| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2022 Intel Corporation |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #ifndef GEMM_KERNEL_H |
| #define GEMM_KERNEL_H |
| |
| #include <x86intrin.h> |
| #include <immintrin.h> |
| #include <type_traits> |
| |
| #include "../../InternalHeaderCheck.h" |
| |
| #define SECOND_FETCH (32) |
| #if (EIGEN_COMP_GNUC_STRICT != 0) && !defined(EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS) |
| // Use less registers to load A elements to workaround compiler spills. Loose a |
| // bit of performance (less than ~2%). |
| #define EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS |
| #endif |
| |
| |
| namespace Eigen { |
| namespace internal { |
| |
| template <typename Scalar, bool is_unit_inc> |
| class gemm_class |
| { |
| using vec = typename packet_traits<Scalar>::type; |
| using vec_ymm = typename unpacket_traits<vec>::half; |
| using vec_xmm = typename unpacket_traits<vec_ymm>::half; |
| using umask_t = typename unpacket_traits<vec>::mask_t; |
| |
| static constexpr bool is_f32 = sizeof(Scalar) == sizeof(float); |
| static constexpr bool is_f64 = sizeof(Scalar) == sizeof(double); |
| |
| #ifndef EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS |
| static constexpr bool use_less_a_regs = !is_unit_inc; |
| #else |
| static constexpr bool use_less_a_regs = true; |
| #endif |
| #ifndef EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_B_REGS |
| static constexpr bool use_less_b_regs = !is_unit_inc; |
| #else |
| static constexpr bool use_less_b_regs = true; |
| #endif |
| |
| static constexpr int a_regs[] = {0, 1, 2, |
| use_less_a_regs ? 0 : 3, |
| use_less_a_regs ? 1 : 4, |
| use_less_a_regs ? 2 : 5 |
| }; |
| static constexpr int b_regs[] = {6, |
| use_less_b_regs ? 6 : 7 |
| }; |
| static constexpr int c_regs[] = { |
| 8 , 16, 24, |
| 9 , 17, 25, |
| 10, 18, 26, |
| 11, 19, 27, |
| 12, 20, 28, |
| 13, 21, 29, |
| 14, 22, 30, |
| 15, 23, 31, |
| }; |
| |
| static constexpr int alpha_load_reg = 0; |
| static constexpr int c_load_regs[] = {1, 2, 6}; |
| |
| static constexpr int a_shift = 128; |
| static constexpr int b_shift = 128; |
| |
| static constexpr int nelems_in_cache_line = is_f32 ? 16 : 8; |
| static constexpr int a_prefetch_size = nelems_in_cache_line * 2; |
| static constexpr int b_prefetch_size = nelems_in_cache_line * 8; |
| |
| vec zmm[32]; |
| umask_t mask; |
| |
| // gemm arguments. |
| Index m; |
| const Index n, k, ldc; |
| const Index inc; |
| const Scalar *alpha; |
| |
| const Scalar *a, *b; |
| Scalar *c; |
| |
| const bool is_alpha1; |
| const bool is_beta0; |
| |
| const Index a_stride, b_stride; |
| const Index a_off, b_off; |
| |
| static EIGEN_ALWAYS_INLINE constexpr int div_up(int a, int b) { |
| return a == 0 ? 0 : (a - 1) / b + 1; |
| } |
| |
| EIGEN_ALWAYS_INLINE void prefetch_a(const Scalar *a_addr) |
| { |
| _mm_prefetch((char *) (a_prefetch_size + a_addr - a_shift), _MM_HINT_T0); |
| } |
| |
| EIGEN_ALWAYS_INLINE void prefetch_b(const Scalar *b_addr) |
| { |
| _mm_prefetch((char *) (b_prefetch_size + b_addr - b_shift), _MM_HINT_T0); |
| } |
| |
| EIGEN_ALWAYS_INLINE void prefetch_x(const Scalar *x_addr) |
| { |
| _mm_prefetch((char *) (x_addr - a_shift), _MM_HINT_T2); |
| } |
| |
| EIGEN_ALWAYS_INLINE void prefetch_c(const Scalar *c_addr) |
| { |
| #if defined(__PRFCHW__) && __PRFCHW__ == 1 |
| _m_prefetchw((void *) c_addr); |
| #else |
| _mm_prefetch((char *) c_addr, _MM_HINT_T0); |
| #endif |
| } |
| |
| template <int nelems> |
| EIGEN_ALWAYS_INLINE void a_load(vec &a_reg, const Scalar *a_addr) |
| { |
| switch (nelems * sizeof(*a_addr) * 8) { |
| default: |
| case 512 * 3: a_reg = ploadu<vec>(a_addr); break; |
| case 512 * 2: a_reg = ploadu<vec>(a_addr); break; |
| case 512 * 1: a_reg = ploadu<vec>(a_addr); break; |
| case 256 * 1: a_reg = preinterpret<vec>(_mm512_broadcast_f64x4(ploadu<Packet4d>(reinterpret_cast<const double *>(a_addr)))); break; |
| case 128 * 1: a_reg = preinterpret<vec>(_mm512_broadcast_f32x4(ploadu<Packet4f>(reinterpret_cast<const float *>(a_addr)))); break; |
| case 64 * 1: a_reg = preinterpret<vec>(pload1<Packet8d>(reinterpret_cast<const double *>(a_addr))); break; |
| case 32 * 1: a_reg = pload1<vec>(a_addr); break; |
| } |
| } |
| |
| EIGEN_ALWAYS_INLINE void b_load(vec &b_reg, const Scalar *b_addr) |
| { |
| b_reg = pload1<vec>(b_addr); |
| } |
| |
| template <int nelems> |
| EIGEN_ALWAYS_INLINE void c_store(Scalar *mem, vec &src) |
| { |
| if (is_unit_inc) { |
| switch (nelems * sizeof(*mem) * 8) { |
| default: |
| case 512 * 3: pstoreu(mem, src); break; |
| case 512 * 2: pstoreu(mem, src); break; |
| case 512 * 1: pstoreu(mem, src); break; |
| case 256 * 1: pstoreu(mem, preinterpret<vec_ymm>(src)); break; |
| case 128 * 1: pstoreu(mem, preinterpret<vec_xmm>(src)); break; |
| case 64 * 1: pstorel(mem, preinterpret<vec_xmm>(src)); break; |
| case 32 * 1: pstores(mem, preinterpret<vec_xmm>(src)); break; |
| } |
| } else { |
| switch (nelems * sizeof(*mem) * 8) { |
| default: |
| case 512 * 3: pscatter(mem, src, inc); break; |
| case 512 * 2: pscatter(mem, src, inc); break; |
| case 512 * 1: pscatter(mem, src, inc); break; |
| case 256 * 1: pscatter(mem, src, inc, mask); break; |
| case 128 * 1: pscatter(mem, src, inc, mask); break; |
