void Permutohedral::compute ( MatrixXf & out, const MatrixXf & in, bool reverse ) const
{
if( out.cols() != in.cols() || out.rows() != in.rows() )
out = 0*in;
if( in.rows() <= 2 )
seqCompute( out.data(), in.data(), in.rows(), reverse );
else
sseCompute( out.data(), in.data(), in.rows(), reverse );
}
void blas_gemm(const MatrixXf& a, const MatrixXf& b, MatrixXf& c)
{
int M = c.rows(); int N = c.cols(); int K = a.cols();
int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
sgemm_(¬rans,¬rans,&M,&N,&K,&fone,
const_cast<float*>(a.data()),&lda,
const_cast<float*>(b.data()),&ldb,&fone,
c.data(),&ldc);
}
void MapOptimizer::scale_stepsize (MatrixXf & dJ) const
{
if (logging) LOG(" computing stepsize");
const size_t XY = dom.size * cod.size;
const float * restrict J_ = m_joint.data();
const float * restrict dJ_ = dJ.data();
float time_to_boundary = INFINITY;
for (size_t xy = 0; xy < XY; ++xy) {
float dJ_xy = dJ_[xy];
if (dJ_xy < 0) {
imin(time_to_boundary, -J_[xy] / dJ_xy);
}
}
float keepaway = 1 / M_E;
float scale = time_to_boundary * keepaway;
if (logging) LOG(" scaling step size by " << scale);
dJ *= scale;
}
double density (const MatrixXf & x)
{
const size_t I = x.rows() * x.cols();
double sum_x1 = 0;
double sum_x2 = 0;
const float * restrict x_ = x.data();
for (size_t i = 0; i < I; ++i) {
double xi = x_[i];
sum_x1 += max(-xi,xi);
sum_x2 += xi * xi;
}
return sqr(sum_x1) / sum_x2 / I;
}
// Compute d/df a^T*K*b
MatrixXf kernelGradient( const MatrixXf & a, const MatrixXf & b ) const {
MatrixXf g = 0*f_;
lattice_.gradient( g.data(), a.data(), b.data(), a.rows() );
return g;
}
void MapOptimizer::constrain_direction (MatrixXf & dJ, float tol) const
{
// Enforce the simultaneous constraints
//
// /\x. sum y. dJ(y,x) = 0
// /\y. sum x. dJ(y,x) = 0
//
// We combine the two constraints by iteratively weakly enforcing both:
// Let Px,Py project to the feasible subspaces for constraints 1,2, resp.
// Each projection has eigenvalues in {0,1}.
// We approximate the desired projection Pxy as a linear combination of Px,Py
// Pxy' = 1 - alpha ((1-Px) + (1-Py))
// which has eigenvalues in {1} u [1 - alpha, 1 - 2 alpha].
// Hence Pxy = lim n->infty Pxy'^n, where convergence rate depends on alpha.
// The optimal alpha is 2/3, yielding Pxy' eigenvalues in {1} u [-1/3,1/3],
// and resulting in project_scale = -alpha below.
