/* * Copyright (c) 2021-2023 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #ifndef SRC_CORE_CL_CL_KERNELS_TILE_HELPERS #define SRC_CORE_CL_CL_KERNELS_TILE_HELPERS // *INDENT-OFF* // clang-format off #define TILE_VECTOR_SIZE1 1 #define TILE_VECTOR_SIZE2 2 #define TILE_VECTOR_SIZE3 3 #define TILE_VECTOR_SIZE4 4 #define TILE_VECTOR_SIZE5 8 #define TILE_VECTOR_SIZE6 8 #define TILE_VECTOR_SIZE7 8 #define TILE_VECTOR_SIZE8 8 #define TILE_VECTOR_SIZE9 16 #define TILE_VECTOR_SIZE10 16 #define TILE_VECTOR_SIZE11 16 #define TILE_VECTOR_SIZE12 16 #define TILE_VECTOR_SIZE13 16 #define TILE_VECTOR_SIZE14 16 #define TILE_VECTOR_SIZE15 16 #define TILE_VECTOR_SIZE16 16 #define TILE_VECTOR_TYPE1(DATA_TYPE) DATA_TYPE##1 #define TILE_VECTOR_TYPE2(DATA_TYPE) DATA_TYPE##2 #define TILE_VECTOR_TYPE3(DATA_TYPE) DATA_TYPE##3 #define TILE_VECTOR_TYPE4(DATA_TYPE) DATA_TYPE##4 #define TILE_VECTOR_TYPE5(DATA_TYPE) DATA_TYPE##8 #define TILE_VECTOR_TYPE6(DATA_TYPE) DATA_TYPE##8 #define TILE_VECTOR_TYPE7(DATA_TYPE) DATA_TYPE##8 #define TILE_VECTOR_TYPE8(DATA_TYPE) DATA_TYPE##8 #define TILE_VECTOR_TYPE9(DATA_TYPE) DATA_TYPE##16 #define TILE_VECTOR_TYPE10(DATA_TYPE) DATA_TYPE##16 #define TILE_VECTOR_TYPE11(DATA_TYPE) DATA_TYPE##16 #define TILE_VECTOR_TYPE12(DATA_TYPE) DATA_TYPE##16 #define TILE_VECTOR_TYPE13(DATA_TYPE) DATA_TYPE##16 #define TILE_VECTOR_TYPE14(DATA_TYPE) DATA_TYPE##16 #define TILE_VECTOR_TYPE15(DATA_TYPE) DATA_TYPE##16 #define TILE_VECTOR_TYPE16(DATA_TYPE) DATA_TYPE##16 /** Tile object * A tile object is a 2D memory block and can be accessed using the following syntax: * -# a[m0].v = access the the vector at row "m0" (OpenCL vector) * -# dst[m0].s[n0] = access the scalar element at row "m0" and column "n0" (scalar access) * * @param[in] DATA_TYPE Data type of the tile * @param[in] H Number of tile rows * @param[in] W Number of tile colums * @param[in] BASENAME Tile's name */ #define TILE(DATA_TYPE, H, W, BASENAME) TILE_STR(DATA_TYPE, H, W, BASENAME) #define TILE_STR(DATA_TYPE, H, W, BASENAME) \ union { \ DATA_TYPE s[TILE_VECTOR_SIZE##W]; \ TILE_VECTOR_TYPE##W(DATA_TYPE) v; \ } BASENAME[H] #define TENSOR4D_IMAGE(name) \ __read_only image2d_t name##_img, \ __global uchar *name##_ptr, \ uint name##_stride_x, \ uint name##_step_x, \ uint name##_stride_y, \ uint name##_step_y, \ uint name##_stride_z, \ uint name##_step_z, \ uint name##_stride_w, \ uint name##_step_w, \ uint name##_offset_first_element_in_bytes #define TENSOR4D_BUFFER(name) \ __global uchar *name##_ptr, \ uint name##_stride_x, \ uint name##_step_x, \ uint name##_stride_y, \ uint name##_step_y, \ uint name##_stride_z, \ uint name##_step_z, \ uint name##_stride_w, \ uint name##_step_w, \ uint name##_offset_first_element_in_bytes #define TENSOR4D_STR(name, type) TENSOR4D_##type(name) #define TENSOR4D(name, type) TENSOR4D_STR(name, type) #define TENSOR4D_T_IMAGE(name) \ __read_only image2d_t name##_img, \ __global uchar *name##_ptr, \ uint name##_stride_y, \ uint name##_stride_z, \ uint name##_stride_w, \ uint name##_c, \ uint name##_w, \ uint name##_h, \ uint name##_n, \ uint name##_offset_first_element_in_bytes #define TENSOR4D_T_BUFFER(name) \ __global uchar *name##_ptr, \ uint name##_stride_y, \ uint name##_stride_z, \ uint name##_stride_w, \ uint name##_c, \ uint name##_w, \ uint name##_h, \ uint name##_n, \ uint name##_offset_first_element_in_bytes #define TENSOR4D_T_STR(name, type) TENSOR4D_T_##type(name) /** Legacy tensor 4D arguments * * @param[in] name Tensor name. The tensor name is the prefix of the tensor components * @param[in] type Tensor type (BUFFER or IMAGE) */ #define TENSOR4D_T(name, type) TENSOR4D_T_STR(name, type) #define TENSOR4D_RO_T_IMAGE(name) \ __read_only image2d_t name##_img, \ TENSOR4D_T_BUFFER(name) #define TENSOR4D_RO_T_BUFFER(name) TENSOR4D_T_BUFFER(name) #define TENSOR4D_RO_T_STR(name, type) TENSOR4D_RO_T_##type(name) /** Read-Only (RO) tensor 4D. * * @param[in] name Tensor name. The tensor name is the prefix of the tensor components * @param[in] type Tensor type (BUFFER or IMAGE) */ #define TENSOR4D_RO_T(name, type) TENSOR4D_RO_T_STR(name, type) #define TENSOR4D_WO_T_IMAGE(name) \ __write_only image2d_t name##_img, \ TENSOR4D_T_BUFFER(name) #define TENSOR4D_WO_T_BUFFER(name) TENSOR4D_T_BUFFER(name) #define TENSOR4D_WO_T_STR(name, type) TENSOR4D_WO_T_##type(name) /** Write-Only (WO) tensor 4D. * * @param[in] name Tensor name. The tensor name is the prefix of the tensor components * @param[in] type Tensor type (BUFFER or IMAGE) */ #define TENSOR4D_WO_T(name, type) TENSOR4D_WO_T_STR(name, type) #define TENSOR3D_T_IMAGE(name) \ __read_only image2d_t name##_img, \ __global uchar *name##_ptr, \ uint name##_stride_y, \ uint name##_stride_z, \ uint