def _test_three_recurrent_children(self, gpu):
# Test if https://github.com/chainer/chainer/issues/6053 is addressed
in_size = 2
out_size = 6
rseq = StatelessRecurrentSequential(
L.NStepLSTM(1, in_size, 3, 0),
L.NStepGRU(2, 3, 4, 0),
L.NStepRNNTanh(5, 4, out_size, 0),
)
if gpu >= 0:
chainer.cuda.get_device_from_id(gpu).use()
rseq.to_gpu()
xp = rseq.xp
seqs_x = [
xp.random.uniform(-1, 1, size=(4, in_size)).astype(np.float32),
xp.random.uniform(-1, 1, size=(1, in_size)).astype(np.float32),
xp.random.uniform(-1, 1, size=(3, in_size)).astype(np.float32),
]
# Make and load a recurrent state to check if the order is correct.
_, rs = rseq.n_step_forward(seqs_x, None, output_mode='concat')
_, _ = rseq.n_step_forward(seqs_x, rs, output_mode='concat')
_, rs = rseq.n_step_forward(seqs_x, None, output_mode='split')
_, _ = rseq.n_step_forward(seqs_x, rs, output_mode='split')
def _test_n_step_forward_with_tuple_output(self, gpu):
in_size = 5
out_size = 6
def split_output(x):
return tuple(F.split_axis(x, [2, 3], axis=1))
rseq = StatelessRecurrentSequential(
L.NStepRNNTanh(1, in_size, out_size, 0),
split_output,
)
if gpu >= 0:
chainer.cuda.get_device_from_id(gpu).use()
rseq.to_gpu()
xp = rseq.xp
# Input is a list of two variables.
seqs_x = [
xp.random.uniform(-1, 1, size=(3, in_size)).astype(np.float32),
xp.random.uniform(-1, 1, size=(2, in_size)).astype(np.float32),
]
# Concatenated output should be a tuple of three variables.
concat_out, concat_state = rseq.n_step_forward(seqs_x,
None,
output_mode='concat')
self.assertIsInstance(concat_out, tuple)
self.assertEqual(len(concat_out), 3)
self.assertEqual(concat_out[0].shape, (5, 2))
self.assertEqual(concat_out[1].shape, (5, 1))
self.assertEqual(concat_out[2].shape, (5, 3))
# Split output should be a list of two tuples, each of which is of
# three variables.
split_out, split_state = rseq.n_step_forward(seqs_x,
None,
output_mode='split')
self.assertIsInstance(split_out, list)
self.assertEqual(len(split_out), 2)
self.assertIsInstance(split_out[0], tuple)
self.assertIsInstance(split_out[1], tuple)
for seq_x, seq_out in zip(seqs_x, split_out):
self.assertEqual(len(seq_out), 3)
self.assertEqual(seq_out[0].shape, (len(seq_x), 2))
self.assertEqual(seq_out[1].shape, (len(seq_x), 1))
self.assertEqual(seq_out[2].shape, (len(seq_x), 3))
# Check if output_mode='concat' and output_mode='split' are consistent
xp.testing.assert_allclose(
F.concat([F.concat(seq_out, axis=1) for seq_out in split_out],
axis=0).array,
F.concat(concat_out, axis=1).array,
)
def _test_n_step_forward_with_tuple_input(self, gpu):
in_size = 5
out_size = 3
def concat_input(*args):
return F.concat(args, axis=1)
rseq = StatelessRecurrentSequential(
concat_input,
L.NStepRNNTanh(1, in_size, out_size, 0),
)
if gpu >= 0:
chainer.cuda.get_device_from_id(gpu).use()
rseq.to_gpu()
xp = rseq.xp
# Input is list of tuples. Each tuple has two variables.
seqs_x = [
(xp.random.uniform(-1, 1, size=(3, 2)).astype(np.float32),
xp.random.uniform(-1, 1, size=(3, 3)).astype(np.float32)),
(xp.random.uniform(-1, 1, size=(1, 2)).astype(np.float32),
xp.random.uniform(-1, 1, size=(1, 3)).astype(np.float32)),
]
