private unsafe void MultiplyScalarU(Span <float> scalar, Span <float> dst)
{
fixed(float *pdst = dst)
fixed(float *psrc = scalar)
{
var pDstEnd = pdst + dst.Length;
var pDstCurrent = pdst;
var scalarVector256 = Avx.BroadcastScalarToVector256(psrc);
while (pDstCurrent + 8 <= pDstEnd)
{
var dstVector = Avx.LoadVector256(pDstCurrent);
dstVector = Avx.Multiply(dstVector, scalarVector256);
Avx.Store(pDstCurrent, dstVector);
pDstCurrent += 8;
}
}
}
// This function implements Algorithm 2 in https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
// Calculate the stochastic gradient and update the model.
public static unsafe void CalculateGradientAndUpdate(int *fieldIndices, int *featureIndices, float *featureValues, float *latentSum, float *linearWeights,
float *latentWeights, float *linearAccumulatedSquaredGrads, float *latentAccumulatedSquaredGrads, float lambdaLinear, float lambdaLatent, float learningRate,
int fieldCount, int latentDim, float weight, int count, float slope)
{
Contracts.Assert(Avx.IsSupported);
int m = fieldCount;
int d = latentDim;
int c = count;
int * pf = fieldIndices;
int * pi = featureIndices;
float *px = featureValues;
float *pq = latentSum;
float *pw = linearWeights;
float *pv = latentWeights;
float *phw = linearAccumulatedSquaredGrads;
float *phv = latentAccumulatedSquaredGrads;
Vector256 <float> wei = Vector256.Create(weight);
Vector256 <float> s = Vector256.Create(slope);
Vector256 <float> lr = Vector256.Create(learningRate);
Vector256 <float> lambdav = Vector256.Create(lambdaLatent);
for (int i = 0; i < count; i++)
{
int f = pf[i];
int j = pi[i];
// Calculate gradient of linear term w_j.
float g = weight * (lambdaLinear * pw[j] + slope * px[i]);
// Accumulate the gradient of the linear term.
phw[j] += g * g;
// Perform ADAGRAD update rule to adjust linear term.
pw[j] -= learningRate / MathF.Sqrt(phw[j]) * g;
// Update latent term, v_j,f', f'=1,...,m.
Vector256 <float> x = Avx.BroadcastScalarToVector256(px + i);
for (int fprime = 0; fprime < m; fprime++)
{
float * vjfprime = pv + j * m * d + fprime * d;
float * hvjfprime = phv + j * m * d + fprime * d;
float * qfprimef = pq + fprime * m * d + f * d;
Vector256 <float> sx = Avx.Multiply(s, x);
for (int k = 0; k + 8 <= d; k += 8)
{
Vector256 <float> v = Avx.LoadVector256(vjfprime + k);
Vector256 <float> q = Avx.LoadVector256(qfprimef + k);
// Calculate L2-norm regularization's gradient.
Vector256 <float> gLatent = Avx.Multiply(lambdav, v);
Vector256 <float> tmp = q;
// Calculate loss function's gradient.
if (fprime == f)
{
tmp = MultiplyAddNegated(v, x, q);
}
gLatent = MultiplyAdd(sx, tmp, gLatent);
gLatent = Avx.Multiply(wei, gLatent);
// Accumulate the gradient of latent vectors.
Vector256 <float> h = MultiplyAdd(gLatent, gLatent, Avx.LoadVector256(hvjfprime + k));
// Perform ADAGRAD update rule to adjust latent vector.
v = MultiplyAddNegated(lr, Avx.Multiply(Avx.ReciprocalSqrt(h), gLatent), v);
Avx.Store(vjfprime + k, v);
Avx.Store(hvjfprime + k, h);
}
}
}
}
// This function implements Algorithm 1 in https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf.
// Compute the output value of the field-aware factorization, as the sum of the linear part and the latent part.
// The linear part is the inner product of linearWeights and featureValues.
// The latent part is the sum of all intra-field interactions in one field f, for all fields possible
public static unsafe void CalculateIntermediateVariables(int *fieldIndices, int *featureIndices, float *featureValues,
float *linearWeights, float *latentWeights, float *latentSum, float *response, int fieldCount, int latentDim, int count)
{
Contracts.Assert(Avx.IsSupported);
// The number of all possible fields.
int m = fieldCount;
int d = latentDim;
int c = count;
int * pf = fieldIndices;
int * pi = featureIndices;
float *px = featureValues;
float *pw = linearWeights;
float *pv = latentWeights;
float *pq = latentSum;
float linearResponse = 0;
float latentResponse = 0;
Unsafe.InitBlock(pq, 0, (uint)(m * m * d * sizeof(float)));
Vector256 <float> y = Vector256 <float> .Zero;
Vector256 <float> tmp = Vector256 <float> .Zero;
for (int i = 0; i < c; i++)
{
int f = pf[i];
int j = pi[i];
linearResponse += pw[j] * px[i];
Vector256 <float> x = Avx.BroadcastScalarToVector256(px + i);
Vector256 <float> xx = Avx.Multiply(x, x);
// tmp -= <v_j,f, v_j,f> * x * x
int vBias = j * m * d + f * d;
// j-th feature's latent vector in the f-th field hidden space.
