| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | // | ||
| 2 | // SPDX-FileCopyrightText: Copyright 2024-2025 Arm Limited and/or its affiliates <open-source-office@arm.com> | ||
| 3 | // | ||
| 4 | // SPDX-License-Identifier: Apache-2.0 | ||
| 5 | // | ||
| 6 | |||
| 7 | #include <gtest/gtest.h> | ||
| 8 | |||
| 9 | #include <array> | ||
| 10 | #include <cstddef> | ||
| 11 | #include <cstdint> | ||
| 12 | #include <cstdlib> | ||
| 13 | #include <functional> | ||
| 14 | #include <limits> | ||
| 15 | #include <random> | ||
| 16 | #include <sstream> | ||
| 17 | #include <string> | ||
| 18 | #include <string_view> | ||
| 19 | |||
| 20 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp1vlx8_qsi4cxp4vlx8_1vlx4vl_sme2_mopa.h" | ||
| 21 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi4cxp4vlx4_1x4vl_sme2_sdot.h" | ||
| 22 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi4cxp4x4_1x4_neon_dotprod.h" | ||
| 23 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp1x8_qsi4cxp4x8_1x4x32_neon_dotprod.h" | ||
| 24 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp1x8_qsi4cxp8x8_1x8x32_neon_dotprod.h" | ||
| 25 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp4x4_qsi4cxp8x4_8x8x32_neon_dotprod.h" | ||
| 26 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x4_16x4x32_neon_dotprod.h" | ||
| 27 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x8_4x4x32_neon_i8mm.h" | ||
| 28 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x8_8x4x32_neon_i8mm.h" | ||
| 29 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp8x8_4x8x32_neon_i8mm.h" | ||
| 30 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp8x8_8x8x32_neon_i8mm.h" | ||
| 31 | #include "kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4cxp/kai_matmul_clamp_f32_qai8dxp_qsi4cxp_interface.h" | ||
| 32 | #include "kai/ukernels/matmul/pack/kai_lhs_quant_pack_qai8dxp_f32.h" | ||
| 33 | #include "kai/ukernels/matmul/pack/kai_rhs_pack_kxn_qsi4cxp_qs4cxs1s0.h" | ||
| 34 | #include "kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4cxp_qs4cxs1s0.h" | ||
| 35 | #include "kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon.h" | ||
| 36 | #include "test/common/buffer.hpp" | ||
| 37 | #include "test/common/compare.hpp" | ||
| 38 | #include "test/common/cpu_info.hpp" | ||
| 39 | #include "test/common/int4.hpp" | ||
| 40 | #include "test/common/matmul_test_common.hpp" | ||
| 41 | #include "test/common/matrix_portion.hpp" | ||
| 42 | #include "test/common/memory.hpp" | ||
| 43 | #include "test/common/round.hpp" | ||
| 44 | #include "test/common/test_suite.hpp" | ||
| 45 | #include "test/reference/cast.hpp" | ||
| 46 | #include "test/reference/fill.hpp" | ||
| 47 | #include "test/reference/matmul.hpp" | ||
| 48 | #include "test/reference/pad.hpp" | ||
| 49 | #include "test/reference/quantize.hpp" | ||
| 50 | #include "test/reference/transpose.hpp" | ||
| 51 | |||
| 52 | namespace kai::test { | ||
| 53 | /// Matrix multiplication test information. | ||
| 54 | |||
| 55 | enum class RhsPackType { NxK, KxN }; | ||
| 56 | |||
| 57 | using ukernel_rhs_pack_function = std::function<decltype(kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0)>; | ||
| 58 | using ukernel_get_rhs_packed_size = std::function<decltype(kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0)>; | ||
| 59 | using ukernel_get_rhs_packed_offset = std::function<decltype(kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0)>; | ||
| 60 | using ukernel_get_rhs_offset = std::function<decltype(kai_get_rhs_offset_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon)>; | ||
| 61 | |||
| 62 | template <typename T> | ||
| 63 | struct UkernelVariantCustom : public UkernelVariant<T> { | ||
| 64 | ukernel_rhs_pack_function run_rhs_pack; | ||
| 65 | ukernel_get_rhs_packed_size get_rhs_packed_size; | ||
| 66 | ukernel_get_rhs_packed_offset get_rhs_packed_offset; | ||
| 67 | ukernel_get_rhs_offset get_rhs_offset; | ||
| 68 | RhsPackType rhs_pack_type; | ||
| 69 | |||
| 70 | UkernelVariantCustom() = delete; | ||
| 71 | |||
| 72 | 40 | UkernelVariantCustom( | |
| 73 | T interface, std::string_view name, const std::function<bool(void)>& fn_is_supported, | ||
| 74 | ukernel_rhs_pack_function run_rhs_pack, ukernel_get_rhs_packed_size get_rhs_packed_size, | ||
| 75 | ukernel_get_rhs_packed_offset get_rhs_packed_offset, ukernel_get_rhs_offset get_rhs_offset, | ||
| 76 | const RhsPackType pack_type) : | ||
| 77 | 20 | UkernelVariant<T>(interface, name, fn_is_supported), | |
| 78 | 20 | run_rhs_pack(std::move(run_rhs_pack)), | |
| 79 | 20 | get_rhs_packed_size(std::move(get_rhs_packed_size)), | |
| 80 | 20 | get_rhs_packed_offset(std::move(get_rhs_packed_offset)), | |
| 81 | 20 | get_rhs_offset(std::move(get_rhs_offset)), | |
| 82 | 40 | rhs_pack_type(pack_type) { | |
| 83 | 40 | } | |
| 84 | }; | ||
| 85 | |||
| 86 | 1 | static const std::array<UkernelVariantCustom<kai_matmul_clamp_f32_qai8dxp_qsi4cxp_ukernel>, 20> | |
| 87 |
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1 | variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp = { |
| 88 |
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21 | {{UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp1vlx8_qsi4cxp4vlx8_1vlx4vl_sme2_mopa), |
| 89 |
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1 | "kai_matmul_clamp_f32_qai8dxp1vlx8_qsi4cxp4vlx8_1vlx4vl_sme2_mopa__RHS_NxK__", cpu_has_sme2, |
| 90 |
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1 | kai_run_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon, |
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1 | kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon, |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon, |
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1 | kai_get_rhs_offset_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon, RhsPackType::NxK}, |
| 94 | |||
| 95 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp1x4_qsi4cxp4vlx4_1x4vl_sme2_sdot), |
| 96 |
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1 | "kai_matmul_clamp_f32_qai8dxp1x4_qsi4cxp4vlx4_1x4vl_sme2_sdot__RHS_NxK__", cpu_has_sme2, |
| 97 |
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1 | kai_run_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon, |
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1 | kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon, |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon, |
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1 | kai_get_rhs_offset_rhs_pack_nxk_qsi4cxps1s0_qsu4cxs1s0_neon, RhsPackType::NxK}, |
| 101 | |||
| 102 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp1x4_qsi4cxp4x4_1x4_neon_dotprod), |
| 103 |
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1 | "kai_matmul_clamp_f32_qai8dxp1x4_qsi4cxp4x4_1x4_neon_dotprod__RHS_NxK__", cpu_has_dotprod, |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 105 |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 106 | RhsPackType::NxK}, | ||
| 107 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp1x4_qsi4cxp4x4_1x4_neon_dotprod), |
| 108 |
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1 | "kai_matmul_clamp_f32_qai8dxp1x4_qsi4cxp4x4_1x4_neon_dotprod__RHS_KxN__", cpu_has_dotprod, |
| 109 |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 110 |
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1 | kai_get_rhs_packed_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 111 | RhsPackType::KxN}, | ||
| 112 | |||
| 113 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp1x8_qsi4cxp4x8_1x4x32_neon_dotprod), |
| 114 |
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1 | "kai_matmul_clamp_f32_qai8dxp1x8_qsi4cxp4x8_1x4x32_neon_dotprod__RHS_NxK__", cpu_has_dotprod, |
| 115 |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 116 |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 117 | RhsPackType::NxK}, | ||
| 118 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp1x8_qsi4cxp4x8_1x4x32_neon_dotprod), |
| 119 |
