mirror of
https://github.com/marian-nmt/marian.git
synced 2024-08-15 01:00:23 +03:00
some clean-up
This commit is contained in:
parent
8eb641ad45
commit
cef8a37fc7
@ -2,8 +2,8 @@ cmake_minimum_required(VERSION 3.5.1)
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set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
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project(marian CXX)
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SET(CMAKE_CXX_FLAGS " -std=c++11 -g -O3 -funroll-loops -Wno-unused-result -Wno-deprecated")
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LIST(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -std=c++11; -g; -O3; -arch=sm_35; -lineinfo; --use_fast_math;)
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SET(CMAKE_CXX_FLAGS " -std=c++11 -g -O0 -funroll-loops -Wno-unused-result -Wno-deprecated")
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LIST(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -std=c++11; -g; -O0; -arch=sm_35; -lineinfo; --use_fast_math; -Xcompiler '-fPIC')
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add_definitions(-DCUDA_API_PER_THREAD_DEFAULT_STREAM)
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SET(CUDA_PROPAGATE_HOST_FLAGS OFF)
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@ -8,6 +8,8 @@ add_library(libcommon OBJECT
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cuda_add_executable(
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marian
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test.cu
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expressions.cu
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tensor_operators.cu
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$<TARGET_OBJECTS:libcommon>
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)
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@ -5,25 +5,25 @@
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#include <functional>
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namespace marian {
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typedef float Float;
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typedef float Float;
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typedef std::vector<int> Shape;
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const int whatevs{-1};
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}
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#include "keywords.h"
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#include "tensor.h"
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namespace marian {
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typedef std::vector<int> Shape;
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const int whatevs{-1};
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class Tensor;
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namespace keywords {
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KEY(init, std::function<void(Tensor)>)
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KEY(axis, int)
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KEY(name, std::string)
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KEY(shape, Shape)
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KEY(value, float)
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KEY(lazy_shape, std::function<Shape()>)
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KEY(lazy_value, std::function<float()>)
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}
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namespace keywords {
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KEY(axis, int)
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KEY(name, std::string)
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KEY(shape, Shape)
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KEY(value, float)
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KEY(lazy_shape, std::function<Shape()>)
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KEY(lazy_value, std::function<float()>)
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KEY(init, std::function<void(Tensor)>)
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}
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}
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@ -7,8 +7,8 @@
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namespace marian {
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template <typename ...Args>
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inline Expr data(Args ...args) {
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return Expr(new DataNode(args...));
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inline Expr input(Args ...args) {
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return Expr(new InputNode(args...));
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}
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template <typename ...Args>
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@ -76,30 +76,31 @@ inline Expr dot(Expr a, Expr b) {
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/******************************************************/
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Expr broadcast(Shape shape, Expr a) {
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if(a.val().shape() == shape) {
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Expr broadcast(Shape bShape, Expr a) {
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const Shape& aShape = a.node()->shape();
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if(aShape == bShape) {
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return a;
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}
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else {
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size_t dimsA = a.val().shape().size();
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size_t dimsB = shape.size();
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size_t dimsA = aShape.size();
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size_t dimsB = bShape.size();
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UTIL_THROW_IF2(dimsA != dimsB,
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"Tensor and shape have different number of dimensions");
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for(size_t i = 0; i < dimsA; ++i) {
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int dimA = a.val().shape()[i];
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int dimB = shape[i];
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int dimA = aShape[i];
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int dimB = bShape[i];
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bool broadcastable = (dimA == dimB || dimA == 1);
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UTIL_THROW_IF2(!broadcastable,
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"Cannot broadcast tensor dimension "
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<< dimA << " to " << dimB);
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if(dimA == 1 && dimB > 1) {
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if(dimA == 1 && dimB != 1) {
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std::cerr << "Broadcasting dim " << i << " from " << dimA << " to " << dimB << std::endl;
