtype=dtype)
                 self.append(RNN(cell, is_reverse, time_major))
-        elif direction == "bidirectional":
+        elif direction in bidirectional_list:
             cell_fw = LSTMCell(input_size, hidden_size, dtype=dtype)
             cell_bw = LSTMCell(input_size, hidden_size, dtype=dtype)
             self.append(BiRNN(cell_fw, cell_bw, time_major))
@@ -487,7 +488,7 @@ class LSTM(RNNMixin):
         self.input_size = input_size
         self.hidden_size = hidden_size
         self.dropout = dropout
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.time_major = time_major
         self.num_layers = num_layers
         self.state_components = 2
@@ -504,14 +505,15 @@ class GRU(RNNMixin):
                  dtype="float64"):
         super(GRU, self).__init__()
 
-        if direction in ["forward", "backward"]:
-            is_reverse = direction == "backward"
+        bidirectional_list = ["bidirectional", "bidirect"]
+        if direction in ["forward"]:
+            is_reverse = False
             cell = GRUCell(input_size, hidden_size, dtype=dtype)
             self.append(RNN(cell, is_reverse, time_major))
             for i in range(1, num_layers):
                 cell = GRUCell(hidden_size, hidden_size, dtype=dtype)
                 self.append(RNN(cell, is_reverse, time_major))
-        elif direction == "bidirectional":
+        elif direction in bidirectional_list:
             cell_fw = GRUCell(input_size, hidden_size, dtype=dtype)
             cell_bw = GRUCell(input_size, hidden_size, dtype=dtype)
             self.append(BiRNN(cell_fw, cell_bw, time_major))
@@ -527,7 +529,7 @@ class GRU(RNNMixin):
         self.input_size = input_size
         self.hidden_size = hidden_size
         self.dropout = dropout
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.time_major = time_major
         self.num_layers = num_layers
         self.state_components = 1

python/paddle/fluid/tests/unittests/rnn/test_rnn_nets.py

@@ -22,13 +22,15 @@ import unittest
 from convert import convert_params_for_net
 from rnn_numpy import SimpleRNN, LSTM, GRU
 
+bidirectional_list = ["bidirectional", "bidirect"]
+
 
 class TestSimpleRNN(unittest.TestCase):
     def __init__(self, time_major=True, direction="forward", place="cpu"):
         super(TestSimpleRNN, self).__init__("runTest")
         self.time_major = time_major
         self.direction = direction
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.place = place
 
     def setUp(self):
@@ -109,7 +111,7 @@ class TestGRU(unittest.TestCase):
         super(TestGRU, self).__init__("runTest")
         self.time_major = time_major
         self.direction = direction
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.place = place
 
     def setUp(self):
@@ -196,7 +198,7 @@ class TestLSTM(unittest.TestCase):
         super(TestLSTM, self).__init__("runTest")
         self.time_major = time_major
         self.direction = direction
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.place = place
 
     def setUp(self):
@@ -339,7 +341,7 @@ def load_tests(loader, tests, pattern):
     suite = unittest.TestSuite()
     devices = ["cpu", "gpu"] if paddle.fluid.is_compiled_with_cuda() \
         else ["cpu"]
-    for direction in ["forward", "backward", "bidirectional"]:
+    for direction in ["forward", "bidirectional", "bidirect"]:
         for time_major in [True, False]:
             for device in devices:
                 for test_class in [TestSimpleRNN, TestLSTM, TestGRU]:

python/paddle/fluid/tests/unittests/rnn/test_rnn_nets_static.py

@@ -23,13 +23,15 @@ import unittest
 from convert import convert_params_for_net_static
 from rnn_numpy import SimpleRNN, LSTM, GRU
 
+bidirectional_list = ["bidirectional", "bidirect"]
+
 
 class TestSimpleRNN(unittest.TestCase):
     def __init__(self, time_major=True, direction="forward", place="cpu"):
         super(TestSimpleRNN, self).__init__("runTest")
         self.time_major = time_major
         self.direction = direction
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.place = place
 
     def setUp(self):
@@ -173,7 +175,7 @@ class TestGRU(unittest.TestCase):
         super(TestGRU, self).__init__("runTest")
         self.time_major = time_major
         self.direction = direction
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.place = place
 
     def setUp(self):
@@ -319,7 +321,7 @@ class TestLSTM(unittest.TestCase):
         super(TestLSTM, self).__init__("runTest")
         self.time_major = time_major
         self.direction = direction
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.place = place
 
     def setUp(self):
@@ -469,9 +471,13 @@ def load_tests(loader, tests, pattern):
     suite = unittest.TestSuite()
     devices = ["cpu", "gpu"] if paddle.fluid.is_compiled_with_cuda() \
         else ["cpu"]
-    for direction in ["forward", "backward", "bidirectional"]:
+    for direction in ["forward", "bidirectional", "bidirect"]:
         for time_major in [True, False]:
             for device in devices:
                 for test_class in [TestSimpleRNN, TestLSTM, TestGRU]:
                     suite.addTest(test_class(time_major, direction, device))
     return suite
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/layer/rnn.py