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1 | "kai_matmul_clamp_f32_qai8dxp1x8_qsi4cxp4x8_1x4x32_neon_dotprod__RHS_KxN__", cpu_has_dotprod, |
| 120 |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 121 |
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1 | kai_get_rhs_packed_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 122 | RhsPackType::KxN}, | ||
| 123 | |||
| 124 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp1x8_qsi4cxp8x8_1x8x32_neon_dotprod), |
| 125 |
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1 | "kai_matmul_clamp_f32_qai8dxp1x8_qsi4cxp8x8_1x8x32_neon_dotprod__RHS_NxK__", cpu_has_dotprod, |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 128 | RhsPackType::NxK}, | ||
| 129 | |||
| 130 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp1x8_qsi4cxp8x8_1x8x32_neon_dotprod), |
| 131 |
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1 | "kai_matmul_clamp_f32_qai8dxp1x8_qsi4cxp8x8_1x8x32_neon_dotprod__RHS_KxN__", cpu_has_dotprod, |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 133 |
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1 | kai_get_rhs_packed_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 134 | RhsPackType::KxN}, | ||
| 135 | |||
| 136 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp4x4_16x4x32_neon_dotprod), |
| 137 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x4_16x4x32_neon_dotprod__RHS_NxK__", cpu_has_dotprod, |
| 138 |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 139 |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 140 | RhsPackType::NxK}, | ||
| 141 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp4x4_16x4x32_neon_dotprod), |
| 142 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x4_16x4x32_neon_dotprod__RHS_KxN__", cpu_has_dotprod, |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 144 |
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1 | kai_get_rhs_packed_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 145 | RhsPackType::KxN}, | ||
| 146 | |||
| 147 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x4_qsi4cxp8x4_8x8x32_neon_dotprod), |
| 148 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x4_qsi4cxp8x4_8x8x32_neon_dotprod__RHS_NxK__", cpu_has_dotprod, |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 150 |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 151 | RhsPackType::NxK}, | ||
| 152 | |||
| 153 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x4_qsi4cxp8x4_8x8x32_neon_dotprod), |
| 154 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x4_qsi4cxp8x4_8x8x32_neon_dotprod__RHS_KxN__", cpu_has_dotprod, |
| 155 |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
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| 157 | RhsPackType::KxN}, | ||
| 158 | |||
| 159 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp4x8_4x4x32_neon_i8mm), |
| 160 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x8_4x4x32_neon_i8mm__RHS_NxK__", cpu_has_i8mm, |
| 161 |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 163 | RhsPackType::NxK}, | ||
| 164 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp4x8_4x4x32_neon_i8mm), |
| 165 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x8_4x4x32_neon_i8mm__RHS_KxN__", cpu_has_i8mm, |
| 166 |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 167 |
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1 | kai_get_rhs_packed_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 168 | RhsPackType::KxN}, | ||
| 169 | |||
| 170 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp4x8_8x4x32_neon_i8mm), |
| 171 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x8_8x4x32_neon_i8mm__RHS_NxK__", cpu_has_i8mm, |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 174 | RhsPackType::NxK}, | ||
| 175 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp4x8_8x4x32_neon_i8mm), |
| 176 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp4x8_8x4x32_neon_i8mm__RHS_KxN__", cpu_has_i8mm, |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 178 |
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1 | kai_get_rhs_packed_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 179 | RhsPackType::KxN}, | ||
| 180 | |||
| 181 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp8x8_4x8x32_neon_i8mm), |
| 182 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp8x8_4x8x32_neon_i8mm__RHS_NxK__", cpu_has_i8mm, |
| 183 |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 184 |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 185 | RhsPackType::NxK}, | ||
| 186 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp8x8_4x8x32_neon_i8mm), |
| 187 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp8x8_4x8x32_neon_i8mm__RHS_KxN__", cpu_has_i8mm, |
| 188 |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 189 |
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1 | kai_get_rhs_packed_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 190 | RhsPackType::KxN}, | ||
| 191 | |||
| 192 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp8x8_8x8x32_neon_i8mm), |
| 193 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp8x8_8x8x32_neon_i8mm__RHS_NxK__", cpu_has_i8mm, |
| 194 |
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1 | kai_run_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 195 |
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1 | kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0, |
| 196 | RhsPackType::NxK}, | ||
| 197 |
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2 | {UKERNEL_MATMUL_VARIANT(clamp_f32_qai8dxp4x8_qsi4cxp8x8_8x8x32_neon_i8mm), |
| 198 |
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1 | "kai_matmul_clamp_f32_qai8dxp4x8_qsi4cxp8x8_8x8x32_neon_i8mm__RHS_KxN__", cpu_has_i8mm, |
| 199 |
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1 | kai_run_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_packed_size_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 200 |
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1 | kai_get_rhs_packed_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, kai_get_rhs_offset_rhs_pack_kxn_qsi4cxp_qs4cxs1s0, |
| 201 | RhsPackType::KxN}} | ||
| 202 | |||
| 203 | }; | ||
| 204 | |||
| 205 | class MatMulTest_f32_qai8dxp_qsi4cxp : public ::testing::TestWithParam<MatMulTestPortionedParams> {}; | ||
| 206 | |||
| 207 |
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2402 | TEST_P(MatMulTest_f32_qai8dxp_qsi4cxp, Offset_RHS) { |
| 208 | 4800 | const auto& [variant_index, matmul_shape, portion] = GetParam(); | |
| 209 | 1600 | const auto& ukernel_variant = variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp.at(variant_index); | |
| 210 | |||
| 211 |
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800 | if (ukernel_variant.fn_is_supported && !ukernel_variant.fn_is_supported()) { |
| 212 | ✗ | GTEST_SKIP() << "Unsupported CPU feature"; | |
| 213 | } | ||
| 214 | |||
| 215 | 1600 | const size_t M = matmul_shape.m; | |
| 216 | 1600 | const size_t N = matmul_shape.n; | |
| 217 | 1600 | const size_t K = matmul_shape.k; | |
| 218 | |||
| 219 | 800 | auto m_step = ukernel_variant.interface.get_m_step(); | |
| 220 | 800 | auto n_step = ukernel_variant.interface.get_n_step(); | |
| 221 | |||
| 222 | 1600 | const auto rect = portion.compute_portion(M, N, m_step, n_step); | |
| 223 |
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800 | if (rect.height() == 0 || rect.width() == 0) { |
| 224 | ✗ | GTEST_SKIP() << "Empty dimension of matrix(" << rect.width() << "," << rect.height() << ")"; | |
| 225 | } | ||
| 226 | |||
| 227 | 800 | const auto nr = ukernel_variant.interface.get_nr(); | |
| 228 | 800 | const auto kr = ukernel_variant.interface.get_kr(); | |
| 229 | 800 | const auto sr = ukernel_variant.interface.get_sr(); | |
| 230 | |||
| 231 | 800 | const auto rhs_start_row = rect.start_col(); | |
| 232 | 800 | auto rhs_packed_offset = ukernel_variant.get_rhs_packed_offset(rhs_start_row, K, nr, kr, sr); | |
| 233 | 800 | auto rhs_matmul_offset = ukernel_variant.interface.get_rhs_packed_offset(rhs_start_row, K); | |