| case 64 * 1: pscatter(mem, src, inc, mask); break; |
| case 32 * 1: pscatter(mem, src, inc, mask); break; |
| } |
| } |
| } |
| |
| template <int nelems> |
| EIGEN_ALWAYS_INLINE void vaddm(vec &dst, const Scalar *mem, vec &src, vec ®) |
| { |
| if (is_unit_inc) { |
| switch (nelems * sizeof(*mem) * 8) { |
| default: |
| case 512 * 3: dst = padd(src, ploadu<vec>(mem)); break; |
| case 512 * 2: dst = padd(src, ploadu<vec>(mem)); break; |
| case 512 * 1: dst = padd(src, ploadu<vec>(mem)); break; |
| case 256 * 1: dst = preinterpret<vec>(padd(preinterpret<vec_ymm>(src), ploadu<vec_ymm>(mem))); break; |
| case 128 * 1: dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadu<vec_xmm>(mem))); break; |
| case 64 * 1: dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem))); break; |
| case 32 * 1: dst = preinterpret<vec>(padds(preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem))); break; |
| } |
| } else { |
| // Zero out scratch register |
| reg = pzero(reg); |
| |
| switch (nelems * sizeof(*mem) * 8) { |
| default: |
| case 512 * 3: reg = pgather<Scalar, vec>(mem, inc); dst = padd(src, reg); break; |
| case 512 * 2: reg = pgather<Scalar, vec>(mem, inc); dst = padd(src, reg); break; |
| case 512 * 1: reg = pgather<Scalar, vec>(mem, inc); dst = padd(src, reg); break; |
| case 256 * 1: reg = preinterpret<vec>(pgather<Scalar, vec_ymm>(mem, inc)); dst = preinterpret<vec>(padd(preinterpret<vec_ymm>(src), preinterpret<vec_ymm>(reg))); break; |
| case 128 * 1: reg = preinterpret<vec>(pgather<Scalar, vec_xmm>(mem, inc)); dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg))); break; |
| case 64 * 1: if (is_f32) { |
| reg = pgather(reg, mem, inc, mask); |
| dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg))); |
| } else { |
| dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem))); |
| } |
| break; |
| case 32 * 1: dst = preinterpret<vec>(padds(preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem))); break; |
| } |
| } |
| } |
| |
| EIGEN_STRONG_INLINE void vfmadd(vec &dst, const vec &src1, const vec &src2) { |
| dst = pmadd(src1, src2, dst); |
| |
| #if (EIGEN_COMP_GNUC != 0) || (EIGEN_COMP_CLANG != 0) |
| // Workaround register spills for gcc and clang |
| __asm__ ("#" : [dst] "+v" (dst) : [src1] "%v" (src1), [src2] "v" (src2)); |
| #endif |
| } |
| |
| template <int nelems> |
| EIGEN_ALWAYS_INLINE void vfmaddm(vec &dst, const Scalar *mem, vec &src, vec &scale, vec ®) |
| { |
| if (is_unit_inc) { |
| switch (nelems * sizeof(*mem) * 8) { |
| default: |
| case 512 * 3: dst = pmadd(scale, src, ploadu<vec>(mem)); break; |
| case 512 * 2: dst = pmadd(scale, src, ploadu<vec>(mem)); break; |
| case 512 * 1: dst = pmadd(scale, src, ploadu<vec>(mem)); break; |
| case 256 * 1: dst = preinterpret<vec>(pmadd(preinterpret<vec_ymm>(scale), preinterpret<vec_ymm>(src), ploadu<vec_ymm>(mem))); break; |
| case 128 * 1: dst = preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadu<vec_xmm>(mem))); break; |
| case 64 * 1: dst = preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem))); break; |
| case 32 * 1: dst = preinterpret<vec>(pmadds(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem))); break; |
| } |
| } else { |
| // Zero out scratch register |
| reg = pzero(reg); |
| |
| switch (nelems * sizeof(*mem) * 8) { |
| default: |
| case 512 * 3: reg = pgather<Scalar, vec>(mem, inc); dst = pmadd(scale, src, reg); break; |
| case 512 * 2: reg = pgather<Scalar, vec>(mem, inc); dst = pmadd(scale, src, reg); break; |
| case 512 * 1: reg = pgather<Scalar, vec>(mem, inc); dst = pmadd(scale, src, reg); break; |
| case 256 * 1: reg = preinterpret<vec>(pgather<Scalar, vec_ymm>(mem, inc)); dst = preinterpret<vec>(pmadd(preinterpret<vec_ymm>(scale), preinterpret<vec_ymm>(src), preinterpret<vec_ymm>(reg))); break; |
| case 128 * 1: reg = preinterpret<vec>(pgather<Scalar, vec_xmm>(mem, inc)); dst = preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg))); break; |
| case 64 * 1: if (is_f32) { |
| reg = pgather(reg, mem, inc, mask); |
| dst = preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg))); |
| } else { |
| dst = preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem))); |
| } |
| break; |
| case 32 * 1: dst = preinterpret<vec>(pmadds(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem))); break; |
| } |
| } |
| } |
| |
| template <int j, int endX, int i, int endY, int nelems> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(j > endX) || (i > endY)> |
| a_loads(const Scalar *ao) |
| { |
| EIGEN_UNUSED_VARIABLE(ao); |
| } |
| |
| template <int j, int endX, int i, int endY, int nelems> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(j <= endX) && (i <= endY)> |
| a_loads(const Scalar *ao) |
| { |
| if (j < endX) { |
| if (i < endY) { |
| auto &a_reg = zmm[a_regs[i + (j % 2) * 3]]; |
| const Scalar *a_addr = ao + nelems * j + nelems_in_cache_line * i - a_shift; |
| a_load<nelems>(a_reg, a_addr); |
| |
| a_loads<j, endX, i + 1, endY, nelems>(ao); |
| } else { |
| a_loads<j + 1, endX, 0, endY, nelems>(ao); |
| } |
| } |
| } |
| |
| template <int un, int max_b_unroll, int i, int um_vecs, int a_unroll, int b_unroll> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(un > max_b_unroll) || (i > um_vecs)> |
| prefetch_cs(const Scalar *co1, const Scalar *co2) |