name##_w, \ uint name##_h, \ uint name##_n, \ uint name##_offset_first_element_in_bytes #define TENSOR3D_T_BUFFER(name) \ __global uchar *name##_ptr, \ uint name##_stride_y, \ uint name##_stride_z, \ uint name##_w, \ uint name##_h, \ uint name##_n, \ uint name##_offset_first_element_in_bytes #define TENSOR3D_T_STR(name, type) TENSOR3D_T_##type(name) #define TENSOR3D_T(name, type) TENSOR3D_T_STR(name, type) #if !defined(UNROLL_WITH_PRAGMA) #define UNROLL_INCR(idx, step, macro) idx += (step); (macro) #define LOOP_UNROLLING_1(idx, step, macro) (macro) #define LOOP_UNROLLING_2(idx, step, macro) LOOP_UNROLLING_1(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_3(idx, step, macro) LOOP_UNROLLING_2(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_4(idx, step, macro) LOOP_UNROLLING_3(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_5(idx, step, macro) LOOP_UNROLLING_4(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_6(idx, step, macro) LOOP_UNROLLING_5(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_7(idx, step, macro) LOOP_UNROLLING_6(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_8(idx, step, macro) LOOP_UNROLLING_7(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_9(idx, step, macro) LOOP_UNROLLING_8(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_10(idx, step, macro) LOOP_UNROLLING_9(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_11(idx, step, macro) LOOP_UNROLLING_10(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_12(idx, step, macro) LOOP_UNROLLING_11(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_13(idx, step, macro) LOOP_UNROLLING_12(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_14(idx, step, macro) LOOP_UNROLLING_13(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_15(idx, step, macro) LOOP_UNROLLING_14(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_16(idx, step, macro) LOOP_UNROLLING_15(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_17(idx, step, macro) LOOP_UNROLLING_16(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_18(idx, step, macro) LOOP_UNROLLING_17(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_19(idx, step, macro) LOOP_UNROLLING_18(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_20(idx, step, macro) LOOP_UNROLLING_19(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_21(idx, step, macro) LOOP_UNROLLING_20(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_22(idx, step, macro) LOOP_UNROLLING_21(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_23(idx, step, macro) LOOP_UNROLLING_22(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_24(idx, step, macro) LOOP_UNROLLING_23(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_25(idx, step, macro) LOOP_UNROLLING_24(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_26(idx, step, macro) LOOP_UNROLLING_25(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_27(idx, step, macro) LOOP_UNROLLING_26(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_28(idx, step, macro) LOOP_UNROLLING_27(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_29(idx, step, macro) LOOP_UNROLLING_28(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_30(idx, step, macro) LOOP_UNROLLING_29(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_31(idx, step, macro) LOOP_UNROLLING_30(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_32(idx, step, macro) LOOP_UNROLLING_31(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_33(idx, step, macro) LOOP_UNROLLING_32(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_34(idx, step, macro) LOOP_UNROLLING_33(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_35(idx, step, macro) LOOP_UNROLLING_34(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_36(idx, step, macro) LOOP_UNROLLING_35(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_37(idx, step, macro) LOOP_UNROLLING_36(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_38(idx, step, macro) LOOP_UNROLLING_37(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_39(idx, step, macro) LOOP_UNROLLING_38(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_40(idx, step, macro) LOOP_UNROLLING_39(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_41(idx, step, macro) LOOP_UNROLLING_40(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_42(idx, step, macro) LOOP_UNROLLING_41(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_43(idx, step, macro) LOOP_UNROLLING_42(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_44(idx, step, macro) LOOP_UNROLLING_43(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_45(idx, step, macro) LOOP_UNROLLING_44(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_46(idx, step, macro) LOOP_UNROLLING_45(