| 234 |
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800 | ASSERT_EQ(rhs_packed_offset, rhs_matmul_offset); |
| 235 | 800 | } | |
| 236 | |||
| 237 |
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2402 | TEST_P(MatMulTest_f32_qai8dxp_qsi4cxp, Offset_LHS) { |
| 238 | 4800 | const auto& [variant_index, matmul_shape, portion] = GetParam(); | |
| 239 | 1600 | const auto& ukernel_variant = variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp.at(variant_index); | |
| 240 | |||
| 241 |
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800 | if (ukernel_variant.fn_is_supported && !ukernel_variant.fn_is_supported()) { |
| 242 | ✗ | GTEST_SKIP() << "Unsupported CPU feature"; | |
| 243 | } | ||
| 244 | |||
| 245 | 1600 | const size_t M = matmul_shape.m; | |
| 246 | 1600 | const size_t N = matmul_shape.n; | |
| 247 | 1600 | const size_t K = matmul_shape.k; | |
| 248 | |||
| 249 | 800 | auto m_step = ukernel_variant.interface.get_m_step(); | |
| 250 | 800 | auto n_step = ukernel_variant.interface.get_n_step(); | |
| 251 | |||
| 252 | 1600 | const auto rect = portion.compute_portion(M, N, m_step, n_step); | |
| 253 |
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800 | if (rect.height() == 0 || rect.width() == 0) { |
| 254 | ✗ | GTEST_SKIP() << "Empty dimension of matrix(" << rect.width() << "," << rect.height() << ")"; | |
| 255 | } | ||
| 256 | |||
| 257 | 800 | const auto mr = ukernel_variant.interface.get_mr(); | |
| 258 | 800 | const auto kr = ukernel_variant.interface.get_kr(); | |
| 259 | 800 | const auto sr = ukernel_variant.interface.get_sr(); | |
| 260 | |||
| 261 | 800 | const auto lhs_start_row = rect.start_row(); | |
| 262 | 800 | auto lhs_packed_offset = kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, K, mr, kr, sr); | |
| 263 | 800 | auto lhs_matmul_offset = ukernel_variant.interface.get_lhs_packed_offset(lhs_start_row, K); | |
| 264 | |||
| 265 |
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800 | ASSERT_EQ(lhs_packed_offset, lhs_matmul_offset); |
| 266 | 800 | } | |
| 267 | |||
| 268 |
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2402 | TEST_P(MatMulTest_f32_qai8dxp_qsi4cxp, EndToEnd_RHS_nxk_qsi4cx) { |
| 269 | 3360 | const auto& [variant_index, matmul_shape, portion] = GetParam(); | |
| 270 | 1600 | const auto& ukernel_variant = variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp.at(variant_index); | |
| 271 | |||
| 272 |
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800 | if (ukernel_variant.fn_is_supported && !ukernel_variant.fn_is_supported()) { |
| 273 | ✗ | GTEST_SKIP() << "Unsupported CPU feature"; | |
| 274 | } | ||
| 275 |
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800 | if (ukernel_variant.rhs_pack_type == RhsPackType::KxN) { |
| 276 |
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360 | GTEST_SKIP() << "Wrong type. This test for NxK"; |
| 277 | } | ||
| 278 | |||
| 279 | 440 | const uint32_t seed = 0; | |
| 280 | |||
| 281 | 880 | const size_t M = matmul_shape.m; | |
| 282 | 880 | const size_t N = matmul_shape.n; | |
| 283 | 880 | const size_t K = matmul_shape.k; | |
| 284 | |||
| 285 | 440 | const auto mr = ukernel_variant.interface.get_mr(); | |
| 286 | 440 | const auto nr = ukernel_variant.interface.get_nr(); | |
| 287 | 440 | const auto kr = ukernel_variant.interface.get_kr(); | |
| 288 | 440 | const auto sr = ukernel_variant.interface.get_sr(); | |
| 289 | |||
| 290 | // Generates input data. | ||
| 291 | 440 | const auto ref_lhs = fill_random<float>(M * K, seed + 0); | |
| 292 |
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440 | const auto ref_biases = fill_random<float>(N, seed + 2); |
| 293 | |||
| 294 |
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440 | std::uniform_real_distribution<float> dist(-10.0, 1.0); |
| 295 |
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440 | std::mt19937 rnd(seed + 1); |
| 296 |
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1890680 | const auto ref_rhs = fill_matrix_raw<float>(1, N * K, [&dist, &rnd](size_t, size_t) { return dist(rnd); }); |
| 297 | |||
| 298 | // Runs the reference implementation. | ||
| 299 | // * Quantizes the LHS matrix using 8-bit asymmetric quantization. | ||
| 300 | // * Quantizes the RHS matrix using 4-bit symmetric quantization. | ||
| 301 | // * Performs GEMM. | ||
| 302 | 1320 | const auto [ref_lhs_qvalues, ref_lhs_scales, ref_lhs_zero_points] = | |
| 303 |
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440 | quantize_asymmetric_per_block_dynamic<float, int8_t, float, int32_t>(ref_lhs.data(), M, K, K); |
| 304 | 2200 | const auto [ref_rhs_qsi4, ref_rhs_scales] = | |
| 305 |
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440 | quantize_symmetric_per_block_dynamic<float, Int4, float>(ref_rhs.data(), N, K, K); |
| 306 | |||
| 307 |
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880 | const auto ref_dst = matmul_clamp_nt_t<int8_t, float, int32_t, Int4, float, int32_t, float, int32_t, float>( |
| 308 |
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880 | M, N, K, ref_lhs_qvalues.data(), ref_lhs_scales.data(), ref_lhs_zero_points.data(), K, ref_rhs_qsi4.data(), |
| 309 |
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440 | ref_rhs_scales.data(), nullptr, K, ref_biases.data(), std::numeric_limits<float>::lowest(), |
| 310 | 440 | std::numeric_limits<float>::max()); | |
| 311 | |||
| 312 |
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440 | auto m_step = ukernel_variant.interface.get_m_step(); |
| 313 |
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440 | ASSERT_TRUE(m_step % mr == 0); |
| 314 | |||
| 315 |
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440 | auto n_step = ukernel_variant.interface.get_n_step(); |
| 316 |
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440 | ASSERT_TRUE(n_step % nr == 0); |
| 317 | |||
| 318 |
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880 | const auto rect = portion.compute_portion(M, N, m_step, n_step); |
| 319 |
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440 | if (rect.height() == 0 || rect.width() == 0) { |
| 320 | ✗ | GTEST_SKIP() << "Empty dimension of matrix(" << rect.width() << "," << rect.height() << ")"; | |
| 321 | } | ||
| 322 | |||
| 323 |
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440 | const auto lhs_start_row = rect.start_row(); |
| 324 | 440 | size_t lhs_stride = K * sizeof(float); | |
| 325 | |||
| 326 | // Runs the LHS packing micro-kernel. | ||
| 327 |
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440 | const auto imp_packed_lhs_size = kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32(M, K, mr, kr, sr); |
| 328 |
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440 | Buffer imp_packed_lhs(imp_packed_lhs_size); |
| 329 | |||
| 330 |
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440 | auto lhs_offset = kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, lhs_stride); |
| 331 |
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440 | auto lhs_packed_offset = kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, K, mr, kr, sr); |
| 332 |
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440 | auto lhs_matmul_offset = ukernel_variant.interface.get_lhs_packed_offset(lhs_start_row, K); |
| 333 |
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440 | ASSERT_EQ(lhs_packed_offset, lhs_matmul_offset); |
| 334 | |||
| 335 |
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440 | kai_run_lhs_quant_pack_qai8dxp_f32( |
| 336 |
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440 | rect.height() /* m */, K, mr, kr, sr, 0 /* m_idx_start*/, |
| 337 |
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440 | reinterpret_cast<const float*>(ref_lhs.data() + lhs_offset), lhs_stride, |
| 338 |
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440 | imp_packed_lhs.data() + lhs_packed_offset); |
| 339 | |||
| 340 | // Runs the RHS packing micro-kernel. | ||
| 341 | // * Generates the 4-bit unsigned symmetric quantized input for the micro-kernel. | ||
