|
| 1 | +//==-------- joint_matrix_su_int8.cpp - DPC++ joint_matrix------------ ----==// |
| 2 | +// |
| 3 | +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| 4 | +// See https://llvm.org/LICENSE.txt for license information. |
| 5 | +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| 6 | +// |
| 7 | +//===----------------------------------------------------------------------===// |
| 8 | +// REQUIRES: matrix |
| 9 | + |
| 10 | +// RUN: %clangxx -fsycl %s -o %t.out |
| 11 | +// RUN: %CPU_RUN_PLACEHOLDER %t.out |
| 12 | +// RUN: %GPU_RUN_PLACEHOLDER %t.out |
| 13 | + |
| 14 | +#include <CL/sycl.hpp> |
| 15 | +#include <iostream> |
| 16 | + |
| 17 | +using namespace sycl; |
| 18 | +using namespace sycl::ext::oneapi::experimental::matrix; |
| 19 | + |
| 20 | +#define SG_SZ 8 |
| 21 | + |
| 22 | +#define TM 8 |
| 23 | +#define TN SG_SZ |
| 24 | +#define TK 32 |
| 25 | + |
| 26 | +template <typename T, size_t NUM_ROWS, size_t NUM_COLS> struct big_matrix { |
| 27 | +public: |
| 28 | + T *mat; |
| 29 | + |
| 30 | +public: |
| 31 | + T *get_data() { return mat; } |
| 32 | + void set_data(T *data) { mat = data; } |
| 33 | + big_matrix(T *data) : mat(data) {} |
| 34 | +}; |
| 35 | + |
| 36 | +template <typename T1, typename T2, typename T3, size_t NUM_ROWS_A, |
| 37 | + size_t NUM_COLS_A, size_t NUM_ROWS_B, size_t NUM_COLS_B, |
| 38 | + size_t NUM_ROWS_C, size_t NUM_COLS_C> |
| 39 | +void matrix_multiply(big_matrix<T1, NUM_ROWS_C, NUM_COLS_C> &C, |
| 40 | + big_matrix<T2, NUM_ROWS_A, NUM_COLS_A> &A, |
| 41 | + big_matrix<T3, NUM_ROWS_B, NUM_COLS_B> &B) { |
| 42 | + size_t M = NUM_ROWS_C; |
| 43 | + size_t N = NUM_COLS_C; |
| 44 | + size_t K = NUM_COLS_A; |
| 45 | + // B => K/4 x N*4, A => M x K, C => M, N |
| 46 | + // stride should be X's cols, e.g., B's stirde = N*4 |
| 47 | + assert(NUM_ROWS_C == NUM_ROWS_A && NUM_COLS_A == NUM_ROWS_B * 4); |
| 48 | + size_t NDRangeM = M / TM; |
| 49 | + size_t NDRangeN = N / TN; |
| 50 | + buffer<int8_t, 2> bufA(A.get_data(), range<2>(M, K)); |
| 51 | + buffer<uint8_t, 2> bufB(B.get_data(), range<2>(K, N)); |
| 52 | + buffer<int32_t, 2> bufC(C.get_data(), range<2>(M, N)); |
| 53 | + |
| 54 | + queue q; |
| 55 | + q.submit([&](handler &cgh) { |
| 56 | + auto accC = bufC. get_access<access::mode::read_write>(cgh); |
| 57 | + auto accA = bufA. get_access<access::mode::read_write>(cgh); |
| 58 | + auto accB = bufB. get_access<access::mode::read_write>(cgh); |
| 59 | + |
| 60 | + cgh.parallel_for<class imatrix>( |
| 61 | + nd_range<2>({NDRangeM, NDRangeN * SG_SZ}, {1, 1 * SG_SZ}), |
| 62 | + [accA, accB, accC, M, N, K](nd_item<2> spmd_item) |
| 63 | + |
| 64 | + { |
| 65 | + // The submatrix API has to be accessed by all the workitems in a |
| 66 | + // subgroup these functions will be called once by the subgroup no |
| 67 | + // code divergence between the workitems |
| 68 | + const auto global_idx = spmd_item.get_global_id(0); |
| 69 | + const auto global_idy = spmd_item.get_global_id(1); |
| 70 | + const auto sg_startx = global_idx - spmd_item.get_local_id(0); |
| 71 | + const auto sg_starty = global_idy - spmd_item.get_local_id(1); |
| 72 | + |
| 73 | + ext::oneapi::sub_group sg = spmd_item.get_sub_group(); |
| 74 | + joint_matrix<int8_t, TM, TK> sub_a(sg); |
| 75 | + // For B, since current implementation does not support non-packed |
| 76 | + // layout, users need to specify the updated VNNI sizes along with |
| 77 | + // the packed_b layout. By default, the layout is row_major and size |
| 78 | + // is (TK, TN). |
| 79 | + joint_matrix<uint8_t, TK, TN, matrix_layout::packed_b> sub_b(sg); |
| 80 | + joint_matrix<int32_t, TM, TN> sub_c(sg); |
| 81 | + |
| 82 | + // AMX: 8 register tiles : 1k byte size, SMmaxxSKmax =16x64 |
