Adding upstream version 124.0.1.upstream/124.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 381 insertions, 0 deletions

diff --git a/mfbt/FastBernoulliTrial.h b/mfbt/FastBernoulliTrial.h
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+/* -*- Mode: C++; tab-width: 8; indent-tabs-mode: nil; c-basic-offset: 2 -*- */
+/* vim: set ts=8 sts=2 et sw=2 tw=80: */
+/* This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
+
+#ifndef mozilla_FastBernoulliTrial_h
+#define mozilla_FastBernoulliTrial_h
+
+#include "mozilla/Assertions.h"
+#include "mozilla/XorShift128PlusRNG.h"
+
+#include <cmath>
+#include <stdint.h>
+
+namespace mozilla {
+
+/**
+ * class FastBernoulliTrial: Efficient sampling with uniform probability
+ *
+ * When gathering statistics about a program's behavior, we may be observing
+ * events that occur very frequently (e.g., function calls or memory
+ * allocations) and we may be gathering information that is somewhat expensive
+ * to produce (e.g., call stacks). Sampling all the events could have a
+ * significant impact on the program's performance.
+ *
+ * Why not just sample every N'th event? This technique is called "systematic
+ * sampling"; it's simple and efficient, and it's fine if we imagine a
+ * patternless stream of events. But what if we're sampling allocations, and the
+ * program happens to have a loop where each iteration does exactly N
+ * allocations? You would end up sampling the same allocation every time through
+ * the loop; the entire rest of the loop becomes invisible to your measurements!
+ * More generally, if each iteration does M allocations, and M and N have any
+ * common divisor at all, most allocation sites will never be sampled. If
+ * they're both even, say, the odd-numbered allocations disappear from your
+ * results.
+ *
+ * Ideally, we'd like each event to have some probability P of being sampled,
+ * independent of its neighbors and of its position in the sequence. This is
+ * called "Bernoulli sampling", and it doesn't suffer from any of the problems
+ * mentioned above.
+ *
+ * One disadvantage of Bernoulli sampling is that you can't be sure exactly how
+ * many samples you'll get: technically, it's possible that you might sample
+ * none of them, or all of them. But if the number of events N is large, these
+ * aren't likely outcomes; you can generally expect somewhere around P * N
+ * events to be sampled.
+ *
+ * The other disadvantage of Bernoulli sampling is that you have to generate a
+ * random number for every event, which can be slow.
+ *
+ * [significant pause]
+ *
+ * BUT NOT WITH THIS CLASS! FastBernoulliTrial lets you do true Bernoulli
+ * sampling, while generating a fresh random number only when we do decide to
+ * sample an event, not on every trial. When it decides not to sample, a call to
+ * |FastBernoulliTrial::trial| is nothing but decrementing a counter and
+ * comparing it to zero. So the lower your sampling probability is, the less
+ * overhead FastBernoulliTrial imposes.
+ *
+ * Probabilities of 0 and 1 are handled efficiently. (In neither case need we
+ * ever generate a random number at all.)
+ *
+ * The essential API:
+ *
+ * - FastBernoulliTrial(double P)
+ *   Construct an instance that selects events with probability P.
+ *
+ * - FastBernoulliTrial::trial()
+ *   Return true with probability P. Call this each time an event occurs, to
+ *   decide whether to sample it or not.
+ *
+ * - FastBernoulliTrial::trial(size_t n)
+ *   Equivalent to calling trial() |n| times, and returning true if any of those
+ *   calls do. However, like trial, this runs in fast constant time.
+ *
+ *   What is this good for? In some applications, some events are "bigger" than
+ *   others. For example, large allocations are more significant than small
+ *   allocations. Perhaps we'd like to imagine that we're drawing allocations
+ *   from a stream of bytes, and performing a separate Bernoulli trial on every
+ *   byte from the stream. We can accomplish this by calling |t.trial(S)| for
+ *   the number of bytes S, and sampling the event if that returns true.
+ *
+ *   Of course, this style of sampling needs to be paired with analysis and
+ *   presentation that makes the size of the event apparent, lest trials with
+ *   large values for |n| appear to be indistinguishable from those with small
+ *   values for |n|.