| 342 | // * Packs the RHS matrix. | ||
| 343 |
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880 | const auto ref_rhs_qsi4_padded = pad_row<Int4>( |
| 344 |
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440 | ref_rhs_qsi4.data(), N, K, K, round_up_multiple(K, 2), round_up_division(N * round_up_multiple(K, 2), 2)); |
| 345 | |||
| 346 |
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440 | const auto imp_packed_rhs_size = ukernel_variant.get_rhs_packed_size(N, K, nr, kr, sr); |
| 347 |
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440 | const auto rhs_start_row = rect.start_col(); |
| 348 |
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440 | auto rhs_packed_offset = ukernel_variant.get_rhs_packed_offset(rhs_start_row, K, nr, kr, sr); |
| 349 |
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440 | auto rhs_matmul_offset = ukernel_variant.interface.get_rhs_packed_offset(rhs_start_row, K); |
| 350 |
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440 | ASSERT_EQ(rhs_packed_offset, rhs_matmul_offset); |
| 351 | |||
| 352 |
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440 | auto rhs_offset = ukernel_variant.get_rhs_offset(rhs_start_row, round_up_division(K, 2)); |
| 353 | 440 | size_t bias_offset = rhs_start_row * sizeof(float); | |
| 354 | 440 | size_t scale_offset = rhs_start_row * sizeof(float); | |
| 355 | |||
| 356 |
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440 | Buffer imp_packed_rhs(imp_packed_rhs_size); |
| 357 | 440 | kai_rhs_pack_nxk_qsi4cxp_qs4cxs1s0_params params{}; | |
| 358 | 440 | params.lhs_zero_point = 1; | |
| 359 | 440 | params.rhs_zero_point = 0; | |
| 360 | |||
| 361 |
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880 | ukernel_variant.run_rhs_pack( |
| 362 |
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440 | 1, rect.width() /* n */, K, nr, kr, sr, |
| 363 |
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440 | reinterpret_cast<const uint8_t*>(ref_rhs_qsi4_padded.data() + rhs_offset), |
| 364 |
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440 | reinterpret_cast<const float*>(ref_biases.data() + bias_offset), |
| 365 |
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440 | reinterpret_cast<const float*>(ref_rhs_scales.data() + scale_offset), imp_packed_rhs.data() + rhs_packed_offset, |
| 366 | 0, ¶ms); | ||
| 367 | |||
| 368 | 440 | const auto dst_stride = N * sizeof(float); | |
| 369 |
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440 | const auto dst_offset = ukernel_variant.interface.get_dst_offset(rect.start_row(), rect.start_col(), dst_stride); |
| 370 |
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440 | const auto ref_dst_offset = rect.start_row() * dst_stride + rect.start_col() * sizeof(float); |
| 371 |
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440 | ASSERT_EQ(dst_offset, ref_dst_offset); |
| 372 | |||
| 373 | // Runs the GEMM micro-kernel. | ||
| 374 |
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440 | const auto imp_dst_size = ukernel_variant.interface.get_dst_size(M, N); |
| 375 |
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440 | ASSERT_EQ(imp_dst_size, ref_dst.size()); |
| 376 |
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440 | Buffer imp_dst(imp_dst_size); |
| 377 |
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880 | ukernel_variant.interface.run_matmul( |
| 378 |
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440 | rect.height(), rect.width(), K, imp_packed_lhs.data() + lhs_matmul_offset, |
| 379 |
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440 | imp_packed_rhs.data() + rhs_matmul_offset, reinterpret_cast<float*>(imp_dst.data() + dst_offset), |
| 380 | 440 | N * sizeof(float), sizeof(float), std::numeric_limits<float>::lowest(), std::numeric_limits<float>::max()); | |
| 381 | |||
| 382 | // Compares the output of the micro-kernels against the output of the reference implementation for the portion | ||
| 383 | // tested. | ||
| 384 |
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11968 | for (size_t y = 0; y < rect.height(); ++y) { |
| 385 |
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848380 | for (size_t x = 0; x < rect.width(); ++x) { |
| 386 | 1673704 | const auto imp_value = | |
| 387 |
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836852 | read_array<float>(imp_dst.data(), (rect.start_row() + y) * N + (x + rect.start_col())); |
| 388 | 1673704 | const auto ref_value = | |
| 389 |
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836852 | read_array<float>(ref_dst.data(), (rect.start_row() + y) * N + (x + rect.start_col())); |
| 390 |
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836852 | const auto rel_error = ref_value != 0 ? std::abs((imp_value - ref_value) / ref_value) : imp_value; |
| 391 | |||
| 392 |
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836852 | if (rel_error > 0.0001F) { |
| 393 | ✗ | ASSERT_EQ(imp_value, ref_value); | |
| 394 | ✗ | } | |
| 395 | 836852 | } | |
| 396 | 11528 | } | |
| 397 | 800 | } | |
| 398 | |||
| 399 |
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2402 | TEST_P(MatMulTest_f32_qai8dxp_qsi4cxp, EndToEnd_RHS_nxk_qsu4cx) { |
| 400 | 3360 | const auto& [variant_index, matmul_shape, portion] = GetParam(); | |
| 401 | 1600 | const auto& ukernel_variant = variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp.at(variant_index); | |
| 402 | |||
| 403 |
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800 | if (ukernel_variant.fn_is_supported && !ukernel_variant.fn_is_supported()) { |
| 404 | ✗ | GTEST_SKIP() << "Unsupported CPU feature"; | |
| 405 | } | ||
| 406 |
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800 | if (ukernel_variant.rhs_pack_type == RhsPackType::KxN) { |
| 407 |
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360 | GTEST_SKIP() << "Wrong type. This test for NxK"; |
| 408 | } | ||
| 409 | |||
| 410 | 440 | const uint32_t seed = 0; | |
| 411 | |||
| 412 | 880 | const size_t M = matmul_shape.m; | |
| 413 | 880 | const size_t N = matmul_shape.n; | |
| 414 | 880 | const size_t K = matmul_shape.k; | |
| 415 | |||
| 416 | 440 | const auto mr = ukernel_variant.interface.get_mr(); | |
| 417 | 440 | const auto nr = ukernel_variant.interface.get_nr(); | |
| 418 | 440 | const auto kr = ukernel_variant.interface.get_kr(); | |
| 419 | 440 | const auto sr = ukernel_variant.interface.get_sr(); | |
| 420 | |||
| 421 | // Generates input data. | ||
| 422 | 440 | const auto ref_lhs = fill_random<float>(M * K, seed + 0); | |
| 423 | |||
| 424 |
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440 | std::uniform_real_distribution<float> dist(-10.0, 1.0); |
| 425 |
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440 | std::mt19937 rnd(seed + 1); |
| 426 |
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1890680 | const auto ref_rhs = fill_matrix_raw<float>(1, N * K, [&dist, &rnd](size_t, size_t) { return dist(rnd); }); |
| 427 | |||
| 428 |
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440 | const auto ref_biases = fill_random<float>(N, seed + 2); |
| 429 | |||
| 430 | // Runs the reference implementation. | ||
| 431 | // * Quantizes the LHS matrix using 8-bit asymmetric quantization. | ||
| 432 | // * Quantizes the RHS matrix using 4-bit symmetric quantization. | ||
| 433 | // * Performs GEMM. | ||
| 434 | 1320 | const auto [ref_lhs_qvalues, ref_lhs_scales, ref_lhs_zero_points] = | |
| 435 |
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440 | quantize_asymmetric_per_block_dynamic<float, int8_t, float, int32_t>(ref_lhs.data(), M, K, K); |
| 436 | 2200 | const auto [ref_rhs_qsi4, ref_rhs_scales] = | |
| 437 |
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440 | quantize_symmetric_per_block_dynamic<float, Int4, float>(ref_rhs.data(), N, K, K); |
| 438 | |||
| 439 |
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880 | const auto ref_dst = matmul_clamp_nt_t<int8_t, float, int32_t, Int4, float, int32_t, float, int32_t, float>( |
| 440 |