if (logging) LOG(" constraining direction");
const size_t X = dom.size;
const size_t Y = cod.size;
const MatrixXf & J = m_joint;
const VectorXf & sum_y_J = m_dom_prior;
const VectorXf & sum_x_J = m_cod_prior;
const float sum_xy_J = m_cod_prior.sum();
VectorXf sum_y_dJ(J.cols());
VectorXf sum_x_dJ(J.rows());
// this is iterative, so we hand-optimize by merging loops
const float * restrict J_ = J.data();
const float * restrict sum_y_J_ = sum_y_J.data();
const float * restrict sum_x_J_ = sum_x_J.data();
float * restrict dJ_ = dJ.data();
float * restrict project_y_ = sum_y_dJ.data();
float * restrict project_x_ = sum_x_dJ.data();
const float project_scale = -2/3.0;
Vector<float> accum_x_dJ(Y);
float * restrict accum_x_dJ_ = accum_x_dJ;
// accumulate first projection
accum_x_dJ.zero();
for (size_t x = 0; x < X; ++x) {
const float * restrict dJ_x_ = dJ_ + Y * x;
float accum_y_dJ = 0;
for (size_t y = 0; y < Y; ++y) {
float dJ_xy = dJ_x_[y];
accum_y_dJ += dJ_xy;
accum_x_dJ_[y] += dJ_xy;
}
project_y_[x] = project_scale * accum_y_dJ / sum_y_J_[x];
}
for (size_t y = 0; y < Y; ++y) {
project_x_[y] = project_scale * accum_x_dJ_[y] / sum_x_J_[y];
accum_x_dJ_[y] = 0;
}
// apply previous projection and accumulate next projection
for (size_t iter = 0; iter < 100; ++iter) {
float error = 0;
for (size_t x = 0; x < X; ++x) {
const float * restrict J_x_ = J_ + Y * x;
float * restrict dJ_x_ = dJ_ + Y * x;
float accum_y_dJ = 0;
for (size_t y = 0; y < Y; ++y) {
float dJ_xy = dJ_x_[y] += J_x_[y] * (project_x_[y] + project_y_[x]);
accum_y_dJ += dJ_xy;
accum_x_dJ_[y] += dJ_xy;
}
project_y_[x] = project_scale * accum_y_dJ / sum_y_J_[x];
imax(error, max(-accum_y_dJ, accum_y_dJ));
}
for (size_t y = 0; y < Y; ++y) {
float accum_x_dJ_y = accum_x_dJ_[y];
accum_x_dJ_[y] = 0;
project_x_[y] = project_scale * accum_x_dJ_y / sum_x_J_[y];
imax(error, max(-accum_x_dJ_y, accum_x_dJ_y));
}
if (error < tol) {
if (logging) {
LOG(" after " << (1+iter) << " iterations, error < " << error);
}
break;
}
}
// apply final projection
for (size_t x = 0; x < X; ++x) {
const float * restrict J_x_ = J_ + Y * x;
float * restrict dJ_x_ = dJ_ + Y * x;
for (size_t y = 0; y < Y; ++y) {
dJ_x_[y] += J_x_[y] * (project_x_[y] + project_y_[x]);
}
}
if (debug) {
sum_y_dJ = dJ.colwise().sum();
sum_x_dJ = dJ.rowwise().sum();
float sum_xy_dJ = sum_x_dJ.sum();
DEBUG("max constraint errors = "
<< sqrt(sum_x_dJ.array().square().maxCoeff())<< ", "
<< sqrt(sum_y_dJ.array().square().maxCoeff())<< ", "
<< sum_xy_dJ);
sum_y_dJ.array() /= sum_y_J.array();
sum_x_dJ.array() /= sum_x_J.array();
sum_xy_dJ /= sum_xy_J;
DEBUG("max relative constraints errors = "
<< sqrt(sum_x_dJ.array().square().maxCoeff()) << ", "
<< sqrt(sum_y_dJ.array().square().maxCoeff()) << ", "
<< sum_xy_dJ);
DEBUG("max(|dJ|) = " << dJ.array().abs().maxCoeff()
<< ", rms(dJ) = " << sqrt(dJ.array().square().mean()));
DEBUG("max(J) / min(J) = " << (J.maxCoeff() / J.minCoeff()));
DEBUG("max(sum x. J) / min(sum x. J) = "
<< (sum_x_J.maxCoeff() / sum_x_J.minCoeff()));
DEBUG("max(sum y. J) / min(sum y. J) = "
<< (sum_y_J.maxCoeff() / sum_y_J.minCoeff()));
}
}
MatrixXf M1(2,6); // Column-major storage
M1 << 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12;
Map<MatrixXf> M2(M1.data(), 6,2);
cout << "M2:" << endl << M2 << endl;
int main (int argc, char *argv[]) {
// handle cmd args
// TODO: add <hidden_layer_sizes> (i.e., "100-100-50") argument processing
int batch_size;
if ( argc > 1 ) {
printf( " Usage: ./neuralnet_mpi <batch_size>");
exit( 0 );
} else if ( argc == 1 ) {
batch_size = atoi( argv[0] ); // mini-batch processing
} else {
batch_size = INT_MIN; // batch processing
}
// initialize/populate mpi specific vars local to each node
int numtasks, taskid, len, dest, source;
char hostname[MPI_MAX_PROCESSOR_NAME];
MPI_Status status;
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &numtasks);
MPI_Comm_rank(MPI_COMM_WORLD,&taskid);
MPI_Get_processor_name(hostname, &len);
/***** MASTER TASK ONLY ******/
// perform data preprocessing based on number of workers and batch_size
if (taskid == MASTER) {
printf( "MASTER: Number of MPI tasks is: %d\n",numtasks );
/* DATA PREPROCESSING */
// Load dataset
// TEMP: populate fictitious dataset
// NOTE: record max and min for each column in the max/min arrays
long datasize = 16;
long numfeats = 4; // to be populated while loading dataset
long numlabels = 1; // for testing binary classification only
//float min[numfeats]; // stores the overall min for each column
//float max[numfeats]; // stores the overall max for each column
MatrixXf data = MatrixXf( datasize, numfeats ); // row-major order!
