public override void Forward(Executor executor)
{
var input = executor.GetTensor(Input);
var states = executor.GetTensor(States);
var weight = executor.GetTensor(Weight);
Util.EnsureTrue(input.Shape.Rank == 2);
Util.EnsureTrue(states.Shape.Rank == 3, "states shape: (steps, n, n)");
Util.EnsureTrue(states.Shape[1] == states.Shape[2], "states shape: (steps, n, n)");
var steps = states.Shape[0];
var n = states.Shape[1];
var intermediate = executor.GetTensor(Intermediate, Shape.Create(steps - 1, n, n));
var output = executor.GetTensor(Output, Shape.Create(n, n));
var subExecutor = (Executor)executor.Objects[SubExecutor];
for (var i = 0; i < steps; ++i)
{
var input_i = i == 0 ? input : intermediate.Slice(i - 1).Reshape(n, n);
var state_i = states.Slice(i).Reshape(n, n);
var output_i = i == steps - 1 ? output : intermediate.Slice(i).Reshape(n, n);
subExecutor.SetTensor(SubInput, input_i);
subExecutor.SetTensor(SubWeight, weight);
subExecutor.SetTensor(SubState, state_i);
subExecutor.SetTensor(SubOutput, output_i);
subExecutor.Forward();
}
}
public override void Initialize(Executor executor)
{
var subExecutor = new Executor(executor.Context, SubOutput)
{
AssignAllGradient = true
};
executor.Objects[SubExecutor] = subExecutor;
base.Initialize(executor);
}
public static void TestAttentionReduce()
{
var n = 3;
var b = 4;
var d = 5;
var statesData = new double[n, b, d];
UniformRandomArray(statesData);
var softmaxData = new double[n, b];
UniformRandomArray(softmaxData);
var softmax = Variable <double>(PartialShape.Create(-1, b));
var states = Variable <double>(PartialShape.Create(-1, b, d));
var reduce = new AttentionReduce <double>(softmax, states);
var ctx = Context.GpuContext(0);
var exe = new Executor(ctx, reduce.Output)
{
AssignAllGradient = true
};
exe.Initalize();
var dOutputData = new double[b, d];
UniformRandomArray(dOutputData);
exe.AssignTensor(softmax, softmaxData.AsTensor());
exe.AssignTensor(states, statesData.AsTensor());
exe.Forward();
exe.AssignGradient(reduce.Output, dOutputData.AsTensor(), replace: true);
exe.Backward();
var dSoftmax = exe.GetGradient(reduce.Softmax);
var dStates = exe.GetGradient(reduce.States);
var bump = 1e-6;
var dSoftmaxFd = GradientChecker.FiniteDifferenceGradient(exe, softmax, bump: bump);
AreClose(dSoftmaxFd.ToArray2D(), dSoftmax.ToArray2D(), 1e-7);
var dStatesFd = GradientChecker.FiniteDifferenceGradient(exe, states, bump: bump);
AreClose(dStatesFd.ToArray3D(), dStates.ToArray3D(), 1e-7);
//var dVectorsFdArray = dVectorsFd.Reshape(-1).ToArray();
//var dVectorsBackpropArray = dStates.Reshape(-1).ToArray();
//var err = MaxAbsDiff(dVectorsFdArray, dVectorsBackpropArray);
}
public override void Backward(Executor executor)
{
var states = executor.GetTensor(States);
var softmax = executor.GetTensor(Softmax);
var dOutput = executor.GetGradient(Output);
var n = states.Shape[0];
var b = states.Shape[1];
var d = states.Shape[2];
executor.AssignGradient(States, softmax.Reshape(n, b, 1) * dOutput);
// states (n,b,d) * dOutput (b,d) => (n,b,d)
// softmax (n,b)
executor.AssignGradient(Softmax, ReduceSum((states * dOutput).Reshape(n * b, d), 1).Reshape(n, b));
}
public override void Forward(Executor executor)
{
var states = executor.GetTensor(States);
var softmax = executor.GetTensor(Softmax);
var n = states.Shape[0];
var b = states.Shape[1];
var d = states.Shape[2];
var prod = softmax.Reshape(n, b, 1) * states;
// currently reduce sum only works up to 2d tensor
// then we do a reduce to make it an 2d tensor
// after reduce, we reshape it back.