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_47(idx, step, macro) LOOP_UNROLLING_46(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_48(idx, step, macro) LOOP_UNROLLING_47(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_49(idx, step, macro) LOOP_UNROLLING_48(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_50(idx, step, macro) LOOP_UNROLLING_49(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_51(idx, step, macro) LOOP_UNROLLING_50(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_52(idx, step, macro) LOOP_UNROLLING_51(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_53(idx, step, macro) LOOP_UNROLLING_52(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_54(idx, step, macro) LOOP_UNROLLING_53(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_55(idx, step, macro) LOOP_UNROLLING_54(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_56(idx, step, macro) LOOP_UNROLLING_55(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_57(idx, step, macro) LOOP_UNROLLING_56(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_58(idx, step, macro) LOOP_UNROLLING_57(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_59(idx, step, macro) LOOP_UNROLLING_58(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_60(idx, step, macro) LOOP_UNROLLING_59(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_61(idx, step, macro) LOOP_UNROLLING_60(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_62(idx, step, macro) LOOP_UNROLLING_61(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_63(idx, step, macro) LOOP_UNROLLING_62(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_64(idx, step, macro) LOOP_UNROLLING_63(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_65(idx, step, macro) LOOP_UNROLLING_64(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_66(idx, step, macro) LOOP_UNROLLING_65(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_67(idx, step, macro) LOOP_UNROLLING_66(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_68(idx, step, macro) LOOP_UNROLLING_67(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_69(idx, step, macro) LOOP_UNROLLING_68(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_70(idx, step, macro) LOOP_UNROLLING_69(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_71(idx, step, macro) LOOP_UNROLLING_70(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_72(idx, step, macro) LOOP_UNROLLING_71(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_73(idx, step, macro) LOOP_UNROLLING_72(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_74(idx, step, macro) LOOP_UNROLLING_73(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_75(idx, step, macro) LOOP_UNROLLING_74(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_76(idx, step, macro) LOOP_UNROLLING_75(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_77(idx, step, macro) LOOP_UNROLLING_76(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_78(idx, step, macro) LOOP_UNROLLING_77(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_79(idx, step, macro) LOOP_UNROLLING_78(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_80(idx, step, macro) LOOP_UNROLLING_79(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_81(idx, step, macro) LOOP_UNROLLING_80(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_82(idx, step, macro) LOOP_UNROLLING_81(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_83(idx, step, macro) LOOP_UNROLLING_82(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_84(idx, step, macro) LOOP_UNROLLING_83(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_85(idx, step, macro) LOOP_UNROLLING_84(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_86(idx, step, macro) LOOP_UNROLLING_85(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_87(idx, step, macro) LOOP_UNROLLING_86(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_88(idx, step, macro) LOOP_UNROLLING_87(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_89(idx, step, macro) LOOP_UNROLLING_88(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_90(idx, step, macro) LOOP_UNROLLING_89(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_91(idx, step, macro) LOOP_UNROLLING_90(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_92(idx, step, macro) LOOP_UNROLLING_91(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_93(idx, step, macro) LOOP_UNROLLING_92(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_94(idx, step, macro) LOOP_UNROLLING_93(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_95(idx, step, macro) LOOP_UNROLLING_94(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_96(idx, step, macro) LOOP_UNROLLING_95(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_97(idx, step, macro) LOOP_UNROLLING_96(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_98(idx, step, macro) LOOP_UNROLLING_97(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_99(idx, step, macro) LOOP_UNROLLING_98(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_100(idx, step, macro) LOOP_UNROLLING_99(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_101(idx, step, macro) LOOP_UNROLLING_100(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_102(idx, step, macro) LOOP_UNROLLING_101(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_103(idx, step, macro) LOOP_UNROLLING_102(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_104(idx, step, macro) LOOP_UNROLLING_103(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_105(idx, step, macro) LOOP_UNROLLING_104(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_106(idx, step, macro) LOOP_UNROLLING_105(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_107(idx, step, macro) LOOP_UNROLLING_106(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_108(idx, step, macro) LOOP_UNROLLING_107(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_109(idx, step, macro) LOOP_UNROLLING_108(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_110(idx, step, macro) LOOP_UNROLLING_109(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_111(idx, step, macro) LOOP_UNROLLING_110(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_112(idx, step, macro) LOOP_UNROLLING_111(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_113(idx, step, macro) LOOP_UNROLLING_112(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_114(idx, step, macro) LOOP_UNROLLING_113(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_115(idx, step, macro) LOOP_UNROLLING_114(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_116(idx, step, macro) LOOP_UNROLLING_115(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_117(idx, step, macro) LOOP_UNROLLING_116(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_118(idx, step, macro) LOOP_UNROLLING_117(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_119(idx, step, macro) LOOP_UNROLLING_118(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_120(idx, step, macro) LOOP_UNROLLING_119(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_121(idx, step, macro) LOOP_UNROLLING_120(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_122(idx, step, macro) LOOP_UNROLLING_121(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_123(idx, step, macro) LOOP_UNROLLING_122(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_124(idx, step, macro) LOOP_UNROLLING_123(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_125(idx, step, macro) LOOP_UNROLLING_124(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_126(idx, step, macro) LOOP_UNROLLING_125(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_127(idx, step, macro) LOOP_UNROLLING_126(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_128(idx, step, macro) LOOP_UNROLLING_127(idx, step, macro); UNROLL_INCR(idx, step, macro) #define LOOP_UNROLLING_STR(type, idx, start, step, num, macro) \ { \ type idx = start; \ LOOP_UNROLLING_##num(idx, step, macro); \ } #else // !defined(UNROLL_WITH_PRAGMA) #define LOOP_UNROLLING_STR(type, idx, start, step, num, macro) \ { \ _Pragma("unroll") \ for(type idx = start; idx < (num * step); idx += step) \ { \ (macro); \ } \ } #endif // !defined(UNROLL_WITH_PRAGMA) #define LOOP_UNROLLING(type, idx, start, step, num, macro) LOOP_UNROLLING_STR(type, idx, start, step, num, macro) /** Get the get_global_id with partial N0. This function is useful when the dimension is not multiple of N0 and we need to use a partial N0 * to avoid out-of-bound read/write * * @note PARTIAL_N0 is used for get_global_id(n) = 0. * * @param[in] IDX get_global_id index (0,1 and 2 only) * @param[in] N0 Number of elements read/written on the IDX direction * @param[in] PARTIAL_N0 Number of elements read/written on the IDX direction for get_global_id(IDX) = 0. If zero, * the Number of elements read/written on the IDX direction for get_global_id(IDX) = 0 is N0 */ #define GET_SPATIAL_IDX(IDX, N0, PARTIAL_N0) (max((int)(get_global_id(IDX) * N0 - (N0 - PARTIAL_N0) % N0), 0)) /** Dot product integet 8bit function * * @note Performs: c += dot(a, b) * * @param[in] A_DATA_TYPE A (lhs) data type * @param[in] B_DATA_TYPE B (rhs) data type * @param[in] C_DATA_TYPE C (accumulator) data type * @param[in] K0 Number of accumulations * @param[in] a OpenCL vector a * @param[in] b OpenCL vector b * @param[in] c Scalar variable c */ #define DOT_PRODUCT_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, b, c) DOT_PRODUCT_INTEGER8_STR(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, b, c) #define