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moved Linear sintrigification to .cpp file and fixed err in neuron weights

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Shin
2016-02-16 23:23:48 +01:00
parent 5bb132d84e
commit 69b530d597
6 changed files with 96 additions and 88 deletions
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5
CMakeLists.txt
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@@ -10,13 +10,13 @@ OPTION(ENABLE_TESTS "enables tests" ON)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wextra -pedantic -Weffc++ -Wshadow -Wstrict-aliasing -ansi -Woverloaded-virtual -Wdelete-non-virtual-dtor -Wno-unused-function")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} --std=c++14")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g")
#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g")
#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=native -mtune=native -O3")
if(USE_AVX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_AVX")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx -DUSE_AVX")
endif(USE_AVX)
if(USE_SSE)
@@ -49,6 +49,7 @@ set (LIBRARY_SOURCES
include/NeuralNetwork/Stringifiable.h
src/NeuralNetwork/Learning/BackPropagation.cpp include/NeuralNetwork/Learning/BackPropagation.h
include/sse_mathfun.h
src/NeuralNetwork/BasisFunction/Linear.cpp
src/NeuralNetwork/FeedForward/Layer.cpp
src/NeuralNetwork/FeedForward/Network.cpp
src/NeuralNetwork/Recurrent/Network.cpp

71
include/NeuralNetwork/BasisFunction/Linear.h
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@@ -7,6 +7,8 @@
#include <pmmintrin.h>
#include <immintrin.h>
#include <cassert>
#include "./StreamingBasisFunction.h"
#include "../../sse_mathfun.h"
@@ -18,74 +20,7 @@ namespace BasisFunction {
public:
Linear() {}
inline virtual float operator()(const std::vector<float>& weights, const std::vector<float>& input) const override {
#ifdef USE_AVX
//TODO: check sizes!!!
std::size_t inputSize=input.size();
size_t alignedPrev=inputSize-inputSize%8;
const float* weightsData=weights.data();
const float* inputData=input.data();
union {
__m256 avx;
float f[8];
} partialSolution;
partialSolution.avx=_mm256_setzero_ps();
for(size_t k=0;k<alignedPrev;k+=8) {
//TODO: asignement!! -- possible speedup
partialSolution.avx=_mm256_add_ps(partialSolution.avx,_mm256_mul_ps(_mm256_loadu_ps(weightsData+k),_mm256_loadu_ps(inputData+k)));
}
for(size_t k=alignedPrev;k<inputSize;k++) {
partialSolution.avx=_mm256_add_ps(partialSolution.avx,_mm256_mul_ps(_mm256_set_ps(weightsData[k],0,0,0,0,0,0,0),_mm256_set_ps(inputData[k],0,0,0,0,0,0,0)));
}
partialSolution.avx = _mm256_add_ps(partialSolution.avx, _mm256_permute2f128_ps(partialSolution.avx , partialSolution.avx , 1));
partialSolution.avx = _mm256_hadd_ps(partialSolution.avx, partialSolution.avx);
partialSolution.avx = _mm256_hadd_ps(partialSolution.avx, partialSolution.avx);
return partialSolution.f[0];
#else
#ifdef USE_SSE
size_t inputSize=input.size();
size_t alignedPrev=inputSize-inputSize%4;
const float* weightsData=weights.data();
const float* inputData=input.data();
vec4f partialSolution;
partialSolution.sse =_mm_setzero_ps();
//TODO prefetch ??
for(register size_t k=0;k<alignedPrev;k+=4) {
partialSolution.sse=_mm_add_ps(partialSolution.sse,_mm_mul_ps(_mm_load_ps(weightsData+k),_mm_load_ps(inputData+k)));
}
for(register size_t k=alignedPrev;k<inputSize;k++) {
partialSolution.sse=_mm_add_ps(partialSolution.sse,_mm_mul_ps(_mm_load_ss(weightsData+k),_mm_load_ss(inputData+k)));
}
#ifdef USE_SSE2 //pre-SSE3 solution
partialSolution.sse= _mm_add_ps(_mm_movehl_ps(partialSolution.sse, partialSolution.sse), partialSolution.sse);
partialSolution.sse=_mm_add_ss(partialSolution.sse, _mm_shuffle_ps(partialSolution.sse,partialSolution.sse, 1));
#else
partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
#endif
return partialSolution.f[0];
#else
register float tmp = 0;
size_t inputSize=input.size();
for(size_t k=0;k<inputSize;k++) {
tmp+=input[k]*weights[k];
}
return tmp;
#endif
#endif
}
virtual float operator()(const std::vector<float>& weights, const std::vector<float>& input) const override;
virtual BasisFunction* clone() const override {
return new Linear();

