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https://github.com/marian-nmt/marian.git
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merge
This commit is contained in:
commit
517fb6c385
@ -36,11 +36,16 @@ cuda_add_executable(
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validate_mnist_batch
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validate_mnist_batch.cu
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)
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cuda_add_executable(
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validate_encoder_decoder
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validate_encoder_decoder.cu
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)
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target_link_libraries(validate_mnist marian_lib)
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target_link_libraries(validate_mnist_batch marian_lib)
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target_link_libraries(validate_encoder_decoder marian_lib)
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foreach(exec marian train_mnist validate_mnist validate_mnist_batch )
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foreach(exec marian train_mnist validate_mnist validate_mnist_batch validate_encoder_decoder)
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target_link_libraries(${exec} ${EXT_LIBS} cuda cudnn)
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cuda_add_cublas_to_target(${exec})
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set_target_properties(${exec} PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
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|
@ -17,6 +17,8 @@ struct Chainable {
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virtual void set_zero_adjoint() { }
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virtual void allocate(size_t) = 0;
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virtual std::string graphviz() = 0;
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virtual const Shape& shape() = 0;
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virtual DataType &val() = 0;
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|
@ -48,6 +48,18 @@ class ExpressionGraph {
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v->forward();
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}
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std::string graphviz() {
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std::stringstream ss;
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ss << "digraph ExpressionGraph {" << std::endl;
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ss << "rankdir=BT" << std::endl;
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typedef typename ChainableStack::reverse_iterator It;
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for(It it = stack_->rbegin(); it != stack_->rend(); ++it)
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ss << (*it)->graphviz();
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ss << "}" << std::endl;
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return ss.str();
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}
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void backward() {
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for(auto&& v : *stack_)
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v->set_zero_adjoint();
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|
@ -22,6 +22,13 @@ struct InputNode : public Node {
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void forward() {}
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void backward() {}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"parallelogram\", label=\"input\", style=\"filled\", fillcolor=\"lawngreen\"]" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct ConstantNode : public Node {
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@ -35,6 +42,13 @@ struct ConstantNode : public Node {
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void forward() {}
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void backward() {}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"diamond\", label=\"const\"]" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct ParamNode : public Node {
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@ -60,6 +74,13 @@ struct ParamNode : public Node {
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}
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"hexagon\", label=\"param\", style=\"filled\", fillcolor=\"orangered\"]" << std::endl << std::endl;
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return ss.str();
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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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@ -89,6 +110,14 @@ struct LogitNodeOp : public UnaryNodeOp {
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Element(_1 += _2 * _3 * (1 - _3),
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a_->grad(), adj_, val_);
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"logit\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct TanhNodeOp : public UnaryNodeOp {
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@ -105,6 +134,14 @@ struct TanhNodeOp : public UnaryNodeOp {
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Element(_1 += _2 * (1 - _3 * _3),
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a_->grad(), adj_, val_);
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"tanh\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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// @TODO, make this numerically safe(r):
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@ -131,6 +168,14 @@ struct SoftmaxNodeOp : public UnaryNodeOp {
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SubtractMean(&result, val_);
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Element(_1 += _2 * _3, a_->grad(), val_, result);
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"softmax\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct LogNodeOp : public UnaryNodeOp {
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@ -146,6 +191,14 @@ struct LogNodeOp : public UnaryNodeOp {
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Element(_1 += _2 * 1.f / _3,
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a_->grad(), adj_, a_->val());
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"log\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct ExpNodeOp : public UnaryNodeOp {
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@ -161,6 +214,14 @@ struct ExpNodeOp : public UnaryNodeOp {
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Element(_1 += _2 * Exp(_3),
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a_->grad(), adj_, a_->val());
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"exp\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct NegNodeOp : public UnaryNodeOp {
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@ -175,6 +236,14 @@ struct NegNodeOp : public UnaryNodeOp {
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void backward() {
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Element(_1 += -_2, a_->grad(), adj_);
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"-\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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/******************************************************/
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@ -220,6 +289,15 @@ struct DotNodeOp : public BinaryNodeOp {
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Prod(a_->grad(), adj_, b_->val(), false, true, 1.0);
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Prod(b_->grad(), a_->val(), adj_, true, false, 1.0);
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"×\", style=\"filled\", fillcolor=\"orange\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl;
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ss << "\"" << &*b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct PlusNodeOp : public BinaryNodeOp {
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@ -238,6 +316,15 @@ struct PlusNodeOp : public BinaryNodeOp {
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Element(_1 += _2,