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880 | M, N, K, ref_lhs_qvalues.data(), ref_lhs_scales.data(), ref_lhs_zero_points.data(), K, ref_rhs_qsi4.data(), |
| 441 |
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440 | ref_rhs_scales.data(), nullptr, K, ref_biases.data(), std::numeric_limits<float>::lowest(), |
| 442 | 440 | std::numeric_limits<float>::max()); | |
| 443 | |||
| 444 |
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440 | auto m_step = ukernel_variant.interface.get_m_step(); |
| 445 |
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440 | ASSERT_TRUE(m_step % mr == 0); |
| 446 | |||
| 447 |
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440 | auto n_step = ukernel_variant.interface.get_n_step(); |
| 448 |
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440 | ASSERT_TRUE(n_step % nr == 0); |
| 449 | |||
| 450 |
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880 | const auto rect = portion.compute_portion(M, N, m_step, n_step); |
| 451 |
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440 | if (rect.height() == 0 || rect.width() == 0) { |
| 452 | ✗ | GTEST_SKIP() << "Empty dimension of matrix(" << rect.width() << "," << rect.height() << ")"; | |
| 453 | } | ||
| 454 | |||
| 455 |
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440 | const auto lhs_start_row = rect.start_row(); |
| 456 | 440 | size_t lhs_stride = K * sizeof(float); | |
| 457 | |||
| 458 | // Runs the LHS packing micro-kernel. | ||
| 459 |
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440 | const auto imp_packed_lhs_size = kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32(M, K, mr, kr, sr); |
| 460 |
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440 | Buffer imp_packed_lhs(imp_packed_lhs_size); |
| 461 | |||
| 462 |
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440 | auto lhs_offset = kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, lhs_stride); |
| 463 |
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440 | auto lhs_packed_offset = kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, K, mr, kr, sr); |
| 464 |
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440 | auto lhs_matmul_offset = ukernel_variant.interface.get_lhs_packed_offset(lhs_start_row, K); |
| 465 |
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440 | ASSERT_EQ(lhs_packed_offset, lhs_matmul_offset); |
| 466 | |||
| 467 |
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440 | kai_run_lhs_quant_pack_qai8dxp_f32( |
| 468 |
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440 | rect.height() /* m */, K, mr, kr, sr, 0 /* m_idx_start*/, |
| 469 |
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440 | reinterpret_cast<const float*>(ref_lhs.data() + lhs_offset), lhs_stride, |
| 470 |
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440 | imp_packed_lhs.data() + lhs_packed_offset); |
| 471 | |||
| 472 |
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880 | const auto ref_rhs_qsu4 = cast_qsu4_qsi4(ref_rhs_qsi4.data(), N * K); |
| 473 | // Runs the RHS packing micro-kernel. | ||
| 474 | // * Generates the 4-bit unsigned symmetric quantized input for the micro-kernel. | ||
| 475 | // * Packs the RHS matrix. | ||
| 476 |
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880 | const auto ref_rhs_qsu4_padded = pad_row<UInt4>( |
| 477 |
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440 | ref_rhs_qsu4.data(), N, K, K, round_up_multiple(K, 2), round_up_division(N * round_up_multiple(K, 2), 2)); |
| 478 | |||
| 479 |
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440 | const auto imp_packed_rhs_size = ukernel_variant.get_rhs_packed_size(N, K, nr, kr, sr); |
| 480 |
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440 | const auto rhs_start_row = rect.start_col(); |
| 481 |
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440 | auto rhs_packed_offset = ukernel_variant.get_rhs_packed_offset(rhs_start_row, K, nr, kr, sr); |
| 482 |
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440 | auto rhs_matmul_offset = ukernel_variant.interface.get_rhs_packed_offset(rhs_start_row, K); |
| 483 |
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440 | ASSERT_EQ(rhs_packed_offset, rhs_matmul_offset); |
| 484 | |||
| 485 |
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440 | auto rhs_offset = ukernel_variant.get_rhs_offset(rhs_start_row, round_up_division(K, 2)); |
| 486 | 440 | size_t bias_offset = rhs_start_row * sizeof(float); | |
| 487 | 440 | size_t scale_offset = rhs_start_row * sizeof(float); | |
| 488 | |||
| 489 |
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440 | Buffer imp_packed_rhs(imp_packed_rhs_size); |
| 490 | 440 | kai_rhs_pack_nxk_qsi4cxp_qs4cxs1s0_params params{}; | |
| 491 | 440 | params.lhs_zero_point = 1; | |
| 492 | 440 | params.rhs_zero_point = 8; | |
| 493 |
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880 | ukernel_variant.run_rhs_pack( |
| 494 |
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440 | 1, rect.width() /* n */, K, nr, kr, sr, |
| 495 |
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440 | reinterpret_cast<const uint8_t*>(ref_rhs_qsu4_padded.data() + rhs_offset), |
| 496 |
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440 | reinterpret_cast<const float*>(ref_biases.data() + bias_offset), |
| 497 |
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440 | reinterpret_cast<const float*>(ref_rhs_scales.data() + scale_offset), imp_packed_rhs.data() + rhs_packed_offset, |
| 498 | 0, ¶ms); | ||
| 499 | |||
| 500 | 440 | const auto dst_stride = N * sizeof(float); | |
| 501 |
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440 | const auto dst_offset = ukernel_variant.interface.get_dst_offset(rect.start_row(), rect.start_col(), dst_stride); |
| 502 |
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440 | const auto ref_dst_offset = rect.start_row() * dst_stride + rect.start_col() * sizeof(float); |
| 503 |
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440 | ASSERT_EQ(dst_offset, ref_dst_offset); |
| 504 | // Runs the GEMM micro-kernel. | ||
| 505 |
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440 | const auto imp_dst_size = ukernel_variant.interface.get_dst_size(M, N); |
| 506 |
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440 | ASSERT_EQ(imp_dst_size, ref_dst.size()); |
| 507 |
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440 | Buffer imp_dst(imp_dst_size); |
| 508 |
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880 | ukernel_variant.interface.run_matmul( |
| 509 |
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440 | rect.height(), rect.width(), K, imp_packed_lhs.data() + lhs_matmul_offset, |
| 510 |
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440 | imp_packed_rhs.data() + rhs_matmul_offset, reinterpret_cast<float*>(imp_dst.data() + dst_offset), |
| 511 | 440 | N * sizeof(float), sizeof(float), std::numeric_limits<float>::lowest(), std::numeric_limits<float>::max()); | |
| 512 | |||
| 513 | // Compares the output of the micro-kernels against the output of the reference implementation for the portion | ||
| 514 | // tested. | ||
| 515 |
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11968 | for (size_t y = 0; y < rect.height(); ++y) { |
| 516 |
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848380 | for (size_t x = 0; x < rect.width(); ++x) { |
| 517 | 1673704 | const auto imp_value = | |
| 518 |
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836852 | read_array<float>(imp_dst.data(), (rect.start_row() + y) * N + (x + rect.start_col())); |
| 519 | 1673704 | const auto ref_value = | |
| 520 |
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836852 | read_array<float>(ref_dst.data(), (rect.start_row() + y) * N + (x + rect.start_col())); |
| 521 |
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836852 | const auto rel_error = ref_value != 0 ? std::abs((imp_value - ref_value) / ref_value) : imp_value; |
| 522 | |||
| 523 |
