def testStateSaverFailsIfPaddedLengthIsNotMultipleOfNumUnroll(self):
with self.test_session() as sess:
batch_size = constant_op.constant(32)
num_unroll = 17
bad_padded_length = 3
length = array_ops.placeholder(dtypes.int32)
key = array_ops.placeholder(dtypes.string)
sequences = {
"seq1": array_ops.placeholder(dtypes.float32, shape=(None, 5))
}
context = {}
initial_states = {
"state1": array_ops.placeholder(dtypes.float32, shape=())
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states)
with self.assertRaisesOpError(
"should be a multiple of: 17, but saw value: %d" %
bad_padded_length):
sess.run(
[state_saver.prefetch_op],
feed_dict={
length: 1,
key: "key",
sequences["seq1"]:
np.random.rand(bad_padded_length, 5),
initial_states["state1"]: 1.0
})
def _testStateSaverFailsIfCapacityTooSmall(self, batch_size):
with self.test_session() as sess:
num_unroll = 2
length = array_ops.placeholder(dtypes.int32)
key = array_ops.placeholder(dtypes.string)
sequences = {
"seq1": array_ops.placeholder(dtypes.float32, shape=(None, 5)),
"seq2": array_ops.placeholder(dtypes.float32, shape=(None, ))
}
context = {}
initial_states = {
"state1": array_ops.placeholder(dtypes.float32, shape=())
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states,
capacity=10)
sess.run(
[state_saver.prefetch_op],
feed_dict={
length: 1,
key: "key",
sequences["seq1"]: np.random.rand(num_unroll, 5),
sequences["seq2"]: np.random.rand(num_unroll),
initial_states["state1"]: 1.0
})
def testStateSaverScopeNames(self):
batch_size = constant_op.constant(2)
sqss_scope_name = "unique_scope_name_for_sqss"
num_unroll = 2
length = 3
key = string_ops.string_join([
"key_", string_ops.as_string(
math_ops.cast(10000 * random_ops.random_uniform(()), dtypes.int32))
])
padded_length = 4
sequences = {
"seq1": np.random.rand(padded_length, 5),
"seq2": np.random.rand(padded_length, 4, 2)
}
context = {"context1": [3, 4]}
initial_states = {
"state1": np.random.rand(6, 7),
"state2": np.random.rand(8)
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states,
name=sqss_scope_name)
prefetch_op = state_saver.prefetch_op
next_batch = state_saver.next_batch
self.assertTrue(
state_saver.barrier.barrier_ref.name.startswith("%s/" %
sqss_scope_name))
self.assertTrue(prefetch_op.name.startswith("%s/" % sqss_scope_name))
self.assertTrue(next_batch.key.name.startswith("%s/" % sqss_scope_name))
def testStateSaverFailsIfInconsistentWriteState(self):
