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some clean-up

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This commit is contained in:
Marcin Junczys-Dowmunt 2016-08-28 00:24:41 +02:00
parent 8eb641ad45
commit cef8a37fc7
16 changed files with 905 additions and 372 deletions
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4
CMakeLists.txt
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@ -2,8 +2,8 @@ cmake_minimum_required(VERSION 3.5.1)
set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
project(marian CXX)
SET(CMAKE_CXX_FLAGS " -std=c++11 -g -O3 -funroll-loops -Wno-unused-result -Wno-deprecated")
LIST(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -std=c++11; -g; -O3; -arch=sm_35; -lineinfo; --use_fast_math;)
SET(CMAKE_CXX_FLAGS " -std=c++11 -g -O0 -funroll-loops -Wno-unused-result -Wno-deprecated")
LIST(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -std=c++11; -g; -O0; -arch=sm_35; -lineinfo; --use_fast_math; -Xcompiler '-fPIC')
add_definitions(-DCUDA_API_PER_THREAD_DEFAULT_STREAM)
SET(CUDA_PROPAGATE_HOST_FLAGS OFF)

2
src/CMakeLists.txt
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@ -8,6 +8,8 @@ add_library(libcommon OBJECT
cuda_add_executable(
marian
test.cu
expressions.cu
tensor_operators.cu
$<TARGET_OBJECTS:libcommon>
)

26
src/definitions.h
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@ -5,25 +5,25 @@
#include <functional>
namespace marian {
typedef float Float;
typedef float Float;
typedef std::vector<int> Shape;
const int whatevs{-1};
}
#include "keywords.h"
#include "tensor.h"
namespace marian {
typedef std::vector<int> Shape;
const int whatevs{-1};
class Tensor;
namespace keywords {
KEY(init, std::function<void(Tensor)>)
KEY(axis, int)
KEY(name, std::string)
KEY(shape, Shape)
KEY(value, float)
KEY(lazy_shape, std::function<Shape()>)
KEY(lazy_value, std::function<float()>)
}
namespace keywords {
KEY(axis, int)
KEY(name, std::string)
KEY(shape, Shape)
KEY(value, float)
KEY(lazy_shape, std::function<Shape()>)
KEY(lazy_value, std::function<float()>)
KEY(init, std::function<void(Tensor)>)
}
}

