public static void TestLstmAgainstReferenceResults()
{
var mfr = new MatFileReader(@"lstm_small.mat");
var inputSize = mfr.GetInt("InputSize");
var seqLength = mfr.GetInt("SeqLength");
var hiddenSize = mfr.GetInt("HiddenSize");
var batchSize = mfr.GetInt("BatchSize");
var x = Variable <float>(PartialShape.Create(seqLength, batchSize, inputSize));
var lstm = new Lstm <float>(x, hiddenSize);
var ctx = Context.GpuContext(0);
var exe = new Executor(ctx, lstm.Y);
exe.Initalize();
var h0 = mfr.GetDoubleArray("h0").Select(n => (float)n).ToArray();
var c0 = mfr.GetDoubleArray("c0").Select(n => (float)n).ToArray();
exe.AssignTensor(lstm.CX, c0.AsTensor(Shape.Create(batchSize, hiddenSize)));
exe.AssignTensor(lstm.HX, h0.AsTensor(Shape.Create(batchSize, hiddenSize)));
var input = mfr.GetDoubleArray("X").Select(n => (float)n).ToArray();
exe.AssignTensor(x, input.AsTensor(Shape.Create(seqLength, batchSize, inputSize)));
var w = mfr.GetDoubleArray("W").Select(n => (float)n).ToArray();
w.AsTensor(Shape.Create(inputSize + hiddenSize + 1, 4 * hiddenSize)).Print();
exe.AssignTensor(lstm.W, w.AsTensor(Shape.Create(inputSize + hiddenSize + 1, 4 * hiddenSize)));
exe.Forward();
var H = mfr.GetDoubleArray("H").Select(n => (float)n).ToArray();
H.AsTensor(Shape.Create(seqLength * batchSize, hiddenSize)).Print();
var myH = exe.GetTensor(lstm.Y).ToArray();
myH.AsTensor(Shape.Create(seqLength * batchSize, hiddenSize)).Print();
AreClose(H, myH, 1e-6);
var CN = mfr.GetDoubleArray("cn").Select(n => (float)n).ToArray();
CN.AsTensor(Shape.Create(batchSize, hiddenSize)).Print();
var myCN = exe.GetTensor(lstm.CY).ToArray();
myCN.AsTensor(Shape.Create(batchSize, hiddenSize)).Print();
AreClose(CN, myCN, 1e-6);
var HN = mfr.GetDoubleArray("hn").Select(n => (float)n).ToArray();
HN.AsTensor(Shape.Create(batchSize, hiddenSize)).Print();
var myHN = exe.GetTensor(lstm.HY).ToArray();
myHN.AsTensor(Shape.Create(batchSize, hiddenSize)).Print();
AreClose(HN, myHN, 1e-6);
var dH = mfr.GetDoubleArray("dH").Select(n => (float)n).ToArray();
exe.AssignGradient(lstm.Y, dH.AsTensor(Shape.Create(seqLength, batchSize, hiddenSize)), replace: true);
exe.Backward();
var dX = mfr.GetDoubleArray("dX").Select(n => (float)n).ToArray();
dX.AsTensor(Shape.Create(seqLength * batchSize, inputSize)).Print();
var dXmy = exe.GetGradient(lstm.X).ToArray();
dXmy.AsTensor(Shape.Create(seqLength * batchSize, inputSize)).Print();
AreClose(dX, dXmy, 1e-6);
var dW = mfr.GetDoubleArray("dW").Select(n => (float)n).ToArray();
dW.AsTensor(Shape.Create(inputSize + hiddenSize + 1, 4 * hiddenSize)).Print();
var dWmy = exe.GetGradient(lstm.W).ToArray();
dWmy.AsTensor(Shape.Create(lstm.W.Shape.AsArray)).Print();
AreClose(dW, dWmy, 1e-6);
var dc0 = mfr.GetDoubleArray("dc0").Select(n => (float)n).ToArray();
dc0.AsTensor(Shape.Create(batchSize, hiddenSize)).Print();
var dc0my = exe.GetGradient(lstm.CX).ToArray();
dc0my.AsTensor(Shape.Create(batchSize, hiddenSize)).Print();
AreClose(dc0, dc0my, 1e-6);
var dh0 = mfr.GetDoubleArray("dh0").Select(n => (float)n).ToArray();
dh0.AsTensor(Shape.Create(batchSize, hiddenSize)).Print();
var dh0my = exe.GetGradient(lstm.HX).ToArray();
dh0my.AsTensor(Shape.Create(batchSize, hiddenSize)).Print();
AreClose(dh0, dh0my, 1e-6);
ctx.ToGpuContext().Stream.Synchronize();
}
public Model(Context ctx, Config cfg, bool isTraining = true, bool usingCuDnn = true)
{
Config = cfg;
IsTraining = isTraining;
UsingCuDnn = usingCuDnn;
Inputs = Variable <int>(PartialShape.Create(cfg.NumSteps, cfg.BatchSize));
Targets = Variable <int>(PartialShape.Create(cfg.NumSteps, cfg.BatchSize));
// embedding
Embedding = new Embedding <float>(Inputs, cfg.VocabSize, cfg.HiddenSize, initScale: cfg.InitScale);
// add dropout
EmbeddedOutput = Embedding.Output;
if (isTraining && cfg.KeepProb < 1.0)
{
var dropout = new Dropout <float>(EmbeddedOutput, dropoutProb: 1.0 - cfg.KeepProb);
EmbeddedOutput = dropout.Output;
}
// rnn layer, dropout for intermediate lstm layers and for output
if (usingCuDnn)
{
RnnAccelerated = new Rnn <float>(new LstmRnnType(forgetBiasInit: 0.0), EmbeddedOutput, cfg.NumLayers, cfg.HiddenSize, isTraining: isTraining, dropout: isTraining && cfg.KeepProb < 1.0 ? 1.0 - Config.KeepProb : 0.0);
RnnOutput = RnnAccelerated.Y;
if (isTraining && cfg.KeepProb < 1.0)
{
var dropout = new Dropout <float>(RnnOutput, dropoutProb: 1.0 - cfg.KeepProb);
RnnOutput = dropout.Output;
}
}
else
{
RnnDirect = new Lstm <float> [cfg.NumLayers];
for (var i = 0; i < cfg.NumLayers; ++i)
{
var lstm = new Lstm <float>(i == 0 ? EmbeddedOutput : RnnOutput, cfg.HiddenSize, forgetBiasInit: 0.0);
RnnDirect[i] = lstm;
RnnOutput = lstm.Y;
if (isTraining && cfg.KeepProb < 1.0)
{
var dropout = new Dropout <float>(RnnOutput, dropoutProb: 1.0 - cfg.KeepProb);
RnnOutput = dropout.Output;
}
}
}
FC = new FullyConnected <float>(RnnOutput.Reshape(RnnOutput.Shape[0] * RnnOutput.Shape[1], RnnOutput.Shape[2]), cfg.VocabSize);
Loss = new SoftmaxCrossEntropySparse <float>(FC.Output, Targets.Reshape(Targets.Shape[0] * Targets.Shape[1]));
Optimizer = new GradientDescentOptimizer(ctx, Loss.Loss, cfg.LearningRate, new GlobalNormGradientClipper(cfg.MaxGradNorm));
// warmup to force JIT compilation to get timings without JIT overhead
Optimizer.Initalize();
ResetStates();
Optimizer.AssignTensor(Inputs, Fill(Shape.Create(Inputs.Shape.AsArray), 0));
Optimizer.AssignTensor(Targets, Fill(Shape.Create(Targets.Shape.AsArray), 0));
Optimizer.Forward();
if (isTraining)
{
Optimizer.Backward();
}
// now reset states
Optimizer.Initalize();
ResetStates();
}
public static void TestLstmAgainstCuDnnVersion()
{
var ctx = Context.GpuContext(0);
var inputSize = 5;
var seqLength = 3;
var batchSize = 2;
var hiddenSize = 4;
var error = 1e-5;
var data = Context.CpuContext.Eval((2.0f.AsScalar() *
RandomUniform <float>(Shape.Create(seqLength, batchSize, inputSize)) -