# TODO(b/26910386): Identify why this infrequently causes timeouts.
with self.test_session() as sess:
batch_size = constant_op.constant(1)
num_unroll = 17
length = array_ops.placeholder(dtypes.int32)
key = array_ops.placeholder(dtypes.string)
sequences = {
"seq1": array_ops.placeholder(dtypes.float32, shape=(None, 5))
}
context = {}
initial_states = {
"state1": array_ops.placeholder(dtypes.float32, shape=())
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states)
next_batch = state_saver.next_batch
with self.assertRaisesRegexp(KeyError,
"state was not declared: state2"):
save_op = next_batch.save_state("state2", None)
with self.assertRaisesRegexp(ValueError,
"Rank check failed for.*state1"):
save_op = next_batch.save_state("state1", np.random.rand(1, 1))
with self.assertRaisesOpError(
r"convert_state1:0 should be: 1, shape received:\] \[1 1\]"
):
state_input = array_ops.placeholder(dtypes.float32)
with ops.control_dependencies([state_saver.prefetch_op]):
save_op = next_batch.save_state("state1", state_input)
sess.run(
[save_op],
feed_dict={
length: 1,
key: "key",
sequences["seq1"]: np.random.rand(num_unroll, 5),
initial_states["state1"]: 1.0,
state_input: np.random.rand(1, 1)
})
def testStateSaverFailsIfInconsistentPaddedLength(self):
with self.cached_session() as sess:
batch_size = constant_op.constant(32)
num_unroll = 17
length = array_ops.placeholder(dtypes.int32)
key = array_ops.placeholder(dtypes.string)
sequences = {
"seq1": array_ops.placeholder(
dtypes.float32, shape=(None, 5)),
"seq2": array_ops.placeholder(
dtypes.float32, shape=(None,))
}
context = {}
initial_states = {
"state1": array_ops.placeholder(
dtypes.float32, shape=())
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states)
with self.assertRaisesOpError(
"Dimension 0 of tensor labeled sorted_sequences_seq2 "
"should be: %d, shape received: %d" % (num_unroll, 2 * num_unroll)):
sess.run([state_saver.prefetch_op],
feed_dict={
length: 1,
key: "key",
sequences["seq1"]: np.random.rand(num_unroll, 5),
sequences["seq2"]: np.random.rand(2 * num_unroll),
initial_states["state1"]: 1.0
})
def testStateSaverWithTwoSimpleSteps(self):
with self.test_session() as sess:
batch_size_value = 2
batch_size = constant_op.constant(batch_size_value)
num_unroll = 2
length = 3
key = string_ops.string_join([
"key_",
string_ops.as_string(
math_ops.cast(10000 * random_ops.random_uniform(()),
dtypes.int32))
])
padded_length = 4
sequences = {
"seq1": np.random.rand(padded_length, 5),
"seq2": np.random.rand(padded_length, 4, 2)
}
context = {"context1": [3, 4]}
initial_states = {
"state1": np.random.rand(6, 7),
"state2": np.random.rand(8)
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states)
initial_key_value_0, _ = sess.run((key, state_saver.prefetch_op))
initial_key_value_1, _ = sess.run((key, state_saver.prefetch_op))
initial_key_value_0 = initial_key_value_0.decode("ascii")
initial_key_value_1 = initial_key_value_1.decode("ascii")
# Step 1
next_batch = state_saver.next_batch
(key_value, next_key_value, seq1_value, seq2_value, context1_value,
state1_value, state2_value, length_value, _, _) = sess.run(
(next_batch.key, next_batch.next_key,
next_batch.sequences["seq1"], next_batch.sequences["seq2"],
next_batch.context["context1"], next_batch.state("state1"),
next_batch.state("state2"), next_batch.length,
next_batch.save_state("state1",
next_batch.state("state1") + 1),
next_batch.save_state("state2",
next_batch.state("state2") - 1)))
expected_first_keys = set(
("00000_of_00002:%s" % x).encode("ascii")
for x in (initial_key_value_0, initial_key_value_1))
expected_second_keys = set(
("00001_of_00002:%s" % x).encode("ascii")
for x in (initial_key_value_0, initial_key_value_1))
expected_final_keys = set(
("STOP:%s" % x).encode("ascii")
for x in (initial_key_value_0, initial_key_value_1))