# Concatenated output should be a variable.
concat_out, concat_state = rseq.n_step_forward(seqs_x,
None,
output_mode='concat')
self.assertEqual(concat_out.shape, (4, out_size))
# Split output should be a list of variables.
split_out, split_state = rseq.n_step_forward(seqs_x,
None,
output_mode='split')
self.assertIsInstance(split_out, list)
self.assertEqual(len(split_out), len(seqs_x))
for seq_x, seq_out in zip(seqs_x, split_out):
self.assertEqual(seq_out.shape, (len(seq_x), out_size))
# Check if output_mode='concat' and output_mode='split' are consistent
xp.testing.assert_allclose(
F.concat(split_out, axis=0).array,
concat_out.array,
)
def _test_n_step_forward(self, gpu):
in_size = 2
out_size = 6
rseq = StatelessRecurrentSequential(
L.Linear(in_size, 3),
F.elu,
L.NStepLSTM(1, 3, 4, 0),
L.Linear(4, 5),
L.NStepRNNTanh(1, 5, out_size, 0),
F.tanh,
)
if gpu >= 0:
chainer.cuda.get_device_from_id(gpu).use()
rseq.to_gpu()
xp = rseq.xp
linear1 = rseq._layers[0]
lstm = rseq._layers[2]
linear2 = rseq._layers[3]
rnn = rseq._layers[4]
seqs_x = [
xp.random.uniform(-1, 1, size=(4, in_size)).astype(np.float32),
xp.random.uniform(-1, 1, size=(1, in_size)).astype(np.float32),
xp.random.uniform(-1, 1, size=(3, in_size)).astype(np.float32),
]
concat_out, concat_state = rseq.n_step_forward(
seqs_x, None, output_mode='concat')
self.assertEqual(concat_out.shape, (8, out_size))
split_out, split_state = rseq.n_step_forward(
seqs_x, None, output_mode='split')
self.assertIsInstance(split_out, list)
self.assertEqual(len(split_out), len(seqs_x))
for seq_x, seq_out in zip(seqs_x, split_out):
self.assertEqual(seq_out.shape, (len(seq_x), out_size))
# Check if output_mode='concat' and output_mode='split' are consistent
xp.testing.assert_allclose(
F.concat(split_out, axis=0).array,
concat_out.array,
)
(concat_lstm_h, concat_lstm_c), concat_rnn_h = concat_state
(split_lstm_h, split_lstm_c), split_rnn_h = split_state
xp.testing.assert_allclose(concat_lstm_h.array, split_lstm_h.array)
xp.testing.assert_allclose(concat_lstm_c.array, split_lstm_c.array)
xp.testing.assert_allclose(concat_rnn_h.array, split_rnn_h.array)
# Check if the output matches that of step-by-step execution
def manual_n_step_forward(seqs_x):
sorted_seqs_x = sorted(seqs_x, key=len, reverse=True)
transposed_x = F.transpose_sequence(sorted_seqs_x)
lstm_h = None
lstm_c = None
rnn_h = None
ys = []
for batch in transposed_x:
if lstm_h is not None:
lstm_h = lstm_h[:len(batch)]
lstm_c = lstm_c[:len(batch)]
rnn_h = rnn_h[:len(batch)]
h = linear1(batch)
h = F.elu(h)
h, (lstm_h, lstm_c) = _step_lstm(lstm, h, (lstm_h, lstm_c))
h = linear2(h)
h, rnn_h = _step_rnn_tanh(rnn, h, rnn_h)
y = F.tanh(h)
ys.append(y)
sorted_seqs_y = F.transpose_sequence(ys)
# Undo sort
seqs_y = [sorted_seqs_y[0], sorted_seqs_y[2], sorted_seqs_y[1]]
return seqs_y
manual_split_out = manual_n_step_forward(seqs_x)
for man_seq_out, seq_out in zip(manual_split_out, split_out):
xp.testing.assert_allclose(
man_seq_out.array, seq_out.array, rtol=1e-5)
# Finally, check the gradient (wrt linear1.W)
concat_grad, = chainer.grad([F.sum(concat_out)], [linear1.W])
split_grad, = chainer.grad(
[F.sum(F.concat(split_out, axis=0))], [linear1.W])
manual_split_grad, = chainer.grad(