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if(i == 0) {
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Expr one = ones(keywords::shape={shape[0], 1});
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Expr one = ones(keywords::shape={bShape[0], 1});
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a = dot(one, a);
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}
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else if(i == 1) {
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Expr one = ones(keywords::shape={1, shape[1]});
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Expr one = ones(keywords::shape={1, bShape[1]});
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a = dot(a, one);
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}
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else {
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@ -120,20 +121,23 @@ inline Expr sum(Expr a, Args ...args) {
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Keywords params(args...);
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int ax = params.Get<int>(axis, whatevs);
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ChainPtr n = a.node();
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if(ax == 0) {
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auto lshape = [&a]() -> Shape {
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int rows = a.val().shape()[0];
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auto lshape = [n]() -> Shape {
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int rows = n->val().shape()[0];
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return {1, rows};
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};
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Expr one = ones(lazy_shape=lshape);
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Expr one = ones(shape={1, n->shape()[0]},
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lazy_shape=lshape);
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return dot(one, a);
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}
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else if(ax == 1) {
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auto lshape = [&a]() -> Shape {
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int cols = a.val().shape()[1];
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auto lshape = [n]() -> Shape {
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int cols = n->val().shape()[1];
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return {cols, 1};
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};
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Expr one = ones(lazy_shape=lshape);
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Expr one = ones(shape={n->shape()[1], 1},
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lazy_shape=lshape);
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return dot(a, one);
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}
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else if(ax == 2) {
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@ -159,16 +163,17 @@ inline Expr mean(Expr a, Args ...args) {
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Keywords params(args...);
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size_t ax = params.Get<int>(axis, whatevs);
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ChainPtr n = a.node();
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switch (ax) {
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case 0:
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return sum(a, axis=0) / constant(shape={1, 1},
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lazy_value=[&a]() -> Float {
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return a.val().shape()[0];
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lazy_value=[n]() -> Float {
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return n->val().shape()[0];
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});
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case 1:
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return sum(a, axis=1) / constant(shape={1, 1},
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lazy_value=[&a]() -> Float {
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return a.val().shape()[1];
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lazy_value=[n]() -> Float {
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return n->val().shape()[1];
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});
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case 2:
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UTIL_THROW2("Not implemented");
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@ -176,8 +181,8 @@ inline Expr mean(Expr a, Args ...args) {
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UTIL_THROW2("Not implemented");
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default:
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return sum(a) / constant(shape={1, 1},
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lazy_value=[&a]() -> Float {
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return a.val().size();
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lazy_value=[n]() -> Float {
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return n->val().size();
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});
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}
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}
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53
src/expressions.cu
Normal file
53
src/expressions.cu
Normal file
@ -0,0 +1,53 @@
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#include "expressions.h"
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#include "graph_operators.h"
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namespace marian {
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Expr::Expr(Chainable<Tensor>* chainable) : pimpl_(chainable) {}
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Expr::Expr(Float v) : pimpl_(new ConstantNode(keywords::value=v,
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keywords::shape={1,1})) {}
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Tensor Expr::val() {
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return pimpl_->val();
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}
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Tensor Expr::grad() {
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return pimpl_->grad();
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}
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ChainPtr Expr::node() {
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return pimpl_;
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}
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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 << "a" << std::endl;
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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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std::cerr << "f" << std::endl;