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b_->grad(), adj_);
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"+\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl;
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ss << "\"" << &*b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct MinusNodeOp : public BinaryNodeOp {
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@ -256,6 +343,15 @@ struct MinusNodeOp : public BinaryNodeOp {
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Element(_1 -= _2,
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b_->grad(), adj_);
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"-\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl;
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ss << "\"" << &*b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct MultNodeOp : public BinaryNodeOp {
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@ -274,6 +370,15 @@ struct MultNodeOp : public BinaryNodeOp {
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Element(_1 += _2 * _3,
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b_->grad(), adj_, a_->val());
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"•\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl;
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ss << "\"" << &*b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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struct DivNodeOp : public BinaryNodeOp {
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@ -292,6 +397,15 @@ struct DivNodeOp : public BinaryNodeOp {
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Element(_1 -= _2 * _3 / (_4 * _4),
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b_->grad(), adj_, a_->val(), b_->val());
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}
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virtual std::string graphviz() {
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std::stringstream ss;
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ss << "\"" << this << "\" [shape=\"box\", label=\"÷\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
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ss << "\"" << a_ << "\" -> \"" << this << "\"" << std::endl;
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ss << "\"" << b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
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return ss.str();
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};
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};
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}
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|
@ -90,6 +90,8 @@ int main(int argc, char** argv) {
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Y[t] = Yt;
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}
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std::cout << g.graphviz() << std::endl;
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g.forward(batch_size);
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g.backward();
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|
142
src/validate_encoder_decoder.cu
Normal file
142
src/validate_encoder_decoder.cu
Normal file
@ -0,0 +1,142 @@
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#include "marian.h"
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#include "mnist.h"
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#if 0
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ExpressionGraph build_graph() {
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std::cerr << "Loading model params...";
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}
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// read parallel corpus from file
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std::fstream sourceFile("../examples/mt/dev/newstest2013.de");
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std::fstream targetFile("../examples/mt/dev/newstest2013.en");
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std::string sourceLine, targetLine;
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while (getline(sourceFile, sourceLine)) {
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getline(targetFile, targetLine);
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std::vector<size_t> sourceIds = sourceVocab.ProcessSentence(sourceLine);
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std::vector<size_t> targetIds = sourceVocab.ProcessSentence(targetLine);
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}
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#endif
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int main(int argc, char** argv) {
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using namespace marian;
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using namespace keywords;
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int input_size = 10;
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int output_size = 15;
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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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int num_outputs = 6;
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ExpressionGraph g(0);
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std::vector<Expr*> X(num_inputs+1); // For the stop symbol.
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std::vector<Expr*> Y(num_outputs);
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std::vector<Expr*> H(num_inputs+1); // For the stop symbol.
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std::vector<Expr*> S(num_outputs);
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// For the stop symbol.
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for (int t = 0; t <= num_inputs; ++t) {
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X[t] = new Expr(g.input(shape={batch_size, input_size}));
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}
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// For the stop symbol.
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for (int t = 0; t <= num_outputs; ++t) {
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Y[t] = new Expr(g.input(shape={batch_size, output_size}));
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}
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Expr Wxh = g.param(shape={input_size, hidden_size}, init=uniform(), name="Wxh");
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Expr Whh = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh");
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Expr bh = g.param(shape={1, hidden_size}, init=uniform(), name="bh");
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Expr h0 = g.param(shape={1, hidden_size}, init=uniform(), name="h0");
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std::cerr << "Building encoder 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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|
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Expr Wxh_d = g.param(shape={output_size, hidden_size}, init=uniform(), name="Wxh_d");
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Expr Whh_d = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh_d");
|
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Expr bh_d = g.param(shape={1, hidden_size}, init=uniform(), name="bh_d");
|
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|
||||
std::cerr << "Building decoder RNN..." << std::endl;
|
||||
auto h0_d = *H[num_inputs];
|
||||
S[0] = new Expr(tanh(dot(*Y[0], Wxh_d) + dot(h0_d, Whh_d) + bh_d));
|
||||
for (int t = 1; t < num_outputs; ++t) {
|
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S[t] = new Expr(tanh(dot(*Y[t], Wxh_d) + dot(*S[t-1], Whh_d) + bh_d));
|
||||
}
|
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|
||||
Expr Why = g.param(shape={hidden_size, output_size}, init=uniform(), name="Why");
|
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Expr by = g.param(shape={1, output_size}, init=uniform(), name="by");
|
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|
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std::cerr << "Building output layer..." << std::endl;
|
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std::vector<Expr*> Yp(num_outputs+1); // For the stop symbol.
