/// <summary>
/// Compute L1-norm. L1-norm computation doesn't subtract the mean from the source values.
/// However, we substract the mean here in case subMean is true (if subMean is false, mean is zero).
/// </summary>
private static Float L1Norm(Float[] values, int count, Float mean = 0)
{
if (count == 0)
{
return(0);
}
return(SseUtils.SumAbs(mean, values, 0, count));
}
/// <summary>
/// Compute L2-norm. L2-norm computation doesn't subtract the mean from the source values.
/// However, we substract the mean here in case subMean is true (if subMean is false, mean is zero).
/// </summary>
private static Float L2Norm(Float[] values, int count, Float mean = 0)
{
if (count == 0)
{
return(0);
}
return(MathUtils.Sqrt(SseUtils.SumSq(mean, values, 0, count)));
}
/// <summary>
/// Compute LInf-norm. LInf-norm computation doesn't subtract the mean from the source values.
/// However, we substract the mean here in case subMean is true (if subMean is false, mean is zero).
/// </summary>
private static Float LInfNorm(Float[] values, int count, Float mean = 0)
{
if (count == 0)
{
return(0);
}
return(SseUtils.MaxAbsDiff(mean, values, count));
}
/// <summary>
/// Returns sum of elements in array
/// </summary>
public static Float Sum(Float[] a)
{
if (a == null || a.Length == 0)
{
return(0);
}
return(SseUtils.Sum(a, a.Length));
}
private static Float Mean(Float[] src, int count, int length)
{
if (length == 0 || count == 0)
{
return(0);
}
return(SseUtils.Sum(src, 0, count) / length);
}
/// <summary>
/// Matrix multiplication:
/// if (add)
/// dst = mat * src
/// else
/// dest += mat * src
/// </summary>
/// <param name="add">The addition flag</param>
/// <param name="mat">The multiplier matrix</param>
/// <param name="src">The source vector</param>
/// <param name="dst">The destination vector</param>
public static void MatTimesSrc(bool add, ICpuFullMatrix mat, ICpuVector src, ICpuVector dst)
{
bool colMajor = typeof(TMatrix) == typeof(CpuAlignedMatrixCol);
AssertCompatible(mat, src, dst);
var m = A(mat);
SseUtils.MatTimesSrc(colMajor, add, m.Items, A(src).Items, A(dst).Items, m.RunCnt);
}
/// <summary>
/// Adds a multiple of an array to a second array.
/// </summary>
/// <param name="src">Array to add</param>
/// <param name="dst">Array to add to</param>
/// <param name="c">Multiple</param>
public static void AddMult(Float[] src, Float[] dst, Float c)
{
Contracts.Check(src.Length == dst.Length, "Arrays must have the same dimensionality.");
if (c == 0)
{
return;
}
SseUtils.AddScale(c, src, dst, src.Length);
}
/// <summary>
/// Perform in-place vector addition <c><paramref name="dst"/> += <paramref name="src"/></c>.
/// </summary>
public static void Add(Float[] src, Float[] dst)
{
Contracts.CheckValue(src, nameof(src));
Contracts.CheckValue(dst, nameof(dst));
Contracts.CheckParam(src.Length == dst.Length, nameof(dst), "Arrays must have the same dimensionality.");
if (src.Length == 0)
{
return;
}
SseUtils.Add(src, dst, src.Length);
}
public static Float DotProduct(Float[] a, ref VBuffer <Float> b)
{
Contracts.Check(Utils.Size(a) == b.Length, "Vectors must have the same dimensionality.");
if (b.Count == 0)
{
return(0);
}
if (b.IsDense)
{
return(SseUtils.DotProductDense(a, b.Values, b.Length));
}
return(SseUtils.DotProductSparse(a, b.Values, b.Indices, b.Count));
}
private static Float L2DiffSquaredDense(Float[] valuesA, Float[] valuesB, int length)
{
Contracts.AssertValueOrNull(valuesA);
Contracts.AssertValueOrNull(valuesB);
Contracts.Assert(0 <= length && length <= Utils.Size(valuesA));
Contracts.Assert(0 <= length && length <= Utils.Size(valuesB));
if (length == 0)
{
return(0);
}
return(SseUtils.L2DistSquared(valuesA, valuesB, length));
}
/// <summary>
/// Multiplies arrays Dst *= A element by element and returns the result in <paramref name="dst"/> (Hadamard product).