| { |
| EIGEN_UNUSED_VARIABLE(co1); |
| EIGEN_UNUSED_VARIABLE(co2); |
| } |
| |
| /* C prefetch loop structure. |
| * for (int un = 0; un < 8; un++) { |
| * if (b_unroll >= un + 1) { |
| * if (un == 4) co2 = co1 + 4 * ldc; |
| * |
| * for (int i = 0; i < um_vecs; i++) { |
| * Scalar *co = (un + 1 <= 4) ? co1 : co2; |
| * auto co_off = (un % 4) * ldc + a_unroll - 1 + i * nelems_in_cache_line * sizeof *co; |
| * prefetch_c(co + co_off); |
| * } |
| * } |
| * } |
| */ |
| |
| template <int un, int max_b_unroll, int i, int um_vecs, int a_unroll, int b_unroll> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(un <= max_b_unroll) && (i <= um_vecs)> |
| prefetch_cs(Scalar *&co1, Scalar *&co2) |
| { |
| if (un < max_b_unroll) { |
| |
| if (b_unroll >= un + 1) { |
| if (un == 4 && i == 0) co2 = co1 + 4 * ldc; |
| |
| if (i < um_vecs) { |
| Scalar *co = (un + 1 <= 4) ? co1 : co2; |
| auto co_off = (un % 4) * ldc + a_unroll - 1 + i * nelems_in_cache_line * sizeof *co; |
| prefetch_c(co + co_off); |
| |
| prefetch_cs<un, max_b_unroll, i + 1, um_vecs, a_unroll, b_unroll>(co1, co2); |
| } else { |
| prefetch_cs<un + 1, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2); |
| } |
| |
| } else { |
| prefetch_cs<un + 1, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2); |
| } |
| } |
| } |
| |
| // load_c |
| template <int i, int um_vecs, int idx, int nelems> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(i > um_vecs)> |
| scale_load_c(const Scalar *cox, vec &alpha_reg) |
| { |
| EIGEN_UNUSED_VARIABLE(cox); |
| EIGEN_UNUSED_VARIABLE(alpha_reg); |
| } |
| |
| template <int i, int um_vecs, int idx, int nelems> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(i <= um_vecs)> |
| scale_load_c(const Scalar *cox, vec &alpha_reg) |
| { |
| if (i < um_vecs) { |
| auto &c_reg = zmm[c_regs[i + idx * 3]]; |
| auto &c_load_reg = zmm[c_load_regs[i % 3]]; |
| auto c_mem = cox; |
| if (is_unit_inc) |
| c_mem += i * nelems_in_cache_line; |
| else |
| c_mem += i * nelems_in_cache_line * inc; |
| |
| |
| if (!is_beta0 && is_alpha1) |
| vaddm<nelems>(c_reg, c_mem, c_reg, c_load_reg); |
| else if (!is_beta0 && !is_alpha1) |
| vfmaddm<nelems>(c_reg, c_mem, c_reg, alpha_reg, c_load_reg); |
| else if (is_beta0 && !is_alpha1) |
| c_reg = pmul(alpha_reg, c_reg); |
| |
| scale_load_c<i + 1, um_vecs, idx, nelems>(cox, alpha_reg); |
| } |
| } |
| |
| // store_c |
| template <int i, int um_vecs, int idx, int nelems> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(i > um_vecs)> |
| write_c(Scalar *cox) |
| { |
| EIGEN_UNUSED_VARIABLE(cox); |
| } |
| |
| template <int i, int um_vecs, int idx, int nelems> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(i <= um_vecs)> |
| write_c(Scalar *cox) |
| { |
| if (i < um_vecs) { |
| auto &c_reg = zmm[c_regs[i + idx * 3]]; |
| auto c_mem = cox; |
| if (is_unit_inc) |
| c_mem += i * nelems_in_cache_line; |
| else |
| c_mem += i * nelems_in_cache_line * inc; |
| |
| c_store<nelems>(c_mem, c_reg); |
| c_reg = pzero(c_reg); |
| |
| write_c<i + 1, um_vecs, idx, nelems>(cox); |
| } |
| } |
| |
| /* C update loop structure. |
| * co2 = co1 + ldc; |
| * |
| * auto &alpha_reg = zmm[alpha_load_reg]; |
| * if (!is_alpha1) alpha_reg = pload1<vec>(alpha); |
| * |
| * int idx = 0; |
| * for (pow = 1; pow <= 8; pow <<= 1) { |
| * |
| * if (b_unroll >= pow) { |
| * for (count = 1; count < (pow + 1) / 2 + 1; count++) { |
| * if (pow >= 4) co2 += ldc; |
| * |
| * const Scalar *cox = (idx == 0) ? co1 : co2; |
| * |
| * const int um_vecs = div_up(a_unroll, nelems_in_cache_line); |
| * scale_load_c<0, um_vecs, idx, a_unroll>(cox, alpha_reg); |
| * write_c<0, um_vecs, idx, a_unroll>(cox); |
| * |
| * idx++; |
| * } |
| * } |
| * } |
| * |
| * if (b_unroll == 1) |
| * co1 += ldc; |
| * else |
| * co1 = co2 + ldc; |
| */ |
| |
| template <int pow, int a_unroll, int idx> |
| EIGEN_ALWAYS_INLINE void c_update_1count(Scalar *&cox) |
| { |
| if (pow >= 4) cox += ldc; |
| |
| const int um_vecs = div_up(a_unroll, nelems_in_cache_line); |
| auto &alpha_reg = zmm[alpha_load_reg]; |
| |
| scale_load_c<0, um_vecs, idx, a_unroll>(cox, alpha_reg); |
| write_c<0, um_vecs, idx, a_unroll>(cox); |
| } |
| |
| template <int pow, int a_unroll> |
| EIGEN_ALWAYS_INLINE void c_update_1pow(Scalar *&co1, Scalar *&co2) |
| { |
| constexpr int idx = pow / 2; |
| Scalar *&cox = idx == 0 ? co1 : co2; |
| |
| constexpr int max_count = (pow + 1) / 2; |
| static_assert(max_count <= 4, "Unsupported max_count."); |
| |
| if (1 <= max_count) c_update_1count<pow, a_unroll, idx + 0>(cox); |
| if (2 <= max_count) c_update_1count<pow, a_unroll, idx + 1>(cox); |
| if (3 <= max_count) c_update_1count<pow, a_unroll, idx + 2>(cox); |
| if (4 <= max_count) c_update_1count<pow, a_unroll, idx + 3>(cox); |
| } |
| |
| template <int max_b_unroll, int a_unroll, int b_unroll> |
| EIGEN_ALWAYS_INLINE void c_update(Scalar *&co1, Scalar *&co2) |
| { |
| auto &alpha_reg = zmm[alpha_load_reg]; |
| |
| co2 = co1 + ldc; |
| if (!is_alpha1) alpha_reg = pload1<vec>(alpha); |
| if (!is_unit_inc && a_unroll < nelems_in_cache_line) |
| mask = (umask_t)(1 << a_unroll) - 1; |
| |
| static_assert(max_b_unroll <= 8, "Unsupported max_b_unroll"); |
| |
| if (1 <= max_b_unroll && 1 <= b_unroll) c_update_1pow<1, a_unroll>(co1, co2); |