+ */
+class FastBernoulliTrial {
+  /*
+   * This comment should just read, "Generate skip counts with a geometric
+   * distribution", and leave everyone to go look that up and see why it's the
+   * right thing to do, if they don't know already.
+   *
+   * BUT IF YOU'RE CURIOUS, COMMENTS ARE FREE...
+   *
+   * Instead of generating a fresh random number for every trial, we can
+   * randomly generate a count of how many times we should return false before
+   * the next time we return true. We call this a "skip count". Once we've
+   * returned true, we generate a fresh skip count, and begin counting down
+   * again.
+   *
+   * Here's an awesome fact: by exercising a little care in the way we generate
+   * skip counts, we can produce results indistinguishable from those we would
+   * get "rolling the dice" afresh for every trial.
+   *
+   * In short, skip counts in Bernoulli trials of probability P obey a geometric
+   * distribution. If a random variable X is uniformly distributed from [0..1),
+   * then std::floor(std::log(X) / std::log(1-P)) has the appropriate geometric
+   * distribution for the skip counts.
+   *
+   * Why that formula?
+   *
+   * Suppose we're to return |true| with some probability P, say, 0.3. Spread
+   * all possible futures along a line segment of length 1. In portion P of
+   * those cases, we'll return true on the next call to |trial|; the skip count
+   * is 0. For the remaining portion 1-P of cases, the skip count is 1 or more.
+   *
+   * skip:                0                         1 or more
+   *             |------------------^-----------------------------------------|
+   * portion:            0.3                            0.7
+   *                      P                             1-P
+   *
+   * But the "1 or more" section of the line is subdivided the same way: *within
+   * that section*, in portion P the second call to |trial()| returns true, and
+   * in portion 1-P it returns false a second time; the skip count is two or
+   * more. So we return true on the second call in proportion 0.7 * 0.3, and
+   * skip at least the first two in proportion 0.7 * 0.7.
+   *
+   * skip:                0                1              2 or more
+   *             |------------------^------------^----------------------------|
+   * portion:            0.3           0.7 * 0.3          0.7 * 0.7
+   *                      P             (1-P)*P            (1-P)^2
+   *
+   * We can continue to subdivide:
+   *
+   * skip >= 0:  |------------------------------------------------- (1-P)^0 --|
+   * skip >= 1:  |                  ------------------------------- (1-P)^1 --|
+   * skip >= 2:  |                               ------------------ (1-P)^2 --|
+   * skip >= 3:  |                                 ^     ---------- (1-P)^3 --|
+   * skip >= 4:  |                                 .            --- (1-P)^4 --|
+   *                                               .
+   *                                               ^X, see below
+   *
+   * In other words, the likelihood of the next n calls to |trial| returning
+   * false is (1-P)^n. The longer a run we require, the more the likelihood
+   * drops. Further calls may return false too, but this is the probability
+   * we'll skip at least n.
+   *
+   * This is interesting, because we can pick a point along this line segment
+   * and see which skip count's range it falls within; the point X above, for
+   * example, is within the ">= 2" range, but not within the ">= 3" range, so it
+   * designates a skip count of 2. So if we pick points on the line at random
+   * and use the skip counts they fall under, that will be indistinguishable
+   * from generating a fresh random number between 0 and 1 for each trial and
+   * comparing it to P.
+   *
+   * So to find the skip count for a point X, we must ask: To what whole power
+   * must we raise 1-P such that we include X, but the next power would exclude
+   * it? This is exactly std::floor(std::log(X) / std::log(1-P)).
+   *
+   * Our algorithm is then, simply: When constructed, compute an initial skip
+   * count. Return false from |trial| that many times, and then compute a new
+   * skip count.
+   *
+   * For a call to |trial(n)|, if the skip count is greater than n, return false
+   * and subtract n from the skip count. If the skip count is less than n,
+   * return true and compute a new skip count. Since each trial is independent,
+   * it doesn't matter by how much n overshoots the skip count; we can actually
+   * compute a new skip count at *any* time without affecting the distribution.
+   * This is really beautiful.