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836852 | if (rel_error > 0.0001F) { |
| 524 | ✗ | ASSERT_EQ(imp_value, ref_value); | |
| 525 | ✗ | } | |
| 526 | 836852 | } | |
| 527 | 11528 | } | |
| 528 | 800 | } | |
| 529 | |||
| 530 |
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2402 | TEST_P(MatMulTest_f32_qai8dxp_qsi4cxp, EndToEnd_RHS_kxn_qsi4cx) { |
| 531 | 3040 | const auto& [variant_index, matmul_shape, portion] = GetParam(); | |
| 532 | 1600 | const auto& ukernel_variant = variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp.at(variant_index); | |
| 533 | |||
| 534 |
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800 | if (ukernel_variant.fn_is_supported && !ukernel_variant.fn_is_supported()) { |
| 535 | ✗ | GTEST_SKIP() << "Unsupported CPU feature"; | |
| 536 | } | ||
| 537 |
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800 | if (ukernel_variant.rhs_pack_type == RhsPackType::NxK) { |
| 538 |
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440 | GTEST_SKIP() << "Wrong type. This test for KxN"; |
| 539 | } | ||
| 540 | |||
| 541 | 360 | const uint32_t seed = 0; | |
| 542 | |||
| 543 | 720 | const size_t M = matmul_shape.m; | |
| 544 | 720 | const size_t N = matmul_shape.n; | |
| 545 | 720 | const size_t K = matmul_shape.k; | |
| 546 | |||
| 547 | 360 | const auto mr = ukernel_variant.interface.get_mr(); | |
| 548 | 360 | const auto nr = ukernel_variant.interface.get_nr(); | |
| 549 | 360 | const auto kr = ukernel_variant.interface.get_kr(); | |
| 550 | 360 | const auto sr = ukernel_variant.interface.get_sr(); | |
| 551 | |||
| 552 | // Generates input data. | ||
| 553 | 360 | const auto ref_lhs = fill_random<float>(M * K, seed + 0); | |
| 554 | |||
| 555 |
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360 | std::uniform_real_distribution<float> dist(-10.0, 1.0); |
| 556 |
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360 | std::mt19937 rnd(seed + 1); |
| 557 |
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1546920 | const auto ref_rhs = fill_matrix_raw<float>(1, N * K, [&dist, &rnd](size_t, size_t) { return dist(rnd); }); |
| 558 | |||
| 559 |
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360 | const auto ref_biases = fill_random<float>(N, seed + 2); |
| 560 | |||
| 561 | // Transposed(nxk) RHS dimensions | ||
| 562 | 360 | const size_t ref_rhs_qsi4_nxk_stride = K; | |
| 563 | |||
| 564 | // Non-Transposed(kxn) RHS dimensions | ||
| 565 |
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360 | const size_t ref_rhs_qsi4_kxn_stride = round_up_multiple(N, 2); |
| 566 |
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360 | const size_t ref_rhs_qsi4_kxn_size_bytes = round_up_division(K * ref_rhs_qsi4_kxn_stride, 2); |
| 567 | |||
| 568 | // Runs the reference implementation. | ||
| 569 | // * Quantizes the LHS matrix using 8-bit asymmetric quantization. | ||
| 570 | // * Quantizes the RHS matrix using 4-bit symmetric quantization. | ||
| 571 | // * Performs GEMM. | ||
| 572 | 1080 | const auto [ref_lhs_qvalues, ref_lhs_scales, ref_lhs_zero_points] = | |
| 573 |
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360 | quantize_asymmetric_per_block_dynamic<float, int8_t, float, int32_t>(ref_lhs.data(), M, K, K); |
| 574 | 1800 | const auto [ref_rhs_qsi4_transposed, ref_rhs_scales] = | |
| 575 |
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360 | quantize_symmetric_per_block_dynamic<float, Int4, float>(ref_rhs.data(), N, K, K); |
| 576 | |||
| 577 |
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360 | const auto ref_rhs_qsi4 = transpose_with_padding<Int4>( |
| 578 |
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360 | ref_rhs_qsi4_transposed.data(), N, K, ref_rhs_qsi4_nxk_stride, ref_rhs_qsi4_kxn_stride, |
| 579 | 360 | ref_rhs_qsi4_kxn_size_bytes); | |
| 580 | |||
| 581 |
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720 | const auto ref_dst = matmul_clamp_nt_nt<int8_t, float, int32_t, Int4, float, int32_t, float, int32_t, float>( |
| 582 |
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720 | M, N, K, ref_lhs_qvalues.data(), ref_lhs_scales.data(), ref_lhs_zero_points.data(), K, ref_rhs_qsi4.data(), |
| 583 |
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360 | ref_rhs_scales.data(), nullptr, K, ref_biases.data(), std::numeric_limits<float>::lowest(), |
| 584 | 360 | std::numeric_limits<float>::max()); | |
| 585 | |||
| 586 |
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360 | auto m_step = ukernel_variant.interface.get_m_step(); |
| 587 |
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360 | ASSERT_TRUE(m_step % mr == 0); |
| 588 | |||
| 589 |
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360 | auto n_step = ukernel_variant.interface.get_n_step(); |
| 590 |
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360 | ASSERT_TRUE(n_step % nr == 0); |
| 591 | |||
| 592 |
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720 | const auto rect = portion.compute_portion(M, N, m_step, n_step); |
| 593 |
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360 | if (rect.height() == 0 || rect.width() == 0) { |
| 594 | ✗ | GTEST_SKIP() << "Empty dimension of matrix(" << rect.width() << "," << rect.height() << ")"; | |
| 595 | } | ||
| 596 | |||
| 597 |
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360 | const auto lhs_start_row = rect.start_row(); |
| 598 | 360 | size_t lhs_stride = K * sizeof(float); | |
| 599 | |||
| 600 | // Runs the LHS packing micro-kernel. | ||
| 601 |
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360 | const auto imp_packed_lhs_size = kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32(M, K, mr, kr, sr); |
| 602 |
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360 | Buffer imp_packed_lhs(imp_packed_lhs_size); |
| 603 |
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360 | auto lhs_offset = kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, lhs_stride); |
| 604 |
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360 | auto lhs_packed_offset = kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, K, mr, kr, sr); |
| 605 |
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360 | auto lhs_matmul_offset = ukernel_variant.interface.get_lhs_packed_offset(lhs_start_row, K); |
| 606 |
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360 | ASSERT_EQ(lhs_packed_offset, lhs_matmul_offset); |
| 607 | |||
| 608 |
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360 | kai_run_lhs_quant_pack_qai8dxp_f32( |
| 609 |
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360 | rect.height() /* m */, K, mr, kr, sr, 0 /* m_idx_start*/, |
| 610 |
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360 | reinterpret_cast<const float*>(ref_lhs.data() + lhs_offset), lhs_stride, |
| 611 |
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360 | imp_packed_lhs.data() + lhs_packed_offset); |
| 612 | |||
| 613 | // Runs the RHS packing micro-kernel. | ||
| 614 | // * Generates the 4-bit unsigned symmetric quantized input for the micro-kernel. | ||
| 615 | // * Packs the RHS matrix. | ||
| 616 |
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720 | const auto ref_rhs_qsi4_padded = pad_row<Int4>( |
| 617 |
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360 | ref_rhs_qsi4.data(), K, N, N, round_up_multiple(N, 2), round_up_division(K * round_up_multiple(N, 2), 2)); |
| 618 |
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360 | const auto imp_packed_rhs_size = ukernel_variant.get_rhs_packed_size(N, K, nr, kr, sr); |
| 619 | |||
| 620 |
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360 | const auto rhs_start_row = rect.start_col(); |
| 621 |
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360 | auto rhs_packed_offset = ukernel_variant.get_rhs_packed_offset(rhs_start_row, K, nr, kr, sr); |
| 622 |
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360 | auto rhs_matmul_offset = ukernel_variant.interface.get_rhs_packed_offset(rhs_start_row, K); |
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360 | ASSERT_EQ(rhs_packed_offset, rhs_matmul_offset); |