var reduce = ReduceSum(prod.Reshape(n, b * d), 0).Reshape(b, d);
executor.AssignTensor(Output, reduce);
}
public static void LoopStyle()
{
var inputVar = Variable <double>();
var statesVar = Variable <double>();
var weightVar = Variable <double>();
var loop = new LoopDemo(inputVar, statesVar, weightVar);
var outputVar = loop.Output;
// create executor
var ctx = Context.GpuContext(0);
var exe = new Executor(ctx, outputVar)
{
AssignAllGradient = true
};
exe.Initalize();
// fake forward data
const int steps = 4;
const int n = 5;
var input = new double[n, n];
var states = new double[steps, n, n];
var weight = new double[n, n];
var rng = new Random(42);
UniformRandomArray(input, rng);
UniformRandomArray(states, rng);
UniformRandomArray(weight, rng);
exe.AssignTensor(inputVar, input.AsTensor());
exe.AssignTensor(statesVar, states.AsTensor());
exe.AssignTensor(weightVar, weight.AsTensor());
// run forward
exe.Forward();
var outputTensor = exe.GetTensor(outputVar);
outputTensor.Print();
// fake backward data
var dOutput = new double[n, n];
UniformRandomArray(dOutput, rng);
exe.AssignGradient(outputVar, dOutput.AsTensor(), replace: true);
// run backward
exe.Backward();
// verify gradients
var bump = 1e-7;
var dInputTensor = exe.GetGradient(inputVar);
var dInputTensor_FD = GradientChecker.FiniteDifferenceGradient(exe, inputVar, bump: bump);
//dInputTensor.Print();
//dInputTensor_FD.Print();
AreClose(dInputTensor_FD.ToArray2D(), dInputTensor.ToArray2D(), 1e-7);
var dStatesTensor = exe.GetGradient(statesVar);
var dStatesTensor_FD = GradientChecker.FiniteDifferenceGradient(exe, statesVar, bump: bump);
//dStatesTensor.Reshape(steps, -1).Print();
//dStatesTensor_FD.Reshape(steps, -1).Print();
AreClose(dStatesTensor_FD.ToArray3D(), dStatesTensor.ToArray3D(), 1e-7);
var dWeightTensor = exe.GetGradient(weightVar);
var dWeightTensor_FD = GradientChecker.FiniteDifferenceGradient(exe, weightVar, bump: bump);
//dWeightTensor.Print();
//dWeightTensor_FD.Print();
AreClose(dWeightTensor_FD.ToArray2D(), dWeightTensor.ToArray2D(), 1e-3);
}
public override void Backward(Executor executor)
{
var input = executor.GetTensor(Input);
var states = executor.GetTensor(States);
var weight = executor.GetTensor(Weight);
Util.EnsureTrue(input.Shape.Rank == 2);
Util.EnsureTrue(states.Shape.Rank == 3, "states shape: (steps, n, n)");
Util.EnsureTrue(states.Shape[1] == states.Shape[2], "states shape: (steps, n, n)");
var steps = (int)states.Shape[0];
var n = states.Shape[1];
var intermediate = executor.GetTensor(Intermediate);
var output = executor.GetTensor(Output);
var dOutput = executor.GetGradient(Output);
var dIntermediate = executor.GetGradient(Intermediate, intermediate.Shape);
var dStates = executor.GetGradient(States, states.Shape);
var dWeight = executor.GetGradient(Weight, weight.Shape);
var dInput = executor.GetGradient(Input, input.Shape);
var counterInput = executor.GetGradientAggregationCounter(Input);
var counterWeight = executor.GetGradientAggregationCounter(Weight);
var counterStates = executor.GetGradientAggregationCounter(States);
var counterIntermediate = executor.GetGradientAggregationCounter(Intermediate);
var subExecutor = (Executor)executor.Objects[SubExecutor];
for (var i = steps - 1; i >= 0; --i)
{
// need set both input and output tensor and their gradient
var input_i = i == 0 ? input : intermediate.Slice(i - 1).Reshape(n, n);
var state_i = states.Slice(i).Reshape(n, n);
var output_i = i == steps - 1 ? output : intermediate.Slice(i).Reshape(n, n);
subExecutor.SetTensor(SubInput, input_i);
subExecutor.SetTensor(SubWeight, weight);
subExecutor.SetTensor(SubState, state_i);
subExecutor.SetTensor(SubOutput, output_i);
var dInput_i = i == 0 ? dInput : dIntermediate.Slice(i - 1).Reshape(n, n);
var dState_i = dStates.Slice(i).Reshape(n, n);
var dOutput_i = i == steps - 1 ? dOutput : dIntermediate.Slice(i).Reshape(n, n);
// since we have one shared variable, the weight, so we need update the
// gradient aggregation counter ourselves
// set counter = 0 means, you just point the memory for that gradient to another
// tensor, but it contains no value for aggregation
// but since weight is shared, so we need update its counter correctly, it
// will be assigned by steps - 1 times.
