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

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

* fix rnn unittest

* fix test_rnn_nets_static.py bug
revert-31562-mean
Jack Zhou 5 years ago committed by GitHub
parent 613c46bc07
commit 84bae27779
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 45 additions and 37 deletions
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28
python/paddle/fluid/tests/unittests/rnn/rnn_numpy.py
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@ -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

10
python/paddle/fluid/tests/unittests/rnn/test_rnn_nets.py
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@ -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]:

14
python/paddle/fluid/tests/unittests/rnn/test_rnn_nets_static.py
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@ -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()

26
python/paddle/nn/layer/rnn.py
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@ -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.

4
python/paddle/text/datasets/wmt14.py
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@ -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):

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