+   */
+ public:
+  /**
+   * Construct a fast Bernoulli trial generator. Calls to |trial()| return true
+   * with probability |aProbability|. Use |aState0| and |aState1| to seed the
+   * random number generator; both may not be zero.
+   */
+  FastBernoulliTrial(double aProbability, uint64_t aState0, uint64_t aState1)
+      : mProbability(0),
+        mInvLogNotProbability(0),
+        mGenerator(aState0, aState1),
+        mSkipCount(0) {
+    setProbability(aProbability);
+  }
+
+  /**
+   * Return true with probability |mProbability|. Call this each time an event
+   * occurs, to decide whether to sample it or not. The lower |mProbability| is,
+   * the faster this function runs.
+   */
+  bool trial() {
+    if (mSkipCount) {
+      mSkipCount--;
+      return false;
+    }
+
+    return chooseSkipCount();
+  }
+
+  /**
+   * Equivalent to calling trial() |n| times, and returning true if any of those
+   * calls do. However, like trial, this runs in fast constant time.
+   *
+   * What is this good for? In some applications, some events are "bigger" than
+   * others. For example, large allocations are more significant than small
+   * allocations. Perhaps we'd like to imagine that we're drawing allocations
+   * from a stream of bytes, and performing a separate Bernoulli trial on every
+   * byte from the stream. We can accomplish this by calling |t.trial(S)| for
+   * the number of bytes S, and sampling the event if that returns true.
+   *
+   * Of course, this style of sampling needs to be paired with analysis and
+   * presentation that makes the "size" of the event apparent, lest trials with
+   * large values for |n| appear to be indistinguishable from those with small
+   * values for |n|, despite being potentially much more likely to be sampled.
+   */
+  bool trial(size_t aCount) {
+    if (mSkipCount > aCount) {
+      mSkipCount -= aCount;
+      return false;
+    }
+
+    return chooseSkipCount();
+  }
+
+  void setRandomState(uint64_t aState0, uint64_t aState1) {
+    mGenerator.setState(aState0, aState1);
+  }
+
+  void setProbability(double aProbability) {
+    MOZ_ASSERT(0 <= aProbability && aProbability <= 1);
+    mProbability = aProbability;
+    if (0 < mProbability && mProbability < 1) {
+      /*
+       * Let's look carefully at how this calculation plays out in floating-
+       * point arithmetic. We'll assume IEEE, but the final C++ code we arrive
+       * at would still be fine if our numbers were mathematically perfect. So,
+       * while we've considered IEEE's edge cases, we haven't done anything that
+       * should be actively bad when using other representations.
+       *
+       * (In the below, read comparisons as exact mathematical comparisons: when
+       * we say something "equals 1", that means it's exactly equal to 1. We
+       * treat approximation using intervals with open boundaries: saying a
+       * value is in (0,1) doesn't specify how close to 0 or 1 the value gets.
+       * When we use closed boundaries like [2**-53, 1], we're careful to ensure
+       * the boundary values are actually representable.)
+       *
+       * - After the comparison above, we know mProbability is in (0,1).
+       *
+       * - The gaps below 1 are 2**-53, so that interval is (0, 1-2**-53].
+       *
+       * - Because the floating-point gaps near 1 are wider than those near
+       *   zero, there are many small positive doubles ε such that 1-ε rounds to
+       *   exactly 1. However, 2**-53 can be represented exactly. So
+       *   1-mProbability is in [2**-53, 1].
+       *
+       * - log(1 - mProbability) is thus in (-37, 0].
+       *
+       *   That range includes zero, but when we use mInvLogNotProbability, it
+       *   would be helpful if we could trust that it's negative. So when log(1
+       *   - mProbability) is 0, we'll just set mProbability to 0, so that
+       *   mInvLogNotProbability is not used in chooseSkipCount.
+       *
+       * - How much of the range of mProbability does this cause us to ignore?
+       *   The only value for which log returns 0 is exactly 1; the slope of log
+       *   at 1 is 1, so for small ε such that 1 - ε != 1, log(1 - ε) is -ε,
+       *   never 0. The gaps near one are larger than the gaps near zero, so if
+       *   1 - ε wasn't 1, then -ε is representable. So if log(1 - mProbability)
+       *   isn't 0, then 1 - mProbability isn't 1, which means that mProbability
+       *   is at least 2**-53, as discussed earlier. This is a sampling
+       *   likelihood of roughly one in ten trillion, which is unlikely to be
+       *   distinguishable from zero in practice.