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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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for(auto&& v : Chainable<Tensor>::stack)
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v->set_zero_adjoint();
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typedef typename Chainable<Tensor>::ChainableStack::reverse_iterator It;
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pimpl_->init_dependent();
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for(It it = Chainable<Tensor>::stack.rbegin(); it != Chainable<Tensor>::stack.rend(); ++it)
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(*it)->backward();
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}
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Expr::operator ChainPtr() {
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return pimpl_;
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}
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}
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@ -1,52 +1,28 @@
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#pragma once
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#include "definitions.h"
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#include "graph.h"
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namespace marian {
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class Expr {
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public:
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Expr(Chainable<Tensor>* chainable) : pimpl_(chainable) {}
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Expr(Chainable<Tensor>* chainable);
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Expr(Float v);
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Tensor val() {
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return pimpl_->val();
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Expr operator=(Tensor t) {
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pimpl_->setVal(t);
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return *this;
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}
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Tensor grad() {
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return pimpl_->grad();
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}
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Tensor val();
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Tensor grad();
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ChainPtr pimpl() {
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return pimpl_;
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}
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void forward(size_t batchSize);
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void backward();
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void forward() {
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UTIL_THROW_IF2(pimpl_.get() != stack.back(),
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"Trying to call forward on non-root of computation graph");
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std::cerr << "a" << std::endl;
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for(auto&& v : stack)
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v->allocate();
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std::cerr << "f" << std::endl;
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for(auto&& v : stack)
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v->forward();
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}
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void backward() {
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UTIL_THROW_IF2(pimpl_.get() != stack.back(),
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"Trying to call backward on non-root of computation graph");
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for(auto&& v : stack)
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v->set_zero_adjoint();
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typedef ChainableStack::reverse_iterator It;
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pimpl_->init_dependent();
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for(It it = stack.rbegin(); it != stack.rend(); ++it)
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(*it)->backward();
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}
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operator ChainPtr() {
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return pimpl_;
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}
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ChainPtr node();
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operator ChainPtr();
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private:
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ChainPtr pimpl_;
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40
src/graph.h
40
src/graph.h
@ -14,16 +14,23 @@ struct Chainable {
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virtual void init_dependent() { }
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virtual void set_zero_adjoint() { }
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virtual void allocate() = 0;
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virtual void allocate(size_t) = 0;
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virtual const Shape& shape() = 0;
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virtual DataType val() = 0;
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virtual DataType grad() = 0;
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virtual void setVal(Tensor t) {
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UTIL_THROW2("Tensors can only be assigned to input nodes");
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};
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typedef std::vector<Chainable<Tensor>*> ChainableStack;
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static ChainableStack stack;
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};
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typedef std::vector<Chainable<Tensor>*> ChainableStack;
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typedef std::shared_ptr<Chainable<Tensor>> ChainPtr;
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template <class DataType>
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typename Chainable<DataType>::ChainableStack Chainable<DataType>::stack;
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ChainableStack stack;
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typedef std::shared_ptr<Chainable<Tensor>> ChainPtr;
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class Node : public Chainable<Tensor>,
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public keywords::Keywords {
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@ -34,14 +41,27 @@ class Node : public Chainable<Tensor>,
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shape_(Get<Shape>(keywords::shape, {1, 1})),
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name_(Get<std::string>(keywords::name, "none"))
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{
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std::cerr << "Creating node " << name_ << std::endl;