51
src/expression_operators.h
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@ -7,8 +7,8 @@
namespace marian {
template <typename ...Args>
inline Expr data(Args ...args) {
return Expr(new DataNode(args...));
inline Expr input(Args ...args) {
return Expr(new InputNode(args...));
}
template <typename ...Args>
@ -76,30 +76,31 @@ inline Expr dot(Expr a, Expr b) {
/******************************************************/
Expr broadcast(Shape shape, Expr a) {
if(a.val().shape() == shape) {
Expr broadcast(Shape bShape, Expr a) {
const Shape& aShape = a.node()->shape();
if(aShape == bShape) {
return a;
}
else {
size_t dimsA = a.val().shape().size();
size_t dimsB = shape.size();
size_t dimsA = aShape.size();
size_t dimsB = bShape.size();
UTIL_THROW_IF2(dimsA != dimsB,
"Tensor and shape have different number of dimensions");
for(size_t i = 0; i < dimsA; ++i) {
int dimA = a.val().shape()[i];
int dimB = shape[i];
int dimA = aShape[i];
int dimB = bShape[i];
bool broadcastable = (dimA == dimB || dimA == 1);
UTIL_THROW_IF2(!broadcastable,
"Cannot broadcast tensor dimension "
<< dimA << " to " << dimB);
if(dimA == 1 && dimB > 1) {
if(dimA == 1 && dimB != 1) {
std::cerr << "Broadcasting dim " << i << " from " << dimA << " to " << dimB << std::endl;
if(i == 0) {
Expr one = ones(keywords::shape={shape[0], 1});
Expr one = ones(keywords::shape={bShape[0], 1});
a = dot(one, a);
}
else if(i == 1) {
Expr one = ones(keywords::shape={1, shape[1]});
Expr one = ones(keywords::shape={1, bShape[1]});
a = dot(a, one);
}
else {
@ -120,20 +121,23 @@ inline Expr sum(Expr a, Args ...args) {
Keywords params(args...);
int ax = params.Get<int>(axis, whatevs);
ChainPtr n = a.node();
if(ax == 0) {
auto lshape = [&a]() -> Shape {
int rows = a.val().shape()[0];
auto lshape = [n]() -> Shape {
int rows = n->val().shape()[0];
return {1, rows};
};
Expr one = ones(lazy_shape=lshape);
Expr one = ones(shape={1, n->shape()[0]},
lazy_shape=lshape);
return dot(one, a);
}
else if(ax == 1) {
auto lshape = [&a]() -> Shape {
int cols = a.val().shape()[1];
auto lshape = [n]() -> Shape {
int cols = n->val().shape()[1];
return {cols, 1};
};
Expr one = ones(lazy_shape=lshape);
Expr one = ones(shape={n->shape()[1], 1},
lazy_shape=lshape);
return dot(a, one);
}
else if(ax == 2) {
@ -159,16 +163,17 @@ inline Expr mean(Expr a, Args ...args) {
Keywords params(args...);
size_t ax = params.Get<int>(axis, whatevs);
ChainPtr n = a.node();
switch (ax) {
case 0:
return sum(a, axis=0) / constant(shape={1, 1},
lazy_value=[&a]() -> Float {
return a.val().shape()[0];
lazy_value=[n]() -> Float {
return n->val().shape()[0];
});
case 1:
return sum(a, axis=1) / constant(shape={1, 1},
lazy_value=[&a]() -> Float {
return a.val().shape()[1];
lazy_value=[n]() -> Float {
return n->val().shape()[1];
});
case 2:
UTIL_THROW2("Not implemented");
@ -176,8 +181,8 @@ inline Expr mean(Expr a, Args ...args) {
UTIL_THROW2("Not implemented");
default:
return sum(a) / constant(shape={1, 1},
lazy_value=[&a]() -> Float {
return a.val().size();
lazy_value=[n]() -> Float {
return n->val().size();
});
}
}

53
src/expressions.cu Normal file
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@ -0,0 +1,53 @@
#include "expressions.h"
#include "graph_operators.h"
namespace marian {
Expr::Expr(Chainable<Tensor>* chainable) : pimpl_(chainable) {}
Expr::Expr(Float v) : pimpl_(new ConstantNode(keywords::value=v,
keywords::shape={1,1})) {}
Tensor Expr::val() {
return pimpl_->val();
}
Tensor Expr::grad() {
return pimpl_->grad();
}
ChainPtr Expr::node() {
return pimpl_;
}
void Expr::forward(size_t batchSize) {
UTIL_THROW_IF2(pimpl_.get() != Chainable<Tensor>::stack.back(),
"Trying to call forward on non-root of computation graph");
std::cerr << "a" << std::endl;
for(auto&& v : Chainable<Tensor>::stack) {
v->allocate(batchSize);
}
std::cerr << "f" << std::endl;
for(auto&& v : Chainable<Tensor>::stack)
v->forward();
}
void Expr::backward() {
UTIL_THROW_IF2(pimpl_.get() != Chainable<Tensor>::stack.back(),
"Trying to call backward on non-root of computation graph");
for(auto&& v : Chainable<Tensor>::stack)
v->set_zero_adjoint();
typedef typename Chainable<Tensor>::ChainableStack::reverse_iterator It;
pimpl_->init_dependent();
for(It it = Chainable<Tensor>::stack.rbegin(); it != Chainable<Tensor>::stack.rend(); ++it)
(*it)->backward();
}
Expr::operator ChainPtr() {
return pimpl_;
}
}

