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| 1 | +//==----------- element_wise_all_ops_cuda.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: cuda |
| 9 | + |
| 10 | +// RUN: %clangxx -fsycl -fsycl-targets=%sycl_triple -Xsycl-target-backend --cuda-gpu-arch=sm_80 -DSYCL_EXT_ONEAPI_MATRIX=3 %s -o %t.out |
| 11 | +// RUN: %t.out |
| 12 | + |
| 13 | +#include <sycl/sycl.hpp> |
| 14 | + |
| 15 | +using namespace sycl; |
| 16 | +using namespace sycl::ext::oneapi::experimental::matrix; |
| 17 | +using sycl::ext::oneapi::experimental::bfloat16; |
| 18 | + |
| 19 | +#define SG_SZ 32 |
| 20 | +constexpr size_t nWGperDim = 2; |
| 21 | + |
| 22 | +class Logical {}; |
| 23 | + |
| 24 | +template <typename T1, typename T2, size_t M, size_t K, size_t N, typename OP> |
| 25 | +class KernelName; |
| 26 | + |
| 27 | +template <typename T, size_t NUM_ROWS, size_t NUM_COLS> struct big_matrix { |
| 28 | +public: |
| 29 | + T *mat; |
| 30 | + |
| 31 | +public: |
| 32 | + T *get_data() { return mat; } |
| 33 | + void set_data(T *data) { mat = data; } |
| 34 | + big_matrix(T *data) : mat(data) {} |
| 35 | +}; |
| 36 | + |
| 37 | +template <typename T, size_t M, size_t N> |
| 38 | +void assert_ops_ref(T *C, const float ref) { |
| 39 | + for (size_t i = 0; i < M; i++) |
| 40 | + for (size_t j = 0; j < N; j++) { |
| 41 | + auto diff = C[i + j * M] - ref; |
| 42 | + assert(std::fabs(static_cast<float>(diff)) < |
| 43 | + std::numeric_limits<float>::epsilon()); |
| 44 | + } |
| 45 | +} |
| 46 | +template <typename T, typename T2, size_t M, size_t K, size_t N, |
| 47 | + class Operation> |
| 48 | +void matrix_verify_op(queue q, big_matrix<T2, M * nWGperDim, N * nWGperDim> &C, |
| 49 | + nd_range<2> &r, const float ref, Operation Op) { |
| 50 | + { |
| 51 | + buffer<T2, 2> bufC(C.get_data(), range<2>(N * nWGperDim, M * nWGperDim)); |
| 52 | + |
| 53 | + q.submit([&](handler &cgh) { |
| 54 | + accessor<T2, 2, access::mode::read_write, target::device> accC(bufC, |
| 55 | + cgh); |
| 56 | + |
| 57 | + cgh.parallel_for<KernelName<T, T2, M, K, N, Operation>>( |
| 58 | + r, [accC, |
| 59 | + Op](nd_item<2> spmd_item) [[sycl::reqd_sub_group_size(SG_SZ)]] { |
| 60 | + const auto global_idx = spmd_item.get_global_id(0); |
| 61 | + const auto global_idy = spmd_item.get_global_id(1); |
| 62 | + const auto sg_startx = global_idx - spmd_item.get_local_id(0); |
| 63 | + const auto sg_starty = global_idy - spmd_item.get_local_id(1); |
| 64 | + |
| 65 | + auto sg = spmd_item.get_sub_group(); |
| 66 | + |
| 67 | + joint_matrix<T, matrix_use::a, M, K> sub_a; |
| 68 | + joint_matrix<T, matrix_use::b, K, N> sub_b; |
| 69 | + joint_matrix<T2, matrix_use::accumulator, M, N> sub_c; |
| 70 | + |
| 71 | + joint_matrix_fill(sg, sub_a, 3); |
| 72 | + joint_matrix_fill(sg, sub_b, 1); |
| 73 | + joint_matrix_fill(sg, sub_c, -80); |
| 74 | + |
| 75 | + auto wi_slice_a = sub_a.get_wi_data(); |
| 76 | + for (int i = 0; i < wi_slice_a.length(); i++) { |
| 77 | + if constexpr (std::is_same_v<Operation, Logical>) { |
| 78 | + if (wi_slice_a[i]) { |
| 79 | + if (wi_slice_a[i] > 2.0 || wi_slice_a[i] >= 3.0 || |
| 80 | + wi_slice_a[i] < 4.0 || wi_slice_a[i] <= 3.0) { |
| 81 | + T val = (wi_slice_a[i] != (2.0)) ? wi_slice_a[i] |
| 82 | + : static_cast<T>(2.0); |
| 83 | + val = ((val) - (1)); |
| 84 | + val = ((val) + (1)); |
| 85 | + if (wi_slice_a[i] == (2.0)) { |
| 86 | + val = ((val) - (2)); |
| 87 | + val = ((val) * (3)); |
| 88 | + val = ((val) / (2)); |
| 89 | + |
| 90 | + } else { |
| 91 | + val = ((val) + (2)); |
| 92 | + } |
| 93 | + wi_slice_a[i] = val; |
| 94 | + } |
| 95 | + } |
| 96 | + } else { |
| 97 | + wi_slice_a[i] = Op(wi_slice_a[i], 2); |
| 98 | + } |
| 99 | + } |
| 100 | + |
| 101 | + sub_c = joint_matrix_mad(sg, sub_a, sub_b, sub_c); |
| 102 | + |