/// </summary>
public static void MulElementWise(ref VBuffer <Float> a, ref VBuffer <Float> dst)
{
Contracts.Check(a.Length == dst.Length, "Vectors must have the same dimensionality.");
if (a.IsDense && dst.IsDense)
{
SseUtils.MulElementWise(a.Values, dst.Values, dst.Values, a.Length);
}
else
{
VBufferUtils.ApplyWithEitherDefined(ref a, ref dst, (int ind, Float v1, ref Float v2) => { v2 *= v1; });
}
}
/// <summary>
/// Computes the dot product of two arrays
/// Where "offset" is considered to be a's zero index
/// </summary>
/// <param name="a">one array</param>
/// <param name="b">the second array (given as a VBuffer)</param>
/// <param name="offset">offset in 'a'</param>
/// <returns>the dot product</returns>
public static Float DotProductWithOffset(ref VBuffer <Float> a, int offset, ref VBuffer <Float> b)
{
Contracts.Check(0 <= offset && offset <= a.Length);
Contracts.Check(b.Length <= a.Length - offset, "VBuffer b must be no longer than a.Length - offset.");
if (a.Count == 0 || b.Count == 0)
{
return(0);
}
if (a.IsDense)
{
if (b.IsDense)
{
return(SseUtils.DotProductDense(a.Values, offset, b.Values, b.Length));
}
return(SseUtils.DotProductSparse(a.Values, offset, b.Values, b.Indices, b.Count));
}
else
{
Float result = 0;
int aMin = Utils.FindIndexSorted(a.Indices, 0, a.Count, offset);
int aLim = Utils.FindIndexSorted(a.Indices, 0, a.Count, offset + b.Length);
if (b.IsDense)
{
for (int iA = aMin; iA < aLim; ++iA)
{
result += a.Values[iA] * b.Values[a.Indices[iA] - offset];
}
return(result);
}
for (int iA = aMin, iB = 0; iA < aLim && iB < b.Count;)
{
int aIndex = a.Indices[iA];
int bIndex = b.Indices[iB];
int comp = (aIndex - offset) - bIndex;
if (comp == 0)
{
result += a.Values[iA++] * b.Values[iB++];
}
else if (comp < 0)
{
iA++;
}
else
{
iB++;
}
}
return(result);
}
}
/// <summary>
/// Computes the dot product of two arrays
/// Where "offset" is considered to be a's zero index
/// </summary>
/// <param name="a">one array</param>
/// <param name="b">the second array (given as a VBuffer)</param>
/// <param name="offset">offset in 'a'</param>
/// <returns>the dot product</returns>
public static Float DotProductWithOffset(Float[] a, int offset, ref VBuffer <Float> b)
{
Contracts.Check(0 <= offset && offset <= a.Length);
Contracts.Check(b.Length <= a.Length - offset, "VBuffer b must be no longer than a.Length - offset.");
if (b.Count == 0)
{
return(0);
}
if (b.IsDense)
{
return(SseUtils.DotProductDense(a, offset, b.Values, b.Length));
}
return(SseUtils.DotProductSparse(a, offset, b.Values, b.Indices, b.Count));
}
/// <summary>
/// Multiples the array by a real value
/// </summary>
/// <param name="dst">The array</param>
/// <param name="c">Value to multiply vector with</param>
public static void ScaleBy(Float[] dst, Float c)
{
if (c == 1)
{
return;
}
if (c != 0)
{
SseUtils.Scale(c, dst, dst.Length);
}
else
{
Array.Clear(dst, 0, dst.Length);
}
}
/// <summary>
/// Compute Standard Deviation.
/// We have two overloads of StdDev instead of one with <see cref="Nullable{Float}"/> mean for perf reasons.
/// </summary>
private static Float StdDev(Float[] values, int count, int length, Float mean)
{
Contracts.Assert(0 <= count && count <= length);
if (count == 0)
{
return(0);
}
Float sumSq = 0;
if (count != length && mean != 0)
{
// Sparse representation.
Float meanSq = mean * mean;
sumSq = (length - count) * meanSq;
}
sumSq += SseUtils.SumSq(mean, values, 0, count);
return(MathUtils.Sqrt(sumSq / length));
}
/// <summary>
/// Compute Standard Deviation. In case of both subMean and useStd are true, we technically need to compute variance
/// based on centered values (i.e. after subtracting the mean). But since the centered
/// values mean is approximately zero, we can use variance of non-centered values.