1.0f.AsScalar())).ToArray3D();
//data.AsTensor(Shape.Create(seqLength*batchSize, inputSize)).Print();
var h0 = Context.CpuContext.Eval(RandomNormal <float>(Shape.Create(batchSize, hiddenSize))).ToArray2D();
var c0 = Context.CpuContext.Eval(RandomNormal <float>(Shape.Create(batchSize, hiddenSize))).ToArray2D();
var dy = Context.CpuContext.Eval((2.0f.AsScalar() *
RandomUniform <float>(Shape.Create(seqLength, batchSize, hiddenSize)) -
1.0f.AsScalar())).ToArray3D();
//dy.AsTensor(Shape.Create(seqLength * batchSize, hiddenSize)).Print();
var wi = 0.5f;
var wf = 0.4f;
var wo = 0.3f;
var wa = 0.2f;
var ui = 0.5f;
var uf = 0.4f;
var uo = 0.3f;
var ua = 0.1f;
var bi = 0.5f;
var bf = 0.4f;
var bo = 0.3f;
var ba = 0.2f;
float[,,] y1, y2, dx1, dx2;
float[,] cy1, cy2, hy1, hy2;
float[,] dcx1, dcx2, dhx1, dhx2;
float[,] dw1, dw2;
{
// calc with cuDNN
var x = Variable <float>(PartialShape.Create(seqLength, batchSize, inputSize));
var lstm = new Rnn <float>(new LstmRnnType(), x, 1, hiddenSize, dropout: 0.0);
var exe = new Executor(ctx, lstm.Y);
exe.Initalize();
// set input
exe.AssignTensor(lstm.X, data.AsTensor());
// set states
exe.AssignTensor(lstm.CX, c0.AsTensor(Shape.Create(1, batchSize, hiddenSize)));
exe.AssignTensor(lstm.HX, h0.AsTensor(Shape.Create(1, batchSize, hiddenSize)));
// set weigths
// cuDNN matrices order: IFAO
var w = exe.GetTensor(lstm.W).Reshape(inputSize * 4 + hiddenSize * 4 + 2 * 4, hiddenSize);
var offset = 0;
// Wi
ctx.Assign(w.Slice(Range(offset, offset + inputSize)), Fill(Shape.Create(inputSize, hiddenSize), wi));
offset += inputSize;
// Wf
ctx.Assign(w.Slice(Range(offset, offset + inputSize)), Fill(Shape.Create(inputSize, hiddenSize), wf));
offset += inputSize;
// Wa
ctx.Assign(w.Slice(Range(offset, offset + inputSize)), Fill(Shape.Create(inputSize, hiddenSize), wa));
offset += inputSize;
// Wo
ctx.Assign(w.Slice(Range(offset, offset + inputSize)), Fill(Shape.Create(inputSize, hiddenSize), wo));
offset += inputSize;
// Ui
ctx.Assign(w.Slice(Range(offset, offset + hiddenSize)), Fill(Shape.Create(hiddenSize, hiddenSize), ui));
offset += hiddenSize;
// Uf
ctx.Assign(w.Slice(Range(offset, offset + hiddenSize)), Fill(Shape.Create(hiddenSize, hiddenSize), uf));
offset += hiddenSize;
// Ua
ctx.Assign(w.Slice(Range(offset, offset + hiddenSize)), Fill(Shape.Create(hiddenSize, hiddenSize), ua));
offset += hiddenSize;
// Uo
ctx.Assign(w.Slice(Range(offset, offset + hiddenSize)), Fill(Shape.Create(hiddenSize, hiddenSize), uo));
offset += hiddenSize;
// Bi
ctx.Assign(w.Slice(offset), Fill(Shape.Create(1, hiddenSize), bi));
offset++;
// Bf
ctx.Assign(w.Slice(offset), Fill(Shape.Create(1, hiddenSize), bf));
offset++;
// Ba
ctx.Assign(w.Slice(offset), Fill(Shape.Create(1, hiddenSize), ba));
offset++;
// Bo
ctx.Assign(w.Slice(offset), Fill(Shape.Create(1, hiddenSize), bo));
exe.Forward();
y1 = exe.GetTensor(lstm.Y).ToArray3D();
cy1 = exe.GetTensor(lstm.CY).Reshape(batchSize, hiddenSize).ToArray2D();