VectorXf labels_vec = VectorXf( datasize );
for ( long i=0; i<datasize; ++i ) {
for ( long j=0; j<numfeats; ++j ) {
data(i, j) = i + j;
}
labels_vec[i] = 1.0; // populate a vector with labels for each instance
}
data(0,0) = 100.1;
data(1,0) = 200.1;
data(0,1) = 300.1;
// Shuffle data/labels to randomize instances
// Reformat labels for multi-class classification
MatrixXf labels = MatrixXf::Zero( datasize, numlabels );
// TODO: create class_map from unique classes with k:v = <true_label>:<column_index>
for ( long i=0; i<datasize; ++i ) {
// labels( i, class_map[y_vec[i]] ) = 1.0;
}
// Scale features
// TODO: use max/min arrays to scale data to between -1 and 1
// # scales all features in dataset X to values between new_min and new_max
// X_min, X_max = X.min(0), X.max(0)
// return (((X - X_min) / (X_max - X_min + 0.000001)) * (new_max - new_min)) + new_min
/* DATA MARSHALLING */
long chunksize = datasize / numtasks;
// load MASTER data
MatrixXf X = MatrixXf( chunksize, numfeats );
MatrixXf y = MatrixXf( chunksize, numlabels );
memcpy( X.data(), data.data(), chunksize * numfeats * sizeof(float) );
memcpy( y.data(), labels.data(), chunksize * numlabels * sizeof(float) );
std::cout << "MASTER X:\n" << X << std::endl;
std::cout << "MASTER y:\n" << y << std::endl;
// send data to workers
long offset = chunksize;
for (dest=1; dest<numtasks; dest++) {
MPI_Send( &chunksize, 1, MPI_LONG, dest, TAG_0, MPI_COMM_WORLD );
MPI_Send( &numfeats, 1, MPI_LONG, dest, TAG_0, MPI_COMM_WORLD );
MPI_Send( &numlabels, 1, MPI_LONG, dest, TAG_0, MPI_COMM_WORLD );
MPI_Send( data.data() + offset * numfeats, chunksize * numfeats, MPI_FLOAT, dest, TAG_0, MPI_COMM_WORLD );
MPI_Send( labels.data() + offset * numlabels, chunksize * numlabels, MPI_FLOAT, dest, TAG_0, MPI_COMM_WORLD );
printf( "Sent %ld instances to task %d offset= %ld\n", chunksize, dest, offset );
offset += chunksize;
}
/* CLASSIFICATION MODEL INITIALIZATION */
// pass network structure and processing parameters message
// initialze MASTER NN
// initialize network parameters
// TODO: create randomized set of parameters stored in contiguous memory
// to be packed and unpacked as needed. The important part of doing this on
// the MASTER first is that all networks will start with the same parameter set.