16
include/NeuralNetwork/FeedForward/Layer.h
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@@ -71,20 +71,8 @@ namespace FeedForward {
}
using Stringifiable::stringify;
virtual void stringify(std::ostream& out) const override {
out << "{" << std::endl;
out << "\t \"class\": \"NeuralNetwork::FeedForward::Layer\"," << std::endl;
out << "\t \"neurons\": [" << std::endl;
bool first=true;
for(auto &neuron: neurons) {
if(!first)
out << ", ";
out << neuron->stringify();
first=false;
}
out << "]";
out << "}";
}
virtual void stringify(std::ostream& out) const override;
protected:
std::vector<NeuronInterface*> neurons;
};

6
include/NeuralNetwork/Neuron.h
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@@ -104,7 +104,7 @@ namespace NeuralNetwork
Neuron(unsigned long _id=0, const ActivationFunction::ActivationFunction &activationFunction=ActivationFunction::Sigmoid(-4.9)):
NeuronInterface(), basis(new BasisFunction::Linear),
activation(activationFunction.clone()),
id_(_id),weights(_id+1),_output(0),_value(0) {
id_(_id),weights(1),_output(0),_value(0) {
}
Neuron(const Neuron &r): NeuronInterface(), basis(r.basis->clone()), activation(r.activation->clone()),id_(r.id_),
@@ -151,8 +151,8 @@ namespace NeuralNetwork
}
virtual void setInputSize(const std::size_t &size) override {
if(weights.size()<size+1) {
weights.resize(size+1);
if(weights.size()<size) {
weights.resize(size);
}
}

69
src/NeuralNetwork/BasisFunction/Linear.cpp Normal file
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@@ -0,0 +1,69 @@
#include <NeuralNetwork/BasisFunction/Linear.h>
float NeuralNetwork::BasisFunction::Linear::operator()(const std::vector<float> &weights, const std::vector<float> &input) const {
assert(input.size()== weights.size());
std::size_t inputSize=input.size();
#ifdef USE_AVX
std::size_t alignedPrev=inputSize-inputSize%8;
const float* weightsData=weights.data();
const float* inputData=input.data();
union {
__m256 avx;
float f[8];
} partialSolution;
partialSolution.avx=_mm256_setzero_ps();
for(size_t k=0;k<alignedPrev;k+=8) {
//TODO: asignement!! -- possible speedup
partialSolution.avx=_mm256_add_ps(partialSolution.avx,_mm256_mul_ps(_mm256_loadu_ps(weightsData+k),_mm256_loadu_ps(inputData+k)));
}
for(size_t k=alignedPrev;k<inputSize;k++) {
partialSolution.avx=_mm256_add_ps(partialSolution.avx,_mm256_mul_ps(_mm256_set_ps(weightsData[k],0,0,0,0,0,0,0),_mm256_set_ps(inputData[k],0,0,0,0,0,0,0)));
}
partialSolution.avx = _mm256_add_ps(partialSolution.avx, _mm256_permute2f128_ps(partialSolution.avx , partialSolution.avx , 1));
partialSolution.avx = _mm256_hadd_ps(partialSolution.avx, partialSolution.avx);
partialSolution.avx = _mm256_hadd_ps(partialSolution.avx, partialSolution.avx);
return partialSolution.f[0];
#elif USE_SSE
std::size_t alignedPrev=inputSize-inputSize%4;
const float* weightsData=weights.data();
const float* inputData=input.data();
vec4f partialSolution;
partialSolution.sse =_mm_setzero_ps();
//TODO prefetch ??
for(register size_t k=0;k<alignedPrev;k+=4) {
partialSolution.sse=_mm_add_ps(partialSolution.sse,_mm_mul_ps(_mm_load_ps(weightsData+k),_mm_load_ps(inputData+k)));
}
for(register size_t k=alignedPrev;k<inputSize;k++) {
partialSolution.sse=_mm_add_ps(partialSolution.sse,_mm_mul_ps(_mm_load_ss(weightsData+k),_mm_load_ss(inputData+k)));
}
#ifdef USE_SSE2 //pre-SSE3 solution
partialSolution.sse= _mm_add_ps(_mm_movehl_ps(partialSolution.sse, partialSolution.sse), partialSolution.sse);
partialSolution.sse=_mm_add_ss(partialSolution.sse, _mm_shuffle_ps(partialSolution.sse,partialSolution.sse, 1));
#else
partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
#endif
return partialSolution.f[0];
#else
register float tmp = 0;
for(size_t k=0;k<inputSize;k++) {
tmp+=input[k]*weights[k];
}
return tmp;
#endif
}

17
src/NeuralNetwork/FeedForward/Layer.cpp
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@@ -7,4 +7,19 @@ void NeuralNetwork::FeedForward::Layer::solve(const std::vector<float> &input, s
output[neuron->id()]=neuron->operator()(input);
}
}
}
void NeuralNetwork::FeedForward::Layer::stringify(std::ostream &out) const {
out << "{" << std::endl;
out << "\t \"class\": \"NeuralNetwork::FeedForward::Layer\"," << std::endl;
out << "\t \"neurons\": [" << std::endl;
bool first=true;
for(auto &neuron: neurons) {
if(!first)
out << ", ";
out << neuron->stringify();
first=false;
}
out << "]";
out << "}";
}