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Initial implementation of an RNN.
This commit is contained in:
Andre Martins
parent
61d8b3cb83
commit
dbd83a5496
168
src/test.cu
168
src/test.cu
@ -5,117 +5,87 @@
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int main(int argc, char** argv) {
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cudaSetDevice(0);
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/*int numImg = 0;*/
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/*auto images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numImg);*/
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/*auto labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numImg);*/
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#if 1
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using namespace marian;
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using namespace keywords;
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Expr x = input(shape={1, 2});
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Expr y = input(shape={1, 2});
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int input_size = 10;
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int output_size = 2;
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int batch_size = 25;
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int hidden_size = 5;
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int num_inputs = 8;
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Expr w = param(shape={2, 2}, name="W0");
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//Expr b = param(shape={1, 2}, name="b0");
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std::vector<Expr*> X(num_inputs);
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std::vector<Expr*> Y(num_inputs);
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std::vector<Expr*> H(num_inputs);
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std::cerr << "Building model...";
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auto predict = softmax_fast(dot(x, w),
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axis=1, name="pred");
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auto graph = -mean(sum(y * log(predict), axis=1),
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axis=0, name="cost");
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for (int t = 0; t < num_inputs; ++t) {
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X[t] = new Expr(input(shape={batch_size, input_size}));
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Y[t] = new Expr(input(shape={batch_size, output_size}));
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}
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Tensor x1t({1, 2});
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std::vector<float> xv = { 0.6, 0.1 };
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thrust::copy(xv.begin(), xv.end(), x1t.begin());
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Expr Wxh = param(shape={input_size, hidden_size}, name="Wxh");
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Expr Whh = param(shape={hidden_size, hidden_size}, name="Whh");
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Expr bh = param(shape={1, hidden_size}, name="bh");
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Expr h0 = param(shape={1, hidden_size}, name="h0");
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Tensor x2t({1, 2});
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std::vector<float> yv = { 0, 1 };
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thrust::copy(yv.begin(), yv.end(), x2t.begin());
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std::cerr << "Building RNN..." << std::endl;
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H[0] = new Expr(tanh(dot(*X[0], Wxh) + dot(h0, Whh) + bh));
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for (int t = 1; t < num_inputs; ++t) {
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H[t] = new Expr(tanh(dot(*X[t], Wxh) + dot(*H[t-1], Whh) + bh));
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}
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x = x1t;
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y = x2t;
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Expr Why = param(shape={hidden_size, output_size}, name="Why");
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Expr by = param(shape={1, output_size}, name="by");
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graph.forward(1);
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std::cerr << "Building output layer..." << std::endl;
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std::vector<Expr*> Yp(num_inputs);
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Expr* cross_entropy = NULL;
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for (int t = 0; t < num_inputs; ++t) {
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Yp[t] = new Expr(softmax_fast(dot(*H[t], Why) + by, name="pred"));
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if (!cross_entropy) {
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cross_entropy = new Expr(sum(*Y[t] * log(*Yp[t]), axis=1));
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} else {
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*cross_entropy = *cross_entropy + sum(*Y[t] * log(*Yp[t]), axis=1);
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}
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}
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auto graph = -mean(*cross_entropy, axis=0, name="cost");
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for (int t = 0; t < num_inputs; ++t) {
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Tensor Xt({batch_size, input_size});
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Tensor Yt({batch_size, output_size});
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float max = 1.;
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std::vector<float> values(batch_size * input_size);
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std::vector<float> classes(batch_size * output_size, 0.0);
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int k = 0;
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int l = 0;
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for (int i = 0; i < batch_size; ++i) {
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for (int j = 0; j < input_size; ++j, ++k) {
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values[k] = max * (2.0*static_cast<float>(rand()) / RAND_MAX - 1.0);
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}
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int gold = output_size * static_cast<float>(rand()) / RAND_MAX;
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classes[l + gold] = 1.0;
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l += output_size;
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}
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thrust::copy(values.begin(), values.end(), Xt.begin());
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thrust::copy(classes.begin(), classes.end(), Yt.begin());
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*X[t] = Xt;
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*Y[t] = Yt;
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}
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graph.forward(batch_size);
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graph.backward();
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std::cerr << graph.val().Debug() << std::endl;
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std::cerr << w.grad().Debug() << std::endl;
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//std::cerr << b.grad().Debug() << std::endl;
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#else
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using namespace marian;
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using namespace keywords;
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using namespace std;
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const size_t BATCH_SIZE = 500;
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const size_t IMAGE_SIZE = 784;
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const size_t LABEL_SIZE = 10;
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Expr x = input(shape={whatevs, IMAGE_SIZE}, name="X");
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Expr y = input(shape={whatevs, LABEL_SIZE}, name="Y");
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Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0");
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Expr b = param(shape={1, LABEL_SIZE}, name="b0");
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Expr z = dot(x, w) + b;
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Expr lr = softmax(z, axis=1, name="pred");
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Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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//cerr << "x=" << Debug(lr.val().shape()) << endl;
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int numofdata;
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//vector<float> images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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//vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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vector<float> images = datasets::mnist::ReadImages("../examples/mnist/train-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/train-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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cerr << "images=" << images.size() << " labels=" << labels.size() << endl;
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cerr << "numofdata=" << numofdata << endl;
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size_t startInd = 0;
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size_t startIndData = 0;
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while (startInd < numofdata) {
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size_t batchSize = (startInd + BATCH_SIZE < numofdata) ? BATCH_SIZE : numofdata - startInd;
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cerr << "startInd=" << startInd
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<< " startIndData=" << startIndData
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<< " batchSize=" << batchSize << endl;
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Tensor tx({numofdata, IMAGE_SIZE}, 1);
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Tensor ty({numofdata, LABEL_SIZE}, 1);
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tx.set(images.begin() + startIndData, images.begin() + startIndData + batchSize * IMAGE_SIZE);
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ty.set(labels.begin() + startInd, labels.begin() + startInd + batchSize);
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//cerr << "tx=" << Debug(tx.shape()) << endl;
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//cerr << "ty=" << Debug(ty.shape()) << endl;
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x = tx;
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y = ty;
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cerr << "x=" << Debug(x.val().shape()) << endl;
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cerr << "y=" << Debug(y.val().shape()) << endl;
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graph.forward(batchSize);
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cerr << "w=" << Debug(w.val().shape()) << endl;
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cerr << "b=" << Debug(b.val().shape()) << endl;
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std::cerr << "z: " << Debug(z.val().shape()) << endl;
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std::cerr << "lr: " << Debug(lr.val().shape()) << endl;
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std::cerr << "Log-likelihood: " << graph.val().Debug() << endl ;
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//std::cerr << "scores=" << scores.val().Debug() << endl;
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//std::cerr << "lr=" << lr.val().Debug() << endl;
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graph.backward();
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std::cerr << w.grad().Debug() << std::endl;
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//std::cerr << graph["pred"].val()[0] << std::endl;
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startInd += batchSize;
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startIndData += batchSize * IMAGE_SIZE;
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}
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#endif
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std::cerr << Whh.grad().Debug() << std::endl;
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std::cerr << bh.grad().Debug() << std::endl;
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std::cerr << Why.grad().Debug() << std::endl;
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std::cerr << by.grad().Debug() << std::endl;
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std::cerr << Wxh.grad().Debug() << std::endl;
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std::cerr << h0.grad().Debug() << std::endl;
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return 0;
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}
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