def test_matrix(self):
num_row = 11
num_col = 10
size = num_col * num_row
workers_num = mv.workers_num()
tbh = mv.MatrixTableHandler(num_row, num_col)
mv.barrier()
for count in xrange(1, 21):
row_ids = [0, 1, 5, 10]
tbh.add(range(size))
tbh.add(
[range(rid * num_col, (1 + rid) * num_col) for rid in row_ids],
row_ids)
mv.barrier()
data = tbh.get()
mv.barrier()
for i, row in enumerate(data):
for j, actual in enumerate(row):
expected = (i * num_col + j) * count * workers_num
if i in row_ids:
expected += (i * num_col + j) * count * workers_num
self.assertEqual(expected, actual)
data = tbh.get(row_ids)
mv.barrier()
for i, row in enumerate(data):
for j, actual in enumerate(row):
expected = (row_ids[i] * num_col +
j) * count * workers_num * 2
self.assertEqual(expected, actual)
def _test_array(self, size):
tbh = mv.ArrayTableHandler(size)
mv.barrier()
for i in xrange(100):
tbh.add(range(1, size + 1))
tbh.add(range(1, size + 1))
mv.barrier()
for j, actual in enumerate(tbh.get()):
self.assertEqual((j + 1) * (i + 1) * 2 * mv.workers_num(),
actual)
mv.barrier()
def _test_sharedvar(self, row, col):
W = sharedvar.mv_shared(value=np.zeros((row, col),
dtype=theano.config.floatX),
name='W',
borrow=True)
delta = np.array(range(1, row * col + 1),
dtype=theano.config.floatX).reshape((row, col))
train_model = theano.function([], updates=[(W, W + delta)])
mv.barrier()
for i in xrange(100):
train_model()
train_model()
sharedvar.sync_all_mv_shared_vars()
mv.barrier()
# to get the newest value, we must sync again
sharedvar.sync_all_mv_shared_vars()
for j, actual in enumerate(W.get_value().reshape(-1)):
self.assertEqual((j + 1) * (i + 1) * 2 * mv.workers_num(),
actual)
mv.barrier()