gemm.hpp

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// Copyright (c) 2019--present, The Simons Foundation
// This file is part of TRIQS/nda and is licensed under the Apache License, Version 2.0.
// SPDX-License-Identifier: Apache-2.0
// See LICENSE in the root of this distribution for details.

 
#pragma once
 
#include "./interface/cxx_interface.hpp"
#include "./tools.hpp"
#include "../concepts.hpp"
#include "../layout_transforms.hpp"
#include "../macros.hpp"
#include "../mem/address_space.hpp"
#include "../traits.hpp"
 
#ifndef NDA_HAVE_DEVICE
#include "../device.hpp"
#endif
 
#include <tuple>
#include <utility>
 
namespace nda::blas {


  template <Matrix A, Matrix B, MemoryMatrix C>
  void gemm_generic(typename A::value_type alpha, A const &a, B const &b, typename A::value_type beta,
                    C &&c) { // NOLINT (temporary views are allowed here)
    EXPECTS(a.extent(1) == b.extent(0));
    EXPECTS(a.extent(0) == c.extent(0));
    EXPECTS(b.extent(1) == c.extent(1));
    for (int i = 0; i < a.extent(0); ++i) {
      for (int j = 0; j < b.extent(1); ++j) {
        c(i, j) = beta * c(i, j);
        for (int k = 0; k < a.extent(1); ++k) c(i, j) += alpha * a(i, k) * b(k, j);
      }
    }
  }

  template <Matrix A, Matrix B, MemoryMatrix C>
    requires((MemoryMatrix<A> or is_conj_array_expr<A>) and (MemoryMatrix<B> or is_conj_array_expr<B>)
             and have_same_value_type_v<A, B, C> and is_blas_lapack_v<get_value_t<A>>)
  void gemm(get_value_t<A> alpha, A const &a, B const &b, get_value_t<A> beta, C &&c) {
    // get underlying matrix in case it is given as a lazy expression
    auto to_mat = []<typename Z>(Z const &z) -> auto & {
      if constexpr (is_conj_array_expr<Z>)
        return std::get<0>(z.a);
      else
        return z;
    };
    auto &mat_a = to_mat(a);
    auto &mat_b = to_mat(b);
 
    // compile-time checks
    using mat_a_type = decltype(mat_a);
    using mat_b_type = decltype(mat_b);
    static_assert(mem::have_compatible_addr_space<mat_a_type, mat_b_type, C>, "Error in nda::blas::gemm: Incompatible memory address spaces");
 
    // runtime checks
    EXPECTS(mat_a.extent(1) == mat_b.extent(0));
    EXPECTS(mat_a.extent(0) == c.extent(0));
    EXPECTS(mat_b.extent(1) == c.extent(1));
    EXPECTS(mat_a.indexmap().min_stride() == 1);
    EXPECTS(mat_b.indexmap().min_stride() == 1);
    EXPECTS(c.indexmap().min_stride() == 1);
 
    // c is in C order: compute the transpose of the product in Fortran order
    if constexpr (has_C_layout<C>) {
      gemm(alpha, transpose(b), transpose(a), beta, transpose(std::forward<C>(c)));
    } else { // c is in Fortran order
      static constexpr bool conj_A = is_conj_array_expr<A>;
      static constexpr bool conj_B = is_conj_array_expr<B>;
      char op_a                    = get_op<conj_A, /* transpose = */ has_C_layout<mat_a_type>>;
      char op_b                    = get_op<conj_B, /* transpose = */ has_C_layout<mat_b_type>>;
      auto [m, k]                  = mat_a.shape();
      auto n                       = mat_b.extent(1);
 
      if constexpr (mem::have_device_compatible_addr_space<mat_a_type, mat_b_type, C>) {
#if defined(NDA_HAVE_DEVICE)
        device::gemm(op_a, op_b, m, n, k, alpha, mat_a.data(), get_ld(mat_a), mat_b.data(), get_ld(mat_b), beta, c.data(), get_ld(c));
#else
        compile_error_no_gpu();
#endif
      } else {
        f77::gemm(op_a, op_b, m, n, k, alpha, mat_a.data(), get_ld(mat_a), mat_b.data(), get_ld(mat_b), beta, c.data(), get_ld(c));
      }
    }
  }

 
} // namespace nda::blas