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https://github.com/marian-nmt/marian.git
synced 2024-09-11 06:15:56 +03:00
fixed backprop error, TODO: check shapes
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parent
cc4e24b3a6
commit
ffb21bb699
@ -24,22 +24,17 @@ ChainPtr Expr::node() {
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void Expr::forward(size_t batchSize) {
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UTIL_THROW_IF2(pimpl_.get() != Chainable<Tensor>::stack.back(),
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"Trying to call forward on non-root of computation graph");
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std::cerr << "forward:" << std::endl;
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"Trying to call forward on non-root of computation graph");
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for(auto&& v : Chainable<Tensor>::stack) {
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v->allocate(batchSize);
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}
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for(auto&& v : Chainable<Tensor>::stack)
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v->forward();
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}
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void Expr::backward() {
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UTIL_THROW_IF2(pimpl_.get() != Chainable<Tensor>::stack.back(),
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"Trying to call backward on non-root of computation graph");
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std::cerr << "backward:" << std::endl;
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"Trying to call backward on non-root of computation graph");
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for(auto&& v : Chainable<Tensor>::stack)
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v->set_zero_adjoint();
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@ -56,7 +51,6 @@ Expr::operator ChainPtr() {
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std::string Expr::Debug() const
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{
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stringstream strm;
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//const Chainable<Tensor> &ct = *pimpl_;
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const Shape &shape = pimpl_->shape();
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strm << marian::Debug(shape);
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return strm.str();
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@ -42,7 +42,8 @@ struct ParamNode : public Node {
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template <typename ...Args>
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ParamNode(Args ...args)
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: Node(args...),
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init_(Get<std::function<void(Tensor)>>(keywords::init, [](Tensor){ }))
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init_(Get<std::function<void(Tensor)>>(keywords::init, [](Tensor){ })),
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initialized_(false)
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{
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UTIL_THROW_IF2(!Has(keywords::shape) &&
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!Has(keywords::lazy_shape),
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@ -51,14 +52,18 @@ struct ParamNode : public Node {
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void forward() {}
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void backward() {}
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virtual void allocate(size_t batchSize) {
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val_.allocate(shape_);
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init_(val_);
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if(!initialized_) {
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init_(val_);
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initialized_ = true;
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}
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}
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private:
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std::function<void(Tensor)> init_;
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bool initialized_;
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};
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struct UnaryNodeOp : public Node {
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@ -139,6 +144,7 @@ struct SoftmaxNodeOp : public UnaryNodeOp {
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SoftmaxNodeOp(ChainPtr a, Args ...args)
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: UnaryNodeOp(a, keywords::shape=newShape(a),
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args...) { }
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Shape newShape(ChainPtr a) {
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Shape shape = a->shape();
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return shape;
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@ -164,8 +170,8 @@ struct SoftmaxNodeOp : public UnaryNodeOp {
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struct LogNodeOp : public UnaryNodeOp {
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template <typename ...Args>
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LogNodeOp(Args ...args)
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: UnaryNodeOp(args...) {}
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LogNodeOp(ChainPtr a, Args ...args)
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: UnaryNodeOp(a, keywords::shape=a->shape(), args...) {}
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void forward() {
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Element(_1 = Log(_2), val_, a_->val());
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@ -180,14 +186,9 @@ struct LogNodeOp : public UnaryNodeOp {
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struct ExpNodeOp : public UnaryNodeOp {
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template <typename ...Args>
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ExpNodeOp(ChainPtr a, Args ...args)
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: UnaryNodeOp(a, keywords::shape=newShape(a),
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: UnaryNodeOp(a, keywords::shape=a->shape(),
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args...) { }
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Shape newShape(ChainPtr a) {
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Shape shape = a->shape();
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return shape;
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}
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void forward() {
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Element(_1 = Exp(_2), val_, a_->val());
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}
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@ -8,7 +8,7 @@ using namespace keywords;
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int main(int argc, char** argv) {
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cudaSetDevice(0);
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cudaSetDevice(1);
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const size_t IMAGE_SIZE = 784;
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const size_t LABEL_SIZE = 10;
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@ -30,18 +30,22 @@ int main(int argc, char** argv) {
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std::cerr << "Done." << std::endl;
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std::cerr << "Building model...";
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auto x = input(shape={whatevs, IMAGE_SIZE}, name="X");
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auto y = input(shape={whatevs, LABEL_SIZE}, name="Y");
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auto w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0",
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auto x = input(shape={whatevs, IMAGE_SIZE});
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auto y = input(shape={whatevs, LABEL_SIZE});
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auto w = param(shape={IMAGE_SIZE, LABEL_SIZE},
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init=[wData](Tensor t) { t.set(wData); });
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auto b = param(shape={1, LABEL_SIZE}, name="b0",
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init=[bData](Tensor t) {t.set(bData); });
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auto b = param(shape={1, LABEL_SIZE},
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init=[bData](Tensor t) { t.set(bData); });
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auto predict = softmax(dot(x, w) + b,
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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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auto zd = dot(x, w);
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auto z = zd + b;
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auto predict = softmax(z, axis=1);
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auto logp = log(predict);
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auto cost = sum(y * logp, axis=1);
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auto graph = -mean(cost, axis=0);
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std::cerr << "Done." << std::endl;
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Tensor xt({BATCH_SIZE, IMAGE_SIZE});
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@ -51,14 +55,20 @@ int main(int argc, char** argv) {
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y = yt << testLabels;
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graph.forward(BATCH_SIZE);
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graph.backward();
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for(size_t i = 0; i < 1000; ++i) {
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graph.backward();
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auto update_rule = _1 -= 0.1 * _2;
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Element(update_rule, w.val(), w.grad());
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Element(update_rule, b.val(), b.grad());
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graph.forward(BATCH_SIZE);
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}
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auto results = predict.val();
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std::vector<float> resultsv(results.size());
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resultsv << results;
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std::cerr << b.grad().Debug() << std::endl;
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size_t acc = 0;
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for (size_t i = 0; i < testLabels.size(); i += LABEL_SIZE) {
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size_t correct = 0;
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@ -69,7 +79,7 @@ int main(int argc, char** argv) {
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}
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acc += (correct == predicted);
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}
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std::cerr << "Accuracy: " << float(acc)/BATCH_SIZE << std::endl;
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std::cerr << "Accuracy: " << float(acc) / BATCH_SIZE << std::endl;
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return 0;
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}
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