21813af360
Before and after logic is equivalent. However, performance could have an impact. PiperOrigin-RevId: 304057589 Change-Id: I2ad9e923b1c966f46eba91ae47e0e632b74cff72
171 lines
7.0 KiB
Python
171 lines
7.0 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for stateless random-number generation ops."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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from tensorflow.python.kernel_tests.random import util as \
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random_test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import stateless_random_ops as stateless
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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class StatelessRandomOpsTest(xla_test.XLATestCase):
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"""Test cases for stateless random-number generator operators."""
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def _random_types(self, include_int=False):
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allowed_types = {dtypes.float64, dtypes.float32, dtypes.bfloat16}
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if include_int:
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allowed_types.update({dtypes.int32, dtypes.int64})
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return self.all_tf_types & allowed_types
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def testDeterminism(self):
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# Stateless values should be equal iff the seeds are equal (roughly)
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with self.session(), self.test_scope():
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seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
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seeds = [(x, y) for x in range(5) for y in range(5)] * 3 # pylint: disable=g-complex-comprehension
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for stateless_op in [
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stateless.stateless_random_uniform, stateless.stateless_random_normal
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]:
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for shape in (), (3,), (2, 5):
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for dtype in self._random_types():
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# Skip bfloat16. The result of bfloat16 is truncated from 32-bit
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# result. With different seeds, the 32-bit results are different,
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# but the truncated 16-bit results might be the same.
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if dtype == dtypes.bfloat16:
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continue
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pure = stateless_op(shape, seed=seed_t, dtype=dtype)
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values = [(seed, pure.eval(feed_dict={
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seed_t: seed
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})) for seed in seeds]
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for s0, v0 in values:
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for s1, v1 in values:
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self.assertEqual(s0 == s1, np.all(v0 == v1))
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def testRandomUniformIsInRange(self):
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with self.session() as sess, self.test_scope():
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for dtype in self._random_types(include_int=True):
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maxval = 1
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if dtype.is_integer:
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maxval = 100
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seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
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x = stateless.stateless_random_uniform(
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shape=[1000], seed=seed_t, maxval=maxval, dtype=dtype)
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y = sess.run(x, {seed_t: [0x12345678, 0xabcdef1]})
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self.assertTrue(np.all(y >= 0))
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self.assertTrue(np.all(y < maxval))
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def testDistributionOfStatelessRandomUniform(self):
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"""Use Pearson's Chi-squared test to test for uniformity."""
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with self.session() as sess, self.test_scope():
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for dtype in self._random_types(include_int=True):
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seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
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n = 1000
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maxval = 1
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if dtype.is_integer:
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maxval = 100
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x = stateless.stateless_random_uniform(
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shape=[n], seed=seed_t, maxval=maxval, dtype=dtype)
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y = sess.run(x, {seed_t: [565656, 121212]})
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# Convert y to float and normalize its value to range [0, 1) when
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# maxval != 1.
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y = y.astype(float) / maxval
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# Tests that the values are distributed amongst 10 bins with equal
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# probability. 16.92 is the Chi^2 value for 9 degrees of freedom with
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# p=0.05. This test is probabilistic and would be flaky if the random
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# seed were not fixed.
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self.assertLess(random_test_util.chi_squared(y, 10), 16.92)
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def testRandomNormalIsFinite(self):
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with self.session() as sess, self.test_scope():
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for dtype in self._random_types():
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seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
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x = stateless.stateless_random_normal(
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shape=[10000], seed=seed_t, dtype=dtype)
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y = sess.run(x, {seed_t: [0x12345678, 0xabcdef1]})
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self.assertTrue(np.all(np.isfinite(y)))
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def testDistributionOfStatelessRandomNormal(self):
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"""Use Anderson-Darling test to test distribution appears normal."""
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with self.session() as sess, self.test_scope():
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for dtype in self._random_types():
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seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
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n = 1000
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x = stateless.stateless_random_normal(
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shape=[n], seed=seed_t, dtype=dtype)
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y = sess.run(x, {seed_t: [25252, 314159]})
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# The constant 2.492 is the 5% critical value for the Anderson-Darling
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# test where the mean and variance are known. This test is probabilistic
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# so to avoid flakiness the seed is fixed.
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self.assertLess(
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random_test_util.anderson_darling(y.astype(float)), 2.492)
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def testTruncatedNormal(self):
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for dtype in self._random_types():
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with self.session() as sess, self.test_scope():
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seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
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n = 10000000
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x = stateless.stateless_truncated_normal(
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shape=[n], seed=seed_t, dtype=dtype)
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y = sess.run(x, {seed_t: [0x12345678, 0xabcdef1]})
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random_test_util.test_truncated_normal(
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self.assertEqual, self.assertAllClose, n, y,
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variance_rtol=6e-3 if dtype == dtypes.bfloat16 else 1e-3)
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class StatelessRandomOpsBenchmark(test.Benchmark):
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"""Microbenchmarks for the stateless random ops."""
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def _benchmarkUniform(self, name, dtype, use_xla_jit):
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def BuilderFn():
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shape = (10, 1000, 1000)
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seed_var = variables.Variable((312, 456),
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dtype=dtypes.int32,
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name='input')
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random_t = stateless.stateless_random_uniform(
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shape, seed=seed_var, dtype=dtype)
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return '%s.shape%s' % (name, shape), [random_t]
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xla_test.Benchmark(self, BuilderFn, use_xla_jit=use_xla_jit, device='cpu')
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def benchmarkUniformF32(self):
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self._benchmarkUniform(
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'uniform_f32', dtype=dtypes.float32, use_xla_jit=False)
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def benchmarkUniformF64(self):
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self._benchmarkUniform(
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'uniform_f64', dtype=dtypes.float64, use_xla_jit=False)
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def benchmarkUniformF32XLA(self):
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self._benchmarkUniform(
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'uniform_f32', dtype=dtypes.float32, use_xla_jit=True)
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def benchmarkUniformF64XLA(self):
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self._benchmarkUniform(
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'uniform_f64', dtype=dtypes.float64, use_xla_jit=True)
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if __name__ == '__main__':
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test.main()
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