// set MASTER NN parameters
/* OPTIMIZATION */
// optimize
offset = 0;
/* PREDICTION */
// predict on validation set
// output prediction results
/* MODEL STORAGE */
// store parameters
}
/***** NON-MASTER TASKS ONLY *****/
if (taskid > MASTER) {
printf ("Hello from task %d on %s!\n", taskid, hostname);
/* DATA INITIALIZATION */
long chunksize, numfeats, numlabels;
source = MASTER;
// recieve data partition
MPI_Recv( &chunksize, 1, MPI_LONG, source, MPI_ANY_TAG, MPI_COMM_WORLD, &status );
MPI_Recv( &numfeats, 1, MPI_LONG, source, MPI_ANY_TAG, MPI_COMM_WORLD, &status );
MPI_Recv( &numlabels, 1, MPI_LONG, source, MPI_ANY_TAG, MPI_COMM_WORLD, &status );
printf( "Task %d chunksize = %ld\n", taskid, chunksize );
printf( "Task %d numfeats = %ld\n", taskid, numfeats );
printf( "Task %d numlabels = %ld\n", taskid, numlabels );
// initialize local data storage
MatrixXf X = MatrixXf( chunksize, numfeats );
MatrixXf y = MatrixXf( chunksize, numlabels );
// receive data and labels
MPI_Recv( X.data(), chunksize * numfeats, MPI_FLOAT, source, MPI_ANY_TAG, MPI_COMM_WORLD, &status );
MPI_Recv( y.data(), chunksize * numlabels, MPI_FLOAT, source, MPI_ANY_TAG, MPI_COMM_WORLD, &status );
std::cout << "task " << taskid << " X:\n" << X << std::endl;
std::cout << "task " << taskid << " y:\n" << y << std::endl;
/* CLASSIFICATION MODEL INITIALIZATION */
// recieve network structure and processing paramters info
// initialize local neuralnet_openmp instance
// TODO: each NN instance is set with identical structure and processing parameters
// recieve network parameters and pass to local instance
// TODO: each NN instance gets the same set of randomized parameters
/* OPTIMIZATION */
// optimize
}
MPI_Finalize();
}
int main(void)
{
cout << "Eigen v" << EIGEN_WORLD_VERSION << "." << EIGEN_MAJOR_VERSION << "." << EIGEN_MINOR_VERSION << endl;
static const int R = 288;
static const int N = R*(R+1)/2;
static const int M = 63;
static const int HALF_M = M/2;
static const float nsigma = 2.5f;
MatrixXf data = MatrixXf::Random(M, N);
MatrixXf mask = MatrixXf::Zero(M, N);
MatrixXf result = MatrixXf::Zero(1, N);
VectorXf std = VectorXf::Zero(N);
VectorXf centroid = VectorXf::Zero(N);
VectorXf mean = VectorXf::Zero(N);
VectorXf minval = VectorXf::Zero(N);
VectorXf maxval = VectorXf::Zero(N);
cout << "computing..." << flush;
double t = GetRealTime();
// computes the exact median
if (M&1)
{
#pragma omp parallel for
for (int i = 0; i < N; i++)
{
vector<float> row(data.data()+i*M, data.data()+(i+1)*M);
nth_element(row.begin(), row.begin()+HALF_M, row.end());
centroid(i) = row[HALF_M];
}
}
// nth_element guarantees x_0,...,x_{n-1} < x_n
else
{
#pragma omp parallel for
for (int i = 0; i < N; i++)
{
vector<float> row(data.data()+i*M, data.data()+(i+1)*M);
nth_element(row.begin(), row.begin()+HALF_M, row.end());
centroid(i) = row[HALF_M];
centroid(i) += *max_element(row.begin(), row.begin()+HALF_M);
centroid(i) *= 0.5f;
}
}
// compute the mean
mean = data.colwise().mean();
// compute std (x) = sqrt ( 1/N SUM_i (x(i) - mean(x))^2 )
std = (((data.rowwise() - mean.transpose()).array().square()).colwise().sum() *
(1.0f / M))
.array()
.sqrt();
// compute n sigmas from centroid
minval = centroid - std * nsigma;
maxval = centroid + std * nsigma;
// compute clip mask
for (int i = 0; i < N; i++)
{
mask.col(i) = (data.col(i).array() > minval(i)).select(VectorXf::Ones(M), 0.0f);
mask.col(i) = (data.col(i).array() < maxval(i)).select(VectorXf::Ones(M), 0.0f);
}
// apply clip mask to data
data.array() *= mask.array();
// compute mean such that we ignore clipped data, this is our final result
result = data.colwise().sum().array() / mask.colwise().sum().array();
t = GetRealTime() - t;
cout << "[done]" << endl << endl;
size_t bytes = data.size()*sizeof(float);
cout << "data: " << M << "x" << N << endl;
cout << "size: " << bytes*1e-6f << " MB" << endl;
cout << "rate: " << bytes/(1e6f*t) << " MB/s" << endl;
cout << "time: " << t << " s" << endl;
return 0;
}
Vector<float> as_vector (const MatrixXf & x)
{
return Vector<float>(x.size(), const_cast<float *>(x.data()));
}