|
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|
||||
Expr* cross_entropy = NULL;
|
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for (int t = 0; t <= num_outputs; ++t) {
|
||||
if (t == 0) {
|
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Yp[t] = new Expr(named(softmax_fast(dot(h0_d, Why) + by), "pred"));
|
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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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Yp[t] = new Expr(named(softmax_fast(dot(*S[t-1], Why) + by), "pred"));
|
||||
*cross_entropy = *cross_entropy + sum(*Y[t] * log(*Yp[t]), axis=1);
|
||||
}
|
||||
}
|
||||
auto graph = -mean(*cross_entropy, axis=0, name="cost");
|
||||
|
||||
// For the stop symbol.
|
||||
for (int t = 0; t <= num_inputs; ++t) {
|
||||
Tensor Xt({batch_size, input_size});
|
||||
|
||||
float max = 1.;
|
||||
std::vector<float> values(batch_size * input_size);
|
||||
std::vector<float> classes(batch_size * output_size, 0.0);
|
||||
int k = 0;
|
||||
for (int i = 0; i < batch_size; ++i) {
|
||||
for (int j = 0; j < input_size; ++j, ++k) {
|
||||
values[k] = max * (2.0*static_cast<float>(rand()) / RAND_MAX - 1.0);
|
||||
}
|
||||
}
|
||||
|
||||
thrust::copy(values.begin(), values.end(), Xt.begin());
|
||||
|
||||
*X[t] = Xt;
|
||||
}
|
||||
|
||||
for (int t = 0; t < num_outputs; ++t) {
|
||||
Tensor Yt({batch_size, output_size});
|
||||
|
||||
std::vector<float> classes(batch_size * output_size, 0.0);
|
||||
int l = 0;
|
||||
for (int i = 0; i < batch_size; ++i) {
|
||||
int gold = output_size * static_cast<float>(rand()) / RAND_MAX;
|
||||
classes[l + gold] = 1.0;
|
||||
l += output_size;
|
||||
}
|
||||
|
||||
thrust::copy(classes.begin(), classes.end(), Yt.begin());
|
||||
|
||||
*Y[t] = Yt;
|
||||
}
|
||||
|
||||
g.forward(batch_size);
|
||||
g.backward();
|
||||
|
||||
std::cerr << graph.val().Debug() << std::endl;
|
||||
|
||||
std::cerr << X[0]->val().Debug() << std::endl;
|
||||
std::cerr << Y[0]->val().Debug() << std::endl;
|
||||
|
||||
std::cerr << Whh.grad().Debug() << std::endl;
|
||||
std::cerr << bh.grad().Debug() << std::endl;
|
||||
std::cerr << Why.grad().Debug() << std::endl;
|
||||
std::cerr << by.grad().Debug() << std::endl;
|
||||
std::cerr << Wxh.grad().Debug() << std::endl;
|
||||
std::cerr << h0.grad().Debug() << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
@ -10,7 +10,7 @@ const size_t IMAGE_SIZE = 784;
|
||||
const size_t LABEL_SIZE = 10;
|
||||
int BATCH_SIZE = 10000;
|
||||
|
||||
ExpressionGraph build_graph(int cudaDevice) {
|
||||
ExpressionGraph build_graph() {
|
||||
std::cerr << "Loading model params...";
|
||||
NpzConverter converter("../scripts/test_model_single/model.npz");
|
||||
|
||||
@ -22,7 +22,7 @@ ExpressionGraph build_graph(int cudaDevice) {
|
||||
|
||||
std::cerr << "Building model...";
|
||||
|
||||
ExpressionGraph g(cudaDevice);
|
||||
ExpressionGraph g(0);
|
||||
auto x = named(g.input(shape={whatevs, IMAGE_SIZE}), "x");
|
||||
auto y = named(g.input(shape={whatevs, LABEL_SIZE}), "y");
|
||||
|
||||
@ -46,12 +46,15 @@ ExpressionGraph build_graph(int cudaDevice) {
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
|
||||
cudaSetDevice(0);
|
||||
|
||||
std::cerr << "Loading test set...";
|
||||
std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", BATCH_SIZE, IMAGE_SIZE);
|
||||
std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", BATCH_SIZE, LABEL_SIZE);
|
||||
std::cerr << "Done." << std::endl;
|
||||
|
||||
ExpressionGraph g = build_graph(1);
|
||||
ExpressionGraph g = build_graph();
|
||||
|
||||
Tensor xt({BATCH_SIZE, IMAGE_SIZE});
|
||||
Tensor yt({BATCH_SIZE, LABEL_SIZE});
|
||||
@ -59,6 +62,8 @@ int main(int argc, char** argv) {
|
||||
g["x"] = (xt << testImages);
|
||||
g["y"] = (yt << testLabels);
|
||||
|
||||
std::cout << g.graphviz() << std::endl;
|
||||
|
||||
g.forward(BATCH_SIZE);
|
||||
|
||||
std::vector<float> results;
|
||||
|
Loading…
Reference in New Issue
Block a user