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pkg/fuzzer: use MAB to distinguish between exec fuzz and exec gen
Let's try to use a plain delta-epsylon MAB for this purpose. To better track its effect, also calculate moving averages of the "new max signal" / "execution time" ratios for exec fuzz and exec gen.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,65 @@ | ||
| // Copyright 2024 syzkaller project authors. All rights reserved. | ||
| // Use of this source code is governed by Apache 2 LICENSE that can be found in the LICENSE file. | ||
|
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| package learning | ||
|
|
||
| import ( | ||
| "math/rand" | ||
| ) | ||
|
|
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| type Action[T comparable] struct { | ||
| Arm T | ||
| index int | ||
| } | ||
|
|
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| type countedValue struct { | ||
| value float64 | ||
| count int64 | ||
| } | ||
|
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||
| func (cv *countedValue) update(value, minStep float64) { | ||
| // Using larger steps at the beginning allows us to | ||
| // converge faster to the actual value. | ||
| // The minStep limit ensures that we can still track | ||
| // non-stationary problems. | ||
| cv.count++ | ||
| step := 1.0 / float64(cv.count) | ||
| if step < minStep { | ||
| step = minStep | ||
| } | ||
| cv.value += (value - cv.value) * step | ||
| } | ||
|
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| // PlainMAB is a very simple epsylon-greedy MAB implementation. | ||
| // It's not thread-safe. | ||
| type PlainMAB[T comparable] struct { | ||
| MinLearningRate float64 | ||
| ExplorationRate float64 | ||
| arms []T | ||
| weights []countedValue | ||
| } | ||
|
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| func (p *PlainMAB[T]) AddArms(arms ...T) { | ||
| for _, arm := range arms { | ||
| p.arms = append(p.arms, arm) | ||
| p.weights = append(p.weights, countedValue{0, 0}) | ||
| } | ||
| } | ||
|
|
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| func (p *PlainMAB[T]) Action(r *rand.Rand) Action[T] { | ||
| var pos int | ||
| if r.Float64() < p.ExplorationRate { | ||
| pos = r.Intn(len(p.arms)) | ||
| } else { | ||
| for i := 1; i < len(p.arms); i++ { | ||
| if p.weights[i].value > p.weights[pos].value { | ||
| pos = i | ||
| } | ||
| } | ||
| } | ||
| return Action[T]{Arm: p.arms[pos], index: pos} | ||
| } | ||
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| func (p *PlainMAB[T]) SaveReward(action Action[T], reward float64) { | ||
| p.weights[action.index].update(reward, p.MinLearningRate) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,66 @@ | ||
| // Copyright 2024 syzkaller project authors. All rights reserved. | ||
| // Use of this source code is governed by Apache 2 LICENSE that can be found in the LICENSE file. | ||
|
|
||
| package learning | ||
|
|
||
| import ( | ||
| "math/rand" | ||
| "testing" | ||
|
|
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| "github.com/google/syzkaller/pkg/testutil" | ||
| "github.com/stretchr/testify/assert" | ||
| ) | ||
|
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| func TestMABSmallDiff(t *testing.T) { | ||
| r := rand.New(testutil.RandSource(t)) | ||
| bandit := &PlainMAB[int]{ | ||
| MinLearningRate: 0.0001, | ||
| ExplorationRate: 0.1, | ||
| } | ||
| arms := []float64{0.65, 0.7} | ||
| for i := range arms { | ||
| bandit.AddArms(i) | ||
| } | ||
| const steps = 40000 | ||
| counts := runMAB(r, bandit, arms, steps) | ||
| t.Logf("counts: %v", counts) | ||
| assert.Greater(t, counts[1], steps/4*3) | ||
| } | ||
|
|
||
| func TestNonStationaryMAB(t *testing.T) { | ||
| r := rand.New(testutil.RandSource(t)) | ||
| bandit := &PlainMAB[int]{ | ||
| MinLearningRate: 0.02, | ||
| ExplorationRate: 0.04, | ||
| } | ||
|
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| arms := []float64{0.2, 0.7, 0.5, 0.1} | ||
| for i := range arms { | ||
| bandit.AddArms(i) | ||
| } | ||
|
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| const steps = 25000 | ||
| counts := runMAB(r, bandit, arms, steps) | ||
| t.Logf("initially: %v", counts) | ||
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| // Ensure that we've found the best arm. | ||
| assert.Greater(t, counts[1], steps/2) | ||
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| // Now change the best arm's avg reward. | ||
| arms[3] = 0.9 | ||
| counts = runMAB(r, bandit, arms, steps) | ||
| t.Logf("after reward change: %v", counts) | ||
| assert.Greater(t, counts[3], steps/2) | ||
| } | ||
|
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| func runMAB(r *rand.Rand, bandit *PlainMAB[int], arms []float64, steps int) []int { | ||
| counts := make([]int, len(arms)) | ||
| for i := 0; i < steps; i++ { | ||
| action := bandit.Action(r) | ||
| // TODO: use normal distribution? | ||
| reward := r.Float64() * arms[action.Arm] | ||
| counts[action.Arm]++ | ||
| bandit.SaveReward(action, reward) | ||
| } | ||
| return counts | ||
| } |
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