hy1 = exe.GetTensor(lstm.HY).Reshape(batchSize, hiddenSize).ToArray2D();
exe.AssignGradient(lstm.Y, dy.AsTensor(), replace: true);
exe.Backward();
dx1 = exe.GetGradient(lstm.X).ToArray3D();
dcx1 = exe.GetGradient(lstm.CX).Reshape(batchSize, hiddenSize).ToArray2D();
dhx1 = exe.GetGradient(lstm.HX).Reshape(batchSize, hiddenSize).ToArray2D();
// we make dw follow the shape as (1 + inputSize + hiddenSize, 4*hiddenSize), need to transpose because cuDNN uses Fortran storge order
var dwCUDNN = exe.GetGradient(lstm.W).ToArray().AsTensor();
dw1 = new float[1 + inputSize + hiddenSize, 4 * hiddenSize];
var dw1Tensor = Reference <float>(dw1);
var cpu = Context.CpuContext;
offset = 0;
// cuDNN order: IFAO, need to transpose because cuDNN uses Fortran storge order
// Wi
cpu.Assign(dw1Tensor.Slice(Range(1, inputSize + 1), Range(0, hiddenSize)), dwCUDNN.Slice(Range(offset, offset + inputSize * hiddenSize)).Reshape(hiddenSize, inputSize).T);
offset += inputSize * hiddenSize;
// Wf
cpu.Assign(dw1Tensor.Slice(Range(1, inputSize + 1), Range(hiddenSize, 2 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + inputSize * hiddenSize)).Reshape(hiddenSize, inputSize).T);
offset += inputSize * hiddenSize;
// Wa
cpu.Assign(dw1Tensor.Slice(Range(1, inputSize + 1), Range(3 * hiddenSize, 4 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + inputSize * hiddenSize)).Reshape(hiddenSize, inputSize).T);
offset += inputSize * hiddenSize;
// Wo
cpu.Assign(dw1Tensor.Slice(Range(1, inputSize + 1), Range(2 * hiddenSize, 3 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + inputSize * hiddenSize)).Reshape(hiddenSize, inputSize).T);
offset += inputSize * hiddenSize;
// Ui
cpu.Assign(dw1Tensor.Slice(Range(inputSize + 1, -1), Range(0, hiddenSize)), dwCUDNN.Slice(Range(offset, offset + hiddenSize * hiddenSize)).Reshape(hiddenSize, hiddenSize).T);
offset += hiddenSize * hiddenSize;
// Uf
cpu.Assign(dw1Tensor.Slice(Range(inputSize + 1, -1), Range(hiddenSize, 2 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + hiddenSize * hiddenSize)).Reshape(hiddenSize, hiddenSize).T);
offset += hiddenSize * hiddenSize;
// Ua
cpu.Assign(dw1Tensor.Slice(Range(inputSize + 1, -1), Range(3 * hiddenSize, 4 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + hiddenSize * hiddenSize)).Reshape(hiddenSize, hiddenSize).T);
offset += hiddenSize * hiddenSize;
// Uo
cpu.Assign(dw1Tensor.Slice(Range(inputSize + 1, -1), Range(2 * hiddenSize, 3 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + hiddenSize * hiddenSize)).Reshape(hiddenSize, hiddenSize).T);
offset += hiddenSize * hiddenSize;
// Bi
cpu.Assign(dw1Tensor.Slice(0, Range(0, hiddenSize)), dwCUDNN.Slice(Range(offset, offset + hiddenSize)).Reshape(hiddenSize, 1).T);
offset += hiddenSize;
// Bf
cpu.Assign(dw1Tensor.Slice(0, Range(hiddenSize, 2 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + hiddenSize)).Reshape(hiddenSize, 1).T);