DOT_PRODUCT_INTEGER8_STR(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, b, c) DOT_PRODUCT##K0##_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) #define DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ c += (C_DATA_TYPE)(a) * (C_DATA_TYPE)(b); \ }) #if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_khr_integer_dot_product) #define DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += dot((A_DATA_TYPE##4)((a).s01, (A_DATA_TYPE##2)(0)), (B_DATA_TYPE##4)(((b).s01), (B_DATA_TYPE##2)(0))); #define DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += dot((A_DATA_TYPE##4)((a).s012, (A_DATA_TYPE)0), (B_DATA_TYPE##4)(((b).s012), (B_DATA_TYPE)0)); #define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += dot((a), (b)); #elif defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_khr_integer_dot_product) #define DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c = arm_dot_acc((A_DATA_TYPE##4)((a).s01, (A_DATA_TYPE##2)(0)), (B_DATA_TYPE##4)(((b).s01), (B_DATA_TYPE##2)(0)), (c)); #define DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c = arm_dot_acc((A_DATA_TYPE##4)((a).s012, (A_DATA_TYPE)0), (B_DATA_TYPE##4)(((b).s012), (B_DATA_TYPE)0), (c)); #define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c = arm_dot_acc((a), (b), (c)); #elif defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) #define DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += arm_dot((A_DATA_TYPE##4)((a).s01, (A_DATA_TYPE##2)(0)), (B_DATA_TYPE##4)(((b).s01), (B_DATA_TYPE##2)(0))); #define DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += arm_dot((A_DATA_TYPE##4)((a).s012, (A_DATA_TYPE)0), (B_DATA_TYPE##4)(((b).s012), (B_DATA_TYPE)0)); #define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += arm_dot((a), (b)); #else // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) #define DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ c += (C_DATA_TYPE)(a).s0 * (C_DATA_TYPE)(b).s0; \ c += (C_DATA_TYPE)(a).s1 * (C_DATA_TYPE)(b).s1; \ }) #define DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c); \ c += (C_DATA_TYPE)(a).s2 * (C_DATA_TYPE)(b).s2; \ }) #define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, x, y, val) \ ({ \ val += (C_DATA_TYPE)(x).s0 * (C_DATA_TYPE)(y).s0; \ val += (C_DATA_TYPE)(x).s1 * (C_DATA_TYPE)(y).s1; \ val += (C_DATA_TYPE)(x).s2 * (C_DATA_TYPE)(y).s2; \ val += (C_DATA_TYPE)(x).s3 * (C_DATA_TYPE)(y).s3; \ }) #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) #define DOT_PRODUCT5_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \ DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s4), ((b).s4), c); \ }) #define DOT_PRODUCT6_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \ DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s45), ((b).s45), c); \ }) #define DOT_PRODUCT7_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \ DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s456), ((b).s456), c); \ }) #define DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).lo), ((b).lo), c); \ DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).hi), ((b).hi), c); \ }) #define DOT_PRODUCT9_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s8), ((b).s8), c); \ }) #define DOT_PRODUCT10_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89), ((b).s89), c); \ }) #define DOT_PRODUCT11_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89A), ((b).s89A), c); \ }) #define DOT_PRODUCT12_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89AB), ((b).s89AB), c); \ }) #define DOT_PRODUCT13_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ DOT_PRODUCT5_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABC), ((b).s89ABC), c); \ }) #define DOT_PRODUCT14_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ DOT_PRODUCT6_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABCD), ((b).s89ABCD), c); \ }) #define DOT_PRODUCT15_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \ DOT_PRODUCT7_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABCDE), ((b).s89ABCDE), c); \ }) #define DOT_PRODUCT16_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \ ({ \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).lo), ((b).lo), c); \ DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).hi), ((b).hi), c); \ }) /** Dot product integet 8bit function * * @note Performs: c += dot(a, b) * * @param[in] A_DATA_TYPE A (lhs) data type * @param[in] B_DATA_TYPE B (rhs) data type * @param[in] C_DATA_TYPE C (accumulator) data type * @param[in] K0 Number of accumulations * @param[in] a OpenCL vector a * @param[in] c Scalar variable c */ #define REDUCE_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, c) REDUCE_INTEGER8_STR(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, c) #define