self.assertEqual(set(key_value), expected_first_keys)
self.assertEqual(set(next_key_value), expected_second_keys)
self.assertAllEqual(
context1_value,
np.tile(context["context1"], (batch_size_value, 1)))
self.assertAllEqual(
seq1_value,
np.tile(sequences["seq1"][np.newaxis, 0:2, :],
(batch_size_value, 1, 1)))
self.assertAllEqual(
seq2_value,
np.tile(sequences["seq2"][np.newaxis, 0:2, :, :],
(batch_size_value, 1, 1, 1)))
self.assertAllEqual(
state1_value,
np.tile(initial_states["state1"], (batch_size_value, 1, 1)))
self.assertAllEqual(
state2_value,
np.tile(initial_states["state2"], (batch_size_value, 1)))
self.assertAllEqual(length_value, [2, 2])
# Step 2
(key_value, next_key_value, seq1_value, seq2_value, context1_value,
state1_value, state2_value, length_value, _, _) = sess.run(
(next_batch.key, next_batch.next_key,
next_batch.sequences["seq1"], next_batch.sequences["seq2"],
next_batch.context["context1"], next_batch.state("state1"),
next_batch.state("state2"), next_batch.length,
next_batch.save_state("state1",
next_batch.state("state1") + 1),
next_batch.save_state("state2",
next_batch.state("state2") - 1)))
self.assertEqual(set(key_value), expected_second_keys)
self.assertEqual(set(next_key_value), expected_final_keys)
self.assertAllEqual(
context1_value,
np.tile(context["context1"], (batch_size_value, 1)))
self.assertAllEqual(
seq1_value,
np.tile(sequences["seq1"][np.newaxis, 2:4, :],
(batch_size_value, 1, 1)))
self.assertAllEqual(
seq2_value,
np.tile(sequences["seq2"][np.newaxis, 2:4, :, :],
(batch_size_value, 1, 1, 1)))
self.assertAllEqual(
state1_value, 1 + np.tile(initial_states["state1"],
(batch_size_value, 1, 1)))
self.assertAllEqual(
state2_value, -1 + np.tile(initial_states["state2"],
(batch_size_value, 1)))
self.assertAllEqual(length_value, [1, 1])
# Finished. Let's make sure there's nothing left in the barrier.
self.assertEqual(0, state_saver.barrier.ready_size().eval())
def testStateSaverCanHandleVariableBatchsize(self):
with self.test_session() as sess:
batch_size = array_ops.placeholder(dtypes.int32)
num_unroll = 17
length = array_ops.placeholder(dtypes.int32)
key = array_ops.placeholder(dtypes.string)
sequences = {
"seq1": array_ops.placeholder(dtypes.float32, shape=(None, 5))
}
context = {
"context1": array_ops.placeholder(dtypes.string, shape=(3, 4))
}
initial_states = {
"state1": array_ops.placeholder(dtypes.float32, shape=())
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states)
next_batch = state_saver.next_batch
update = next_batch.save_state("state1",
1 + next_batch.state("state1"))
for insert_key in range(128):
# Insert varying length inputs
sess.run(
[state_saver.prefetch_op],
feed_dict={
length: np.random.randint(2 * num_unroll),
key: "%05d" % insert_key,
sequences["seq1"]: np.random.rand(2 * num_unroll, 5),
context["context1"]: np.random.rand(3,
4).astype(np.str),
initial_states["state1"]: 0.0
})
all_received_indices = []
# Pull out and validate batch sizes 0, 1, ..., 7
for batch_size_value in range(8):
got_keys, input_index, context1, seq1, state1, _ = sess.run(
[
next_batch.key, next_batch.insertion_index,
next_batch.context["context1"],
next_batch.sequences["seq1"],
next_batch.state("state1"), update
],
feed_dict={batch_size: batch_size_value})
# Indices may have come in out of order within the batch
all_received_indices.append(input_index.tolist())
self.assertEqual(got_keys.size, batch_size_value)
self.assertEqual(input_index.size, batch_size_value)
self.assertEqual(context1.shape, (batch_size_value, 3, 4))
self.assertEqual(seq1.shape, (batch_size_value, num_unroll, 5))
self.assertEqual(state1.shape, (batch_size_value, ))
# Each input was split into 2 iterations (sequences size == 2*num_unroll)
expected_indices = [[], [0], [0, 1], [1, 2, 3], [2, 3, 4, 5],
[4, 5, 6, 7, 8], [6, 7, 8, 9, 10, 11],
