def test_gpu3_mixture_dtype_outputs(self):
def f_rnn(u_t, x_tm1, W_in, W):
return (u_t * W_in + x_tm1 * W,
tensor.cast(u_t + x_tm1, 'int64'))
u = theano.tensor.fvector('u')
x0 = theano.tensor.fscalar('x0')
W_in = theano.tensor.fscalar('win')
W = theano.tensor.fscalar('w')
output, updates = theano.scan(f_rnn,
u,
[x0, None],
[W_in, W],
n_steps=None,
truncate_gradient=-1,
go_backwards=False,
mode=self.mode_with_gpu)
f2 = theano.function([u, x0, W_in, W],
output,
updates=updates,
allow_input_downcast=True,
mode=self.mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out1 = numpy.zeros((4,))
v_out2 = numpy.zeros((4,), dtype='int64')
v_out1[0] = v_u[0] * W_in + v_x0 * W
v_out2[0] = v_u[0] + v_x0
for step in xrange(1, 4):
v_out1[step] = v_u[step] * W_in + v_out1[step - 1] * W
v_out2[step] = numpy.int64(v_u[step] + v_out1[step - 1])
theano_out1, theano_out2 = f2(v_u, v_x0, W_in, W)
utt.assert_allclose(theano_out1, v_out1)
utt.assert_allclose(theano_out2, v_out2)
topo = f2.maker.fgraph.toposort()
scan_node = [node for node in topo
if isinstance(node.op, theano.scan_module.scan_op.Scan)]
assert len(scan_node) == 1
scan_node = scan_node[0]
assert self.is_scan_on_gpu(scan_node)
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