public void SdcaL1UpdateSUTest(string mode, string test, string scale, Dictionary <string, string> environmentVariables)
{
RemoteExecutor.RemoteInvoke((arg0, arg1, arg2) =>
{
CheckProperFlag(arg0);
float defaultScale = float.Parse(arg2, CultureInfo.InvariantCulture);
float[] src = (float[])_testArrays[int.Parse(arg1)].Clone();
float[] v = (float[])src.Clone();
float[] w = (float[])src.Clone();
int[] idx = _testIndexArray;
float[] expected = (float[])w.Clone();
for (int i = 0; i < idx.Length; i++)
{
int index = idx[i];
float value = v[index] + src[i] * defaultScale;
expected[index] = Math.Abs(value) > defaultScale ? (value > 0 ? value - defaultScale : value + defaultScale) : 0;
}
CpuMathUtils.SdcaL1UpdateSparse(defaultScale, idx.Length, src, idx, defaultScale, v, w);
var actual = w;
Assert.Equal(expected, actual, _comparer);
return(RemoteExecutor.SuccessExitCode);
}, mode, test, scale, new RemoteInvokeOptions(environmentVariables));
}
public void SdcaL1UpdateSUTest(int test)
{
float[] src = (float[])testArrays[test].Clone();
float[] v = (float[])src.Clone();
float[] w = (float[])src.Clone();
int[] idx = testIndexArray;
float[] expected = (float[])w.Clone();
for (int i = 0; i < idx.Length; i++)
{
int index = idx[i];
float value = v[index] + src[i] * DEFAULT_SCALE;
expected[index] = Math.Abs(value) > DEFAULT_SCALE ? (value > 0 ? value - DEFAULT_SCALE : value + DEFAULT_SCALE) : 0;
}
CpuMathUtils.SdcaL1UpdateSparse(DEFAULT_SCALE, src.Length, src, idx, idx.Length, DEFAULT_SCALE, v, w);
var actual = w;
Assert.Equal(expected, actual, comparer);
}
public void SdcaL1UpdateSUTest(int test)
{
float[] src = (float[])_testArrays[test].Clone();
float[] v = (float[])src.Clone();
float[] w = (float[])src.Clone();
int[] idx = _testIndexArray;
float[] expected = (float[])w.Clone();
for (int i = 0; i < idx.Length; i++)
{
int index = idx[i];
float value = v[index] + src[i] * DefaultScale;
expected[index] = Math.Abs(value) > DefaultScale ? (value > 0 ? value - DefaultScale : value + DefaultScale) : 0;
}
CpuMathUtils.SdcaL1UpdateSparse(DefaultScale, idx.Length, src, idx, DefaultScale, v, w);
var actual = w;
Assert.Equal(expected, actual, _comparer);
}
/// <inheritdoc/>
private protected override void TrainWithoutLock(IProgressChannelProvider progress, FloatLabelCursor.Factory cursorFactory, Random 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(SdcaTrainerOptions.L1Threshold.HasValue);
Contracts.AssertValueOrNull(idToIdx);
Contracts.AssertValueOrNull(invariants);
Contracts.AssertValueOrNull(featureNormSquared);
int numClasses = Utils.Size(weights);
Contracts.Assert(Utils.Size(biasReg) == numClasses);
Contracts.Assert(Utils.Size(biasUnreg) == numClasses);
int maxUpdateTrials = 2 * numThreads;
var l1Threshold = SdcaTrainerOptions.L1Threshold.Value;
bool l1ThresholdZero = l1Threshold == 0;
var lr = SdcaTrainerOptions.BiasLearningRate * SdcaTrainerOptions.L2Regularization.Value;
var pch = progress != null?progress.StartProgressChannel("Dual update") : null;
using (pch)
using (var cursor = SdcaTrainerOptions.Shuffle ? cursorFactory.Create(rand) : cursorFactory.Create())
{
long rowCount = 0;
if (pch != null)
{
pch.SetHeader(new ProgressHeader("examples"), e => e.SetProgress(0, rowCount));
}
Func <DataViewRowId, long> getIndexFromId = GetIndexFromIdGetter(idToIdx, biasReg.Length);
while (cursor.MoveNext())
{
long idx = getIndexFromId(cursor.Id);
long dualIndexInitPos = idx * numClasses;
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(in features);
if (SdcaTrainerOptions.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(in features, in 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;
}
var weightsEditor = VBufferEditor.CreateFromBuffer(ref weights[iClass]);
var l1IntermediateWeightsEditor =
!l1ThresholdZero?VBufferEditor.CreateFromBuffer(ref l1IntermediateWeights[iClass]) :
default;
// 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(in features, in weights[iClass], biasReg[iClass] + biasUnreg[iClass]);
var dualUpdate = _loss.DualUpdate(output, 1, dual, invariant, numThreads);
// The successive over-relaxation approach 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(in features, weightsEditor.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 (SdcaTrainerOptions.BiasLearningRate == 0)
{
biasReg[iClass] = Math.Abs(l1IntermediateBias[iClass]) - l1Threshold > 0.0
? l1IntermediateBias[iClass] - Math.Sign(l1IntermediateBias[iClass]) * l1Threshold
: 0;
}
var featureValues = features.GetValues();
if (features.IsDense)
{
CpuMathUtils.SdcaL1UpdateDense(-primalUpdate, featureValues.Length, featureValues, l1Threshold, l1IntermediateWeightsEditor.Values, weightsEditor.Values);
}
else if (featureValues.Length > 0)
{
CpuMathUtils.SdcaL1UpdateSparse(-primalUpdate, featureValues.Length, featureValues, features.GetIndices(), l1Threshold, l1IntermediateWeightsEditor.Values, weightsEditor.Values);
}
}
break;
}
}
}
// Updating with label class weights and dual variable.
duals[label + dualIndexInitPos] = labelDual;
biasUnreg[label] += labelAdjustment * lambdaNInv * instanceWeight;
if (l1ThresholdZero)
{
var weightsEditor = VBufferEditor.CreateFromBuffer(ref weights[label]);
VectorUtils.AddMult(in features, weightsEditor.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;
var weightsEditor = VBufferEditor.CreateFromBuffer(ref weights[label]);
var l1IntermediateWeightsEditor = VBufferEditor.CreateFromBuffer(ref l1IntermediateWeights[label]);
var featureValues = features.GetValues();
if (features.IsDense)
{
CpuMathUtils.SdcaL1UpdateDense(labelPrimalUpdate, featureValues.Length, featureValues, l1Threshold, l1IntermediateWeightsEditor.Values, weightsEditor.Values);
}
else if (featureValues.Length > 0)
{
CpuMathUtils.SdcaL1UpdateSparse(labelPrimalUpdate, featureValues.Length, featureValues, features.GetIndices(), l1Threshold, l1IntermediateWeightsEditor.Values, weightsEditor.Values);
}
}
rowCount++;
}
}
}
public void SdcaL1UpdateSU() => CpuMathUtils.SdcaL1UpdateSparse(DefaultScale, _smallInputLength, src, idx, DefaultScale, dst, result);
public void ManagedSdcaL1UpdateSUPerf() => CpuMathUtils.SdcaL1UpdateSparse(DEFAULT_SCALE, LEN, src, idx, IDXLEN, DEFAULT_SCALE, dst, result);