[9, 10, 11, 12, 13, 14, 15]]
self.assertEqual(len(all_received_indices), len(expected_indices))
for received, expected in zip(all_received_indices,
expected_indices):
self.assertAllEqual([x + 2**63 for x in received], expected)
def testStateSaverProcessesExamplesInOrder(self):
with self.test_session() as sess:
batch_size_value = 32
batch_size = constant_op.constant(batch_size_value)
num_unroll = 17
length = array_ops.placeholder(dtypes.int32)
key = array_ops.placeholder(dtypes.string)
sequences = {
"seq1": array_ops.placeholder(dtypes.float32, shape=(None, 5))
}
context = {
"context1": array_ops.placeholder(dtypes.string, shape=(3, 4))
}
initial_states = {
"state1": array_ops.placeholder(dtypes.float32, shape=())
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states)
next_batch = state_saver.next_batch
update = next_batch.save_state("state1",
1 + next_batch.state("state1"))
get_ready_size = state_saver.barrier.ready_size()
get_incomplete_size = state_saver.barrier.incomplete_size()
global_insert_key = [0]
def insert(insert_key):
# Insert varying length inputs
sess.run(
[state_saver.prefetch_op],
feed_dict={
length: np.random.randint(2 * num_unroll),
key: "%05d" % insert_key[0],
sequences["seq1"]: np.random.rand(2 * num_unroll, 5),
context["context1"]: np.random.rand(3,
4).astype(np.str),
initial_states["state1"]: 0.0
})
insert_key[0] += 1
for _ in range(batch_size_value * 100):
insert(global_insert_key)
def process_and_validate(check_key):
true_step = int(check_key[0] / 2) # Each entry has two slices
check_key[0] += 1
got_keys, input_index, _ = sess.run(
[next_batch.key, next_batch.insertion_index, update])
decoded_keys = [
int(x.decode("ascii").split(":")[-1]) for x in got_keys
]
min_key = min(decoded_keys)
min_index = int(min(input_index)) # numpy scalar
max_key = max(decoded_keys)
max_index = int(max(input_index)) # numpy scalar
# The current min key should be above the previous min
self.assertEqual(min_key, true_step * batch_size_value)
self.assertEqual(max_key,
(true_step + 1) * batch_size_value - 1)
self.assertEqual(2**63 + min_index,
true_step * batch_size_value)
self.assertEqual(2**63 + max_index,
(true_step + 1) * batch_size_value - 1)
# There are now (batch_size * 100 * 2) / batch_size = 200 full steps
global_step_key = [0]
for _ in range(200):
process_and_validate(global_step_key)
# Processed everything in the queue
self.assertEqual(get_incomplete_size.eval(), 0)
self.assertEqual(get_ready_size.eval(), 0)
def testStateSaverWithManyInputsReadWriteThread(self):
batch_size_value = 32
num_proc_threads = 100
with self.test_session() as sess:
batch_size = constant_op.constant(batch_size_value)
num_unroll = 17
length = array_ops.placeholder(dtypes.int32)
key = array_ops.placeholder(dtypes.string)
sequences = {
"seq1": array_ops.placeholder(dtypes.float32, shape=(None, 5)),
"seq2": array_ops.placeholder(dtypes.float32,
shape=(None, 4, 2)),
"seq3": array_ops.placeholder(dtypes.float64, shape=(None, ))
}
context = {
"context1": array_ops.placeholder(dtypes.string, shape=(3, 4)),
"context2": array_ops.placeholder(dtypes.int64, shape=())
}
initial_states = {
"state1": array_ops.placeholder(dtypes.float32, shape=(6, 7)),
"state2": array_ops.placeholder(dtypes.int32, shape=())
}
state_saver = sqss.SequenceQueueingStateSaver(
batch_size=batch_size,
num_unroll=num_unroll,
input_length=length,
input_key=key,
input_sequences=sequences,
input_context=context,
initial_states=initial_states)
next_batch = state_saver.next_batch
cancel_op = state_saver.close(cancel_pending_enqueues=True)
update_1 = next_batch.save_state("state1",
1 + next_batch.state("state1"))
update_2 = next_batch.save_state("state2",
-1 + next_batch.state("state2"))
original_values = dict()
def insert(which):
for i in range(20):
