public void TestAtomicAdd()
{
int batchSize = 5;
int vocabSize = 20;
TensorAllocator.InitDevices(ProcessorTypeEnums.CPU, new int[] { 0 });
var graph = new ComputeGraphTensor(new WeightTensorFactory(), 0, true);
var tensorA = BuildRandomTensor(shape: new long[2] {
batchSize, vocabSize
}, name: "tensorA", isTrainable: true);
var tensorAWeights = tensorA.ToWeightArray();
float sumA = tensorAWeights.Sum();
var tensorB = BuildRandomTensor(shape: new long[2] {
batchSize, vocabSize
}, name: "tensorB", isTrainable: true);
var tensorBWeights = tensorB.ToWeightArray();
float sumB = tensorBWeights.Sum();
float sum = sumA + sumB;
Ops.AtomicAdd(tensorA.TWeight, tensorB.TWeight);
var tensorSumWeights = tensorA.ToWeightArray();
float sum2 = tensorSumWeights.Sum();
Assert.IsTrue(Math.Round(sum, 5) == Math.Round(sum2, 5));
}
public void TestAddTensorTensorBP()
{
TensorAllocator.InitDevices(ProcessorTypeEnums.CPU, new int[] { 0 });
var graph = new ComputeGraphTensor(new WeightTensorFactory(), 0, true);
var tensorA = new WeightTensor(new long[2] {
2, 2
}, 1, 0, name: "tensorA", isTrainable: true);
var tensorB = new WeightTensor(new long[2] {
2, 2
}, 2, 0, name: "tensorB", isTrainable: true);
var tensorSum = graph.Add(tensorA, tensorB);
tensorSum.CopyWeightsToGradients(tensorSum);
graph.Backward();
float gA = tensorA.GetGradientAt(new long[] { 1, 1 });
float gB = tensorB.GetGradientAt(new long[] { 1, 1, });
Assert.IsTrue(gA == 3.0f);
Assert.IsTrue(gB == 3.0f);
}
public void TestSum()
{
int batchSize = 5;
int vocabSize = 20;
TensorAllocator.InitDevices(ProcessorTypeEnums.CPU, new int[] { 0 });
var graph = new ComputeGraphTensor(new WeightTensorFactory(), 0, true);
var tensorA = BuildRandomTensor(shape: new long[2] {
batchSize, vocabSize
}, name: "tensorA", isTrainable: true);
var tensorAWeights = tensorA.ToWeightArray();
float sum1 = tensorAWeights.Sum();
var tensorSum = graph.Sum(tensorA, 1);
var tensorSumWeights = tensorSum.ToWeightArray();
float sum2 = tensorSumWeights.Sum();
sum1 = (float)Math.Round(sum1, 5);
sum2 = (float)Math.Round(sum2, 5);
Logger.WriteLine($"sum from .net core = '{sum1}', sum from sum operator = '{sum2}'");
Assert.IsTrue(sum1 == sum2);
}
public void TestAddTensorTensor()
{
TensorAllocator.InitDevices(ProcessorTypeEnums.CPU, new int[] { 0 });
var graph = new ComputeGraphTensor(new WeightTensorFactory(), 0, true);
var tensorA = new WeightTensor(new long[2] {
2, 2
}, 1, 0, name: "tensorA", isTrainable: true);
var tensorB = new WeightTensor(new long[2] {
2, 2
}, 2, 0, name: "tensorB", isTrainable: true);
var tensorSum = graph.Add(tensorA, tensorB);
float v = tensorSum.GetWeightAt(new long[] { 1, 1 });
Assert.IsTrue(v == 3.0f);
}
public void TestAddSubGradients()
{
int batchSize = 5;
int vocabSize = 20;
TensorAllocator.InitDevices(ProcessorTypeEnums.CPU, new int[] { 0 });
var graph = new ComputeGraphTensor(new WeightTensorFactory(), 0, true);
var tensorA = new WeightTensor(new long[2] {
batchSize, vocabSize
}, 1, 0, name: "tensorA", isTrainable: true);
var tensorB = new WeightTensor(new long[2] {
batchSize, vocabSize
}, 1, 0, name: "tensorB", isTrainable: true);
var tensorIdx = BuildRandomLabelTensor(batchSize, vocabSize, "tensorIdx");
var tensorANeg = graph.Mul(tensorA, -1.0f);
var tensorANegSum = graph.Add(tensorANeg, 100.0f);
var tensorSub = graph.Sub(100.0f, tensorB);
float v1 = tensorANegSum.GetWeightAt(new long[] { 1, 1 });
float v2 = tensorSub.GetWeightAt(new long[] { 1, 1 });
Assert.IsTrue(v1 == v2);
var softmax1 = graph.Softmax(tensorANegSum);
var softmax2 = graph.Softmax(tensorSub);
graph.CrossEntropyLoss(softmax1, tensorIdx);
graph.CrossEntropyLoss(softmax2, tensorIdx);
graph.Backward();
float gA = tensorA.GetGradientAt(new long[] { 1, 1 });
float gB = tensorB.GetGradientAt(new long[] { 1, 1, });
Assert.IsTrue(gA == gB);
}
public void TestCrossEntropyLoss()
{
int batchSize = 5;
int vocabSize = 20;
TensorAllocator.InitDevices(ProcessorTypeEnums.CPU, new int[] { 0 });
var graph = new ComputeGraphTensor(new WeightTensorFactory(), 0, true);
var tensorA = BuildRandomTensor(shape: new long[2] {
batchSize, vocabSize
}, name: "tensorA", isTrainable: true);
var tensorIdx = BuildRandomLabelTensor(batchSize, vocabSize, "tensorIdx");
var probs = graph.Softmax(tensorA);
float[] softmaxWeights = probs.ToWeightArray();
graph.CrossEntropyLoss(probs, tensorIdx);
graph.Backward();
//Check if graidents are correct
for (int i = 0; i < batchSize; i++)
{
for (int j = 0; j < vocabSize; j++)
{
float softmaxWeight = softmaxWeights[i * vocabSize + j];
float tensorAGrad = tensorA.GetGradientAt(new long[] { i, j });
if (tensorIdx.GetWeightAt(new long[] { i, 0 }) != j)
{
Assert.IsTrue(Math.Round(tensorAGrad, 5) == Math.Round(softmaxWeight, 5));
}
else
{
Assert.IsTrue(Math.Round(tensorAGrad, 5) == Math.Round(softmaxWeight - 1.0f, 5));
}
}
}
}
public BaseSeq2SeqFramework(int[] deviceIds, ProcessorTypeEnums processorType, string modelFilePath, float memoryUsageRatio = 0.9f, string[] compilerOptions = null)
{
m_deviceIds = deviceIds;
m_modelFilePath = modelFilePath;
TensorAllocator.InitDevices(processorType, m_deviceIds, memoryUsageRatio, compilerOptions);
}
public BaseSeq2SeqFramework(int[] deviceIds, ProcessorTypeEnums processorType, string modelFilePath)
{
m_deviceIds = deviceIds;
m_modelFilePath = modelFilePath;
TensorAllocator.InitDevices(processorType, m_deviceIds);
}