@@ -858,11 +858,12 @@ class RNNBase(LayerList):
                  bias_ih_attr=None,
                  bias_hh_attr=None):
         super(RNNBase, self).__init__()
+        bidirectional_list = ["bidirectional", "bidirect"]
         self.mode = mode
         self.input_size = input_size
         self.hidden_size = hidden_size
         self.dropout = dropout
-        self.num_directions = 2 if direction == "bidirectional" else 1
+        self.num_directions = 2 if direction in bidirectional_list else 1
         self.time_major = time_major
         self.num_layers = num_layers
         self.state_components = 2 if mode == "LSTM" else 1
@@ -882,14 +883,14 @@ class RNNBase(LayerList):
             rnn_cls = SimpleRNNCell
             kwargs["activation"] = self.activation
 
-        if direction in ["forward", "backward"]:
-            is_reverse = direction == "backward"
+        if direction in ["forward"]:
+            is_reverse = False
             cell = rnn_cls(input_size, hidden_size, **kwargs)
             self.append(RNN(cell, is_reverse, time_major))
             for i in range(1, num_layers):
                 cell = rnn_cls(hidden_size, hidden_size, **kwargs)
                 self.append(RNN(cell, is_reverse, time_major))
-        elif direction == "bidirectional":
+        elif direction in bidirectional_list:
             cell_fw = rnn_cls(input_size, hidden_size, **kwargs)
             cell_bw = rnn_cls(input_size, hidden_size, **kwargs)
             self.append(BiRNN(cell_fw, cell_bw, time_major))
@@ -899,13 +900,12 @@ class RNNBase(LayerList):
                 self.append(BiRNN(cell_fw, cell_bw, time_major))
         else:
             raise ValueError(
-                "direction should be forward, backward or bidirectional, "
+                "direction should be forward or bidirect (or bidirectional), "
                 "received direction = {}".format(direction))
 
         self.could_use_cudnn = True
-        self.could_use_cudnn &= direction != "backward"
         self.could_use_cudnn &= len(self.parameters()) == num_layers * 4 * (
-            2 if direction == "bidirectional" else 1)
+            2 if direction in bidirectional_list else 1)
 
         # Expose params as RNN's attribute, which can make it compatible when
         # replacing small ops composed rnn with cpp rnn kernel.
@@ -1079,8 +1079,8 @@ class SimpleRNN(RNNBase):
         input_size (int): The input size for the first layer's cell.
         hidden_size (int): The hidden size for each layer's cell.
         num_layers (int, optional): Number of layers. Defaults to 1.
-        direction (str, optional): The direction of the network. It can be "forward", 
-            "backward" and "bidirectional". When "bidirectional", the way to merge
+        direction (str, optional): The direction of the network. It can be "forward"
+            or "bidirect"(or "bidirectional"). When "bidirect", the way to merge
             outputs of forward and backward is concatenating. Defaults to "forward".
         time_major (bool, optional): Whether the first dimension of the input means the
             time steps. Defaults to False.
@@ -1195,8 +1195,8 @@ class LSTM(RNNBase):
         input_size (int): The input size for the first layer's cell.
         hidden_size (int): The hidden size for each layer's cell.
         num_layers (int, optional): Number of layers. Defaults to 1.
-        direction (str, optional): The direction of the network. It can be "forward", 
-            "backward" and "bidirectional". When "bidirectional", the way to merge
+        direction (str, optional): The direction of the network. It can be "forward"
+            or "bidirect"(or "bidirectional"). When "bidirect", the way to merge
             outputs of forward and backward is concatenating. Defaults to "forward".
         time_major (bool, optional): Whether the first dimension of the input 
             means the time steps. Defaults to False.
@@ -1300,8 +1300,8 @@ class GRU(RNNBase):
         input_size (int): The input size for the first layer's cell.
         hidden_size (int): The hidden size for each layer's cell.
         num_layers (int, optional): Number of layers. Defaults to 1.
-        direction (str, optional): The direction of the network. It can be "forward",
-            "backward" and "bidirectional". When "bidirectional", the way to merge
+        direction (str, optional): The direction of the network. It can be "forward"
+            or "bidirect"(or "bidirectional"). When "bidirect", the way to merge
             outputs of forward and backward is concatenating. Defaults to "forward".
         time_major (bool, optional): Whether the first dimension of the input 
             means the time steps. Defaults to False.

python/paddle/text/datasets/wmt14.py

@@ -43,7 +43,7 @@ class WMT14(Dataset):
     Implementation of `WMT14 <http://www.statmt.org/wmt14/>`_ test dataset.
     The original WMT14 dataset is too large and a small set of data for set is
     provided. This module will download dataset from
-    http://paddlepaddle.bj.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz
+    http://paddlemodels.bj.bcebos.com/wmt/wmt14.tgz .
 
     Args:
         data_file(str): path to data tar file, can be set None if
@@ -70,8 +70,6 @@ class WMT14(Dataset):
                 def forward(self, src_ids, trg_ids, trg_ids_next):
                     return paddle.sum(src_ids), paddle.sum(trg_ids), paddle.sum(trg_ids_next)
 
-            paddle.disable_static()
-
             wmt14 = WMT14(mode='train', dict_size=50)
 
             for i in range(10):