Adds context parameter to check_rnn_layer_forward calls in test_lstmp (…

…apache#14529)

tests/python/gpu/test_gluon_gpu.py

@@ -88,7 +88,8 @@ def test_lstmp():
     rtol, atol = 1e-2, 1e-2
     batch_size, seq_len = 7, 11
     input_size = 5
-    lstm_input = mx.nd.uniform(shape=(seq_len, batch_size, input_size), ctx=mx.gpu(0))
+    ctx = mx.gpu(0)
+    lstm_input = mx.nd.uniform(shape=(seq_len, batch_size, input_size), ctx=ctx)
     shapes = {'i2h_weight': (hidden_size*4, input_size),
               'h2h_weight': (hidden_size*4, projection_size),
               'i2h_bias': (hidden_size*4,),
@@ -101,8 +102,8 @@ def test_lstmp():
                                             projection_size=projection_size,
                                             input_size=input_size,
                                             prefix='lstm0_l0_')
-    lstm_layer.initialize(ctx=mx.gpu(0))
-    lstm_cell.initialize(ctx=mx.gpu(0))
+    lstm_layer.initialize(ctx=ctx)
+    lstm_cell.initialize(ctx=ctx)
     layer_params = lstm_layer.collect_params()
     cell_params = lstm_cell.collect_params()
     for k, v in weights.items():
@@ -121,14 +122,14 @@ def test_lstmp():
         print('checking gradient for {}'.format('lstm0_l0_'+k))
         assert_almost_equal(layer_grad.asnumpy(), cell_grad.asnumpy(),
                             rtol=rtol, atol=atol)
-    check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5), mx.nd.ones((8, 3, 20)))
-    check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5, bidirectional=True), mx.nd.ones((8, 3, 20)), [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))])
+    check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5), mx.nd.ones((8, 3, 20)), ctx=ctx)
+    check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5, bidirectional=True), mx.nd.ones((8, 3, 20)), [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))], ctx=ctx)
 
     check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, dropout=0.5, projection_size=5), mx.nd.ones((8, 3, 20)),
-                            run_only=True)
+                            run_only=True, ctx=ctx)
     check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, bidirectional=True, dropout=0.5, projection_size=5),
                             mx.nd.ones((8, 3, 20)),
-                            [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))], run_only=True)
+                            [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))], run_only=True, ctx=ctx)
 
 
 @with_seed()