| 83 | + // strideX = X's cols, so strideC = N, strideA = K, strideB = N*4 |
| 84 | + joint_matrix_load(sg, sub_c, |
| 85 | + accC.get_pointer() + (sg_startx * TM) * N + |
| 86 | + sg_starty / SG_SZ * TN, |
| 87 | + N, matrix_layout::row_major); |
| 88 | + for (int k = 0; k < K / TK; k += 1) { |
| 89 | + joint_matrix_load( |
| 90 | + sg, sub_a, accA.get_pointer() + (sg_startx * TM) * K + k * TK, |
| 91 | + K, matrix_layout::packed_a); |
| 92 | + // Assuming B data is already in VNNI format. |
| 93 | + joint_matrix_load(sg, sub_b, |
| 94 | + accB.get_pointer() + (k * TK / 4) * (N * 4) + |
| 95 | + sg_starty / SG_SZ * TN * 4, |
| 96 | + N * 4, matrix_layout::packed_b); |
| 97 | + sub_c = joint_matrix_mad(sg, sub_a, sub_b, sub_c); |
| 98 | + } |
| 99 | + joint_matrix_store(sg, sub_c, |
| 100 | + accC.get_pointer() + (sg_startx * TM) * N + |
| 101 | + sg_starty / SG_SZ * TN, |
| 102 | + N, matrix_layout::row_major); |
| 103 | + }); // parallel for |
| 104 | + }).wait(); |
| 105 | +} |
| 106 | + |
| 107 | +static constexpr size_t MATRIX_M = TM * 2; |
| 108 | +static constexpr size_t MATRIX_N = TN * 2; |
| 109 | +static constexpr size_t MATRIX_K = TK * 2; |
| 110 | +int8_t A[MATRIX_M][MATRIX_K]; |
| 111 | +uint8_t B[MATRIX_K / 4][MATRIX_N * 4]; |
| 112 | +int32_t C[MATRIX_M][MATRIX_N]; |
| 113 | +int32_t D[MATRIX_M][MATRIX_N]; |
| 114 | + |
| 115 | +void matrix_multiply_ref(int32_t *A_mem, int32_t *B_mem, int32_t *C_mem, int M, |
| 116 | + int N, int K) { |
| 117 | + // tiling |
| 118 | + for (int m = 0; m < M; m++) |
| 119 | + for (int n = 0; n < N; n++) { |
| 120 | + for (int k = 0; k < K; k++) { |
| 121 | + char *va = (char *)(A_mem + m * K + k); |
| 122 | + char *vb = (char *)(B_mem + k * N + n); |
| 123 | + int acc = *(C_mem + m * N + n); |
| 124 | + for (int i = 0; i < 4; i++) { |
| 125 | + acc += (va[i] * vb[i]); |
| 126 | + } |
| 127 | + *(C_mem + m * N + n) = acc; |
| 128 | + } |
| 129 | + } |
| 130 | +} |
| 131 | + |
| 132 | +int main() { |
| 133 | + for (int i = 0; i < MATRIX_M; i++) { |
| 134 | + for (int j = 0; j < MATRIX_K; j++) { |
| 135 | + A[i][j] = i + 2 * j; |
| 136 | + } |
| 137 | + } |
| 138 | + for (int i = 0; i < MATRIX_K / 4; i++) { |
| 139 | + for (int j = 0; j < MATRIX_N * 4; j++) { |
| 140 | + B[i][j] = i + j; |
| 141 | + } |
| 142 | + } |
| 143 | + for (int i = 0; i < MATRIX_M; i++) { |
| 144 | + for (int j = 0; j < MATRIX_N; j++) { |
| 145 | + C[i][j] = 1; |
| 146 | + D[i][j] = 1; |
| 147 | + } |
| 148 | + } |
| 149 | + |
| 150 | + big_matrix<int32_t, MATRIX_M, MATRIX_N> MC((int32_t *)&C); |
| 151 | + big_matrix<int32_t, MATRIX_M, MATRIX_N> MD((int32_t *)&D); |
| 152 | + big_matrix<int8_t, MATRIX_M, MATRIX_K> MA((int8_t *)&A); |
| 153 | + big_matrix<uint8_t, MATRIX_K / 4, MATRIX_N * 4> MB((uint8_t *)&B); |
| 154 | + matrix_multiply(MC, MA, MB); |
| 155 | + matrix_multiply_ref((int32_t *)A, (int32_t *)B, (int32_t *)D, MATRIX_M, |
| 156 | + MATRIX_N, MATRIX_K / 4); |
| 157 | + |
| 158 | + bool res = true; |
| 159 | + for (int i = 0; i < MATRIX_M; i++) { |
| 160 | + for (int j = 0; j < MATRIX_N; j++) { |
| 161 | + if (C[i][j] != D[i][j]) |
| 162 | + res = false; |
| 163 | + } |
| 164 | + } |
| 165 | + if (res) |
| 166 | + std::cout << "passed\n"; |
| 167 | + else |
| 168 | + std::cout << "failed\n"; |
| 169 | + for (int i = 0; i < MATRIX_M; i++) { |
| 170 | + for (int j = 0; j < MATRIX_N; j++) |
| 171 | + std::cout << C[i][j] << ", "; |
| 172 | + std::cout << "\n"; |
| 173 | + } |
| 174 | + std::cout << std::endl; |
| 175 | + for (int i = 0; i < MATRIX_M; i++) { |
| 176 | + for (int j = 0; j < MATRIX_N; j++) |
| 177 | + std::cout << D[i][j] << ", "; |
| 178 | + std::cout << "\n"; |
| 179 | + } |
| 180 | +} |
0 commit comments