| if (2 <= max_b_unroll && 2 <= b_unroll) c_update_1pow<2, a_unroll>(co1, co2); |
| if (4 <= max_b_unroll && 4 <= b_unroll) c_update_1pow<4, a_unroll>(co1, co2); |
| if (8 <= max_b_unroll && 8 <= b_unroll) c_update_1pow<8, a_unroll>(co1, co2); |
| |
| if (b_unroll == 1) |
| co1 += ldc; |
| else |
| co1 = co2 + ldc; |
| } |
| |
| // compute |
| template <int um, int um_vecs, int idx, int uk, bool fetch_x, bool ktail> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(um > um_vecs)> |
| compute(const Scalar *ao, const Scalar *bo, int &fetchA_idx, int &fetchB_idx, vec &b_reg) |
| { |
| EIGEN_UNUSED_VARIABLE(ao); |
| EIGEN_UNUSED_VARIABLE(bo); |
| EIGEN_UNUSED_VARIABLE(fetchA_idx); |
| EIGEN_UNUSED_VARIABLE(fetchB_idx); |
| EIGEN_UNUSED_VARIABLE(b_reg); |
| } |
| |
| template <int um, int um_vecs, int idx, int uk, bool fetch_x, bool ktail> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(um <= um_vecs)> |
| compute(const Scalar *ao, const Scalar *bo, int &fetchA_idx, int &fetchB_idx, vec &b_reg) |
| { |
| if (um < um_vecs) { |
| auto &c_reg = zmm[c_regs[um + idx * 3]]; |
| auto &a_reg = zmm[a_regs[um + (uk % 2) * 3]]; |
| |
| vfmadd(c_reg, a_reg, b_reg); |
| |
| if (!fetch_x && um == 0 && (((idx == 0 || idx == 6) && (uk % 2 == 0 || is_f64 || ktail)) || (idx == 3 && (uk % 2 == 1 || is_f64 || ktail)))) { |
| prefetch_a(ao + nelems_in_cache_line * fetchA_idx); |
| fetchA_idx++; |
| } |
| |
| if (um == 0 && idx == 1 && (uk % 2 == 0 || is_f64 || ktail)) { |
| prefetch_b(bo + nelems_in_cache_line * fetchB_idx); |
| fetchB_idx++; |
| } |
| |
| compute<um + 1, um_vecs, idx, uk, fetch_x, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg); |
| } |
| } |
| |
| // load_a |
| template <int um, int um_vecs, int uk, int nelems, bool ktail> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(um > um_vecs)> |
| load_a(const Scalar *ao) |
| { |
| EIGEN_UNUSED_VARIABLE(ao); |
| } |
| |
| template <int um, int um_vecs, int uk, int nelems, bool ktail> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(um <= um_vecs)> |
| load_a(const Scalar *ao) |
| { |
| if (um < um_vecs) { |
| auto &a_reg = zmm[a_regs[um + (uk % 2) * 3]]; |
| const Scalar *a_addr = ao |
| + nelems * (1 + !ktail * !use_less_a_regs + uk) |
| + nelems_in_cache_line * um - a_shift; |
| a_load<nelems>(a_reg, a_addr); |
| |
| load_a<um + 1, um_vecs, uk, nelems, ktail>(ao); |
| } |
| } |
| template<int uk, int pow, int count, int um_vecs, int b_unroll, bool ktail, bool fetch_x, bool c_fetch> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(count > (pow + 1) / 2)> |
| innerkernel_1pow(const Scalar *&aa, const Scalar * const &ao, const Scalar * const &bo, Scalar *&co2, int &fetchA_idx, int &fetchB_idx) |
| { |
| EIGEN_UNUSED_VARIABLE(aa); |
| EIGEN_UNUSED_VARIABLE(ao); |
| EIGEN_UNUSED_VARIABLE(bo); |
| EIGEN_UNUSED_VARIABLE(co2); |
| EIGEN_UNUSED_VARIABLE(fetchA_idx); |
| EIGEN_UNUSED_VARIABLE(fetchB_idx); |
| } |
| |
| template<int uk, int pow, int count, int um_vecs, int b_unroll, bool ktail, bool fetch_x, bool c_fetch> |
| EIGEN_ALWAYS_INLINE std::enable_if_t<(count <= (pow + 1) / 2)> |
| innerkernel_1pow(const Scalar *&aa, const Scalar * const &ao, const Scalar * const &bo, Scalar *&co2, int &fetchA_idx, int &fetchB_idx) |
| { |
| const int idx = (pow / 2) + count; |
| |
| if (count < (pow + 1) / 2) { |
| auto &b_reg = zmm[b_regs[idx % 2]]; |
| |
| if (fetch_x && uk == 3 && idx == 0) prefetch_x(aa); |
| if (fetch_x && uk == 3 && idx == 4) aa += 8; |
| |
| if (b_unroll >= pow) { |
| |
| compute<0, um_vecs, idx, uk, fetch_x, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg); |
| |
| const Scalar *b_addr = bo + b_unroll * uk + idx + 1 |
| + (b_unroll > 1) * !use_less_b_regs - b_shift; |
| b_load(b_reg, b_addr); |
| } |
| |
| // Go to the next count. |
| innerkernel_1pow<uk, pow, count + 1, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| |
| } else { |
| // Maybe prefetch C data after count-loop. |
| if (pow == 2 && c_fetch) { |
| if (uk % 3 == 0 && uk > 0) { |
| co2 += ldc; |
| } else { |
| prefetch_c(co2 + (uk % 3) * nelems_in_cache_line); |
| } |
| } |
| } |
| } |
| |
| template<int uk, int max_b_unroll, int a_unroll, int b_unroll, bool ktail, bool fetch_x, bool c_fetch> |
| EIGEN_ALWAYS_INLINE void innerkernel_1uk(const Scalar *&aa, const Scalar * const &ao, const Scalar * const &bo, Scalar *&co2, int &fetchA_idx, int &fetchB_idx) |
| { |
| const int um_vecs = div_up(a_unroll, nelems_in_cache_line); |
| |
| if (max_b_unroll >= 1) innerkernel_1pow<uk, 1, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| if (max_b_unroll >= 2) innerkernel_1pow<uk, 2, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| if (max_b_unroll >= 4) innerkernel_1pow<uk, 4, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| if (max_b_unroll >= 8) innerkernel_1pow<uk, 8, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| |
| // Load A after pow-loop. |
| load_a<0, um_vecs, uk, a_unroll, ktail>(ao); |
| } |
| |
| /* Inner kernel loop structure. |
| * for (int uk = 0; uk < kfactor; uk++) { |
| * int idx = 0; |
| * |
| * for (pow = 1; pow < max_b_unroll << 1; pow <<= 1) { |
| * for (int count = 0; count < (pow + 1) / 2; count++) { |
| * auto &b_reg = zmm[b_regs[idx % 2]]; |
| * |