+       *
+       *   So by forbidding zero, we've tightened our range to (-37, -2**-53].
+       *
+       * - Finally, 1 / log(1 - mProbability) is in [-2**53, -1/37). This all
+       *   falls readily within the range of an IEEE double.
+       *
+       * ALL THAT HAVING BEEN SAID: here are the five lines of actual code:
+       */
+      double logNotProbability = std::log(1 - mProbability);
+      if (logNotProbability == 0.0)
+        mProbability = 0.0;
+      else
+        mInvLogNotProbability = 1 / logNotProbability;
+    }
+
+    chooseSkipCount();
+  }
+
+ private:
+  /* The likelihood that any given call to |trial| should return true. */
+  double mProbability;
+
+  /*
+   * The value of 1/std::log(1 - mProbability), cached for repeated use.
+   *
+   * If mProbability is exactly 0 or exactly 1, we don't use this value.
+   * Otherwise, we guarantee this value is in the range [-2**53, -1/37), i.e.
+   * definitely negative, as required by chooseSkipCount. See setProbability for
+   * the details.
+   */
+  double mInvLogNotProbability;
+
+  /* Our random number generator. */
+  non_crypto::XorShift128PlusRNG mGenerator;
+
+  /* The number of times |trial| should return false before next returning true.
+   */
+  size_t mSkipCount;
+
+  /*
+   * Choose the next skip count. This also returns the value that |trial| should
+   * return, since we have to check for the extreme values for mProbability
+   * anyway, and |trial| should never return true at all when mProbability is 0.
+   */
+  bool chooseSkipCount() {
+    /*
+     * If the probability is 1.0, every call to |trial| returns true. Make sure
+     * mSkipCount is 0.
+     */
+    if (mProbability == 1.0) {
+      mSkipCount = 0;
+      return true;
+    }
+
+    /*
+     * If the probabilility is zero, |trial| never returns true. Don't bother us
+     * for a while.
+     */
+    if (mProbability == 0.0) {
+      mSkipCount = SIZE_MAX;
+      return false;
+    }
+
+    /*
+     * What sorts of values can this call to std::floor produce?
+     *
+     * Since mGenerator.nextDouble returns a value in [0, 1-2**-53], std::log
+     * returns a value in the range [-infinity, -2**-53], all negative. Since
+     * mInvLogNotProbability is negative (see its comments), the product is
+     * positive and possibly infinite. std::floor returns +infinity unchanged.
+     * So the result will always be positive.
+     *
+     * Converting a double to an integer that is out of range for that integer
+     * is undefined behavior, so we must clamp our result to SIZE_MAX, to ensure
+     * we get an acceptable value for mSkipCount.
+     *
+     * The clamp is written carefully. Note that if we had said:
+     *
+     *    if (skipCount > double(SIZE_MAX))
+     *       mSkipCount = SIZE_MAX;
+     *    else
+     *       mSkipCount = skipCount;
+     *
+     * that leads to undefined behavior 64-bit machines: SIZE_MAX coerced to
+     * double can equal 2^64, so if skipCount equaled 2^64 converting it to
+     * size_t would induce undefined behavior.
+     *
+     * Jakob Olesen cleverly suggested flipping the sense of the comparison to
+     * skipCount < double(SIZE_MAX).  The conversion will evaluate to 2^64 or
+     * the double just below it: either way, skipCount is guaranteed to have a
+     * value that's safely convertible to size_t.
+     *
+     * (On 32-bit machines, all size_t values can be represented exactly in
+     * double, so all is well.)
+     */
+    double skipCount =
+        std::floor(std::log(mGenerator.nextDouble()) * mInvLogNotProbability);
+    if (skipCount < double(SIZE_MAX))
+      mSkipCount = skipCount;
+    else
+      mSkipCount = SIZE_MAX;
+
+    return true;
+  }
+};
+
+} /* namespace mozilla */
+
+#endif /* mozilla_FastBernoulliTrial_h */