subExecutor.ClearGradientAggregationCounters();
subExecutor.SetGradient(SubInput, dInput_i, counter: i == 0 ? counterInput : counterIntermediate);
subExecutor.SetGradient(SubWeight, dWeight, counter: counterWeight + steps - 1 - i);
subExecutor.SetGradient(SubState, dState_i, counter: counterStates);
subExecutor.SetGradient(SubOutput, dOutput_i);
// do backward without clearing the counter, because we set the counter ourselves.
subExecutor.Backward(clearGradientAggretionCounter: false);
}
executor.IncreaseGradientAggregationCounter(Input);
executor.IncreaseGradientAggregationCounter(Weight);
executor.IncreaseGradientAggregationCounter(States);
executor.IncreaseGradientAggregationCounter(Intermediate);
}
public static void UnrollingStyle()
{
// create unrolling graph
const int steps = 4;
var inputVar = Variable <double>();
var stateVars = Enumerable.Range(0, steps).Select(_ => Variable <double>()).ToArray();
var weightVar = Variable <double>();
var outputVar = CreateUnrollingGraph(inputVar, stateVars, weightVar);
// create executor
var ctx = Context.GpuContext(0);
var exe = new Executor(ctx, outputVar)
{
AssignAllGradient = true
};
exe.Initalize();
// fake forward data
const int n = 5;
var input = new double[n, n];
var states = Enumerable.Range(0, steps).Select(_ => new double[n, n]).ToArray();
var weight = new double[n, n];
var rng = new Random(42);
UniformRandomArray(input, rng);
foreach (var state in states)
{
UniformRandomArray(state, rng);
}
UniformRandomArray(weight, rng);
exe.AssignTensor(inputVar, input.AsTensor());
for (var i = 0; i < steps; ++i)
{
exe.AssignTensor(stateVars[i], states[i].AsTensor());
}
exe.AssignTensor(weightVar, weight.AsTensor());
// run forward
exe.Forward();
var outputTensor = exe.GetTensor(outputVar);
outputTensor.Print();
// fake backward data
var dOutput = new double[n, n];
UniformRandomArray(dOutput, rng);
exe.AssignGradient(outputVar, dOutput.AsTensor(), replace: true);
// run backward
exe.Backward();
// verify gradients
var bump = 1e-7;
var dInputTensor = exe.GetGradient(inputVar);
var dInputTensor_FD = GradientChecker.FiniteDifferenceGradient(exe, inputVar, bump: bump);
//dInputTensor.Print();
//dInputTensor_FD.Print();
AreClose(dInputTensor_FD.ToArray2D(), dInputTensor.ToArray2D(), 1e-7);
for (var i = 0; i < steps; ++i)
{
var stateVar = stateVars[i];
var dStateTensor = exe.GetGradient(stateVar);
var dStateTensor_FD = GradientChecker.FiniteDifferenceGradient(exe, stateVar, bump: bump);
//dStateTensor.Print();
//dStateTensor_FD.Print();
AreClose(dStateTensor_FD.ToArray2D(), dStateTensor.ToArray2D(), 1e-7);
}
var dWeightTensor = exe.GetGradient(weightVar);
var dWeightTensor_FD = GradientChecker.FiniteDifferenceGradient(exe, weightVar, bump: bump);
//dWeightTensor.Print();
//dWeightTensor_FD.Print();
AreClose(dWeightTensor_FD.ToArray2D(), dWeightTensor.ToArray2D(), 1e-3);
}
public static void TestAttention()
{
