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authorGravatar Abseil Team <absl-team@google.com>2019-11-21 10:30:22 -0800
committerGravatar Gennadiy Civil <misterg@google.com>2019-11-22 11:35:22 -0500
commit16d9fd58a51c6083234e2e9f8f50e49ed5ed02e4 (patch)
treec4d5da856d0ee16401bb6d9694c998e51b2f604c /absl
parentbcaae6009c0833b73c6fa7bdd972921d8081a724 (diff)
Export of internal Abseil changes
-- db8dbd0e8a7b0125a4819dfc81c9bd2496849c71 by Abseil Team <absl-team@google.com>: Create GetSkipCount() and GetStride() methods and add rounding bias correction. PiperOrigin-RevId: 281780897 GitOrigin-RevId: db8dbd0e8a7b0125a4819dfc81c9bd2496849c71 Change-Id: I56a97288b1cb38a9357c065747f8d9bc4b187fee
Diffstat (limited to 'absl')
-rw-r--r--absl/base/internal/exponential_biased.cc45
-rw-r--r--absl/base/internal/exponential_biased.h86
-rw-r--r--absl/base/internal/exponential_biased_test.cc35
-rw-r--r--absl/base/internal/periodic_sampler.cc7
-rw-r--r--absl/base/internal/periodic_sampler_benchmark.cc7
-rw-r--r--absl/base/internal/periodic_sampler_test.cc12
6 files changed, 162 insertions, 30 deletions
diff --git a/absl/base/internal/exponential_biased.cc b/absl/base/internal/exponential_biased.cc
index d7ffd184..3007f9b4 100644
--- a/absl/base/internal/exponential_biased.cc
+++ b/absl/base/internal/exponential_biased.cc
@@ -16,6 +16,7 @@
#include <stdint.h>
+#include <algorithm>
#include <atomic>
#include <cmath>
#include <limits>
@@ -26,6 +27,42 @@
namespace absl {
namespace base_internal {
+
+int64_t ExponentialBiased::GetSkipCount(int64_t mean) {
+ if (ABSL_PREDICT_FALSE(!initialized_)) {
+ Initialize();
+ }
+
+ uint64_t rng = NextRandom(rng_);
+ rng_ = rng;
+
+ // Take the top 26 bits as the random number
+ // (This plus the 1<<58 sampling bound give a max possible step of
+ // 5194297183973780480 bytes.)
+ // The uint32_t cast is to prevent a (hard-to-reproduce) NAN
+ // under piii debug for some binaries.
+ double q = static_cast<uint32_t>(rng >> (kPrngNumBits - 26)) + 1.0;
+ // Put the computed p-value through the CDF of a geometric.
+ double interval = bias_ + (std::log2(q) - 26) * (-std::log(2.0) * mean);
+ // Very large values of interval overflow int64_t. To avoid that, we will
+ // cheat and clamp any huge values to (int64_t max)/2. This is a potential
+ // source of bias, but the mean would need to be such a large value that it's
+ // not likely to come up. For example, with a mean of 1e18, the probability of
+ // hitting this condition is about 1/1000. For a mean of 1e17, standard
+ // calculators claim that this event won't happen.
+ if (interval > static_cast<double>(std::numeric_limits<int64_t>::max() / 2)) {
+ // Assume huge values are bias neutral, retain bias for next call.
+ return std::numeric_limits<int64_t>::max() / 2;
+ }
+ double value = std::round(interval);
+ bias_ = interval - value;
+ return value;
+}
+
+int64_t ExponentialBiased::GetStride(int64_t mean) {
+ return GetSkipCount(mean - 1) + 1;
+}
+
// The algorithm generates a random number between 0 and 1 and applies the
// inverse cumulative distribution function for an exponential. Specifically:
// Let m be the inverse of the sample period, then the probability
@@ -51,7 +88,7 @@ int64_t ExponentialBiased::Get(int64_t mean) {
// under piii debug for some binaries.
double q = static_cast<uint32_t>(rng >> (kPrngNumBits - 26)) + 1.0;
// Put the computed p-value through the CDF of a geometric.