offset += hiddenSize;
// Ba
cpu.Assign(dw1Tensor.Slice(0, Range(3 * hiddenSize, 4 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + hiddenSize)).Reshape(hiddenSize, 1).T);
offset += hiddenSize;
// Bo
cpu.Assign(dw1Tensor.Slice(0, Range(2 * hiddenSize, 3 * hiddenSize)), dwCUDNN.Slice(Range(offset, offset + hiddenSize)).Reshape(hiddenSize, 1).T);
}
{
// calc with direct LSTM implementation
var x = Variable <float>(PartialShape.Create(seqLength, batchSize, inputSize));
var lstm = new Lstm <float>(x, hiddenSize, forgetBiasInit: 0.0);
var exe = new Executor(ctx, lstm.Y);
exe.Initalize();
// set input
exe.AssignTensor(lstm.X, data.AsTensor());
// set states
exe.AssignTensor(lstm.CX, c0.AsTensor());
exe.AssignTensor(lstm.HX, h0.AsTensor());
// set weights
var w = exe.GetTensor(lstm.W);
// Wi
ctx.Assign(w.Slice(Range(1, inputSize + 1), Range(0, hiddenSize)), Fill(Shape.Create(inputSize, hiddenSize), wi));
// Wf
ctx.Assign(w.Slice(Range(1, inputSize + 1), Range(hiddenSize, 2 * hiddenSize)), Fill(Shape.Create(inputSize, hiddenSize), wf));
// Wo
ctx.Assign(w.Slice(Range(1, inputSize + 1), Range(2 * hiddenSize, 3 * hiddenSize)), Fill(Shape.Create(inputSize, hiddenSize), wo));
// Wa
ctx.Assign(w.Slice(Range(1, inputSize + 1), Range(3 * hiddenSize, 4 * hiddenSize)), Fill(Shape.Create(inputSize, hiddenSize), wa));
// Ui
ctx.Assign(w.Slice(Range(inputSize + 1, -1), Range(0, hiddenSize)), Fill(Shape.Create(hiddenSize, hiddenSize), ui));
// Uf
ctx.Assign(w.Slice(Range(inputSize + 1, -1), Range(hiddenSize, 2 * hiddenSize)), Fill(Shape.Create(hiddenSize, hiddenSize), uf));
// Uo
ctx.Assign(w.Slice(Range(inputSize + 1, -1), Range(2 * hiddenSize, 3 * hiddenSize)), Fill(Shape.Create(hiddenSize, hiddenSize), uo));
// Ua
ctx.Assign(w.Slice(Range(inputSize + 1, -1), Range(3 * hiddenSize, 4 * hiddenSize)), Fill(Shape.Create(hiddenSize, hiddenSize), ua));
// Bi
ctx.Assign(w.Slice(0, Range(0, hiddenSize)), Fill(Shape.Create(1, hiddenSize), bi));
// Bf
ctx.Assign(w.Slice(0, Range(hiddenSize, 2 * hiddenSize)), Fill(Shape.Create(1, hiddenSize), bf));
// Bo
ctx.Assign(w.Slice(0, Range(2 * hiddenSize, 3 * hiddenSize)), Fill(Shape.Create(1, hiddenSize), bo));
// Ba
ctx.Assign(w.Slice(0, Range(3 * hiddenSize, 4 * hiddenSize)), Fill(Shape.Create(1, hiddenSize), ba));
exe.Forward();
y2 = exe.GetTensor(lstm.Y).ToArray3D();
cy2 = exe.GetTensor(lstm.CY).ToArray2D();
hy2 = exe.GetTensor(lstm.HY).ToArray2D();
exe.AssignGradient(lstm.Y, dy.AsTensor(), replace: true);
exe.Backward();
dx2 = exe.GetGradient(lstm.X).ToArray3D();
dcx2 = exe.GetGradient(lstm.CX).Reshape(batchSize, hiddenSize).ToArray2D();
dhx2 = exe.GetGradient(lstm.HX).Reshape(batchSize, hiddenSize).ToArray2D();
dw2 = exe.GetGradient(lstm.W).ToArray2D();
}
AreClose(y1, y2, error);
AreClose(cy1, cy2, error);
AreClose(hy1, hy2, error);
AreClose(dx1, dx2, error);
AreClose(dcx1, dcx2, error);
AreClose(dhx1, dhx2, error);
AreClose(dw1, dw2, error);
}