//var batch = 4;
//var encoderHiddenSize = 5;
//var decoderHiddenSize = 4;
//var attentionDim = 3;
var batch = 10;
var encoderHiddenSize = 20;
var decoderHiddenSize = 25;
var attentionDim = 30;
// (encoderSeqLength, batch, encoderHiddenSize)
var encoderHiddenStates = Variable <double>(PartialShape.Create(-1, batch, encoderHiddenSize));
var decoderHiddenStates = Variable <double>(PartialShape.Create(batch, decoderHiddenSize));
var attention = new Attention <double>(encoderHiddenStates, decoderHiddenStates, attentionDim);
var ctx = Context.GpuContext(0);
var exe = new Executor(ctx, attention.Output)
{
AssignAllGradient = true
};
exe.Initalize();
// encoderSeqLength is flexibly at runtime
var encoderSeqLength = 3;
var dataEncoderHiddenStates = new double[encoderSeqLength, batch, encoderHiddenSize];
UniformRandomArray(dataEncoderHiddenStates);
var dataDecoderHiddenStates = new double[batch, decoderHiddenSize];
UniformRandomArray(dataDecoderHiddenStates);
exe.AssignTensor(encoderHiddenStates, dataEncoderHiddenStates.AsTensor());
exe.AssignTensor(decoderHiddenStates, dataDecoderHiddenStates.AsTensor());
exe.Forward();
var tensorOutput = exe.GetTensor(attention.Output);
//Console.WriteLine(tensorOutput.Shape);
//tensorOutput.Print();
var dataDOutput = new double[batch, encoderHiddenSize];
UniformRandomArray(dataDOutput);
exe.AssignGradient(attention.Output, dataDOutput.AsTensor(), replace: true);
exe.Backward();
var tensorDWh = exe.GetGradient(attention.Wh);
//tensorDWh.Print();
var tensorDWd = exe.GetGradient(attention.Wd);
//tensorDWd.Print();
var tensorDH = exe.GetGradient(attention.EncoderHiddenStates);
//Console.WriteLine(tensorDH.Shape);
//tensorDH.Reshape(-1, encoderHiddenSize).Print();
var tensorDD = exe.GetGradient(attention.DecoderHiddenStates);
//Console.WriteLine(tensorDD.Shape);
//tensorDD.Print();
var bump = 1e-7;
var tensorDWh_fd = GradientChecker.FiniteDifferenceGradient(exe, attention.Wh, bump: bump);
//tensorDWh.Print();
//tensorDWh_fd.Print();
AreClose(tensorDWh.ToArray2D(), tensorDWh_fd.ToArray2D(), 1e-7);
var tensorDWd_fd = GradientChecker.FiniteDifferenceGradient(exe, attention.Wd, bump: bump);
//tensorDWd.Print();
//tensorDWd_fd.Print();
AreClose(tensorDWd.ToArray2D(), tensorDWd_fd.ToArray2D(), 1e-7);
var tensorDH_fd = GradientChecker.FiniteDifferenceGradient(exe, attention.EncoderHiddenStates, bump: bump);
//tensorDH.Reshape(-1, encoderHiddenSize).Print();
//tensorDH_fd.Reshape(-1, encoderHiddenSize).Print();
AreClose(tensorDH.ToArray3D(), tensorDH_fd.ToArray3D(), 1e-7);
var tensorDD_fd = GradientChecker.FiniteDifferenceGradient(exe, attention.DecoderHiddenStates, bump: bump);
//tensorDD.Print();
//tensorDD_fd.Print();
AreClose(tensorDD.ToArray2D(), tensorDD_fd.ToArray2D(), 1e-7);
}
public virtual void Initialize(Executor executor)
{
}
public abstract void Backward(Executor executor);
public abstract void Forward(Executor executor);