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stack.push_back(this);
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}
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virtual ~Node() {};
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virtual void allocate() {
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val_.allocate(shape_);
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virtual void allocate(size_t batchSize) {
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for(auto&& d : shape_) {
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if(d == whatevs)
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d = batchSize;
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}
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if(Has(keywords::lazy_shape)) {
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auto defaultShape = [this]() -> Shape { return shape_; };
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shape_ = Get<std::function<Shape()>>(keywords::lazy_shape, defaultShape)();
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}
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if(Has(keywords::lazy_value))
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val_.allocate(shape_, Get<std::function<float()>>(
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keywords::lazy_value, []()->Float{return 0.f;})());
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else if(Has(keywords::value))
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val_.allocate(shape_, Get<Float>(keywords::value, 0));
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else
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val_.allocate(shape_);
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}
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virtual void init_dependent() {
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@ -71,7 +91,11 @@ class Node : public Chainable<Tensor>,
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UTIL_THROW_IF2(!adj_, "Tensor has not been allocated");
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return adj_;
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};
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virtual const Shape& shape() {
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return shape_;
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}
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protected:
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Shape shape_;
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std::string name_;
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@ -1,16 +1,26 @@
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#pragma once
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#include "graph.h"
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#include "expressions.h"
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//#include "expression_operators.h"
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#include "graph.h"
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#include "tensor_operators.h"
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namespace marian {
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struct DataNode : public Node {
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struct InputNode : public Node {
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template <typename ...Args>
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DataNode(Args ...args)
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: Node(args...) { }
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InputNode(Args ...args)
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: Node(args...) {
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UTIL_THROW_IF2(!Has(keywords::shape) &&
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!Has(keywords::lazy_shape),
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"Data items require shape information");
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}
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virtual void setVal(Tensor t) {
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val_ = t;
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shape_ = t.shape();
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//@todo, shape checking
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};
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void forward() {}
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void backward() {}
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};
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@ -18,7 +28,11 @@ struct DataNode : public Node {
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struct ConstantNode : public Node {
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template <typename ...Args>
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ConstantNode(Args ...args)
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: Node(args...) { }
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: Node(args...) {
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UTIL_THROW_IF2(!Has(keywords::shape) &&
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!Has(keywords::lazy_shape),
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"Constant items require shape information");
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}
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void forward() {}
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void backward() {}
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@ -29,12 +43,16 @@ struct ParamNode : public Node {
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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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{ }
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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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"Param items require shape information");
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}
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void forward() {}
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void backward() {}
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virtual void allocate() {
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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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}
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@ -86,9 +104,7 @@ struct TanhNodeOp : 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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std::cerr << "log" << std::endl;
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}
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: UnaryNodeOp(args...) {}
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void forward() {
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Element(_1 = Log(_2), val_, a_->val());
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@ -145,13 +161,15 @@ struct BinaryNodeOp : public Node {
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struct DotNodeOp : public BinaryNodeOp {
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template <typename ...Args>
