Updates to CP

external/versions.cmake

@@ -17,8 +17,8 @@ set(TA_TRACKED_MADNESS_PREVIOUS_TAG bd84a52766ab497dedc2f15f2162fb0eb7ec4653)
 set(TA_TRACKED_MADNESS_VERSION 0.10.1)
 set(TA_TRACKED_MADNESS_PREVIOUS_VERSION 0.10.1)
 
-set(TA_TRACKED_BTAS_TAG 62d57d9b1e0c733b4b547bc9cfdd07047159dbca)
-set(TA_TRACKED_BTAS_PREVIOUS_TAG 1cfcb12647c768ccd83b098c64cda723e1275e49)
+set(TA_TRACKED_BTAS_TAG 74dd9277c71043c564f931c9d1f07842548d2349)
+set(TA_TRACKED_BTAS_PREVIOUS_TAG 26646416e5f5829dc13d0d97fb15ae5c01b78e82)
 
 set(TA_TRACKED_LIBRETT_TAG 6eed30d4dd2a5aa58840fe895dcffd80be7fbece)
 set(TA_TRACKED_LIBRETT_PREVIOUS_TAG 354e0ccee54aeb2f191c3ce2c617ebf437e49d83)

src/TiledArray/math/solvers/cp.h

@@ -28,10 +28,12 @@
 
 #include <TiledArray/math/solvers/cp/cp.h>
 #include <TiledArray/math/solvers/cp/cp_als.h>
+#include <TiledArray/math/solvers/cp/cp_thc_als.h>
 #include <TiledArray/math/solvers/cp/cp_reconstruct.h>
 
 namespace TiledArray {
 using TiledArray::math::cp::CP_ALS;
+using TiledArray::math::cp::CP_THC_ALS;
 using TiledArray::math::cp::cp_reconstruct;
 }  // namespace TiledArray
 

src/TiledArray/math/solvers/cp/cp.h

@@ -37,6 +37,56 @@ static inline char intToAlphabet(int i) { return static_cast<char>('a' + i); }
 
 }  // namespace detail
 
+/// normalizes "columns" (aka rows) of an updated factor matrix
+
+/// rows of factor matrices produced by least-squares are not unit
+/// normalized. This takes each row and makes it unit normalized,
+/// with inverse of the normalization factor stored in this->lambda
+/// \param[in,out] factor in: unnormalized factor matrix, out:
+/// normalized factor matrix
+template<typename Array>
+void normalize_factor(Array& factor, Array & lambda) {
+  using Tile = typename Array::value_type;
+  auto& world = factor.world();
+  // this is what the code should look like, but expressions::einsum seems to
+  // be buggy lambda contains squared norms of rows
+  lambda = expressions::einsum(factor("r,n"), factor("r,n"), "r");
+
+  // element-wise square root to convert squared norms to norms
+  TiledArray::foreach_inplace(
+      lambda,
+      [](Tile& tile) {
+        auto lo = tile.range().lobound_data();
+        auto up = tile.range().upbound_data();
+        for (auto R = lo[0]; R < up[0]; ++R) {
+          const auto norm_squared_RR = tile({R});
+          using std::sqrt;
+          tile({R}) = sqrt(norm_squared_RR);
+        }
+      },
+      /* fence = */ true);
+  lambda.truncate();
+  lambda.make_replicated();
+  auto lambda_eig = array_to_eigen(lambda);
+
+  TiledArray::foreach_inplace(
+      factor,
+      [&lambda_eig](Tile& tile) {
+        auto lo = tile.range().lobound_data();
+        auto up = tile.range().upbound_data();
+        for (auto R = lo[0]; R < up[0]; ++R) {
+          const auto lambda_R = lambda_eig(R, 0);
+          if (lambda_R < 1e-12) continue;
+          auto scale_by = 1.0 / lambda_R;
+          for (auto N = lo[1]; N < up[1]; ++N) {
+            tile(R, N) *= scale_by;
+          }
+        }
+      },
+      /* fence = */ true);
+  factor.truncate();
+}
+
 /**
  * This is a base class for the canonical polyadic (CP)
  * decomposition solver. The decomposition, in general,
@@ -91,7 +141,7 @@ class CP {
   /// \returns the fit: \f$ 1.0 - |T_{\text{exact}} - T_{\text{approx}} | \f$
   double compute_rank(size_t rank, size_t rank_block_size = 0,
                       bool build_rank = false, double epsilonALS = 1e-3,
-                      bool verbose = false) {
+                      bool verbose = false, int niters = 100) {
     rank_block_size = (rank_block_size == 0 ? rank : rank_block_size);
     double epsilon = 1.0;
     fit_tol = epsilonALS;
@@ -101,15 +151,15 @@ class CP {
       do {
         rank_trange = TiledRange1::make_uniform(cur_rank, rank_block_size);
         build_guess(cur_rank, rank_trange);
-        ALS(cur_rank, 100, verbose);
+        ALS(cur_rank, niters, verbose);
         ++cur_rank;
       } while (cur_rank < rank);
     } else {
       rank_trange = TiledRange1::make_uniform(rank, rank_block_size);
       build_guess(rank, rank_trange);
-      ALS(rank, 100, verbose);
+      ALS(rank, niters, verbose);
     }
-    return epsilon;
+    return this->final_fit;
   }
 