- double interval = (std::log2(q) - 26) * (-std::log(2.0) * mean);
+ double interval = bias_ + (std::log2(q) - 26) * (-std::log(2.0) * mean);
// Very large values of interval overflow int64_t. To avoid that, we will cheat
// and clamp any huge values to (int64_t max)/2. This is a potential source of
// bias, but the mean would need to be such a large value that it's not likely
@@ -59,10 +96,12 @@ int64_t ExponentialBiased::Get(int64_t mean) {
// this condition is about 1/1000. For a mean of 1e17, standard calculators
// claim that this event won't happen.
if (interval > static_cast<double>(std::numeric_limits<int64_t>::max() / 2)) {
+ // Assume huge values are bias neutral, retain bias for next call.
return std::numeric_limits<int64_t>::max() / 2;
}
-
- return static_cast<int64_t>(interval);
+ int64_t value = std::max<int64_t>(1, std::round(interval));
+ bias_ = interval - value;
+ return value;
}
void ExponentialBiased::Initialize() {
diff --git a/absl/base/internal/exponential_biased.h b/absl/base/internal/exponential_biased.h
index cac2d8ad..571505d3 100644
--- a/absl/base/internal/exponential_biased.h
+++ b/absl/base/internal/exponential_biased.h
@@ -17,24 +17,56 @@
#include <stdint.h>
+#include "absl/base/macros.h"
+
namespace absl {
namespace base_internal {
// ExponentialBiased provides a small and fast random number generator for a
-// rounded exponential distribution. This generator doesn't requires very little
-// state doesn't impose synchronization overhead, which makes it useful in some
-// specialized scenarios.
+// rounded exponential distribution. This generator manages very little state,
+// and imposes no synchronization overhead. This makes it useful in specialized
+// scenarios requiring minimum overhead, such as stride based periodic sampling.
+//
+// ExponentialBiased provides two closely related functions, GetSkipCount() and
+// GetStride(), both returning a rounded integer defining a number of events
+// required before some event with a given mean probability occurs.
+//
+// The distribution is useful to generate a random wait time or some periodic
+// event with a given mean probability. For example, if an action is supposed to
+// happen on average once every 'N' events, then we can get a random 'stride'
+// counting down how long before the event to happen. For example, if we'd want
+// to sample one in every 1000 'Frobber' calls, our code could look like this:
+//
+// Frobber::Frobber() {
+// stride_ = exponential_biased_.GetStride(1000);
+// }
+//
+// void Frobber::Frob(int arg) {
+// if (--stride == 0) {
+// SampleFrob(arg);
+// stride_ = exponential_biased_.GetStride(1000);
+// }
+// ...
+// }
+//
+// The rounding of the return value creates a bias, especially for smaller means
+// where the distribution of the fraction is not evenly distributed. We correct
+// this bias by tracking the fraction we rounded up or down on each iteration,
+// effectively tracking the distance between the cumulative value, and the
+// rounded cumulative value. For example, given a mean of 2:
//
-// For the generated variable X, X ~ floor(Exponential(1/mean)). The floor
-// operation introduces a small amount of bias, but the distribution is useful
-// to generate a wait time. That is, if an operation is supposed to happen on
-// average to 1/mean events, then the generated variable X will describe how
-// many events to skip before performing the operation and computing a new X.
+// raw = 1.63076, cumulative = 1.63076, rounded = 2, bias = -0.36923
+// raw = 0.14624, cumulative = 1.77701, rounded = 2, bias = 0.14624
+// raw = 4.93194, cumulative = 6.70895, rounded = 7, bias = -0.06805
+// raw = 0.24206, cumulative = 6.95101, rounded = 7, bias = 0.24206
+// etc...
//
-// The mathematically precise distribution to use for integer wait times is a
-// Geometric distribution, but a Geometric distribution takes slightly more time
-// to compute and when the mean is large (say, 100+), the Geometric distribution
-// is hard to distinguish from the result of ExponentialBiased.
+// Adjusting with rounding bias is relatively trivial:
+//
+// double value = bias_ + exponential_distribution(mean)();
+// double rounded_value = std::round(value);
+// bias_ = value - rounded_value;
+// return rounded_value;
//
// This class is thread-compatible.
class ExponentialBiased {
@@ -42,9 +74,32 @@ class ExponentialBiased {
// The number of bits set by NextRandom.
static constexpr int kPrngNumBits = 48;
- // Generates the floor of an exponentially distributed random variable by
- // rounding the value down to the nearest integer. The result will be in the
- // range [0, int64_t max / 2].