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DotNodeOp(ChainPtr a, ChainPtr b, Args ...args)
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: BinaryNodeOp(a, b, args...) { }
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: BinaryNodeOp(a, b,
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keywords::shape=newShape(a,b),
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args...) { }
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Shape shape(ChainPtr a, ChainPtr b) {
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UTIL_THROW_IF2(a->val().shape()[1] != b->val().shape()[0],
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Shape newShape(ChainPtr a, ChainPtr b) {
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Shape shape1 = a->shape();
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Shape shape2 = b->shape();
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UTIL_THROW_IF2(shape1[1] != shape2[0],
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"matrix product requires dimensions to match");
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Shape shape1 = a->val().shape();
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Shape shape2 = b->val().shape();
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shape1[1] = shape2[1];
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return shape1;
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}
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@ -177,23 +195,26 @@ Expr broadcast(Shape shape, Expr a);
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struct BroadcastingNodeOp : public BinaryNodeOp {
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template <typename ...Args>
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BroadcastingNodeOp(Expr a, Expr b, Args ...args)
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: BinaryNodeOp(broadcast(shape(a ,b), a),
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broadcast(shape(a ,b), b),
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args...) {}
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: BinaryNodeOp(broadcast(newShape(a ,b), a),
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broadcast(newShape(a ,b), b),
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keywords::shape=newShape(a, b),
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args...) {}
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static Shape shape(ChainPtr a, ChainPtr b) {
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size_t dimsA = a->val().shape().size();
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size_t dimsB = b->val().shape().size();
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static Shape newShape(ChainPtr a, ChainPtr b) {
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size_t dimsA = a->shape().size();
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size_t dimsB = b->shape().size();
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UTIL_THROW_IF2(dimsA != dimsB,
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"Tensors have different numbers of dimensions");
|
||||
Shape shape(dimsA);
|
||||
for(size_t i = 0; i < dimsA; ++i) {
|
||||
int dimA = a->val().shape()[i];
|
||||
int dimB = b->val().shape()[i];
|
||||
int dimA = a->shape()[i];
|
||||
int dimB = b->shape()[i];
|
||||
bool broadcastable = (dimA == dimB || dimA == 1 || dimB == 1);
|
||||
UTIL_THROW_IF2(!broadcastable, "Different dimensions in elementwise "
|
||||
<< "operation cannot be broadcasted: " << dimA << " != " << dimB);
|
||||
shape[i] = std::max(dimA, dimB);
|
||||
if(dimA == whatevs || dimB == whatevs)
|
||||
shape[i] = whatevs;
|
||||
}
|
||||
return shape;
|
||||
}
|
||||
|
@ -15,24 +15,23 @@ namespace keywords {
|
||||
public:
|
||||
typedef Value value_type;
|
||||
|
||||
struct pair {
|
||||
Keyword<key, Value> first;
|
||||
Value second;
|
||||
};
|
||||
Keyword(const std::string& name, Value value)
|
||||
: name_(name), value_(value) {}
|
||||
|
||||
Keyword(const std::string& name)
|
||||
: name_(name) {}
|
||||
: name_(name), value_() {}
|
||||
|
||||
pair operator=(Value value) {
|
||||
return pair{*this, value};
|
||||
Keyword<key, Value> operator=(Value value) const {
|
||||
return Keyword<key, Value>(name_, value);
|
||||
}
|
||||
|
||||
const std::string& operator()() const {
|
||||
return name_;
|
||||
const Value& operator()() const {
|
||||
return value_;
|
||||
}
|
||||
|
||||
private:
|
||||
std::string name_;
|
||||
const std::string name_;
|
||||
const Value value_;
|
||||
};
|
||||
|
||||
struct Keywords {
|
||||
@ -45,12 +44,12 @@ namespace keywords {
|
||||
|
||||
template <typename Head>
|
||||
void add(Head head) {
|
||||
map_[std::type_index(typeid(head.first))] = head.second;
|
||||
map_[std::type_index(typeid(head))] = head();
|
||||
}
|
||||
|
||||
template <typename Head, typename ...Tail>
|
||||
void add(Head head, Tail ...tail) {
|
||||
map_[std::type_index(typeid(head.first))] = head.second;
|
||||
map_[std::type_index(typeid(head))] = head();
|
||||
add(tail...);
|
||||
}
|
||||
|
||||
@ -63,12 +62,66 @@ namespace keywords {
|
||||
return default_value;
|
||||
}
|
||||
|
||||
template <typename Key>
|
||||
bool Has(Key key) {
|
||||
auto it = map_.find(std::type_index(typeid(key)));
|
||||
return it != map_.end();
|
||||
}
|
||||
|
||||
private:
|
||||
std::unordered_map<std::type_index, boost::any> map_;
|
||||
};
|
||||
|
||||
#include <type_traits>
|
||||
|
||||
//template <typename...>
|
||||
//struct is_one_of {
|
||||
// static constexpr bool value = false;
|
||||
//};
|
||||
//
|
||||
//template <typename F, typename S, typename... T>
|
||||
//struct is_one_of<F, S, T...> {
|
||||
// static constexpr bool value =
|
||||
// std::is_same<F, S>::value || is_one_of<F, T...>::value;
|
||||
//};
|
||||
//
|
||||
//template <class T, class Tuple>
|
||||
//struct Index;
|
||||
//
|
||||
//template <class T, class... Types>
|
||||
//struct Index<T, std::tuple<T, Types...>> {
|
||||
// static constexpr std::size_t value = 0;
|
||||
//};
|
||||
//
|
||||
//template <class T, class U, class... Types>
|
||||
//struct Index<T, std::tuple<U, Types...>> {
|
||||
// static constexpr std::size_t value = 1 + Index<T, std::tuple<Types...>>::value;
|
||||
//};
|
||||
//
|
||||
//struct True {};
|
||||
//struct False {};
|
||||
//
|
||||
//template <typename Match, typename ...Args>
|
||||
//typename Match::value_type opt(True foo, Args... args) {
|
||||
// std::tuple<const Args...> t(args...);
|
||||
// return std::get<Index<Match, std::tuple<const Args...>>::value>(t)();
|
||||
//}
|
||||
//
|
||||
//template <typename Match, typename ...Args>
|
||||
//typename Match::value_type opt(False foo, Args... args) {
|
||||
// return typename Match::value_type();
|
||||
//}
|
||||
//
|
||||
//template <typename Match, typename ...Args>
|
||||
//typename Match::value_type Get(Args ...args) {
|
||||
// constexpr bool match = is_one_of<Match, const Args...>::value;
|
||||
// typename std::conditional<match, True, False>::type condition;
|
||||
// return opt<Match>(condition, args...);
|
||||
//}
|
||||
|
||||
|
||||
#define KEY(name, value_type) \
|
||||
typedef Keyword<COMPILE_TIME_CRC32_STR(#name),value_type> name ## _k; \
|
||||
typedef const Keyword<COMPILE_TIME_CRC32_STR(#name),value_type> name ## _k; \
|
||||
name ## _k name(#name);
|
||||
}
|
||||
|
||||