| * if (fetch_x && uk == 3 && idx == 0) prefetch_x(aa); |
| * if (fetch_x && uk == 3 && idx == 4) aa += 8; |
| * |
| * if (b_unroll >= pow) { |
| * compute<0, um_vecs, idx, uk, fetchx, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg); |
| * |
| * const Scalar *b_addr = bo + b_unroll * uk + idx + 1 + (b_unroll > 1) - b_shift ; |
| * b_load(b_reg, b_addr); |
| * } |
| * idx++; |
| * } |
| * |
| * Maybe prefetch C data. |
| * if (pow == 2 && c_fetch) { |
| * if (uk % 3 == 0 && uk > 0) { |
| * co2 += ldc; |
| * } else { |
| * prefetch_c(co2 + (uk % 3) * nelems_in_cache_line); |
| * } |
| * } |
| * } |
| * |
| * Load A. |
| * load_a<0, um_vecs, uk, ktail, a_unroll>(ao); |
| * } |
| * |
| * Advance A/B pointers after uk-loop. |
| * ao += a_unroll * kfactor; |
| * bo += b_unroll * kfactor; |
| */ |
| |
| template <int a_unroll, int b_unroll, int k_factor, int max_b_unroll, int max_k_factor, bool c_fetch> |
| EIGEN_ALWAYS_INLINE void innerkernel(const Scalar *&aa, const Scalar *&ao, const Scalar *&bo, Scalar *&co2) |
| { |
| int fetchA_idx = 0; |
| int fetchB_idx = 0; |
| |
| const bool fetch_x = k_factor == max_k_factor; |
| const bool ktail = k_factor == 1; |
| |
| static_assert(k_factor <= 4 && k_factor > 0, |
| "innerkernel maximum k_factor supported is 4"); |
| |
| if (k_factor > 0) innerkernel_1uk<0, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| if (k_factor > 1) innerkernel_1uk<1, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| if (k_factor > 2) innerkernel_1uk<2, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| if (k_factor > 3) innerkernel_1uk<3, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx); |
| |
| // Advance A/B pointers after uk-loop. |
| ao += a_unroll * k_factor; |
| bo += b_unroll * k_factor; |
| } |
| |
| |
| template <int a_unroll, int b_unroll, int max_b_unroll> |
| EIGEN_ALWAYS_INLINE void kloop(const Scalar *&aa, const Scalar *&ao, const Scalar *&bo, Scalar *&co1, Scalar *&co2) |
| { |
| const int um_vecs = div_up(a_unroll, nelems_in_cache_line); |
| if (!use_less_a_regs) |
| a_loads<0, 2, 0, um_vecs, a_unroll>(ao); |
| else |
| a_loads<0, 1, 0, um_vecs, a_unroll>(ao); |
| |
| b_load(zmm[b_regs[0]], bo - b_shift + 0); |
| if (!use_less_b_regs) |
| b_load(zmm[b_regs[1]], bo - b_shift + 1); |
| |
| #ifndef SECOND_FETCH |
| prefetch_cs<0, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2); |
| #endif // SECOND_FETCH |
| |
| // Unrolling k-loop by a factor of 4. |
| const int max_k_factor = 4; |
| Index loop_count = k / max_k_factor; |
| |
| if (loop_count > 0) { |
| #ifdef SECOND_FETCH |
| loop_count -= SECOND_FETCH; |
| #endif |
| while (loop_count > 0) { |
| innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2); |
| loop_count--; |
| } |
| #ifdef SECOND_FETCH |
| co2 = co1 + nelems_in_cache_line - 1; |
| |
| loop_count += b_unroll; |
| while (loop_count > 0) { |
| innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 1>(aa, ao, bo, co2); |
| loop_count--; |
| } |
| |
| loop_count += SECOND_FETCH - b_unroll; |
| while (loop_count > 0) { |
| innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2); |
| loop_count--; |
| } |
| #endif |
| } |
| |
| // k-loop remainder handling. |
| loop_count = k % max_k_factor; |
| while (loop_count > 0) { |
| innerkernel<a_unroll, b_unroll, 1, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2); |
| loop_count--; |
| } |
| |
| // Update C matrix. |
| c_update<max_b_unroll, a_unroll, b_unroll>(co1, co2); |
| } |
| |
| template <int a_unroll, int b_unroll, int max_b_unroll> |
| EIGEN_ALWAYS_INLINE void nloop(const Scalar *&aa, const Scalar *&ao, const Scalar *&bo, Scalar *&co1, Scalar *&co2) |
| { |
| // Set A matrix pointer. |
| ao = a + a_off * a_unroll; |
| |
| // Set B matrix pointer if needed. |
| bo += b_unroll * b_off; |
| |
| kloop<a_unroll, b_unroll, max_b_unroll>(aa, ao, bo, co1, co2); |
| |
| // Advance B matrix pointer if needed. |
| bo += b_unroll * (b_stride - k - b_off); |
| |
| // Advance prefetch A pointer. |
| aa += 16; |
| } |
| |
| template <int a_unroll, int max_a_unroll, int max_b_unroll> |
| EIGEN_ALWAYS_INLINE void mloop(const Scalar *&ao, const Scalar *&bo, Scalar *&co1, Scalar *&co2) |
| { |
| // Set prefetch A pointers. |
| const Scalar *aa = a + a_unroll * a_stride; |
| |
| // Set C matrix pointers. |
| co1 = c; |
| if (a_unroll >= max_a_unroll) co2 = c + 2 * ldc; |
| if (is_unit_inc) |
| c += a_unroll; |
| else |
| c += a_unroll * inc; |
| |
| // Set B matrix pointer. |
| bo = b; |
| |
| // Main n-loop. |
| for (Index i = n / max_b_unroll; i > 0; i--) |
| nloop<a_unroll, max_b_unroll, max_b_unroll>(aa, ao, bo, co1, co2); |
| |
| // n-remainders. |
| if (n & 4 && max_b_unroll > 4) nloop<a_unroll, 4, max_b_unroll>(aa, ao, bo, co1, co2); |
| #if 0 |
| if (n & 2 && max_b_unroll > 2) nloop<a_unroll, 2, max_b_unroll>(aa, ao, bo, co1, co2); |
| if (n & 1 && max_b_unroll > 1) nloop<a_unroll, 1, max_b_unroll>(aa, ao, bo, co1, co2); |
| #else |
| // Copy kernels don't support tails of n = 2 for single/double precision. |
| // Loop over ones. |
| int n_rem = 2 * ((n & 2) != 0) + 1 * ((n & 1) != 0); |
| while (n_rem > 0) {nloop<a_unroll, 1, max_b_unroll>(aa, ao, bo, co1, co2); n_rem--;} |
| #endif |
| |
| // Advance A matrix pointer. |
| a = ao + a_unroll * (a_stride - k - a_off); |
