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diff --git a/mfbt/FastBernoulliTrial.h b/mfbt/FastBernoulliTrial.h new file mode 100644 index 0000000000..7e38b70ab4 --- /dev/null +++ b/mfbt/FastBernoulliTrial.h @@ -0,0 +1,379 @@ +/* -*- 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 > SIZE_MAX) + * skipCount = SIZE_MAX; + * + * that leads to undefined behavior 64-bit machines: SIZE_MAX coerced to + * double is 2^64, not 2^64-1, so this doesn't actually set skipCount to a + * value that can be safely assigned to mSkipCount. + * + * Jakub Oleson cleverly suggested flipping the sense of the comparison: if + * we require that skipCount < SIZE_MAX, then because of the gaps (2048) + * between doubles at that magnitude, the highest double less than 2^64 is + * 2^64 - 2048, which is fine to store in a 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 < SIZE_MAX) + mSkipCount = skipCount; + else + mSkipCount = SIZE_MAX; + + return true; + } +}; + +} /* namespace mozilla */ + +#endif /* mozilla_FastBernoulliTrial_h */ |