/// </summary>
private static Float StdDev(Float[] values, int count, int length)
{
Contracts.Assert(0 <= count && count <= length);
if (count == 0)
{
return(0);
}
// We need a mean to compute variance.
Float tmpMean = SseUtils.Sum(values, 0, count) / length;
Float sumSq = 0;
if (count != length && tmpMean != 0)
{
// Sparse representation.
Float meanSq = tmpMean * tmpMean;
sumSq = (length - count) * meanSq;
}
sumSq += SseUtils.SumSq(tmpMean, values, 0, count);
return(MathUtils.Sqrt(sumSq / length));
}
private static Float L2DistSquaredHalfSparse(Float[] valuesA, int lengthA, Float[] valuesB, int[] indicesB, int countB)
{
Contracts.AssertValueOrNull(valuesA);
Contracts.AssertValueOrNull(valuesB);
Contracts.AssertValueOrNull(indicesB);
Contracts.Assert(0 <= lengthA && lengthA <= Utils.Size(valuesA));
Contracts.Assert(0 <= countB && countB <= Utils.Size(indicesB));
Contracts.Assert(countB <= Utils.Size(valuesB));
var normA = SseUtils.SumSq(valuesA, 0, lengthA);
if (countB == 0)
{
return(normA);
}
var normB = SseUtils.SumSq(valuesB, 0, countB);
var dotP = SseUtils.DotProductSparse(valuesA, valuesB, indicesB, countB);
var res = normA + normB - 2 * dotP;
return(res < 0 ? 0 : res);
}
/// <summary>
/// Adds a multiple of a <see cref="VBuffer{T}"/> to a <see cref="Float"/> array.
/// </summary>
/// <param name="src">Buffer to add</param>
/// <param name="dst">Array to add to</param>
/// <param name="c">Coefficient</param>
public static void AddMult(ref VBuffer <Float> src, Float[] dst, Float c)
{
Contracts.CheckValue(dst, nameof(dst));
Contracts.CheckParam(src.Length == dst.Length, nameof(dst), "Arrays must have the same dimensionality.");
if (src.Count == 0 || c == 0)
{
return;
}
if (src.IsDense)
{
SseUtils.AddScale(c, src.Values, dst, src.Count);
}
else
{
for (int i = 0; i < src.Count; i++)
{
dst[src.Indices[i]] += c * src.Values[i];
}
}
}
public static Float DotProduct(ref VBuffer <Float> a, ref VBuffer <Float> b)
{
Contracts.Check(a.Length == b.Length, "Vectors must have the same dimensionality.");
if (a.Count == 0 || b.Count == 0)
{
return(0);
}
if (a.IsDense)
{
if (b.IsDense)
{
return(SseUtils.DotProductDense(a.Values, b.Values, a.Length));
}
return(SseUtils.DotProductSparse(a.Values, b.Values, b.Indices, b.Count));
}
if (b.IsDense)
{
return(SseUtils.DotProductSparse(b.Values, a.Values, a.Indices, a.Count));
}
return(DotProductSparse(a.Values, a.Indices, 0, a.Count, b.Values, b.Indices, 0, b.Count, 0));
}
private static void FillValues(IExceptionContext ectx, ref VBuffer <Float> src, ref VBuffer <Float> dst, Float divisor, Float scale, Float offset = 0)
{
int count = src.Count;
int length = src.Length;
ectx.Assert(Utils.Size(src.Values) >= count);
ectx.Assert(divisor >= 0);
if (count == 0)
{
dst = new VBuffer <Float>(length, 0, dst.Values, dst.Indices);
return;
}
ectx.Assert(count > 0);
ectx.Assert(length > 0);
Float normScale = scale;
if (divisor > 0)
{
normScale /= divisor;
}
// Don't normalize small values.
if (normScale < MinScale)
{
normScale = 1;
}
if (offset == 0)
{
var dstValues = dst.Values;
if (Utils.Size(dstValues) < count)
{
dstValues = new Float[count];
}
var dstIndices = dst.Indices;
if (!src.IsDense)
{
if (Utils.Size(dstIndices) < count)
{
dstIndices = new int[count];
}
Array.Copy(src.Indices, dstIndices, count);
}
SseUtils.Scale(normScale, src.Values, dstValues, count);
dst = new VBuffer <Float>(length, count, dstValues, dstIndices);
return;
}
// Subtracting the mean requires a dense representation.
src.CopyToDense(ref dst);
if (normScale != 1)
{
SseUtils.ScaleAdd(normScale, -offset, dst.Values, length);
}
else
{
SseUtils.Add(-offset, dst.Values, length);
}
}
/// <summary>
/// Returns a dot product of dense vector 'a' starting from offset 'aOffset' and sparse vector 'b'
/// with first 'count' valid elements and their corresponding 'indices'.
