func testRBatcher(t *testing.T, rv autofunc.RVector, b batchFuncR, in autofunc.RResult,
n int, params []*autofunc.Variable) {
funcRBatcher := autofunc.RFuncBatcher{F: b}
t.Run("Forward", func(t *testing.T) {
expected := funcRBatcher.BatchR(rv, in, n)
actual := b.BatchR(rv, in, n)
diff := actual.Output().Copy().Scale(-1).Add(expected.Output()).MaxAbs()
if diff > 1e-5 {
t.Errorf("expected output %v but got %v", expected, actual)
}
diff = actual.ROutput().Copy().Scale(-1).Add(expected.ROutput()).MaxAbs()
if diff > 1e-5 {
t.Errorf("expected r-output %v but got %v", expected, actual)
}
})
t.Run("Backward", func(t *testing.T) {
expectedOut := funcRBatcher.BatchR(rv, in, n)
actualOut := b.BatchR(rv, in, n)
expected := autofunc.NewGradient(params)
actual := autofunc.NewGradient(params)
expectedR := autofunc.NewRGradient(params)
actualR := autofunc.NewRGradient(params)
outGrad := make(linalg.Vector, len(expectedOut.Output()))
outGradR := make(linalg.Vector, len(expectedOut.Output()))
for i := range outGrad {
outGrad[i] = rand.NormFloat64()
outGradR[i] = rand.NormFloat64()
}
expectedOut.PropagateRGradient(outGrad.Copy(), outGradR.Copy(), expectedR, expected)
actualOut.PropagateRGradient(outGrad, outGradR, actualR, actual)
for i, variable := range params {
actualVec := actual[variable]
expectedVec := expected[variable]
diff := actualVec.Copy().Scale(-1).Add(expectedVec).MaxAbs()
if diff > 1e-5 {
t.Errorf("variable %d (grad): expected %v got %v", i, expectedVec, actualVec)
}
actualVec = actualR[variable]
expectedVec = expectedR[variable]
diff = actualVec.Copy().Scale(-1).Add(expectedVec).MaxAbs()
if diff > 1e-5 {
t.Errorf("variable %d (rgrad): expected %v got %v", i, expectedVec, actualVec)
}
}
})
}
func (b *BlockChecker) testNilUpstreamR(t *testing.T) {
t.Run("Nil Upstream R", func(t *testing.T) {
out := b.B.ApplyBlockR(b.RV, []rnn.RState{b.B.StartRState(b.RV)},
[]autofunc.RResult{autofunc.NewRVariable(b.Input[0][0], b.RV)})
g1 := autofunc.NewGradient(b.Vars)
rg1 := autofunc.NewRGradient(b.Vars)
initLen1 := len(g1)
c := len(out.PropagateRGradient(nil, nil, nil, rg1, g1))
if c != 1 {
t.Errorf("expected %d downstream states, got %d", 1, c)
}
g2 := autofunc.NewGradient(b.Vars)
rg2 := autofunc.NewRGradient(b.Vars)
initLen2 := len(g2)
zeroUpstream := make([]linalg.Vector, len(out.Outputs()))
for i, x := range out.Outputs() {
zeroUpstream[i] = make(linalg.Vector, len(x))
}
nilStateUpstream := make([]rnn.RStateGrad, len(out.RStates()))
c = len(out.PropagateRGradient(zeroUpstream, zeroUpstream, nilStateUpstream, rg2, g2))
if c != 1 {
t.Errorf("expected %d downstream states, got %d", 1, c)
}
if len(g1) != initLen1 {
t.Errorf("all nil gradient length changed from %d to %d", initLen1, len(g1))
}
if len(rg1) != initLen1 {
t.Errorf("all nil r-gradient length changed from %d to %d", initLen1, len(rg1))
}
if len(g2) != initLen2 {
t.Errorf("non-nil gradient length changed from %d to %d", initLen2, len(g2))
}
if len(rg2) != initLen2 {
t.Errorf("non-nil r-gradient length changed from %d to %d", initLen2, len(rg2))
}
for i, variable := range b.Vars {
val1 := g1[variable]
val2 := g2[variable]
if !b.vecsEqual(val1, val2) {
t.Errorf("gradients for var %d don't match: %v and %v", i, val1, val2)
}
val1 = rg1[variable]
val2 = rg2[variable]
if !b.vecsEqual(val1, val2) {
t.Errorf("r-gradients for var %d don't match: %v and %v", i, val1, val2)
}
}
})
}
func (b *SingleRGradienter) RGradient(rv autofunc.RVector, s sgd.SampleSet) (autofunc.Gradient,
autofunc.RGradient) {
if b.gradCache == nil {
b.gradCache = autofunc.NewGradient(b.Learner.Parameters())
} else {
b.gradCache.Zero()
}
if b.rgradCache == nil {
b.rgradCache = autofunc.NewRGradient(b.Learner.Parameters())
} else {
b.rgradCache.Zero()
}
for i := 0; i < s.Len(); i++ {
sample := s.GetSample(i)
vs := sample.(VectorSample)
output := vs.Output
inVar := &autofunc.Variable{vs.Input}
rVar := autofunc.NewRVariable(inVar, rv)
result := b.Learner.ApplyR(rv, rVar)
cost := b.CostFunc.CostR(rv, output, result)
cost.PropagateRGradient(linalg.Vector{1}, linalg.Vector{0},
b.rgradCache, b.gradCache)
}
return b.gradCache, b.rgradCache
}
func (g *gradientCache) AllocR() autofunc.RGradient {
if len(g.rGradients) == 0 {
res := autofunc.NewRGradient(g.variables)
return res
}
res := g.rGradients[len(g.rGradients)-1]
g.rGradients = g.rGradients[:len(g.rGradients)-1]
res.Zero()
return res
}