|
@ -5,5 +5,4 @@
|
||||
#include "graph_operators.h"
|
||||
#include "expressions.h"
|
||||
#include "expression_operators.h"
|
||||
//#include "tensor.h"
|
||||
//#include "tensor_operators.h"
|
||||
|
||||
|
399
src/tensor.cu
Normal file
399
src/tensor.cu
Normal file
@ -0,0 +1,399 @@
|
||||
#pragma once
|
||||
|
||||
#include <memory>
|
||||
#include <functional>
|
||||
#include <vector>
|
||||
#include <cmath>
|
||||
|
||||
#include <cudnn.h>
|
||||
#include <cublas_v2.h>
|
||||
#include <thrust/device_vector.h>
|
||||
#include <thrust/functional.h>
|
||||
|
||||
#include "definitions.h"
|
||||
#include "exception.h"
|
||||
#include "thrust_functions.h"
|
||||
|
||||
namespace marian {
|
||||
|
||||
struct Handles {
|
||||
cudnnHandle_t cudnnHandle;
|
||||
cublasHandle_t cublasHandle;
|
||||
|
||||
cudnnOpTensorDescriptor_t add;
|
||||
|
||||
Handles() {
|
||||
cudnnCreate(&cudnnHandle);
|
||||
cublasCreate(&cublasHandle);
|
||||
cudnnCreateOpTensorDescriptor(&add);
|
||||
cudnnSetOpTensorDescriptor(add, CUDNN_OP_TENSOR_ADD, CUDNN_DATA_FLOAT, CUDNN_NOT_PROPAGATE_NAN);
|
||||
}
|
||||
|
||||
~Handles() {
|
||||
cudnnDestroy(cudnnHandle);
|
||||
cublasDestroy(cublasHandle);
|
||||
cudnnDestroyOpTensorDescriptor(add);
|
||||
}
|
||||
};
|
||||
|
||||
Handles handles;
|
||||
|
||||
typedef std::vector<int> Shape;
|
||||
|
||||
template<class Float>
|
||||
class TensorImpl {
|
||||
private:
|
||||
Shape shape_;
|
||||
thrust::device_vector<Float> data_;
|
||||
cudnnTensorDescriptor_t desc_;
|
||||
size_t tno_;
|
||||
static size_t tensorCounter;
|
||||
|
||||
cudnnDataType_t dataType() {
|
||||
switch(sizeof(Float)) {
|
||||
case 2: return CUDNN_DATA_HALF;
|
||||
case 8: return CUDNN_DATA_DOUBLE;
|
||||
default: return CUDNN_DATA_FLOAT;
|
||||
}
|
||||
}
|
||||
|
||||
public:
|
||||
typedef Float value_type;
|
||||
|
||||
TensorImpl(const Shape& shape, value_type value = 0)
|
||||
: shape_(shape), tno_(tensorCounter++)
|
||||
{
|
||||
|
||||
// @TODO:
|
||||
UTIL_THROW_IF2(shape_.size() != 2,
|
||||
"For now, only 2D Tensors, will be fixed later.");
|
||||
|
||||
UTIL_THROW_IF2(shape_.size() < 1 || shape_.size() > 4,
|
||||
"Wrong number of dimensions: " << shape_.size());
|
||||
|
||||
std::cerr << "Allocating : " << shape[0] << " " << shape[1] << std::endl;
|
||||
|
||||
int size = std::accumulate(shape_.begin(), shape_.end(),
|
||||
1, std::multiplies<int>());
|
||||
data_.resize(size, value);
|
||||
cudnnCreateTensorDescriptor(&desc_);
|
||||
switch (shape_.size()) {
|
||||
case 1:
|
||||
cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
|
||||
shape_[0], 1, 1, 1); break;
|
||||
case 2:
|
||||
cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
|
||||
shape_[0], shape_[1], 1, 1); break;
|
||||
case 3:
|
||||
cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
|
||||
shape_[0], shape_[1], shape_[2], 1); break;
|
||||
case 4:
|
||||
cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
|
||||
shape_[0], shape_[1], shape_[2], shape_[3]); break;
|
||||
}
|
||||
}
|
||||
|
||||
TensorImpl(const TensorImpl&) = delete;
|
||||
TensorImpl(TensorImpl&&) = delete;
|
||||
|
||||
~TensorImpl() {
|
||||
cudnnDestroyTensorDescriptor(desc_);
|
||||
}
|
||||
|
||||
value_type operator[](size_t i) const {
|
||||
return data_[i];
|
||||
}
|
||||
|
||||
auto begin() -> decltype( data_.begin() ) {
|
||||
return data_.begin();
|
||||
}
|
||||
|
||||
auto begin() const -> decltype( data_.begin() ) {
|
||||
return data_.begin();
|
||||
}
|
||||
|
||||
auto end() -> decltype( data_.end() ) {
|
||||
return data_.end();
|
||||
}
|
||||
|
||||
auto end() const -> decltype( data_.end() ) {
|
||||
return data_.end();
|
||||
}
|
||||
|
||||
const Shape& shape() const {
|
||||
return shape_;
|
||||
}
|
||||
|
||||
size_t size() const {
|
||||
return data_.size();
|
||||
}
|
||||
|
||||
value_type* data() {
|
||||
return thrust::raw_pointer_cast(data_.data());
|
||||
}
|
||||
|
||||
cudnnTensorDescriptor_t desc() const {
|
||||
return desc_;
|
||||
}
|
||||
|
||||
size_t id() const {
|
||||
return tno_;
|
||||
}
|
||||
|
||||
void set(value_type value) {
|
||||
thrust::fill(data_.begin(), data_.end(), value);
|
||||
}
|
||||
};
|
||||
|
||||
template <typename Type>
|
||||
size_t TensorImpl<Type>::tensorCounter = 0;
|
||||
|
||||
class Tensor {
|
||||
private:
|
||||
std::shared_ptr<TensorImpl<Float>> pimpl_;
|
||||
|
||||
public:
|
||||
typedef TensorImpl<Float>::value_type value_type;
|
||||
|
||||
Tensor() {}
|
||||
~Tensor() {}
|
||||
|
||||
void allocate(Shape shape, value_type value = 0) {
|
||||
pimpl_.reset(new TensorImpl<Float>(shape, value));
|
||||
}
|
||||
|
||||
value_type operator[](size_t i) const {
|
||||
return (*pimpl_)[i];
|
||||
}
|
||||
|
||||
size_t size() const {
|
||||
return pimpl_->size();
|
||||
}
|
||||
|
||||
value_type* data() {
|
||||
return pimpl_->data();
|
||||
}
|
||||
|
||||
const value_type* data() const {
|
||||
return pimpl_->data();
|
||||
}
|
||||
|
||||
auto begin() -> decltype( pimpl_->begin() ) {
|
||||
return pimpl_->begin();
|
||||
}
|
||||
|
||||
auto begin() const -> decltype( pimpl_->begin() ) {
|
||||
return pimpl_->begin();
|
||||
}
|
||||
|
||||
auto end() -> decltype( pimpl_->begin() ) {
|
||||
return pimpl_->begin();
|
||||
}
|
||||
|
||||
auto end() const -> decltype( pimpl_->begin() ) {
|
||||
return pimpl_->begin();
|
||||
}
|
||||
|
||||
const Shape& shape() const {
|
||||
return pimpl_->shape();
|
||||
}
|
||||
|
||||
cudnnTensorDescriptor_t desc() const {
|
||||
return pimpl_->desc();
|
||||
}
|
||||
|
||||
void set(value_type value) {
|
||||
pimpl_->set(value);
|
||||
}
|
||||
|
||||
size_t id() const {
|
||||
return pimpl_->id();
|
||||
}
|
||||
|
||||
operator bool() {
|
||||
return pimpl_ != nullptr;
|
||||
}
|
||||
};
|
||||
|
||||
Tensor uniform(Tensor t, Float a=-0.1, Float b=0.1) {
|
||||
std::vector<Float> r(t.size());
|
||||
for(int i = 0; i < r.size(); i++)
|
||||
r[i] = (Float(rand() % 2000) - 1000.0)/10000.0;
|
||||
thrust::copy(r.begin(), r.end(), t.begin());
|
||||
return t;
|
||||
};
|
||||
|
||||
using namespace thrust::placeholders;
|
||||
#define MAX_THREADS 512
|
||||
#define MAX_BLOCKS 65535
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor, Float* out,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i]);;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn = in + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn[i]);;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in1, const Float* in2,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn1 = in1 + j * cols;
|
||||
const Float* rowIn2 = in2 + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn1[i], rowIn2[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in1,
|
||||
const Float* in2, const Float* in3,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn1 = in1 + j * cols;
|
||||
const Float* rowIn2 = in2 + j * cols;
|
||||
const Float* rowIn3 = in3 + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn1[i], rowIn2[i], rowIn3[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// @TODO add broadcasting
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor, Tensor Out) {
|
||||
Float* d_out = Out.data();
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In) {
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in = In.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In1, const Tensor In2) {
