MatrixXf diff = user->rr.rowwise() - r0.transpose(); VectorXf one = diff.rowwise().norm();floatsum = one.sum();floatsum2 = one.dot(one); F = sum2 - sum*sum/user->rr.rows();if(user->report)std::cout<<"r0: "<<1000*r0[0] <<", r1: "<<1000*r0[1] <<", r2: ...
// compute weight norm at::acc_type<cache_t, true> weight_sum_square = 0.0; #pragma unroll kMaxVecsPerThread for (int32_t i = 0; i < kMaxVecsPerThread && 4 * kThreadGroupSize * i + threadIdx.x * 4 < D; ++i) { int32_t d = 4 * kThreadGroupSize * i + threadIdx.x ...
}if(params.trainBiases) {VectorLBFGSbiasesGrad(g + offset, mDimOut -1); biasesGrad = diff.rowwise().sum().bottomRows(mDimOut -1) / normConst; biasesGrad += params.regularizeBiases.gradient(biases); } }// return negative average log-likelihood in bitsdoublevalue = -(logProb * output...
#init model weights model.init_weights() #parallelize the first embedding and the last linear out projection model = parallelize_module( model, tp_mesh, { "tok_embeddings": RowwiseParallel( # **Here's the problem** input_layouts=Replicate(), output_layouts=Shard(1), ), "norm": Sequence...
/* return word nearest neighbors in the embedding space */voidgetnn(FILE* fout, Eigen::MatrixXf m,constintidx){// find nearest neighbourEigen::VectorXf dist = (m.rowwise() - m.row(idx)).rowwise().squaredNorm();std::vector<int> sortidx = REDSVD::Util::ascending_order(dist);for...
rows_dot_self(constEigen::Matrix<T,R,C>& x) {returnx.rowwise().squaredNorm(); } 开发者ID:javaosos,项目名称:stan,代码行数:4,代码来源:rows_dot_self.hpp 示例2: unibak Eigen::Matrix<double,4, Eigen::Dynamic> bb_cluster_confidence(Eigen::Matrix<double,4,20>const& iBB, Eigen::Matrix...
// compute weight norm at::acc_type<cache_t, true> weight_sum_square = 0.0; #pragma unroll kMaxVecsPerThread for (int32_t i = 0; i < kMaxVecsPerThread && 4 * kThreadGroupSize * i + threadIdx.x * 4 < D; ++i) { int32_t d = 4 * kThreadGroupSize * i + threadIdx.x ...
Float dir_sq_len = dir.squaredNorm();if(dir_sq_len ==0.0) {throwRuntimeError("Zero edge encountered."); } VectorF proj = (loop.rowwise() - v0.transpose()) * dir / dir_sq_len;return((proj.array() >0.0).all() && (proj.array() <1.0).all()); ...