def testMatchStatefulMultinomial(self):
# Stateless ops should be the same as stateful ops on the first call
# after seed scrambling.
key = 0x3ec8f720, 0x02461e29
num_samples = 4
for logits_dtype in np.float16, np.float32, np.float64:
for output_dtype in dtypes.int32, dtypes.int64:
for seed in (7, 17), (11, 5), (2, 3):
preseed = invert_philox(
key, (seed[0], 0, seed[1], 0)).astype(np.uint64)
preseed = preseed[::2] | preseed[1::2] << 32
random_seed.set_random_seed(seed[0])
with self.test_session(use_gpu=True):
for logits in ([[0.1, 0.25, 0.5, 0.15]], [[0.5, 0.5],
[0.8, 0.2],
[0.25,
0.75]]):
logits_t = constant_op.constant(logits,
dtype=logits_dtype)
stateful = random_ops.multinomial(
logits_t,
num_samples,
seed=seed[1],
output_dtype=output_dtype)
pure = stateless.stateless_multinomial(
logits_t,
num_samples,
seed=preseed,
output_dtype=output_dtype)
self.assertAllEqual(stateful.eval(), pure.eval())
def testMatchStatefulMultinomial(self):
# Stateless ops should be the same as stateful ops on the first call
# after seed scrambling.
key = 0x3ec8f720, 0x02461e29
num_samples = 4
for logits_dtype in np.float16, np.float32, np.float64:
for output_dtype in dtypes.int32, dtypes.int64:
for seed in (7, 17), (11, 5), (2, 3):
preseed = invert_philox(key,
(seed[0], 0, seed[1], 0)).astype(np.uint64)
preseed = preseed[::2] | preseed[1::2] << 32
random_seed.set_random_seed(seed[0])
with self.test_session(use_gpu=True):
for logits in ([[0.1, 0.25, 0.5, 0.15]], [[0.5, 0.5], [0.8, 0.2],
[0.25, 0.75]]):
logits_t = constant_op.constant(logits, dtype=logits_dtype)
stateful = random_ops.multinomial(
logits_t,
num_samples,
seed=seed[1],
output_dtype=output_dtype)
pure = stateless.stateless_multinomial(
logits_t,
num_samples,
seed=preseed,
output_dtype=output_dtype)
self.assertAllEqual(stateful.eval(), pure.eval())
def testDeterminismMultinomial(self):
# Stateless values should be equal iff the seeds are equal (roughly)
num_samples = 10
with self.test_session(use_gpu=True):
for seed_type in [dtypes.int32, dtypes.int64]:
seed_t = array_ops.placeholder(seed_type, shape=[2])
seeds = [(x, y) for x in range(5) for y in range(5)] * 3
for logits in ([[0.1, 0.25, 0.5, 0.15]], [[0.5, 0.5], [0.8, 0.2],
[0.25, 0.75]]):
pure = stateless.stateless_multinomial(
logits, num_samples, seed=seed_t)
values = [
(seed, pure.eval(feed_dict={seed_t: seed})) for seed in seeds
]
for s0, v0 in values:
for s1, v1 in values:
self.assertEqual(s0 == s1, np.all(v0 == v1))
def testDeterminismMultinomial(self):
# Stateless values should be equal iff the seeds are equal (roughly)
num_samples = 10
with self.test_session(use_gpu=True):
for seed_type in [dtypes.int32, dtypes.int64]:
seed_t = array_ops.placeholder(seed_type, shape=[2])
seeds = [(x, y) for x in range(5) for y in range(5)] * 3
for logits in ([[0.1, 0.25, 0.5, 0.15]], [[0.5, 0.5],
[0.8, 0.2],
[0.25, 0.75]]):
pure = stateless.stateless_multinomial(logits,
num_samples,
seed=seed_t)
values = [(seed, pure.eval(feed_dict={seed_t: seed}))
for seed in seeds]
for s0, v0 in values:
for s1, v1 in values:
self.assertEqual(s0 == s1, np.all(v0 == v1))
def select_dataset(logits, seed):
return array_ops.squeeze(stateless.stateless_multinomial(logits,
1,
seed=seed),
axis=[0, 1])
def select_dataset_varying_logits(logits, seed):
return array_ops.squeeze(
stateless.stateless_multinomial(logits, 1, seed=seed), axis=[0, 1])
def select_dataset(seed):
return array_ops.squeeze(
stateless.stateless_multinomial([logits], 1, seed=seed), axis=[0, 1])