|
||||
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in1 = In1.data();
|
||||
const Float* d_in2 = In2.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in1, d_in2,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In1,
|
||||
const Tensor In2, const Tensor In3) {
|
||||
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in1 = In1.data();
|
||||
const Float* d_in2 = In2.data();
|
||||
const Float* d_in3 = In3.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in1, d_in2, d_in3,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
|
||||
bool transA, bool transB, Float beta) {
|
||||
Float alpha = 1.0;
|
||||
|
||||
size_t m = A.shape()[0];
|
||||
size_t k = A.shape()[1];
|
||||
if(transA)
|
||||
std::swap(m, k);
|
||||
|
||||
size_t l = B.shape()[0];
|
||||
size_t n = B.shape()[1];
|
||||
if(transB)
|
||||
std::swap(l, n);
|
||||
|
||||
size_t lda = A.shape()[1];
|
||||
size_t ldb = B.shape()[1];
|
||||
size_t ldc = B.shape()[1];
|
||||
|
||||
if(transB)
|
||||
ldc = B.shape()[0];
|
||||
|
||||
cublasOperation_t opA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
|
||||
cublasOperation_t opB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
|
||||
|
||||
cublasSgemm(handle, opB, opA,
|
||||
n, m, k, &alpha, B.data(), ldb, A.data(), lda, &beta, C.data(), ldc);
|
||||
return C;
|
||||
}
|
||||
|
||||
Tensor Prod(Tensor C, const Tensor A, const Tensor B,
|
||||
bool transA, bool transB, Float beta = 0) {
|
||||
|
||||
return Prod(handles.cublasHandle, C, A, B, transA, transB, beta);
|
||||
}
|
||||
|
||||
}
|
199
src/tensor.h
199
src/tensor.h
@ -1,10 +1,5 @@
|
||||
#pragma once
|
||||
|
||||
#include <memory>
|
||||
#include <functional>
|
||||
#include <vector>
|
||||
#include <cmath>
|
||||
|
||||
#include <cudnn.h>
|
||||
#include <cublas_v2.h>
|
||||
#include <thrust/device_vector.h>
|
||||
@ -36,7 +31,7 @@ struct Handles {
|
||||
}
|
||||
};
|
||||
|
||||
Handles handles;
|
||||
const Handles handles;
|
||||
|
||||
typedef std::vector<int> Shape;
|
||||
|
||||
@ -63,12 +58,16 @@ class TensorImpl {
|
||||
TensorImpl(const Shape& shape, value_type value = 0)
|
||||
: shape_(shape), tno_(tensorCounter++)
|
||||
{
|
||||
|
||||
// @TODO:
|
||||
UTIL_THROW_IF2(shape_.size() != 2,
|
||||
"For now, only 2D Tensors, will be fixed later.");
|
||||
|
||||
UTIL_THROW_IF2(shape_.size() < 1 || shape_.size() > 4,
|
||||
"Wrong number of dimensions: " << shape_.size());
|
||||
|
||||
std::cerr << "Allocating : " << shape[0] << " " << shape[1] << std::endl;
|
||||
|
||||
int size = std::accumulate(shape_.begin(), shape_.end(),
|
||||
1, std::multiplies<int>());
|
||||
data_.resize(size, value);
|
||||
@ -152,10 +151,15 @@ class Tensor {
|
||||
typedef TensorImpl<Float>::value_type value_type;
|
||||
|
||||
Tensor() {}
|
||||
Tensor(Shape shape, value_type value = 0) {
|
||||
allocate(shape, value);
|
||||
}
|
||||
|
||||
~Tensor() {}
|
||||
|
||||
void allocate(Shape shape, value_type value = 0) {
|
||||
pimpl_.reset(new TensorImpl<Float>(shape, value));
|
||||
if(!pimpl_)
|
||||
pimpl_.reset(new TensorImpl<Float>(shape, value));
|
||||
}
|
||||
|
||||
value_type operator[](size_t i) const {
|
||||
@ -211,185 +215,4 @@ class Tensor {
|
||||
}
|
||||
};
|
||||
|
||||
Tensor uniform(Tensor t, Float a=-0.1, Float b=0.1) {
|
||||
std::vector<Float> r(t.size());
|
||||
for(int i = 0; i < r.size(); i++)
|
||||
r[i] = (Float(rand() % 2000) - 1000.0)/10000.0;
|
||||
thrust::copy(r.begin(), r.end(), t.begin());
|
||||
return t;
|
||||
};
|
||||
|
||||
using namespace thrust::placeholders;
|
||||
#define MAX_THREADS 512
|
||||
#define MAX_BLOCKS 65535
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor, Float* out,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i]);;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn = in + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn[i]);;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in1, const Float* in2,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn1 = in1 + j * cols;
|
||||
const Float* rowIn2 = in2 + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn1[i], rowIn2[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in1,
|
||||
const Float* in2, const Float* in3,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn1 = in1 + j * cols;
|
||||
const Float* rowIn2 = in2 + j * cols;
|
||||
const Float* rowIn3 = in3 + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn1[i], rowIn2[i], rowIn3[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// @TODO add broadcasting
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor, Tensor Out) {
|
||||
Float* d_out = Out.data();
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In) {
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in = In.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In1, const Tensor In2) {
|
||||
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in1 = In1.data();
|
||||
const Float* d_in2 = In2.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in1, d_in2,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In1,
|
||||
const Tensor In2, const Tensor In3) {
|
||||
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in1 = In1.data();
|
||||
const Float* d_in2 = In2.data();
|
||||
const Float* d_in3 = In3.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in1, d_in2, d_in3,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
|
||||
bool transA, bool transB, Float beta) {
|
||||
Float alpha = 1.0;
|
||||
|
||||
size_t m = A.shape()[0];
|
||||
size_t k = A.shape()[1];
|
||||
if(transA)
|
||||
std::swap(m, k);
|
||||
|
||||
size_t l = B.shape()[0];
|
||||
size_t n = B.shape()[1];
|
||||
if(transB)
|
||||
std::swap(l, n);
|
||||
|
||||
size_t lda = A.shape()[1];
|
||||
size_t ldb = B.shape()[1];
|
||||
size_t ldc = B.shape()[1];
|
||||
|
||||
if(transB)
|
||||
ldc = B.shape()[0];
|
||||
|
||||
cublasOperation_t opA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
|
||||
cublasOperation_t opB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
|
||||
|
||||
cublasSgemm(handle, opB, opA,
|
||||
n, m, k, &alpha, B.data(), ldb, A.data(), lda, &beta, C.data(), ldc);
|
||||
return C;
|
||||
}
|
||||
|
||||
Tensor Prod(Tensor C, const Tensor A, const Tensor B,
|
||||
bool transA, bool transB, Float beta = 0) {
|
||||
|
||||
return Prod(handles.cublasHandle, C, A, B, transA, transB, beta);
|
||||
}
|
||||
|
||||
}
|
40
src/tensor_operators.cu
Normal file
40
src/tensor_operators.cu
Normal file
@ -0,0 +1,40 @@
|
||||
#include "tensor_operators.h"
|
||||
|
||||
namespace marian {
|
||||
|
||||
Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
|
||||
bool transA, bool transB, Float beta) {
|
||||
Float alpha = 1.0;
|
||||
|
||||
size_t m = A.shape()[0];
|
||||
size_t k = A.shape()[1];
|
||||
if(transA)
|
||||
std::swap(m, k);
|
||||
|
||||
size_t l = B.shape()[0];
|
||||
size_t n = B.shape()[1];
|
||||
if(transB)
|
||||
std::swap(l, n);
|
||||
|
||||
size_t lda = A.shape()[1];
|
||||
size_t ldb = B.shape()[1];
|
||||
size_t ldc = B.shape()[1];
|
||||
|
||||
if(transB)
|
||||
ldc = B.shape()[0];
|
||||
|
||||
cublasOperation_t opA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
|
||||
cublasOperation_t opB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
|
||||
|
||||
cublasSgemm(handle, opB, opA,
|
||||
n, m, k, &alpha, B.data(), ldb, A.data(), lda, &beta, C.data(), ldc);