float *vjf = pv + vBias;
for (int k = 0; k + 8 <= d; k += 8)
{
Vector256 <float> vjfBuffer = Avx.LoadVector256(vjf + k);
tmp = MultiplyAddNegated(Avx.Multiply(vjfBuffer, vjfBuffer), xx, tmp);
}
for (int fprime = 0; fprime < m; fprime++)
{
vBias = j * m * d + fprime * d;
int qBias = f * m * d + fprime * d;
float *vjfprime = pv + vBias;
float *qffprime = pq + qBias;
// q_f,f' += v_j,f' * x
for (int k = 0; k + 8 <= d; k += 8)
{
Vector256 <float> vjfprimeBuffer = Avx.LoadVector256(vjfprime + k);
Vector256 <float> q = Avx.LoadVector256(qffprime + k);
q = MultiplyAdd(vjfprimeBuffer, x, q);
Avx.Store(qffprime + k, q);
}
}
}
for (int f = 0; f < m; f++)
{
// tmp += <q_f,f, q_f,f>
float *qff = pq + f * m * d + f * d;
for (int k = 0; k + 8 <= d; k += 8)
{
Vector256 <float> qffBuffer = Avx.LoadVector256(qff + k);
// Intra-field interactions.
tmp = MultiplyAdd(qffBuffer, qffBuffer, tmp);
}
// y += <q_f,f', q_f',f>, f != f'
// Whis loop handles inter - field interactions because f != f'.
for (int fprime = f + 1; fprime < m; fprime++)
{
float *qffprime = pq + f * m * d + fprime * d;
float *qfprimef = pq + fprime * m * d + f * d;
for (int k = 0; k + 8 <= d; k += 8)
{
// Inter-field interaction.
Vector256 <float> qffprimeBuffer = Avx.LoadVector256(qffprime + k);
Vector256 <float> qfprimefBuffer = Avx.LoadVector256(qfprimef + k);
y = MultiplyAdd(qffprimeBuffer, qfprimefBuffer, y);
}
}
}
y = MultiplyAdd(_point5, tmp, y);
tmp = Avx.Add(y, Avx.Permute2x128(y, y, 1));
tmp = Avx.HorizontalAdd(tmp, tmp);
y = Avx.HorizontalAdd(tmp, tmp);
Sse.StoreScalar(&latentResponse, y.GetLower()); // The lowest slot is the response value.
*response = linearResponse + latentResponse;
}
protected override unsafe double CalculateImpl(double x, double stepThreshold, int maxN)
{
if (!Avx.IsSupported)
{
Status = TaylorSeriesStatus.NotSupported;
return(Double.NaN);
}
const int vectorSize = 256 / 8 / sizeof(double);
// v8888 <- (8, 8, 8, 8)
var value8 = 8.0;
var v8888 = Avx.BroadcastScalarToVector256(&value8);
// xPow8 <- (x^8, x^8, x^8, x^8)
var xPow8 = Avx.BroadcastScalarToVector256(&x);
xPow8 = Avx.Multiply(xPow8, xPow8);
xPow8 = Avx.Multiply(xPow8, xPow8);
xPow8 = Avx.Multiply(xPow8, xPow8);
// up <- (x^(-1), x^(-3), x^(-5), x^(-7))
var upSa = stackalloc double[vectorSize];
var xDiv2iPlus1 = 1 / x;
for (var i = 0; i < vectorSize; i++)
{
upSa[i] = xDiv2iPlus1;
xDiv2iPlus1 /= x * x;
}
var up = Avx.LoadVector256(upSa);
// down <- (1, 3, 5, 7)
var downSa = stackalloc double[vectorSize] {
1, 3, 5, 7
};
var down = Avx.LoadVector256(downSa);
// sum <- (0, 0, 0, 0)
var sum = Vector256 <double> .Zero;
N = 0;
while (N < maxN)
{
// div <- up / down
var div = Avx.Divide(up, down);
// sum <- sum + div
sum = Avx.Add(sum, div);
// div = (x1, x2, x3, last)
var last = div.GetElement(vectorSize - 1);
N += vectorSize;
if (Math.Abs(last) < stepThreshold)
{
break;
}
// up <- up / (x^8, x^8, x^8, x^8)
up = Avx.Divide(up, xPow8);
// down <- down + (8, 8, 8, 8)
down = Avx.Add(down, v8888);
}
var resultSa = stackalloc double[vectorSize];
Avx.Store(resultSa, sum);
Status = N >= maxN ? TaylorSeriesStatus.TooManyIterations : TaylorSeriesStatus.Success;
return(resultSa[0] + resultSa[1] + resultSa[2] + resultSa[3]);
}
}