| 624 | |||
| 625 |
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360 | Buffer imp_packed_rhs(imp_packed_rhs_size); |
| 626 | 360 | kai_rhs_pack_kxn_qsi4cxp_qs4cxs1s0_params params{}; | |
| 627 | 360 | params.lhs_zero_point = 1; | |
| 628 | 360 | params.rhs_zero_point = 0; | |
| 629 |
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720 | ukernel_variant.run_rhs_pack( |
| 630 |
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360 | 1, N, K, nr, kr, sr, reinterpret_cast<const uint8_t*>(ref_rhs_qsi4_padded.data()), |
| 631 |
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360 | reinterpret_cast<const float*>(ref_biases.data()), reinterpret_cast<const float*>(ref_rhs_scales.data()), |
| 632 |
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360 | imp_packed_rhs.data(), 0, ¶ms); |
| 633 | |||
| 634 | 360 | const auto dst_stride = N * sizeof(float); | |
| 635 |
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360 | const auto dst_offset = ukernel_variant.interface.get_dst_offset(rect.start_row(), rect.start_col(), dst_stride); |
| 636 |
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360 | const auto ref_dst_offset = rect.start_row() * dst_stride + rect.start_col() * sizeof(float); |
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360 | ASSERT_EQ(dst_offset, ref_dst_offset); |
| 638 | |||
| 639 | // Runs the GEMM micro-kernel. | ||
| 640 |
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360 | const auto imp_dst_size = ukernel_variant.interface.get_dst_size(M, N); |
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360 | ASSERT_EQ(imp_dst_size, ref_dst.size()); |
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360 | Buffer imp_dst(imp_dst_size); |
| 643 |
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720 | ukernel_variant.interface.run_matmul( |
| 644 |
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360 | rect.height(), rect.width(), K, imp_packed_lhs.data() + lhs_matmul_offset, |
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360 | imp_packed_rhs.data() + rhs_matmul_offset, reinterpret_cast<float*>(imp_dst.data() + dst_offset), |
| 646 | 360 | N * sizeof(float), sizeof(float), std::numeric_limits<float>::lowest(), std::numeric_limits<float>::max()); | |
| 647 | |||
| 648 | // Compares the output of the micro-kernels against the output of the reference implementation for the portion | ||
| 649 | // tested. | ||
| 650 |
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9792 | for (size_t y = 0; y < rect.height(); ++y) { |
| 651 |
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667150 | for (size_t x = 0; x < rect.width(); ++x) { |
| 652 | 1315436 | const auto imp_value = | |
| 653 |
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657718 | read_array<float>(imp_dst.data(), (rect.start_row() + y) * N + (x + rect.start_col())); |
| 654 | 1315436 | const auto ref_value = | |
| 655 |
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657718 | read_array<float>(ref_dst.data(), (rect.start_row() + y) * N + (x + rect.start_col())); |
| 656 |
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657718 | const auto rel_error = ref_value != 0 ? std::abs((imp_value - ref_value) / ref_value) : imp_value; |
| 657 | |||
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657718 | if (rel_error > 0.0001F) { |
| 659 | ✗ | ASSERT_EQ(imp_value, ref_value); | |
| 660 | ✗ | } | |
| 661 | 657718 | } | |
| 662 | 9432 | } | |
| 663 | 800 | } | |
| 664 | |||
| 665 |
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2402 | TEST_P(MatMulTest_f32_qai8dxp_qsi4cxp, EndToEnd_RHS_kxn_qsu4cx) { |
| 666 | 3040 | const auto& [variant_index, matmul_shape, portion] = GetParam(); | |
| 667 | 1600 | const auto& ukernel_variant = variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp.at(variant_index); | |
| 668 | |||
| 669 |
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800 | if (ukernel_variant.fn_is_supported && !ukernel_variant.fn_is_supported()) { |
| 670 | ✗ | GTEST_SKIP() << "Unsupported CPU feature"; | |
| 671 | } | ||
| 672 |
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800 | if (ukernel_variant.rhs_pack_type == RhsPackType::NxK) { |
| 673 |
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440 | GTEST_SKIP() << "Wrong type. This test for KxN"; |
| 674 | } | ||
| 675 | |||
| 676 | 360 | const uint32_t seed = 0; | |
| 677 | |||
| 678 | 720 | const size_t M = matmul_shape.m; | |
| 679 | 720 | const size_t N = matmul_shape.n; | |
| 680 | 720 | const size_t K = matmul_shape.k; | |
| 681 | |||
| 682 | 360 | const auto mr = ukernel_variant.interface.get_mr(); | |
| 683 | 360 | const auto nr = ukernel_variant.interface.get_nr(); | |
| 684 | 360 | const auto kr = ukernel_variant.interface.get_kr(); | |
| 685 | 360 | const auto sr = ukernel_variant.interface.get_sr(); | |
| 686 | |||
| 687 | // Generates input data. | ||
| 688 | 360 | const auto ref_lhs = fill_random<float>(M * K, seed + 0); | |
| 689 | |||
| 690 |
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360 | std::uniform_real_distribution<float> dist(-10.0, 1.0); |
| 691 |
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360 | std::mt19937 rnd(seed + 1); |
| 692 |
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1546920 | const auto ref_rhs = fill_matrix_raw<float>(1, N * K, [&dist, &rnd](size_t, size_t) { return dist(rnd); }); |
| 693 | |||
| 694 |
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360 | const auto ref_biases = fill_random<float>(N, seed + 2); |
| 695 | |||
| 696 | // Transposed(nxk) RHS dimensions | ||
| 697 | 360 | const size_t ref_rhs_qsi4_nxk_stride = K; | |
| 698 | |||
| 699 | // Non-Transposed(kxn) RHS dimensions | ||
| 700 |
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360 | const size_t ref_rhs_qsi4_kxn_stride = round_up_multiple(N, 2); |
| 701 | 360 | const size_t ref_rhs_qsi4_kxn_size = K * ref_rhs_qsi4_kxn_stride; | |
| 702 |
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360 | const size_t ref_rhs_qsi4_kxn_size_bytes = round_up_division(ref_rhs_qsi4_kxn_size, 2); |
| 703 | |||
| 704 | // Runs the reference implementation. | ||
| 705 | // * Quantizes the LHS matrix using 8-bit asymmetric quantization. | ||
| 706 | // * Quantizes the RHS matrix using 4-bit symmetric quantization. | ||
| 707 | // * Performs GEMM. | ||
| 708 | 1080 | const auto [ref_lhs_qvalues, ref_lhs_scales, ref_lhs_zero_points] = | |
| 709 |
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360 | quantize_asymmetric_per_block_dynamic<float, int8_t, float, int32_t>(ref_lhs.data(), M, K, K); |
| 710 | 1800 | const auto [ref_rhs_qsi4_transposed, ref_rhs_scales] = | |
| 711 |
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360 | quantize_symmetric_per_block_dynamic<float, Int4, float>(ref_rhs.data(), N, K, K); |
| 712 | |||
| 713 |
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360 | const auto ref_rhs_qsi4 = transpose_with_padding<Int4>( |
| 714 |
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360 | ref_rhs_qsi4_transposed.data(), N, K, ref_rhs_qsi4_nxk_stride, ref_rhs_qsi4_kxn_stride, |
| 715 | 360 | ref_rhs_qsi4_kxn_size_bytes); | |
| 716 | |||
| 717 |
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720 | const auto ref_dst = matmul_clamp_nt_nt<int8_t, float, int32_t, Int4, float, int32_t, float, int32_t, float>( |
| 718 |
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720 | M, N, K, ref_lhs_qvalues.data(), ref_lhs_scales.data(), ref_lhs_zero_points.data(), K, ref_rhs_qsi4.data(), |
| 719 |
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360 | ref_rhs_scales.data(), nullptr, K, ref_biases.data(), std::numeric_limits<float>::lowest(), |
| 720 | 360 | std::numeric_limits<float>::max()); | |
| 721 | |||
| 722 |
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360 | auto m_step = ukernel_variant.interface.get_m_step(); |
| 723 |
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360 | ASSERT_TRUE(m_step % mr == 0); |
| 724 | |||
| 725 |