|
||||
return C;
|
||||
}
|
||||
|
||||
Tensor Prod(Tensor C, const Tensor A, const Tensor B,
|
||||
bool transA, bool transB, Float beta) {
|
||||
|
||||
return Prod(handles.cublasHandle, C, A, B, transA, transB, beta);
|
||||
}
|
||||
|
||||
}
|
@ -0,0 +1,151 @@
|
||||
#pragma once
|
||||
|
||||
#include "tensor.h"
|
||||
|
||||
namespace marian {
|
||||
|
||||
using namespace thrust::placeholders;
|
||||
#define MAX_THREADS 512
|
||||
#define MAX_BLOCKS 65535
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor, Float* out,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i]);;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn = in + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn[i]);;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in1, const Float* in2,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn1 = in1 + j * cols;
|
||||
const Float* rowIn2 = in2 + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn1[i], rowIn2[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
__global__ void gElement(Functor functor,
|
||||
Float* out, const Float* in1,
|
||||
const Float* in2, const Float* in3,
|
||||
size_t rows, size_t cols) {
|
||||
for(int bid = 0; bid < rows; bid += gridDim.x) {
|
||||
int j = bid + blockIdx.x;
|
||||
if(j < rows) {
|
||||
Float* rowOut = out + j * cols;
|
||||
const Float* rowIn1 = in1 + j * cols;
|
||||
const Float* rowIn2 = in2 + j * cols;
|
||||
const Float* rowIn3 = in3 + j * cols;
|
||||
|
||||
for(int tid = 0; tid < cols; tid += blockDim.x) {
|
||||
int i = tid + threadIdx.x;
|
||||
if(i < cols)
|
||||
rowOut[i] = functor(rowOut[i], rowIn1[i], rowIn2[i], rowIn3[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// @TODO add broadcasting
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor, Tensor Out) {
|
||||
Float* d_out = Out.data();
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In) {
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in = In.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In1, const Tensor In2) {
|
||||
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in1 = In1.data();
|
||||
const Float* d_in2 = In2.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in1, d_in2,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
template <class Functor>
|
||||
void Element(Functor functor,
|
||||
Tensor Out, const Tensor In1,
|
||||
const Tensor In2, const Tensor In3) {
|
||||
|
||||
Float* d_out = Out.data();
|
||||
const Float* d_in1 = In1.data();
|
||||
const Float* d_in2 = In2.data();
|
||||
const Float* d_in3 = In3.data();
|
||||
|
||||
int blocks = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
|
||||
int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
|
||||
gElement<<<blocks, threads>>>(functor, d_out, d_in1, d_in2, d_in3,
|
||||
Out.shape()[0], Out.shape()[1]);
|
||||
cudaStreamSynchronize(0);
|
||||
}
|
||||
|
||||
Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
|
||||
bool transA, bool transB, Float beta);
|
||||
|
||||
Tensor Prod(Tensor C, const Tensor A, const Tensor B,
|
||||
bool transA, bool transB, Float beta = 0);
|
||||
|
||||
}
|
64
src/test.cu
64
src/test.cu
@ -6,50 +6,34 @@ int main(int argc, char** argv) {
|
||||
using namespace marian;
|
||||
using namespace keywords;
|
||||
|
||||
auto x = data(shape={whatevs, 784}, name="X");
|
||||
auto y = data(shape={whatevs, 10}, name="Y");
|
||||
|
||||
auto x = input(shape={whatevs, 784}, name="X");
|
||||
auto y = input(shape={whatevs, 10}, name="Y");
|
||||
|
||||
auto w = param(shape={784, 10}, name="W0");
|
||||
auto b = param(shape={1, 10}, name="b0");
|
||||
|
||||
auto lr = softmax(dot(x, w) + b, axis=1);
|
||||
auto cost = -mean(sum(y * log(lr), axis=1), axis=0);
|
||||
|
||||
cost.forward();
|
||||
auto lr = softmax(dot(x, w) + b, axis=1, name="pred");
|
||||
auto graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
|
||||
|
||||
//auto set = [](size_t i, Expr c) {
|
||||
// size_t bid = (i + 1) % batches;
|
||||
// Tensor x = c["X"].val();
|
||||
// thrust::copy(XBatches[bid].begin(), XBatches[bid].end(),
|
||||
// x.begin());
|
||||
// Tensor y = c["Y"].val();
|
||||
// thrust::copy(YBatches[bid].begin(), YBatches[bid].end(),
|
||||
// y.begin());
|
||||
//};
|
||||
//
|
||||
//auto before = [](size_t i, Expr c) {
|
||||
// for(auto&& p : c.params())
|
||||
// clip(p.grad(), type=norm, max=10);
|
||||
//};
|
||||
//
|
||||
//
|
||||
//float sum;
|
||||
//auto after = [&sum](size_t i, Expr c) {
|
||||
// sum += c.val()[0];
|
||||
//
|
||||
// if(i % 100 == 0) {
|
||||
// std::cerr << sum / i << std::endl;
|
||||
// std::cerr << i << " : " << c.val()[0] << std::endl;
|
||||
// }
|
||||
//
|
||||
// if(i % 10000 == 0) {
|
||||
// std::cerr << "Saving model " << i << std::endl;
|
||||
// std::stringstream name;
|
||||
// name << "model.iter" << i << ".yml.gz";
|
||||
// dump(c, name.str());
|
||||
// }
|
||||
//
|
||||
//};
|
||||
Tensor tx({500, 784}, 1);
|
||||
Tensor ty({500, 10}, 1);
|
||||
|
||||
x = tx;
|
||||
y = ty;
|
||||
|
||||
graph.forward(500);
|
||||
//std::cerr << graph["pred"].val()[0] << std::endl;
|
||||
|
||||
|
||||
//hook0(graph);
|
||||
//graph.autodiff();
|
||||
//std::cerr << graph["cost"].val()[0] << std::endl;
|
||||
//hook1(graph);
|
||||
//for(auto p : graph.params()) {
|
||||
// auto update = _1 = _1 - alpha * _2;
|
||||
// Element(update, p.val(), p.grad());
|
||||
//}
|
||||
//hook2(graph);
|
||||
//
|
||||
//auto opt = adadelta(cost_function=cost,
|
||||
// eta=0.9, gamma=0.1,
|
||||
|
@ -11,29 +11,19 @@ namespace thrust
|
||||
{
|
||||
namespace functional
|
||||
{
|
||||
|
||||
// Ugly hacks, but it seems this is neccessary.
|
||||
__host__ __device__
|
||||
float expf2(float x) {
|
||||
float clip = 16;
|
||||
if(x > clip)
|
||||
x = clip;
|
||||
if(x < -clip)
|
||||
x = -clip;
|
||||
return expf(x);
|
||||
}
|
||||
|
||||
__host__ __device__
|
||||
float logf2(float x) {
|
||||
if(x < 10e-10)
|
||||
x = 10e-10;
|
||||
return logf(x);
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
struct unary_exp : public thrust::unary_function<T,T> {
|
||||
__host__ __device__
|
||||
T operator()(const T &x) const { return expf2(x); }
|
||||
T operator()(const T &x) const {
|
||||
float x2 = x;
|
||||
float clip = 16;
|
||||
if(x2 > clip)
|
||||
x2 = clip;
|
||||
if(x2 < -clip)
|
||||
x2 = -clip;
|
||||
return expf(x2);
|
||||
}
|
||||
};
|
||||
|
||||
template<typename Eval>
|
||||
@ -46,7 +36,12 @@ namespace thrust
|
||||
template<typename T>
|
||||
struct unary_log : public thrust::unary_function<T,T> {
|
||||
__host__ __device__
|
||||
T operator()(const T &x) const { return logf2(x); }
|
||||
T operator()(const T &x) const {
|
||||
float x2 = x;
|
||||
if(x2 < 10e-10)
|
||||
x2 = 10e-10;
|
||||
return logf(x2);
|
||||
}
|
||||
};
|
||||
|
||||
template<typename Eval>
|
||||
@ -59,7 +54,15 @@ namespace thrust
|
||||
template<typename T>
|
||||
struct unary_sigma : public thrust::unary_function<T,T> {
|
||||
__host__ __device__
|
||||
T operator()(const T &x) const { return 1.0 / (1.0 + expf2(-x)); }
|
||||
T operator()(const T &x) const {
|
||||
float x2 = x;
|
||||
float clip = 16;
|
||||
if(x2 > clip)
|
||||
x2 = clip;
|
||||
if(x2 < -clip)
|
||||
x2 = -clip;
|
||||
return 1.0 / (1.0 + expf(-x2));
|
||||
}
|
||||
};
|
||||
|
||||
template<typename Eval>
|
||||
|
Loading…
Reference in New Issue
Block a user