| } |
| |
| public: |
| // Compute kernel unrolling C matrix by max_a_unroll x max_b_unroll. |
| template <int max_a_unroll, int max_b_unroll> |
| EIGEN_ALWAYS_INLINE void compute_kern() |
| { |
| a -= -a_shift; |
| b -= -b_shift; |
| |
| const Scalar *ao = nullptr; |
| const Scalar *bo = nullptr; |
| Scalar *co1 = nullptr; |
| Scalar *co2 = nullptr; |
| |
| // Main m-loop. |
| for (; m >= max_a_unroll; m -= max_a_unroll) |
| mloop<max_a_unroll, max_a_unroll, max_b_unroll>(ao, bo, co1, co2); |
| |
| // m-remainders. |
| if (m & 32 && max_a_unroll > 32) mloop<32, max_a_unroll, max_b_unroll>(ao, bo, co1, co2); |
| if (m & 16 && max_a_unroll > 16) mloop<16, max_a_unroll, max_b_unroll>(ao, bo, co1, co2); |
| if (m & 8 && max_a_unroll > 8) mloop< 8, max_a_unroll, max_b_unroll>(ao, bo, co1, co2); |
| if (m & 4 && max_a_unroll > 4) mloop< 4, max_a_unroll, max_b_unroll>(ao, bo, co1, co2); |
| if (m & 2 && max_a_unroll > 2 && is_f64) mloop< 2, max_a_unroll, max_b_unroll>(ao, bo, co1, co2); |
| if (m & 1 && max_a_unroll > 1 && is_f64) mloop< 1, max_a_unroll, max_b_unroll>(ao, bo, co1, co2); |
| |
| // Copy kernels don't support tails of m = 2 for single precision. |
| // Loop over ones. |
| if (is_f32) { |
| int m_rem = 2 * ((m & 2) != 0) + 1 * ((m & 1) != 0); |
| while (m_rem > 0) {mloop< 1, max_a_unroll, max_b_unroll>(ao, bo, co1, co2); m_rem--;} |
| } |
| } |
| |
| gemm_class(Index m_, Index n_, Index k_, Index ldc_, Index inc_, |
| const Scalar *alpha_, |
| const Scalar *a_, const Scalar *b_, Scalar *c_, |
| bool is_alpha1_, bool is_beta0_, |
| Index a_stride_, Index b_stride_, |
| Index a_off_, Index b_off_) |
| : m(m_) |
| , n(n_) |
| , k(k_) |
| , ldc(ldc_) |
| , inc(inc_) |
| , alpha(alpha_) |
| , a(a_) |
| , b(b_) |
| , c(c_) |
| , is_alpha1(is_alpha1_) |
| , is_beta0(is_beta0_) |
| , a_stride(a_stride_) |
| , b_stride(b_stride_) |
| , a_off(a_off_) |
| , b_off(b_off_) |
| { |
| // Zero out all accumulation registers. |
| zmm[8 ] = pzero(zmm[8 ]); |
| zmm[9 ] = pzero(zmm[9 ]); |
| zmm[10] = pzero(zmm[10]); |
| zmm[11] = pzero(zmm[11]); |
| zmm[12] = pzero(zmm[12]); |
| zmm[13] = pzero(zmm[13]); |
| zmm[14] = pzero(zmm[14]); |
| zmm[15] = pzero(zmm[15]); |
| zmm[16] = pzero(zmm[16]); |
| zmm[17] = pzero(zmm[17]); |
| zmm[18] = pzero(zmm[18]); |
| zmm[19] = pzero(zmm[19]); |
| zmm[20] = pzero(zmm[20]); |
| zmm[21] = pzero(zmm[21]); |
| zmm[22] = pzero(zmm[22]); |
| zmm[23] = pzero(zmm[23]); |
| zmm[24] = pzero(zmm[24]); |
| zmm[25] = pzero(zmm[25]); |
| zmm[26] = pzero(zmm[26]); |
| zmm[27] = pzero(zmm[27]); |
| zmm[28] = pzero(zmm[28]); |
| zmm[29] = pzero(zmm[29]); |
| zmm[30] = pzero(zmm[30]); |
| zmm[31] = pzero(zmm[31]); |
| } |
| }; |
| |
| // Compute kernel with max unroll support of: |
| // Single precision: |
| // max_a_unroll: 48, 32, 16, 8, 4, 2, 1 |
| // max_b_unroll: 8, 4, 2, 1 |
| // Double precision: |
| // max_a_unroll: 24, 16, 8, 4, 2, 1 |
| // max_b_unroll: 8, 4, 2, 1 |
| template <typename Scalar, int max_a_unroll, int max_b_unroll, bool is_alpha1, bool is_beta0, bool is_unit_inc> |
| EIGEN_DONT_INLINE void gemm_kern_avx512( |
| Index m, Index n, Index k, |
| Scalar *alpha, const Scalar *a, const Scalar *b, Scalar *c, |
| Index ldc, Index inc = 1, |
| Index a_stride = -1, Index b_stride = -1, |
| Index a_off = 0, Index b_off = 0) |
| { |
| if (a_stride == -1) a_stride = k; |
| if (b_stride == -1) b_stride = k; |
| |
| gemm_class<Scalar, is_unit_inc> g(m, n, k, ldc, inc, alpha, |
| a, b, c, is_alpha1, is_beta0, a_stride, b_stride, a_off, b_off); |
| g.template compute_kern<max_a_unroll, max_b_unroll>(); |
| } |
| |
| template<bool ConjLhs_, bool ConjRhs_, int PacketSize_> |
| class gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Target, PacketSize_> : |
| public gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_> { |
| using Base = gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_>; |
| |
| public: |
| enum {nr = Base::Vectorizable ? 8 : 4}; |
| }; |
| |
| template<bool ConjLhs_, bool ConjRhs_, int PacketSize_> |
| class gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Target, PacketSize_> : |
| public gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_> { |
| using Base = gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_>; |
| |
| public: |
| enum {nr = Base::Vectorizable ? 8 : 4}; |
| }; |
| |
| template<typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode> |
| struct gemm_pack_rhs<Scalar, Index, DataMapper, 8, ColMajor, Conjugate, PanelMode> |
| { |
| typedef typename packet_traits<Scalar>::type Packet; |
| typedef typename DataMapper::LinearMapper LinearMapper; |
| enum { PacketSize = packet_traits<Scalar>::size }; |
| EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0); |
| }; |
| |
| template<typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode> |
| EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, 8, ColMajor, Conjugate, PanelMode> |
| ::operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) |
| { |
| constexpr int nr = 8; |
| EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR"); |
| EIGEN_UNUSED_VARIABLE(stride); |
| EIGEN_UNUSED_VARIABLE(offset); |
| eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); |
| conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; |
| Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0; |
| Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0; |
| Index count = 0; |
| const Index peeled_k = (depth/PacketSize)*PacketSize; |
| if(nr>=8) |
| { |
| for(Index j2=0; j2<packet_cols8; j2+=8) |
| { |
| // skip what we have before |
| if(PanelMode) count += 8 * offset; |
| const LinearMapper dm0 = rhs.getLinearMapper(0, j2+0); |
| const LinearMapper dm1 = rhs.getLinearMapper(0, j2+1); |
| const LinearMapper dm2 = rhs.getLinearMapper(0, j2+2); |
| const LinearMapper dm3 = rhs.getLinearMapper(0, j2+3); |
| const LinearMapper dm4 = rhs.getLinearMapper(0, j2+4); |
| const LinearMapper dm5 = rhs.getLinearMapper(0, j2+5); |
| const LinearMapper dm6 = rhs.getLinearMapper(0, j2+6); |
| const LinearMapper dm7 = rhs.getLinearMapper(0, j2+7); |
| Index k=0; |
| if((PacketSize%8)==0) // TODO enable vectorized transposition for PacketSize==4 |
| { |
| for(; k<peeled_k; k+=PacketSize) { |
| PacketBlock<Packet,(PacketSize%8)==0?8:PacketSize> kernel; |
| |
| kernel.packet[0] = dm0.template loadPacket<Packet>(k); |
| kernel.packet[1] = dm1.template loadPacket<Packet>(k); |
| kernel.packet[2] = dm2.template loadPacket<Packet>(k); |
| kernel.packet[3] = dm3.template loadPacket<Packet>(k); |
| kernel.packet[4] = dm4.template loadPacket<Packet>(k); |
| kernel.packet[5] = dm5.template loadPacket<Packet>(k); |
| kernel.packet[6] = dm6.template loadPacket<Packet>(k); |
| kernel.packet[7] = dm7.template loadPacket<Packet>(k); |
| |
| ptranspose(kernel); |
| |
| pstoreu(blockB+count+0*PacketSize, cj.pconj(kernel.packet[0])); |
| pstoreu(blockB+count+1*PacketSize, cj.pconj(kernel.packet[1%PacketSize])); |
| pstoreu(blockB+count+2*PacketSize, cj.pconj(kernel.packet[2%PacketSize])); |
| pstoreu(blockB+count+3*PacketSize, cj.pconj(kernel.packet[3%PacketSize])); |
| pstoreu(blockB+count+4*PacketSize, cj.pconj(kernel.packet[4%PacketSize])); |
| pstoreu(blockB+count+5*PacketSize, cj.pconj(kernel.packet[5%PacketSize])); |
| pstoreu(blockB+count+6*PacketSize, cj.pconj(kernel.packet[6%PacketSize])); |
| pstoreu(blockB+count+7*PacketSize, cj.pconj(kernel.packet[7%PacketSize])); |
| count+=8*PacketSize; |
| } |
| } |
| for(; k<depth; k++) |
| { |
| blockB[count+0] = cj(dm0(k)); |
| blockB[count+1] = cj(dm1(k)); |
| blockB[count+2] = cj(dm2(k)); |
| blockB[count+3] = cj(dm3(k)); |
| blockB[count+4] = cj(dm4(k)); |
| blockB[count+5] = cj(dm5(k)); |
| blockB[count+6] = cj(dm6(k)); |
| blockB[count+7] = cj(dm7(k)); |
| count += 8; |
| } |
| // skip what we have after |
| if(PanelMode) count += 8 * (stride-offset-depth); |
| } |
| } |
| |
| if(nr>=4) |
| { |
| for(Index j2=packet_cols8; j2<packet_cols4; j2+=4) |
| { |
| // skip what we have before |
| if(PanelMode) count += 4 * offset; |
| const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0); |
| const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1); |
| const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2); |
| const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3); |
| |
| Index k=0; |
| if((PacketSize%4)==0) // TODO enable vectorized transposition for PacketSize==2 ?? |
| { |
| for(; k<peeled_k; k+=PacketSize) { |
| PacketBlock<Packet,(PacketSize%4)==0?4:PacketSize> kernel; |
| kernel.packet[0 ] = dm0.template loadPacket<Packet>(k); |
| kernel.packet[1%PacketSize] = dm1.template loadPacket<Packet>(k); |
| kernel.packet[2%PacketSize] = dm2.template loadPacket<Packet>(k); |
| kernel.packet[3%PacketSize] = dm3.template loadPacket<Packet>(k); |
| ptranspose(kernel); |
| pstoreu(blockB+count+0*PacketSize, cj.pconj(kernel.packet[0])); |
| pstoreu(blockB+count+1*PacketSize, cj.pconj(kernel.packet[1%PacketSize])); |
| pstoreu(blockB+count+2*PacketSize, cj.pconj(kernel.packet[2%PacketSize])); |
| pstoreu(blockB+count+3*PacketSize, cj.pconj(kernel.packet[3%PacketSize])); |
| count+=4*PacketSize; |
| } |
| } |
| for(; k<depth; k++) |
| { |
| blockB[count+0] = cj(dm0(k)); |
| blockB[count+1] = cj(dm1(k)); |
| blockB[count+2] = cj(dm2(k)); |
| blockB[count+3] = cj(dm3(k)); |
| count += 4; |
| } |
| // skip what we have after |
| if(PanelMode) count += 4 * (stride-offset-depth); |
| } |
| } |
| |
| // copy the remaining columns one at a time (nr==1) |
| for(Index j2=packet_cols4; j2<cols; ++j2) |
| { |
| if(PanelMode) count += offset; |
| const LinearMapper dm0 = rhs.getLinearMapper(0, j2); |
| for(Index k=0; k<depth; k++) |
| { |
| blockB[count] = cj(dm0(k)); |
| count += 1; |
| } |
| if(PanelMode) count += (stride-offset-depth); |
| } |
| } |
| |
| template<typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode> |
| struct gemm_pack_rhs<Scalar, Index, DataMapper, 8, RowMajor, Conjugate, PanelMode> |
| { |
| typedef typename packet_traits<Scalar>::type Packet; |
| typedef typename unpacket_traits<Packet>::half HalfPacket; |
| typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket; |
| typedef typename DataMapper::LinearMapper LinearMapper; |
| enum { PacketSize = packet_traits<Scalar>::size, |
| HalfPacketSize = unpacket_traits<HalfPacket>::size, |
| QuarterPacketSize = unpacket_traits<QuarterPacket>::size}; |
| EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0) |
| { |
| constexpr int nr = 8; |
| EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR"); |