   /// This function computes the CP decomposition with an
@@ -140,10 +190,14 @@ class CP {
     return epsilon;
   }
 
-  std::vector<Array> get_factor_matrices() {
+  std::vector<Array> get_factor_matrices(bool with_lambda = false) {
     TA_ASSERT(!cp_factors.empty(),
               "CP factor matrices have not been computed)");
     auto result = cp_factors;
+    if(with_lambda){
+      result.emplace_back(lambda);
+      return result;
+    }
     result.pop_back();
     result.emplace_back(unNormalized_Factor);
     return result;
@@ -185,7 +239,8 @@ class CP {
       final_fit,          // The final fit of the ALS
                           // optimization at fixed rank.
       fit_tol,            // Tolerance for the ALS solver
-      norm_reference;     // used in determining the CP fit.
+      norm_reference,     // used in determining the CP fit.
+      norm_ref_sq;
   std::size_t converged_num =
       0;  // How many times the ALS solver
           // has changed less than the tolerance in a row
@@ -259,7 +314,7 @@ class CP {
     // MtKRP);
     try {
       MtKRP = math::linalg::cholesky_solve(W, MtKRP);
-    } catch (std::runtime_error& ex) {
+    } catch (std::exception& ex){
       // if W is near-singular try LU instead of Cholesky
       if (std::string(ex.what()).find("lapack::posv failed") !=
           std::string::npos) {
@@ -370,16 +425,16 @@ class CP {
       for (size_t i = 1; i < ndim - 1; ++i, ++gram_ptr) {
         W("r,rp") *= (*gram_ptr)("r,rp");
       }
-      auto result = sqrt(W("r,rp").dot(
-          (unNormalized_Factor("r,n") * unNormalized_Factor("rp,n"))));
+      auto result = W("r,rp").dot(
+          (unNormalized_Factor("r,n") * unNormalized_Factor("rp,n")));
       // not sure why need to fence here, but hang periodically without it
       W.world().gop.fence();
       return result;
     };
     // compute the error in the loss function and find the fit
     const auto norm_cp = factor_norm();  // ||T_CP||_2
-    const auto squared_norm_error = norm_reference * norm_reference +
-                                    norm_cp * norm_cp -
+    const auto squared_norm_error = norm_ref_sq +
+                                    norm_cp -
                                     2.0 * ref_dot_cp;  // ||T - T_CP||_2^2
     // N.B. squared_norm_error is very noisy
     // TA_ASSERT(squared_norm_error >= - 1e-8);

src/TiledArray/math/solvers/cp/cp_als.h

@@ -69,6 +69,12 @@ class CP_ALS : public CP<Tile, Policy> {
     first_gemm_dim_last.pop_back();
 
     this->norm_reference = norm2(tref);
+    this->norm_ref_sq = this->norm_reference * this->norm_reference;
+  }
+
+  void set_factor_matrices(std::vector<DistArray<Tile, Policy>> & factors){
+    cp_factors = factors;
+    factors_set = true;
   }
 
  protected:
@@ -77,6 +83,7 @@ class CP_ALS : public CP<Tile, Policy> {
   std::string ref_indices, first_gemm_dim_one, first_gemm_dim_last;
   std::vector<typename Tile::value_type> lambda;
   TiledRange1 rank_trange1;
+  bool factors_set = false;
 
   /// This function constructs the initial CP facotr matrices
   /// stores them in CP::cp_factors vector.
@@ -93,7 +100,10 @@ class CP_ALS : public CP<Tile, Policy> {
             rank_trange, reference.trange().data()[i]);
         cp_factors.emplace_back(factor);
       }
-    } else {
+    }else if(factors_set){
+      // Do nothing and don't throw an error.
+    }
+    else {
       TA_EXCEPTION("Currently no implementation to increase or change rank");
     }