# Insert varying length inputs
pad_i = num_unroll * (1 + (i % 10))
length_i = int(np.random.rand() * pad_i)
key_value = "key_%02d_%04d" % (which, i)
stored_state = {
"length":
length_i,
"seq1":
np.random.rand(pad_i, 5),
"seq2":
np.random.rand(pad_i, 4, 2),
"seq3":
np.random.rand(pad_i),
"context1":
np.random.rand(3, 4).astype(np.str),
"context2":
np.asarray(100 * np.random.rand(), dtype=np.int32),
"state1":
np.random.rand(6, 7),
"state2":
np.asarray(100 * np.random.rand(), dtype=np.int32)
}
original_values[key_value] = stored_state
sess.run(
[state_saver.prefetch_op],
feed_dict={
length: stored_state["length"],
key: key_value,
sequences["seq1"]: stored_state["seq1"],
sequences["seq2"]: stored_state["seq2"],
sequences["seq3"]: stored_state["seq3"],
context["context1"]: stored_state["context1"],
context["context2"]: stored_state["context2"],
initial_states["state1"]: stored_state["state1"],
initial_states["state2"]: stored_state["state2"]
})
processed_count = [0]
def process_and_check_state():
next_batch = state_saver.next_batch
while True:
try:
(got_key, next_key, length, total_length, sequence,
sequence_count, context1, context2, seq1, seq2, seq3,
state1, state2, _, _) = (sess.run([
next_batch.key, next_batch.next_key,
next_batch.length, next_batch.total_length,
next_batch.sequence, next_batch.sequence_count,
next_batch.context["context1"],
next_batch.context["context2"],
next_batch.sequences["seq1"],
next_batch.sequences["seq2"],
next_batch.sequences["seq3"],
next_batch.state("state1"),
next_batch.state("state2"), update_1, update_2
]))
except errors_impl.OutOfRangeError:
# SQSS has been closed
break
self.assertEqual(len(got_key), batch_size_value)
processed_count[0] += len(got_key)
for i in range(batch_size_value):
key_name = got_key[i].decode("ascii").split(":")[1]
# We really saved this unique key
self.assertTrue(key_name in original_values)
# The unique key matches next_key
self.assertEqual(
key_name,
next_key[i].decode("ascii").split(":")[1])
# Pull out the random values we used to create this example
stored_state = original_values[key_name]
self.assertEqual(total_length[i],
stored_state["length"])
self.assertEqual(
"%05d_of_%05d:%s" %
(sequence[i], sequence_count[i], key_name),
got_key[i].decode("ascii"))
expected_length = max(
0,
min(
num_unroll, stored_state["length"] -
sequence[i] * num_unroll))
self.assertEqual(length[i], expected_length)
expected_state1 = stored_state["state1"] + sequence[i]
expected_state2 = stored_state["state2"] - sequence[i]
expected_sequence1 = stored_state[
"seq1"][sequence[i] *
num_unroll:(sequence[i] + 1) * num_unroll]
expected_sequence2 = stored_state[
"seq2"][sequence[i] *
num_unroll:(sequence[i] + 1) * num_unroll]
expected_sequence3 = stored_state[
"seq3"][sequence[i] *
num_unroll:(sequence[i] + 1) * num_unroll]
self.assertAllClose(state1[i], expected_state1)
self.assertAllEqual(state2[i], expected_state2)
# context1 is strings, which come back as bytes
self.assertAllEqual(context1[i].astype(np.str),
stored_state["context1"])
self.assertAllEqual(context2[i],
stored_state["context2"])
self.assertAllClose(seq1[i], expected_sequence1)
self.assertAllClose(seq2[i], expected_sequence2)
self.assertAllClose(seq3[i], expected_sequence3)
# Total number of inserts will be a multiple of batch_size
insert_threads = [
self.checkedThread(insert, args=(which, ))
for which in range(batch_size_value)
]
process_threads = [
self.checkedThread(process_and_check_state)
for _ in range(num_proc_threads)
]
for t in insert_threads:
t.start()
for t in process_threads:
t.start()
for t in insert_threads:
t.join()
time.sleep(3) # Allow the threads to run and process for a while
cancel_op.run()
for t in process_threads:
t.join()
# Each thread processed at least 2 sequence segments
self.assertGreater(processed_count[0], 2 * 20 * batch_size_value)