| EIGEN_UNUSED_VARIABLE(stride); |
| EIGEN_UNUSED_VARIABLE(offset); |
| eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); |
| const bool HasHalf = (int)HalfPacketSize < (int)PacketSize; |
| const bool HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize; |
| conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; |
| Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0; |
| Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0; |
| Index count = 0; |
| |
| if(nr>=8) |
| { |
| for(Index j2=0; j2<packet_cols8; j2+=8) |
| { |
| // skip what we have before |
| if(PanelMode) count += 8 * offset; |
| for(Index k=0; k<depth; k++) |
| { |
| if (PacketSize==8) { |
| // Packet A = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2]); |
| Packet A = rhs.template loadPacket<Packet>(k, j2); |
| pstoreu(blockB+count, cj.pconj(A)); |
| } else if (HasHalf && HalfPacketSize==8) { |
| HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2); |
| pstoreu(blockB+count, cj.pconj(A)); |
| } else if (HasQuarter && QuarterPacketSize==8) { |
| QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2); |
| pstoreu(blockB+count, cj.pconj(A)); |
| } else if (PacketSize==4) { |
| // Packet A = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2]); |
| // Packet B = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2 + PacketSize]); |
| Packet A = rhs.template loadPacket<Packet>(k, j2); |
| Packet B = rhs.template loadPacket<Packet>(k, j2 + PacketSize); |
| pstoreu(blockB+count, cj.pconj(A)); |
| pstoreu(blockB+count+PacketSize, cj.pconj(B)); |
| } else { |
| // const Scalar* b0 = &rhs.data()[k*rhs.stride() + j2]; |
| const LinearMapper dm0 = rhs.getLinearMapper(k, j2); |
| blockB[count+0] = cj(dm0(0)); |
| blockB[count+1] = cj(dm0(1)); |
| blockB[count+2] = cj(dm0(2)); |
| blockB[count+3] = cj(dm0(3)); |
| blockB[count+4] = cj(dm0(4)); |
| blockB[count+5] = cj(dm0(5)); |
| blockB[count+6] = cj(dm0(6)); |
| blockB[count+7] = cj(dm0(7)); |
| } |
| count += 8; |
| } |
| // skip what we have after |
| if(PanelMode) count += 8 * (stride-offset-depth); |
| } |
| } |
| |
| if(nr>=4) |
| { |
| for(Index j2=packet_cols8; j2<packet_cols4; j2+=4) |
| { |
| // skip what we have before |
| if(PanelMode) count += 4 * offset; |
| for(Index k=0; k<depth; k++) |
| { |
| if (PacketSize==4) { |
| Packet A = rhs.template loadPacket<Packet>(k, j2); |
| pstoreu(blockB+count, cj.pconj(A)); |
| count += PacketSize; |
| } else if (HasHalf && HalfPacketSize==4) { |
| HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2); |
| pstoreu(blockB+count, cj.pconj(A)); |
| count += HalfPacketSize; |
| } else if (HasQuarter && QuarterPacketSize==4) { |
| QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2); |
| pstoreu(blockB+count, cj.pconj(A)); |
| count += QuarterPacketSize; |
| } else { |
| const LinearMapper dm0 = rhs.getLinearMapper(k, j2); |
| blockB[count+0] = cj(dm0(0)); |
| blockB[count+1] = cj(dm0(1)); |
| blockB[count+2] = cj(dm0(2)); |
| blockB[count+3] = cj(dm0(3)); |
| count += 4; |
| } |
| } |
| // skip what we have after |
| if(PanelMode) count += 4 * (stride-offset-depth); |
| } |
| } |
| // copy the remaining columns one at a time (nr==1) |
| for(Index j2=packet_cols4; j2<cols; ++j2) |
| { |
| if(PanelMode) count += offset; |
| for(Index k=0; k<depth; k++) |
| { |
| blockB[count] = cj(rhs(k, j2)); |
| count += 1; |
| } |
| if(PanelMode) count += stride-offset-depth; |
| } |
| } |
| }; |
| |
| template<typename Scalar, typename Index, typename DataMapper, int mr, bool ConjugateLhs, bool ConjugateRhs> |
| struct gebp_kernel<Scalar, Scalar, Index, DataMapper, mr, 8, ConjugateLhs, ConjugateRhs> |
| { |
| EIGEN_ALWAYS_INLINE |
| void operator()(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, |
| Index rows, Index depth, Index cols, Scalar alpha, |
| Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0); |
| }; |
| |
| template<typename Scalar, typename Index, typename DataMapper, int mr, bool ConjugateLhs, bool ConjugateRhs> |
| EIGEN_ALWAYS_INLINE |
| void gebp_kernel<Scalar,Scalar,Index,DataMapper,mr,8,ConjugateLhs,ConjugateRhs> |
| ::operator()(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, |
| Index rows, Index depth, Index cols, Scalar alpha, |
| Index strideA, Index strideB, Index offsetA, Index offsetB) |
| { |
| if (res.incr() == 1) { |
| if (alpha == 1) { |
| gemm_kern_avx512<Scalar, mr, 8, true, false, true>(rows, cols, depth, |
| &alpha, blockA, blockB, (Scalar *)res.data(), res.stride(), |
| res.incr(), strideA, strideB, offsetA, offsetB); |
| } else { |
| gemm_kern_avx512<Scalar, mr, 8, false, false, true>(rows, cols, depth, |
| &alpha, blockA, blockB, (Scalar *)res.data(), |
| res.stride(), res.incr(), strideA, strideB, offsetA, offsetB); |
| } |
| } else { |
| if (alpha == 1) { |
| gemm_kern_avx512<Scalar, mr, 8, true, false, false>(rows, cols, depth, |
| &alpha, blockA, blockB, (Scalar *)res.data(), res.stride(), |
| res.incr(), strideA, strideB, offsetA, offsetB); |
| } else { |
| gemm_kern_avx512<Scalar, mr, 8, false, false, false>(rows, cols, depth, |
| &alpha, blockA, blockB, (Scalar *)res.data(), res.stride(), |
| res.incr(), strideA, strideB, offsetA, offsetB); |
| } |
| } |
| } |
| |
| } // namespace Eigen |
| } // namespace internal |
| |
| #endif // GEMM_KERNEL_H |