52
src/expressions.h
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@ -1,52 +1,28 @@
#pragma once
#include "definitions.h"
#include "graph.h"
namespace marian {
class Expr {
public:
Expr(Chainable<Tensor>* chainable) : pimpl_(chainable) {}
Expr(Chainable<Tensor>* chainable);
Expr(Float v);
Tensor val() {
return pimpl_->val();
Expr operator=(Tensor t) {
pimpl_->setVal(t);
return *this;
}
Tensor grad() {
return pimpl_->grad();
}
Tensor val();
Tensor grad();
ChainPtr pimpl() {
return pimpl_;
}
void forward(size_t batchSize);
void backward();
void forward() {
UTIL_THROW_IF2(pimpl_.get() != stack.back(),
"Trying to call forward on non-root of computation graph");
std::cerr << "a" << std::endl;
for(auto&& v : stack)
v->allocate();
std::cerr << "f" << std::endl;
for(auto&& v : stack)
v->forward();
}
void backward() {
UTIL_THROW_IF2(pimpl_.get() != stack.back(),
"Trying to call backward on non-root of computation graph");
for(auto&& v : stack)
v->set_zero_adjoint();
typedef ChainableStack::reverse_iterator It;
pimpl_->init_dependent();
for(It it = stack.rbegin(); it != stack.rend(); ++it)
(*it)->backward();
}
operator ChainPtr() {
return pimpl_;
}
ChainPtr node();
operator ChainPtr();
private:
ChainPtr pimpl_;

40
src/graph.h
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@ -14,16 +14,23 @@ struct Chainable {
virtual void init_dependent() { }
virtual void set_zero_adjoint() { }
virtual void allocate() = 0;
virtual void allocate(size_t) = 0;
virtual const Shape& shape() = 0;
virtual DataType val() = 0;
virtual DataType grad() = 0;
virtual void setVal(Tensor t) {
UTIL_THROW2("Tensors can only be assigned to input nodes");
};
typedef std::vector<Chainable<Tensor>*> ChainableStack;
static ChainableStack stack;
};
typedef std::vector<Chainable<Tensor>*> ChainableStack;
typedef std::shared_ptr<Chainable<Tensor>> ChainPtr;
template <class DataType>
typename Chainable<DataType>::ChainableStack Chainable<DataType>::stack;
ChainableStack stack;
typedef std::shared_ptr<Chainable<Tensor>> ChainPtr;
class Node : public Chainable<Tensor>,
public keywords::Keywords {
@ -34,14 +41,27 @@ class Node : public Chainable<Tensor>,
shape_(Get<Shape>(keywords::shape, {1, 1})),
name_(Get<std::string>(keywords::name, "none"))
{
std::cerr << "Creating node " << name_ << std::endl;
stack.push_back(this);
}
virtual ~Node() {};
virtual void allocate() {
val_.allocate(shape_);
virtual void allocate(size_t batchSize) {
for(auto&& d : shape_) {
if(d == whatevs)
d = batchSize;
}
if(Has(keywords::lazy_shape)) {
auto defaultShape = [this]() -> Shape { return shape_; };
shape_ = Get<std::function<Shape()>>(keywords::lazy_shape, defaultShape)();
}
if(Has(keywords::lazy_value))
val_.allocate(shape_, Get<std::function<float()>>(
keywords::lazy_value, []()->Float{return 0.f;})());
else if(Has(keywords::value))
val_.allocate(shape_, Get<Float>(keywords::value, 0));
else
val_.allocate(shape_);
}
virtual void init_dependent() {
@ -71,7 +91,11 @@ class Node : public Chainable<Tensor>,
UTIL_THROW_IF2(!adj_, "Tensor has not been allocated");
return adj_;
};
virtual const Shape& shape() {
return shape_;
}
protected:
Shape shape_;
std::string name_;