+ // `GetSkipCount()` returns the number of events to skip before some chosen
+ // event happens. For example, randomly tossing a coin, we will on average
+ // throw heads once before we get tails. We can simulate random coin tosses
+ // using GetSkipCount() as:
+ //
+ // ExponentialBiased eb;
+ // for (...) {
+ // int number_of_heads_before_tail = eb.GetSkipCount(1);
+ // for (int flips = 0; flips < number_of_heads_before_tail; ++flips) {
+ // printf("head...");
+ // }
+ // printf("tail\n");
+ // }
+ //
+ int64_t GetSkipCount(int64_t mean);
+
+ // GetStride() returns the number of events required for a specific event to
+ // happen. See the class comments for a usage example. `GetStride()` is
+ // equivalent to `GetSkipCount(mean - 1) + 1`. When to use `GetStride()` or
+ // `GetSkipCount()` depends mostly on what best fits the use case.
+ int64_t GetStride(int64_t mean);
+
+ // Generates a rounded exponentially distributed random variable
+ // by rounding the value to the nearest integer.
+ // The result will be in the range [0, int64_t max / 2].
+ ABSL_DEPRECATED("Use GetSkipCount() or GetStride() instead")
int64_t Get(int64_t mean);
// Computes a random number in the range [0, 1<<(kPrngNumBits+1) - 1]
@@ -56,6 +111,7 @@ class ExponentialBiased {
void Initialize();
uint64_t rng_{0};
+ double bias_{0};
bool initialized_{false};
};
diff --git a/absl/base/internal/exponential_biased_test.cc b/absl/base/internal/exponential_biased_test.cc
index 09b511d1..af003239 100644
--- a/absl/base/internal/exponential_biased_test.cc
+++ b/absl/base/internal/exponential_biased_test.cc
@@ -113,6 +113,35 @@ double AndersonDarlingTest(const std::vector<double>& random_sample) {
return p;
}
+TEST(ExponentialBiasedTest, CoinTossDemoWithGetSkipCount) {
+ ExponentialBiased eb;
+ for (int runs = 0; runs < 10; ++runs) {
+ for (int flips = eb.GetSkipCount(1); flips > 0; --flips) {
+ printf("head...");
+ }
+ printf("tail\n");
+ }
+ int heads = 0;
+ for (int i = 0; i < 10000000; i += 1 + eb.GetSkipCount(1)) {
+ ++heads;
+ }
+ printf("Heads = %d (%f%%)\n", heads, 100.0 * heads / 10000000);
+}
+
+TEST(ExponentialBiasedTest, SampleDemoWithStride) {
+ ExponentialBiased eb;
+ int stride = eb.GetStride(10);
+ int samples = 0;
+ for (int i = 0; i < 10000000; ++i) {
+ if (--stride == 0) {
+ ++samples;
+ stride = eb.GetStride(10);
+ }
+ }
+ printf("Samples = %d (%f%%)\n", samples, 100.0 * samples / 10000000);
+}
+
+
// Testing that NextRandom generates uniform random numbers. Applies the
// Anderson-Darling test for uniformity
TEST(ExponentialBiasedTest, TestNextRandom) {
@@ -153,15 +182,15 @@ TEST(ExponentialBiasedTest, TestNextRandom) {
// variable.