[F.sum(F.concat(manual_split_out, axis=0))], [linear1.W])
xp.testing.assert_allclose(
concat_grad.array, split_grad.array, rtol=1e-5)
xp.testing.assert_allclose(
concat_grad.array, manual_split_grad.array, rtol=1e-5)
def _test_mask_recurrent_state_at(self, gpu):
in_size = 2
out_size = 4
rseq = StatelessRecurrentSequential(
L.Linear(in_size, 3),
F.elu,
L.NStepGRU(1, 3, out_size, 0),
F.softmax,
)
if gpu >= 0:
chainer.cuda.get_device_from_id(gpu).use()
rseq.to_gpu()
xp = rseq.xp
seqs_x = [
xp.random.uniform(-1, 1, size=(2, in_size)).astype(np.float32),
xp.random.uniform(-1, 1, size=(2, in_size)).astype(np.float32),
]
transposed_x = F.transpose_sequence(seqs_x)
print('transposed_x[0]', transposed_x[0])
def no_mask_n_step_forward():
nomask_nstep_out, nstep_rs = rseq.n_step_forward(
seqs_x, None, output_mode='concat')
return F.reshape(nomask_nstep_out, (2, 2, out_size)), nstep_rs
nstep_out, nstep_rs = no_mask_n_step_forward()
# Check if n_step_forward and forward twice results are same
def no_mask_forward_twice():
_, rs = rseq(transposed_x[0], None)
return rseq(transposed_x[1], rs)
nomask_out, nomask_rs = no_mask_forward_twice()
xp.testing.assert_allclose(
nstep_out.array[:, 1],
nomask_out.array,
)
xp.testing.assert_allclose(nstep_rs[0].array, nomask_rs[0].array)
# 1st-only mask forward twice: only 2nd should be the same
def mask0_forward_twice():
_, rs = rseq(transposed_x[0], None)
rs = rseq.mask_recurrent_state_at(rs, 0)
return rseq(transposed_x[1], rs)
mask0_out, mask0_rs = mask0_forward_twice()
with self.assertRaises(AssertionError):
xp.testing.assert_allclose(
nstep_out.array[0, 1],
mask0_out.array[0],
)
xp.testing.assert_allclose(
nstep_out.array[1, 1],
mask0_out.array[1],
)
# 2nd-only mask forward twice: only 1st should be the same
def mask1_forward_twice():
_, rs = rseq(transposed_x[0], None)
rs = rseq.mask_recurrent_state_at(rs, 1)
return rseq(transposed_x[1], rs)
mask1_out, mask1_rs = mask1_forward_twice()
xp.testing.assert_allclose(
nstep_out.array[0, 1],
mask1_out.array[0],
)
with self.assertRaises(AssertionError):
xp.testing.assert_allclose(
nstep_out.array[1, 1],
mask1_out.array[1],
)
# both 1st and 2nd mask forward twice: both should be different
def mask01_forward_twice():
_, rs = rseq(transposed_x[0], None)
rs = rseq.mask_recurrent_state_at(rs, [0, 1])
return rseq(transposed_x[1], rs)
mask01_out, mask01_rs = mask01_forward_twice()
with self.assertRaises(AssertionError):
xp.testing.assert_allclose(
nstep_out.array[0, 1],
mask01_out.array[0],
)
with self.assertRaises(AssertionError):
xp.testing.assert_allclose(
nstep_out.array[1, 1],
mask01_out.array[1],
)
# get and concat recurrent states and resume forward
def get_and_concat_rs_forward():
_, rs = rseq(transposed_x[0], None)
rs0 = rseq.get_recurrent_state_at(rs, 0, unwrap_variable=True)
rs1 = rseq.get_recurrent_state_at(rs, 1, unwrap_variable=True)
concat_rs = rseq.concatenate_recurrent_states([rs0, rs1])
return rseq(transposed_x[1], concat_rs)
getcon_out, getcon_rs = get_and_concat_rs_forward()
xp.testing.assert_allclose(getcon_rs[0].array, nomask_rs[0].array)
xp.testing.assert_allclose(
nstep_out.array[0, 1], getcon_out.array[0])
xp.testing.assert_allclose(
nstep_out.array[1, 1], getcon_out.array[1])
