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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for stateless random ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import numpy as np
from tensorflow.contrib import stateless
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import random_seed
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test
def invert_philox(key, value):
"""Invert the Philox bijection."""
key = np.array(key, dtype=np.uint32)
value = np.array(value, dtype=np.uint32)
step = np.array([0x9E3779B9, 0xBB67AE85], dtype=np.uint32)
for n in range(10)[::-1]:
key0, key1 = key + n * step
v0 = value[3] * 0x991a7cdb & 0xffffffff
v2 = value[1] * 0x6d7cae67 & 0xffffffff
hi0 = v0 * 0xD2511F53 >> 32
hi1 = v2 * 0xCD9E8D57 >> 32
v1 = hi1 ^ value[0] ^ key0
v3 = hi0 ^ value[2] ^ key1
value = v0, v1, v2, v3
return np.array(value)
class StatelessOpsTest(test.TestCase):
def _test_match(self, cases):
# Stateless ops should be the same as stateful ops on the first call
# after seed scrambling.
cases = tuple(cases)
key = 0x3ec8f720, 0x02461e29
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 stateless_op, stateful_op in cases:
stateful = stateful_op(seed=seed[1])
pure = stateless_op(seed=preseed)
self.assertAllEqual(stateful.eval(), pure.eval())
def _test_determinism(self, cases):
# Stateless values should be equal iff the seeds are equal (roughly)
cases = tuple(cases)
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 stateless_op, _ in cases:
pure = stateless_op(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 _float_cases(self, shape_dtypes=(None,)):
float_cases = (
# Uniform distribution, with and without range
(stateless.stateless_random_uniform, random_ops.random_uniform, {}),
(stateless.stateless_random_uniform, random_ops.random_uniform,
dict(minval=2.2, maxval=7.1)),
# Normal distribution, with and without mean+stddev
(stateless.stateless_random_normal, random_ops.random_normal, {}),
(stateless.stateless_random_normal, random_ops.random_normal,
dict(mean=2, stddev=3)),
# Truncated normal distribution, with and without mean+stddev
(stateless.stateless_truncated_normal, random_ops.truncated_normal, {}),
(stateless.stateless_truncated_normal, random_ops.truncated_normal,
dict(mean=3, stddev=4)),
)
for dtype in dtypes.float16, dtypes.float32, dtypes.float64:
for shape_dtype in shape_dtypes:
for shape in (), (3,), (2, 5):
if shape_dtype is not None:
shape = constant_op.constant(shape, dtype=shape_dtype)
for stateless_op, stateful_op, kwds in float_cases:
kwds = dict(shape=shape, dtype=dtype, **kwds)
yield (functools.partial(stateless_op, **kwds),
functools.partial(stateful_op, **kwds))
def _int_cases(self, shape_dtypes=(None,)):
for shape_dtype in shape_dtypes:
for shape in (), (3,), (2, 5):
if shape_dtype is not None:
shape = constant_op.constant(shape, dtype=shape_dtype)
for dtype in dtypes.int32, dtypes.int64:
kwds = dict(minval=2, maxval=11111, dtype=dtype, shape=shape)
yield (functools.partial(stateless.stateless_random_uniform, **kwds),
functools.partial(random_ops.random_uniform, **kwds))
def _multinomial_cases(self):
num_samples = 10
for logits_dtype in np.float16, np.float32, np.float64:
for output_dtype in dtypes.int32, dtypes.int64:
for logits in ([[0.1, 0.25, 0.5, 0.15]], [[0.5, 0.5], [0.8, 0.2],
[0.25, 0.75]]):
kwds = dict(
logits=constant_op.constant(logits, dtype=logits_dtype),
num_samples=num_samples,
output_dtype=output_dtype)
yield (functools.partial(stateless.stateless_multinomial, **kwds),
functools.partial(random_ops.multinomial, **kwds))
def testMatchFloat(self):
self._test_match(self._float_cases())
def testMatchInt(self):
self._test_match(self._int_cases())
def testMatchMultinomial(self):
self._test_match(self._multinomial_cases())
def testDeterminismFloat(self):
self._test_determinism(
self._float_cases(shape_dtypes=(dtypes.int32, dtypes.int64)))
def testDeterminismInt(self):
self._test_determinism(
self._int_cases(shape_dtypes=(dtypes.int32, dtypes.int64)))
def testDeterminismMultinomial(self):
self._test_determinism(self._multinomial_cases())
if __name__ == '__main__':
test.main()
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