TEST(ExponentialBiasedTest, InitializationModes) {
ABSL_CONST_INIT static ExponentialBiased eb_static;
- EXPECT_THAT(eb_static.Get(2), Ge(0));
+ EXPECT_THAT(eb_static.GetSkipCount(2), Ge(0));
#if ABSL_HAVE_THREAD_LOCAL
thread_local ExponentialBiased eb_thread;
- EXPECT_THAT(eb_thread.Get(2), Ge(0));
+ EXPECT_THAT(eb_thread.GetSkipCount(2), Ge(0));
#endif
ExponentialBiased eb_stack;
- EXPECT_THAT(eb_stack.Get(2), Ge(0));
+ EXPECT_THAT(eb_stack.GetSkipCount(2), Ge(0));
}
} // namespace base_internal
diff --git a/absl/base/internal/periodic_sampler.cc b/absl/base/internal/periodic_sampler.cc
index 69656c8a..87c3c85a 100644
--- a/absl/base/internal/periodic_sampler.cc
+++ b/absl/base/internal/periodic_sampler.cc
@@ -22,7 +22,7 @@ namespace absl {
namespace base_internal {
int64_t PeriodicSamplerBase::GetExponentialBiased(int period) noexcept {
- return rng_.Get(period);
+ return rng_.GetStride(period);
}
bool PeriodicSamplerBase::SubtleConfirmSample() noexcept {
@@ -36,13 +36,14 @@ bool PeriodicSamplerBase::SubtleConfirmSample() noexcept {
// Check if this is the first call to Sample()
if (ABSL_PREDICT_FALSE(stride_ == 1)) {
- stride_ = static_cast<uint64_t>(-1 - GetExponentialBiased(current_period));
+ stride_ = static_cast<uint64_t>(-GetExponentialBiased(current_period));
if (static_cast<int64_t>(stride_) < -1) {
++stride_;
return false;
}
}
- stride_ = static_cast<uint64_t>(-1 - GetExponentialBiased(current_period));
+
+ stride_ = static_cast<uint64_t>(-GetExponentialBiased(current_period));
return true;
}
diff --git a/absl/base/internal/periodic_sampler_benchmark.cc b/absl/base/internal/periodic_sampler_benchmark.cc
index f87cef73..03759632 100644
--- a/absl/base/internal/periodic_sampler_benchmark.cc
+++ b/absl/base/internal/periodic_sampler_benchmark.cc
@@ -37,6 +37,13 @@ void BM_SampleMinunumInlined(Sampler* sampler, benchmark::State& state) {
}
}
+void BM_PeriodicSampler_TinySample(benchmark::State& state) {
+ struct Tag {};
+ PeriodicSampler<Tag, 10> sampler;
+ BM_Sample(&sampler, state);
+}
+BENCHMARK(BM_PeriodicSampler_TinySample);
+
void BM_PeriodicSampler_ShortSample(benchmark::State& state) {
struct Tag {};
PeriodicSampler<Tag, 1024> sampler;
diff --git a/absl/base/internal/periodic_sampler_test.cc b/absl/base/internal/periodic_sampler_test.cc
index 1ba61377..29f4d24d 100644
--- a/absl/base/internal/periodic_sampler_test.cc
+++ b/absl/base/internal/periodic_sampler_test.cc
@@ -42,9 +42,9 @@ TEST(PeriodicSamplerBaseTest, Sample) {
EXPECT_CALL(sampler, period()).Times(3).WillRepeatedly(Return(16));
EXPECT_CALL(sampler, GetExponentialBiased(16))
- .WillOnce(Return(1))
.WillOnce(Return(2))
- .WillOnce(Return(3));
+ .WillOnce(Return(3))
+ .WillOnce(Return(4));
EXPECT_FALSE(sampler.Sample());
EXPECT_TRUE(sampler.Sample());
@@ -63,9 +63,9 @@ TEST(PeriodicSamplerBaseTest, ImmediatelySample) {
EXPECT_CALL(sampler, period()).Times(2).WillRepeatedly(Return(16));
EXPECT_CALL(sampler, GetExponentialBiased(16))
- .WillOnce(Return(0))
.WillOnce(Return(1))
- .WillOnce(Return(2));
+ .WillOnce(Return(2))
+ .WillOnce(Return(3));
EXPECT_TRUE(sampler.Sample());
@@ -100,7 +100,7 @@ TEST(PeriodicSamplerBaseTest, Disable) {
StrictMock<MockPeriodicSampler> sampler;
EXPECT_CALL(sampler, period()).WillOnce(Return(16));
- EXPECT_CALL(sampler, GetExponentialBiased(16)).WillOnce(Return(2));
+ EXPECT_CALL(sampler, GetExponentialBiased(16)).WillOnce(Return(3));
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
@@ -119,7 +119,7 @@ TEST(PeriodicSamplerBaseTest, Enable) {
EXPECT_CALL(sampler, period()).Times(2).WillRepeatedly(Return(16));
EXPECT_CALL(sampler, GetExponentialBiased(16))
.Times(2)
- .WillRepeatedly(Return(2));
+ .WillRepeatedly(Return(3));
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());