| 103 | + joint_matrix_store(sg, sub_c, |
| 104 | + accC.get_pointer() + |
| 105 | + (sg_startx * M) * (N * nWGperDim) + |
| 106 | + sg_starty / SG_SZ * N, |
| 107 | + (N * nWGperDim)); |
| 108 | + }); // parallel for |
| 109 | + }).wait(); |
| 110 | + } |
| 111 | + assert_ops_ref<T2, M * nWGperDim, N * nWGperDim>(C.get_data(), ref); |
| 112 | +} |
| 113 | + |
| 114 | +static constexpr size_t MATRIX_M = 16 * nWGperDim; |
| 115 | +static constexpr size_t MATRIX_N = 16 * nWGperDim; |
| 116 | + |
| 117 | +int main() { |
| 118 | + |
| 119 | + float D[MATRIX_M][MATRIX_N]; |
| 120 | + big_matrix<float, MATRIX_M, MATRIX_N> MD_f((float *)&D); |
| 121 | + |
| 122 | + queue q; |
| 123 | + auto computeCapability = |
| 124 | + std::stof(q.get_device().get_info<info::device::backend_version>()); |
| 125 | + nd_range<2> r({nWGperDim, nWGperDim * SG_SZ}, {1, 1 * SG_SZ}); |
| 126 | + |
| 127 | + if (computeCapability >= 7.0) { |
| 128 | + matrix_verify_op<half, float, 16, 16, 16>(q, MD_f, r, 0.0, |
| 129 | + std::plus<half>{}); |
| 130 | + matrix_verify_op<half, float, 16, 16, 16>(q, MD_f, r, 0.0, Logical{}); |
| 131 | + matrix_verify_op<half, float, 16, 16, 16>(q, MD_f, r, 16.0, |
| 132 | + std::multiplies<half>{}); |
| 133 | + matrix_verify_op<half, float, 16, 16, 16>(q, MD_f, r, -56.0, |
| 134 | + std::divides<half>{}); |
| 135 | + matrix_verify_op<half, float, 16, 16, 16>(q, MD_f, r, -64.0, |
| 136 | + std::minus<half>{}); |
| 137 | + } |
| 138 | + |
| 139 | + if (computeCapability >= 7.2) { |
| 140 | + int32_t D_i[MATRIX_M][MATRIX_N]; |
| 141 | + big_matrix<int32_t, MATRIX_M, MATRIX_N> MD_i((int32_t *)&D_i); |
| 142 | + matrix_verify_op<uint8_t, int32_t, 16, 16, 16>(q, MD_i, r, 0, |
| 143 | + std::plus<uint8_t>{}); |
| 144 | + matrix_verify_op<uint8_t, int32_t, 16, 16, 16>(q, MD_i, r, 16, |
| 145 | + std::multiplies<uint8_t>{}); |
| 146 | + matrix_verify_op<uint8_t, int32_t, 16, 16, 16>(q, MD_i, r, -64, |
| 147 | + std::minus<uint8_t>{}); |
| 148 | + matrix_verify_op<int8_t, int32_t, 16, 16, 16>(q, MD_i, r, 0, |
| 149 | + std::plus<int8_t>{}); |
| 150 | + matrix_verify_op<int8_t, int32_t, 16, 16, 16>(q, MD_i, r, 0.0, Logical{}); |
| 151 | + matrix_verify_op<int8_t, int32_t, 16, 16, 16>(q, MD_i, r, 16, |
| 152 | + std::multiplies<int8_t>{}); |
| 153 | + matrix_verify_op<int8_t, int32_t, 16, 16, 16>(q, MD_i, r, -64, |
| 154 | + std::minus<int8_t>{}); |
| 155 | + } |
| 156 | + |
| 157 | + if (computeCapability >= 8.0) { |
| 158 | + |
| 159 | + matrix_verify_op<bfloat16, float, 16, 16, 16>(q, MD_f, r, 0.0, |
| 160 | + std::plus<bfloat16>{}); |
| 161 | + matrix_verify_op<bfloat16, float, 16, 16, 16>(q, MD_f, r, 0.0, Logical{}); |
| 162 | + matrix_verify_op<bfloat16, float, 16, 16, 16>(q, MD_f, r, 16.0, |
| 163 | + std::multiplies<bfloat16>{}); |
| 164 | + matrix_verify_op<bfloat16, float, 16, 16, 16>(q, MD_f, r, -56.0, |
| 165 | + std::divides<bfloat16>{}); |
| 166 | + matrix_verify_op<bfloat16, float, 16, 16, 16>(q, MD_f, r, -64.0, |
| 167 | + std::minus<bfloat16>{}); |
| 168 | + |
| 169 | + double D_d[MATRIX_M / 2][MATRIX_N / 2]; |
| 170 | + big_matrix<double, 8 * nWGperDim, 8 * nWGperDim> MD_d((double *)&D_d); |
| 171 | + |
| 172 | + matrix_verify_op<double, double, 8, 4, 8>(q, MD_d, r, -60.0, |
| 173 | + std::plus<double>{}); |
| 174 | + matrix_verify_op<double, double, 8, 4, 8>(q, MD_d, r, -60.0, Logical{}); |
| 175 | + matrix_verify_op<double, double, 8, 4, 8>(q, MD_d, r, -56.0, |
| 176 | + std::multiplies<double>{}); |
| 177 | + matrix_verify_op<double, double, 8, 4, 8>(q, MD_d, r, -74.0, |
| 178 | + std::divides<double>{}); |
| 179 | + matrix_verify_op<double, double, 8, 4, 8>(q, MD_d, r, -76.0, |
| 180 | + std::minus<double>{}); |
| 181 | + } |
| 182 | + |
| 183 | + return 0; |
| 184 | +} |
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