/// </summary>
private static Float DotProduct(Float[] a, int aOffset, Float[] b, int[] indices, int count)
{
Contracts.Assert(count <= indices.Length);
return(SseUtils.DotProductSparse(a, aOffset, b, indices, count));
}
public static void Add(float[] src, int[] indices, float[] dst, int dstOffset, int count) => SseUtils.Add(src, indices, dst, dstOffset, count);
public static float L2DistSquared(float[] a, float[] b, int count) => SseUtils.L2DistSquared(a, b, count);
public static float DotProductSparse(float[] a, int offset, float[] b, int[] indices, int count) => SseUtils.DotProductSparse(a, offset, b, indices, count);
public static float DotProductDense(float[] a, int offset, float[] b, int count) => SseUtils.DotProductDense(a, offset, b, count);
public static void MulElementWise(float[] src1, float[] src2, float[] dst, int count) => SseUtils.MulElementWise(src1, src2, dst, count);
public static float SumSq(float[] src, int count) => SseUtils.SumSq(src, count);
public static void Scale(float a, float[] dst, int count) => SseUtils.Scale(a, dst, count);
/// <inheritdoc/>
protected override void TrainWithoutLock(IProgressChannelProvider progress, FloatLabelCursor.Factory cursorFactory, IRandom rand,
IdToIdxLookup idToIdx, int numThreads, DualsTableBase duals, Float[] biasReg, Float[] invariants, Float lambdaNInv,
VBuffer <Float>[] weights, Float[] biasUnreg, VBuffer <Float>[] l1IntermediateWeights, Float[] l1IntermediateBias, Float[] featureNormSquared)
{
Contracts.AssertValueOrNull(progress);
Contracts.Assert(_args.L1Threshold.HasValue);
Contracts.AssertValueOrNull(idToIdx);
Contracts.AssertValueOrNull(invariants);
Contracts.AssertValueOrNull(featureNormSquared);
int weightArraySize = WeightArraySize;
Contracts.Assert(weightArraySize == _numClasses);
Contracts.Assert(Utils.Size(weights) == weightArraySize);
Contracts.Assert(Utils.Size(biasReg) == weightArraySize);
Contracts.Assert(Utils.Size(biasUnreg) == weightArraySize);
int maxUpdateTrials = 2 * numThreads;
var l1Threshold = _args.L1Threshold.Value;
bool l1ThresholdZero = l1Threshold == 0;
var lr = _args.BiasLearningRate * _args.L2Const.Value;
var pch = progress != null?progress.StartProgressChannel("Dual update") : null;
using (pch)
using (var cursor = _args.Shuffle ? cursorFactory.Create(rand) : cursorFactory.Create())
{
long rowCount = 0;
if (pch != null)
{
pch.SetHeader(new ProgressHeader("examples"), e => e.SetProgress(0, rowCount));
}
Func <UInt128, long> getIndexFromId = GetIndexFromIdGetter(idToIdx);
while (cursor.MoveNext())
{
long idx = getIndexFromId(cursor.Id);
long dualIndexInitPos = idx * weightArraySize;
var features = cursor.Features;
var label = (int)cursor.Label;
Float invariant;
Float normSquared;
if (invariants != null)
{
invariant = invariants[idx];
Contracts.AssertValue(featureNormSquared);
normSquared = featureNormSquared[idx];
}
else
{
normSquared = VectorUtils.NormSquared(features);
if (_args.BiasLearningRate == 0)
{
normSquared += 1;
}
invariant = _loss.ComputeDualUpdateInvariant(2 * normSquared * lambdaNInv * GetInstanceWeight(cursor));
}
// The output for the label class using current weights and bias.
var labelOutput = WDot(ref features, ref weights[label], biasReg[label] + biasUnreg[label]);
var instanceWeight = GetInstanceWeight(cursor);
// This will be the new dual variable corresponding to the label class.
Float labelDual = 0;
// This will be used to update the weights and regularized bias corresponding to the label class.
Float labelPrimalUpdate = 0;
// This will be used to update the unregularized bias corresponding to the label class.