69
src/graph_operators.h
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@ -1,16 +1,26 @@
#pragma once
#include "graph.h"
#include "expressions.h"
//#include "expression_operators.h"
#include "graph.h"
#include "tensor_operators.h"
namespace marian {
struct DataNode : public Node {
struct InputNode : public Node {
template <typename ...Args>
DataNode(Args ...args)
: Node(args...) { }
InputNode(Args ...args)
: Node(args...) {
UTIL_THROW_IF2(!Has(keywords::shape) &&
!Has(keywords::lazy_shape),
"Data items require shape information");
}
virtual void setVal(Tensor t) {
val_ = t;
shape_ = t.shape();
//@todo, shape checking
};
void forward() {}
void backward() {}
};
@ -18,7 +28,11 @@ struct DataNode : public Node {
struct ConstantNode : public Node {
template <typename ...Args>
ConstantNode(Args ...args)
: Node(args...) { }
: Node(args...) {
UTIL_THROW_IF2(!Has(keywords::shape) &&
!Has(keywords::lazy_shape),
"Constant items require shape information");
}
void forward() {}
void backward() {}
@ -29,12 +43,16 @@ struct ParamNode : public Node {
ParamNode(Args ...args)
: Node(args...),
init_(Get<std::function<void(Tensor)>>(keywords::init, [](Tensor){ }))
{ }
{
UTIL_THROW_IF2(!Has(keywords::shape) &&
!Has(keywords::lazy_shape),
"Param items require shape information");
}
void forward() {}
void backward() {}
virtual void allocate() {
virtual void allocate(size_t batchSize) {
val_.allocate(shape_);
init_(val_);
}
@ -86,9 +104,7 @@ struct TanhNodeOp : public UnaryNodeOp {
struct LogNodeOp : public UnaryNodeOp {
template <typename ...Args>
LogNodeOp(Args ...args)
: UnaryNodeOp(args...) {
std::cerr << "log" << std::endl;
}
: UnaryNodeOp(args...) {}
void forward() {
Element(_1 = Log(_2), val_, a_->val());
@ -145,13 +161,15 @@ struct BinaryNodeOp : public Node {
struct DotNodeOp : public BinaryNodeOp {
template <typename ...Args>
DotNodeOp(ChainPtr a, ChainPtr b, Args ...args)
: BinaryNodeOp(a, b, args...) { }
: BinaryNodeOp(a, b,
keywords::shape=newShape(a,b),
args...) { }
Shape shape(ChainPtr a, ChainPtr b) {
UTIL_THROW_IF2(a->val().shape()[1] != b->val().shape()[0],
Shape newShape(ChainPtr a, ChainPtr b) {
Shape shape1 = a->shape();
Shape shape2 = b->shape();
UTIL_THROW_IF2(shape1[1] != shape2[0],
"matrix product requires dimensions to match");
Shape shape1 = a->val().shape();
Shape shape2 = b->val().shape();
shape1[1] = shape2[1];
return shape1;
}
@ -177,23 +195,26 @@ Expr broadcast(Shape shape, Expr a);
struct BroadcastingNodeOp : public BinaryNodeOp {
template <typename ...Args>
BroadcastingNodeOp(Expr a, Expr b, Args ...args)
: BinaryNodeOp(broadcast(shape(a ,b), a),
broadcast(shape(a ,b), b),
args...) {}
: BinaryNodeOp(broadcast(newShape(a ,b), a),
broadcast(newShape(a ,b), b),
keywords::shape=newShape(a, b),
args...) {}
static Shape shape(ChainPtr a, ChainPtr b) {
size_t dimsA = a->val().shape().size();
size_t dimsB = b->val().shape().size();
static Shape newShape(ChainPtr a, ChainPtr b) {
size_t dimsA = a->shape().size();
size_t dimsB = b->shape().size();
UTIL_THROW_IF2(dimsA != dimsB,
"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;
}

79
src/keywords.h
View File

@ -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);
}

3
src/marian.h
View File

@ -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
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@ -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
View File

@ -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
View 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);
}
}

151
src/tensor_operators.h
View File

@ -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
View File

@ -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,

45
src/thrust_functions.h
View File

@ -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>