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-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 d7ffd184e968..3007f9b46b86 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 cac2d8ad84ff..571505d32677 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 09b511d14e70..af003239f8de 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 69656c8a0fbe..87c3c85ae3e2 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 f87cef73dc16..037596321776 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 1ba61377a0f4..29f4d24d6323 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());
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