add bidirectional test

tests/python/unittest/test_gluon_rnn.py

@@ -16,7 +16,7 @@
 # under the License.
 
 import mxnet as mx
-from mxnet import gluon
+from mxnet import gluon, nd
 import numpy as np
 import copy
 from numpy.testing import assert_allclose
@@ -82,6 +82,7 @@ def test_lstm_cpu_inference():
     model = mx.gluon.rnn.LSTM(2, num_layers=6, bidirectional=True)
     model_cell = model._unfuse()
     model.initialize(mx.init.One())
+
     y = model(x).asnumpy()
     y_cell = model_cell.unroll(2, x, layout='TNC', merge_outputs=True)[0].asnumpy()
 
@@ -242,6 +243,46 @@ def test_bidirectional():
     assert outs == [(10, 200), (10, 200), (10, 200)]
 
 
+@assert_raises_cudnn_disabled()
+def test_layer_bidirectional():
+    class RefBiLSTM(gluon.Block):
+        def __init__(self, size, **kwargs):
+            super(RefBiLSTM, self).__init__(**kwargs)
+            with self.name_scope():
+                self._lstm_fwd = gluon.rnn.LSTM(size, bidirectional=False, prefix='l0')
+                self._lstm_bwd = gluon.rnn.LSTM(size, bidirectional=False, prefix='r0')
+
+        def forward(self, inpt):
+            fwd = self._lstm_fwd(inpt)
+            bwd_inpt = nd.flip(inpt, 0)
+            bwd = self._lstm_bwd(bwd_inpt)
+            bwd = nd.flip(bwd, 0)
+            return nd.concat(fwd, bwd, dim=2)
+
+    size = 7
+    in_size = 5
+    weights = {}
+    for d in ['l', 'r']:
+        weights['lstm_{}0_i2h_weight'.format(d)] = mx.random.uniform(shape=(size*4, in_size))
+        weights['lstm_{}0_h2h_weight'.format(d)] = mx.random.uniform(shape=(size*4, size))
+        weights['lstm_{}0_i2h_bias'.format(d)] = mx.random.uniform(shape=(size*4,))
+        weights['lstm_{}0_h2h_bias'.format(d)] = mx.random.uniform(shape=(size*4,))
+
+    net = gluon.rnn.LSTM(size, bidirectional=True, prefix='lstm_')
+    ref_net = RefBiLSTM(size, prefix='lstm_')
+    net.initialize()
+    ref_net.initialize()
+    net_params = net.collect_params()
+    ref_net_params = ref_net.collect_params()
+    for k in weights:
+        net_params[k].set_data(weights[k])
+        ref_net_params[k.replace('l0', 'l0l0').replace('r0', 'r0l0')].set_data(weights[k])
+
+    data = mx.random.uniform(shape=(3, 10, in_size))
+    assert_allclose(net(data).asnumpy(), ref_net(data).asnumpy())
+
+
+
 def test_zoneout():
     cell = gluon.rnn.ZoneoutCell(gluon.rnn.RNNCell(100, prefix='rnn_'), zoneout_outputs=0.5,
                               zoneout_states=0.5)