Float labelAdjustment = 0;
// Iterates through all classes.
for (int iClass = 0; iClass < _numClasses; iClass++)
{
// Skip the dual/weights/bias update for label class. Will be taken care of at the end.
if (iClass == label)
{
continue;
}
// Loop trials for compare-and-swap updates of duals.
// In general, concurrent update conflict to the same dual variable is rare
// if data is shuffled.
for (int numTrials = 0; numTrials < maxUpdateTrials; numTrials++)
{
long dualIndex = iClass + dualIndexInitPos;
var dual = duals[dualIndex];
var output = labelOutput + labelPrimalUpdate * normSquared - WDot(ref features, ref weights[iClass], biasReg[iClass] + biasUnreg[iClass]);
var dualUpdate = _loss.DualUpdate(output, 1, dual, invariant, numThreads);
// The successive over-relaxation apporach to adjust the sum of dual variables (biasReg) to zero.
// Reference to details: http://stat.rutgers.edu/home/tzhang/papers/ml02_dual.pdf, pp. 16-17.
var adjustment = l1ThresholdZero ? lr * biasReg[iClass] : lr * l1IntermediateBias[iClass];
dualUpdate -= adjustment;
bool success = false;
duals.ApplyAt(dualIndex, (long index, ref Float value) =>
{
success = Interlocked.CompareExchange(ref value, dual + dualUpdate, dual) == dual;
});
if (success)
{
// Note: dualConstraint[iClass] = lambdaNInv * (sum of duals[iClass])
var primalUpdate = dualUpdate * lambdaNInv * instanceWeight;
labelDual -= dual + dualUpdate;
labelPrimalUpdate += primalUpdate;
biasUnreg[iClass] += adjustment * lambdaNInv * instanceWeight;
labelAdjustment -= adjustment;
if (l1ThresholdZero)
{
VectorUtils.AddMult(ref features, weights[iClass].Values, -primalUpdate);
biasReg[iClass] -= primalUpdate;
}
else
{
//Iterative shrinkage-thresholding (aka. soft-thresholding)
//Update v=denseWeights as if there's no L1
//Thresholding: if |v[j]| < threshold, turn off weights[j]
//If not, shrink: w[j] = v[i] - sign(v[j]) * threshold
l1IntermediateBias[iClass] -= primalUpdate;
if (_args.BiasLearningRate == 0)
{
biasReg[iClass] = Math.Abs(l1IntermediateBias[iClass]) - l1Threshold > 0.0
? l1IntermediateBias[iClass] - Math.Sign(l1IntermediateBias[iClass]) * l1Threshold
: 0;
}
if (features.IsDense)
{
SseUtils.SdcaL1UpdateDense(-primalUpdate, features.Length, features.Values, l1Threshold, l1IntermediateWeights[iClass].Values, weights[iClass].Values);
}
else if (features.Count > 0)
{
SseUtils.SdcaL1UpdateSparse(-primalUpdate, features.Length, features.Values, features.Indices, features.Count, l1Threshold, l1IntermediateWeights[iClass].Values, weights[iClass].Values);
}
}
break;
}
}
}
// Updating with label class weights and dual variable.
duals[label + dualIndexInitPos] = labelDual;
biasUnreg[label] += labelAdjustment * lambdaNInv * instanceWeight;
if (l1ThresholdZero)
{
VectorUtils.AddMult(ref features, weights[label].Values, labelPrimalUpdate);
biasReg[label] += labelPrimalUpdate;
}
else
{
l1IntermediateBias[label] += labelPrimalUpdate;
var intermediateBias = l1IntermediateBias[label];
biasReg[label] = Math.Abs(intermediateBias) - l1Threshold > 0.0
? intermediateBias - Math.Sign(intermediateBias) * l1Threshold
: 0;
if (features.IsDense)
{
SseUtils.SdcaL1UpdateDense(labelPrimalUpdate, features.Length, features.Values, l1Threshold, l1IntermediateWeights[label].Values, weights[label].Values);
}
else if (features.Count > 0)
{
SseUtils.SdcaL1UpdateSparse(labelPrimalUpdate, features.Length, features.Values, features.Indices, features.Count, l1Threshold, l1IntermediateWeights[label].Values, weights[label].Values);
}
}
rowCount++;
}
}
}
public static float SumAbs(float[] src, int offset, int count) => SseUtils.SumAbs(src, offset, count);