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360 | auto n_step = ukernel_variant.interface.get_n_step(); |
| 726 |
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360 | ASSERT_TRUE(n_step % nr == 0); |
| 727 | |||
| 728 |
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720 | const auto rect = portion.compute_portion(M, N, m_step, n_step); |
| 729 |
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360 | if (rect.height() == 0 || rect.width() == 0) { |
| 730 | ✗ | GTEST_SKIP() << "Empty dimension of matrix(" << rect.width() << "," << rect.height() << ")"; | |
| 731 | } | ||
| 732 | |||
| 733 |
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360 | const auto lhs_start_row = rect.start_row(); |
| 734 | 360 | size_t lhs_stride = K * sizeof(float); | |
| 735 | |||
| 736 | // Runs the LHS packing micro-kernel. | ||
| 737 |
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360 | const auto imp_packed_lhs_size = kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32(M, K, mr, kr, sr); |
| 738 |
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360 | Buffer imp_packed_lhs(imp_packed_lhs_size); |
| 739 | |||
| 740 |
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360 | auto lhs_offset = kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, lhs_stride); |
| 741 |
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360 | auto lhs_packed_offset = kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32(lhs_start_row, K, mr, kr, sr); |
| 742 |
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360 | auto lhs_matmul_offset = ukernel_variant.interface.get_lhs_packed_offset(lhs_start_row, K); |
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360 | ASSERT_EQ(lhs_packed_offset, lhs_matmul_offset); |
| 744 | |||
| 745 |
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360 | kai_run_lhs_quant_pack_qai8dxp_f32( |
| 746 |
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360 | rect.height() /* m */, K, mr, kr, sr, 0 /* m_idx_start*/, |
| 747 |
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360 | reinterpret_cast<const float*>(ref_lhs.data() + lhs_offset), lhs_stride, |
| 748 |
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360 | imp_packed_lhs.data() + lhs_packed_offset); |
| 749 | |||
| 750 | // Runs the RHS packing micro-kernel. | ||
| 751 | // * Generates the 4-bit unsigned symmetric quantized input for the micro-kernel. | ||
| 752 | // * Packs the RHS matrix. | ||
| 753 |
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360 | const auto ref_rhs_qsu4 = cast_qsu4_qsi4(ref_rhs_qsi4.data(), ref_rhs_qsi4_kxn_size); |
| 754 |
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720 | const auto ref_rhs_qsu4_padded = pad_row<UInt4>( |
| 755 |
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360 | ref_rhs_qsu4.data(), K, N, N, round_up_multiple(N, 2), round_up_division(K * round_up_multiple(N, 2), 2)); |
| 756 |
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360 | const auto imp_packed_rhs_size = ukernel_variant.get_rhs_packed_size(N, K, nr, kr, sr); |
| 757 | |||
| 758 |
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360 | const auto rhs_start_row = rect.start_col(); |
| 759 |
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360 | auto rhs_packed_offset = kai_get_rhs_packed_offset_rhs_pack_nxk_qsi4cxp_qs4cxs1s0(rhs_start_row, K, nr, kr, sr); |
| 760 |
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360 | auto rhs_matmul_offset = ukernel_variant.interface.get_rhs_packed_offset(rhs_start_row, K); |
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360 | ASSERT_EQ(rhs_packed_offset, rhs_matmul_offset); |
| 762 | |||
| 763 |
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360 | Buffer imp_packed_rhs(imp_packed_rhs_size); |
| 764 | 360 | kai_rhs_pack_kxn_qsi4cxp_qs4cxs1s0_params params{}; | |
| 765 | 360 | params.lhs_zero_point = 1; | |
| 766 | 360 | params.rhs_zero_point = 8; | |
| 767 |
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720 | ukernel_variant.run_rhs_pack( |
| 768 |
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360 | 1, N, K, nr, kr, sr, reinterpret_cast<const uint8_t*>(ref_rhs_qsu4_padded.data()), |
| 769 |
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360 | reinterpret_cast<const float*>(ref_biases.data()), reinterpret_cast<const float*>(ref_rhs_scales.data()), |
| 770 |
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360 | imp_packed_rhs.data(), 0, ¶ms); |
| 771 | |||
| 772 | 360 | const auto dst_stride = N * sizeof(float); | |
| 773 |
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360 | const auto dst_offset = ukernel_variant.interface.get_dst_offset(rect.start_row(), rect.start_col(), dst_stride); |
| 774 |
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360 | const auto ref_dst_offset = rect.start_row() * dst_stride + rect.start_col() * sizeof(float); |
| 775 |
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360 | ASSERT_EQ(dst_offset, ref_dst_offset); |
| 776 | |||
| 777 | // Runs the GEMM micro-kernel. | ||
| 778 |
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360 | const auto imp_dst_size = ukernel_variant.interface.get_dst_size(M, N); |
| 779 |
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360 | ASSERT_EQ(imp_dst_size, ref_dst.size()); |
| 780 |
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360 | Buffer imp_dst(imp_dst_size); |
| 781 |
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720 | ukernel_variant.interface.run_matmul( |
| 782 |
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360 | rect.height(), rect.width(), K, imp_packed_lhs.data() + lhs_matmul_offset, |
| 783 |
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360 | imp_packed_rhs.data() + rhs_matmul_offset, reinterpret_cast<float*>(imp_dst.data() + dst_offset), |
| 784 | 360 | N * sizeof(float), sizeof(float), std::numeric_limits<float>::lowest(), std::numeric_limits<float>::max()); | |
| 785 | |||
| 786 | // Compares the output of the micro-kernels against the output of the reference implementation for the portion | ||
| 787 | // tested. | ||
| 788 |
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360 | DefaultMismatchHandler handler(0, 0.1, 0, 0.05); |
| 789 | 360 | DataFormat dst_format = DataFormat(DataType::FP32); | |
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360 | const auto success = compare(imp_dst.data(), ref_dst.data(), dst_format, M, N, rect, handler); |
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360 | ASSERT_TRUE(success); |
| 792 | 800 | } | |
| 793 | |||
| 794 |
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9607 | INSTANTIATE_TEST_SUITE_P( |
| 795 | MatMul, MatMulTest_f32_qai8dxp_qsi4cxp, | ||
| 796 | testing::Combine( | ||
| 797 | testing::Range<size_t>(0, variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp.size()), | ||
| 798 | testing::Values( | ||
| 799 | MatMulShape{16, 32, 64}, // | ||
| 800 | MatMulShape{16, 32, 36}, // | ||
| 801 | MatMulShape{15, 35, 65}, // | ||
| 802 | MatMulShape{8, 32, 64}, // | ||
| 803 | MatMulShape{15, 31, 45}, // | ||
| 804 | MatMulShape{1, 35, 65}, // | ||
| 805 | MatMulShape{1, 128, 32}, // | ||
| 806 | MatMulShape{64, 128, 32}, // | ||
| 807 | MatMulShape{1, 225, 55}, // | ||
| 808 | MatMulShape{125, 200, 56}), | ||
| 809 | testing::Values( | ||
| 810 | MatrixPortion(0, 0, 1, 1), // Full matrix. | ||
| 811 | MatrixPortion(0, 0, 1, 0.25), // Leftmost portion. | ||
| 812 | MatrixPortion(0, 0.75, 1, 1), // Rightmost portion. | ||
| 813 | MatrixPortion(0, 0.5, 1, 0.8) // Somewhere Middle | ||
| 814 | )), | ||
| 815 | [](const auto& info) { | ||
| 816 | const auto variant_idx = std::get<0>(info.param); | ||
| 817 | const std::string name{variants_kai_matmul_clamp_f32_qai8dxp_qsi4cxp.at(variant_idx).name}; | ||
| 818 | const auto shape = std::get<MatMulShape>(info.param); | ||
| 819 | const auto portion = std::get<2>(info.param); | ||
| 820 | |||
| 821 | return test_description(name, shape, portion, true); | ||
| 822 | }); | ||
| 823 | |||
| 824 | } // namespace kai::test | ||
| 825 |