REDUCE_INTEGER8_STR(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, c) DOT_PRODUCT_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, K0, a, (TILE_VECTOR_TYPE##K0(B_DATA_TYPE))1, c) /** Load a vector from global memory (tensor) * * @param[in] DATA_TYPE Data type * @param[in] WIDTH Number of dst columns * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] X Starting X position * @param[in] Y Starting Y position * @param[in] STRIDE_Y Stride Y (in bytes) */ #define V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y) V_LOAD_STR(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y) #define V_LOAD_STR(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y) V_LOAD_##TENSOR_TYPE(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) #define V_LOAD_BUFFER(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) \ VLOAD(WIDTH) \ (0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (Y) * (STRIDE_Y))) #define V_LOAD_IMAGE(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) READ_IMAGE2D(DATA_TYPE, CONVERT_VECTOR_SIZE_TO_PIXEL_UNIT(WIDTH), TENSOR##_img, (X) / 4, (Y)) /** Store a vector in global memory (tensor) * * @param[in] DATA_TYPE Data type * @param[in] WIDTH Number of dst columns * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] X Starting X position * @param[in] Y Starting Y position * @param[in] STRIDE_Y Stride Y (in bytes) * @param[in] VALUES Values to store in memory */ #define V_STORE(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y, VALUES) V_STORE_STR(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y, VALUES) #define V_STORE_STR(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y, VALUES) V_STORE_##TENSOR_TYPE(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y, VALUES) #define V_STORE_BUFFER(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y, VALUES) \ VSTORE(WIDTH) \ (VALUES, 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (Y) * (STRIDE_Y))) #define V_STORE_IMAGE(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y, VALUES) WRITE_IMAGE2D(DATA_TYPE, CONVERT_VECTOR_SIZE_TO_PIXEL_UNIT(WIDTH), TENSOR##_img, (X) / 4, (Y), VALUES) /** Load a tile from global memory (tensor) * * @param[in] DATA_TYPE Data type * @param[in] HEIGHT Number of dst rows * @param[in] WIDTH Number of dst columns * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] X Starting X position * @param[in] Y Starting Y position * @param[in] YI_MULTIPLIER Parameter used to multiply the internal row increment (_i). * In common cases should be 1 but it becomes useful when we want to load rows which are multiple of STRIDE_Y. (e.g. loading the weights of convolution layer). * In this case the address calculation is performed as: (Y + _i * Y_MULTIPLIER) * STRIDE_Y * @param[in] STRIDE_Y Stride Y (in bytes) used to load each row. * @param[out] dst Output tile */ #define T_LOAD(DATA_TYPE, HEIGHT, WIDTH, TENSOR_TYPE, TENSOR, X, Y, YI_MULTIPLIER, STRIDE_Y, dst) \ ({ \ LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \ { \ dst[_i].v = V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, ((Y) + _i * (int)(YI_MULTIPLIER)), STRIDE_Y); \ }) \ }) /** Load a tile from global memory (tensor) using an indirect Y index tile * * @param[in] DATA_TYPE Data type * @param[in] HEIGHT Number of dst rows * @param[in] WIDTH Number of dst columns * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] X Starting X position * @param[in] STRIDE_Y Stride Y (in bytes) * @param[in] indirect_y Indirect Y index tile * @param[out] dst Output tile */ #define T_LOAD_INDIRECT(DATA_TYPE, HEIGHT, WIDTH, TENSOR_TYPE, TENSOR, X, STRIDE_Y, indirect_y, dst) \ ({ \ LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \ { \ dst[_i].v = V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, (indirect_y[_i].v), STRIDE_Y); \ }) \ }) /** Load a tile from global memory (tensor) using an indirect Y index tile and conditionally use a different length for the load * * @note If WIDTH1_CONDITION is true, the load will use the WIDTH1 length for the store * @note The vectors are stored in reverse order so the invalid rows are overwritten by the valid ones * * @param[in] DATA_TYPE Data type * @param[in] HEIGHT Number of dst rows * @param[in] WIDTH0 Store width to use if WIDTH1_CONDITION = false * @param[in] WIDTH1 Store width to use if WIDTH1_CONDITION = true * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] X Starting X position * @param[in] STRIDE_Y Stride Y (in bytes) used to load each row. * @param[in] WIDTH1_CONDITION Condition to select the WIDTH1 store * @param[out] dst Output tile * @param[out] indirect_y Indirect Y index tile */ #define T_LOAD_INDIRECT_WIDTH_SELECT(DATA_TYPE, HEIGHT, WIDTH0, WIDTH1, TENSOR_TYPE, TENSOR, X, STRIDE_Y, WIDTH1_CONDITION, dst, indirect_y) \ ({ \ if(WIDTH1_CONDITION) \ { \ LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \ { \ VLOAD_PARTIAL(WIDTH0, WIDTH1) \ (dst[HEIGHT - 1 - _i].v, 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ }) \ } \ else \ { \ LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \ { \ dst[HEIGHT - 1 - _i].v = V_LOAD(DATA_TYPE, WIDTH0, TENSOR_TYPE, TENSOR, X, (indirect_y[HEIGHT - 1 - _i].v), STRIDE_Y); \ }) \ } \ }) /** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout * * @param[in] DATA_TYPE Data type * @param[in] TILE_HEIGHT Number of elements to load from Y (height) dimension * @param[in] TILE_WIDTH Number of elements to load from X (width) dimension * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] B Starting batch index * @param[in] Y Starting Y index * @param[in] X Starting X index * @param[in] C Starting C index * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension * @param[in] STRIDE_Y Stride Y (in bytes) * @param[out] dst Output tile */ #define T_LOAD_NHWC(DATA_TYPE, TILE_HEIGHT, TILE_WIDTH, TILE_CHANNELS, TENSOR_TYPE, TENSOR, B, Y, X, C, TENSOR_WIDTH, TENSOR_HEIGHT, STRIDE_Y, dst) \ ({ \ LOOP_UNROLLING(int, _yk, 0, 1, TILE_HEIGHT, \ { \ LOOP_UNROLLING(int, _xk, 0, 1, TILE_WIDTH, \ { \ int _src_y = (X) + _xk + ((Y) + _yk) * (TENSOR_WIDTH); \ _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT); \ int _src_valid_y = (((X) + _xk) >= 0 && ((X) + _xk) < (int)(TENSOR_WIDTH) && ((Y) + _yk) >= 0 && ((Y) + _yk) < (int)(TENSOR_HEIGHT)); \ if(_src_valid_y != 0) \ { \ dst[_xk + _yk * (TILE_WIDTH)].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, STRIDE_Y); \ } \ }) \ }) \ }) /** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout with dilation for the X and Y increments * * @param[in] DATA_TYPE Data type * @param[in] TILE_HEIGHT Number of elements to load from Y (height) dimension * @param[in] TILE_WIDTH Number of elements to load from X (width) dimension * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] B Starting batch index * @param[in] Y Starting Y index * @param[in] X Starting X index * @param[in] C Starting C index * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension * @param[in] DILATION_X Dilation for the X increment * @param[in] DILATION_Y Dilation for the Y increment * @param[in] BOUNDARY_CHECK Boundary check flag. If true, it checks for any out-of-bound reads * @param[out] dst Output tile */ #define T_LOAD_NHWC_WITH_DILATION(DATA_TYPE, TILE_HEIGHT, TILE_WIDTH, TILE_CHANNELS, TENSOR_TYPE, TENSOR, B, Y, X, C, TENSOR_WIDTH, TENSOR_HEIGHT, DILATION_X, DILATION_Y, BOUNDARY_CHECK, dst) \ ({ \ LOOP_UNROLLING(int, _yk, 0, 1, TILE_HEIGHT, \ { \ LOOP_UNROLLING(int, _xk, 0, 1, TILE_WIDTH, \ { \ int _src_y = (X) + _xk * (DILATION_X); \ int _src_z = ((Y) + _yk * (DILATION_Y)); \ int _src_w = (B); \ bool _src_valid_y = (((X) + _xk * (DILATION_X)) >= 0) && (((X) + _xk * (DILATION_X)) < (int)(TENSOR_WIDTH)) && (((Y) + _yk * (DILATION_Y)) >= 0) && (((Y) + _yk * (DILATION_Y)) < (int)(TENSOR_HEIGHT)); \ if(!(BOUNDARY_CHECK)) \ { \ dst[_xk + _yk * (TILE_WIDTH)].v = VLOAD(TILE_CHANNELS) \ (0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (C) * sizeof(DATA_TYPE) + (_src_y) * (TENSOR##_stride_y) + (_src_z) * (TENSOR##_stride_z) + (_src_w) * (TENSOR##_stride_w))); \ } \ else \ { \ if(_src_valid_y) \ { \ dst[_xk + _yk * (TILE_WIDTH)].v = VLOAD(TILE_CHANNELS) \ (0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (C) * sizeof(DATA_TYPE) + (_src_y) * (TENSOR##_stride_y) + (_src_z) * (TENSOR##_stride_z) + (_src_w) * (TENSOR##_stride_w))); \ } \ } \ }) \ }) \ }) /** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout using indirect X and Y coordinates * * @param[in] DATA_TYPE Data type * @param[in] TILE_AREA Number of elements to load from Y (height) dimension * Number of elements to load from X (width) dimension * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] B Starting batch index * @param[in] Y Starting Y index * @param[in] X Starting X index * @param[in] C Starting C index * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension * @param[in] STRIDE_Y Stride Y (in bytes) * @param[out] xi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect X coordinate * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate * @param[out] dst Output tile */ #define T_LOAD_NHWC_INDIRECT(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, B, Y, X, C, TENSOR_WIDTH, TENSOR_HEIGHT, STRIDE_Y, xi, yi, dst) \ ({ \ LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \ { \ int _src_y = (X) + xi[_i].v + ((Y) + yi[_i].v) * (TENSOR_WIDTH); \ _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT); \ int _src_valid_y = (((X) + xi[_i].v) >= 0 && ((X) + xi[_i].v) < (int)(TENSOR_WIDTH) && ((Y) + yi[_i].v) >= 0 && ((Y) + yi[_i].v) < (int)(TENSOR_HEIGHT)); \ if(_src_valid_y != 0) \ { \ dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, STRIDE_Y); \ } \ }) \ }) /** Load a tile from global memory (tensor) using an indirect buffer for the Y coordinates * * @param[in] DATA_TYPE Data type * @param[in] TILE_AREA Number of elements to load from Y (height) dimension * Number of elements to load from X (width) dimension * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). * When TENSOR_TYPE=IMAGE, the if condition for the out-of-bound check can be skipped * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] C Starting C index * @param[in] STRIDE_Y Stride Y (in bytes) * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate * 16 is the maximum indirect buffer size. * @param[out] dst Output tile */ #define T_LOAD2D_INDIRECT(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, STRIDE_Y, yi, dst) T_LOAD2D_INDIRECT_STR(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, STRIDE_Y, yi, dst) #define T_LOAD2D_INDIRECT_STR(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, STRIDE_Y, yi, dst) T_LOAD2D_INDIRECT_##TENSOR_TYPE(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, STRIDE_Y, yi, dst) #define T_LOAD2D_INDIRECT_BUFFER(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, STRIDE_Y, yi, dst) \ ({ \ LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \ { \ if(yi[0].s[_i] >= 0) \ { \ dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, yi[0].s[_i], STRIDE_Y); \ } \ }) \ }) #define T_LOAD2D_INDIRECT_IMAGE(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, STRIDE_Y, yi, dst) \ ({ \ LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \ { \ dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, yi[0].s[_i], STRIDE_Y); \ }) \ }) /** Load a tile from global memory (tensor) when the tensor is stored using a NDHWC layout using indirect X, Y and Z coordinates * * @param[in] DATA_TYPE Data type * @param[in] TILE_AREA Number of elements to load from Y (height) dimension * Number of elements to load from X (width) dimension * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16) * @param[in] TENSOR Tensor basename * @param[in] B Starting batch index * @param[in] Z Starting Z index * @param[in] Y Starting Y index * @param[in] X Starting X index * @param[in] C Starting C index * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension * @param[in] TENSOR_DEPTH Number of elements to load from Z (depth) dimension * @param[in] STRIDE_Y Stride Y (in bytes) * @param[out] xi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect X coordinate * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate * @param[out] zi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Z coordinate * @param[out] dst Output tile */ #define T_LOAD_NDHWC_INDIRECT(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, B, Z, Y, X, C, TENSOR_WIDTH, TENSOR_HEIGHT, TENSOR_DEPTH, STRIDE_Y, xi, yi, zi, dst) \ ({ \ LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \ { \ int _src_y = (X) + xi[_i].v + ((Y) + yi[_i].v) * (TENSOR_WIDTH) + ((Z) + zi[_i].v) * (TENSOR_WIDTH * TENSOR_HEIGHT); \ _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT) * (int)(TENSOR_DEPTH); \ int _src_valid_y = (((X) + xi[_i].v) >= 0 && ((X) + xi[_i].v) < (int)(TENSOR_WIDTH) && ((Y) + yi[_i].v) >= 0 && ((Y) + yi[_i].v) < (int)(TENSOR_HEIGHT) \ && ((Z) + zi[_i].v) >= 0 && ((Z) + zi[_i].v) < (int)(TENSOR_DEPTH)); \ if(_src_valid_y != 0) \ { \ dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, STRIDE_Y); \ } \ }) \ }) /** Store a tile to global memory (tensor) using an indirect Y index tile and conditionally use a different length for the store * * @note If WIDTH1_CONDITION is true, the store will use the WIDTH1 length for the store * @note The vectors are stored in reverse order so the invalid rows are overwritten by the valid ones * * @param[in] DATA_TYPE Data type * @param[in] HEIGHT Number of src rows * @param[in] WIDTH0 Store width to use if WIDTH1_CONDITION = false * @param[in] WIDTH1 Store width to use if WIDTH1_CONDITION = true * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported * cl_image is not supported. * @param[in] TENSOR Tensor basename * @param[in] X Starting X position * @param[in] STRIDE_Y Stride Y (in bytes) * @param[in] WIDTH1_CONDITION Condition to select the WIDTH1 store * @param[in] src Input tile * @param[in] indirect_y Indirect Y index tile */ #define T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, HEIGHT, WIDTH0, WIDTH1, TENSOR_TYPE, TENSOR, X, STRIDE_Y, WIDTH1_CONDITION, src, indirect_y) \ ({ \ if(WIDTH1_CONDITION) \ { \ LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \ { \ VSTORE_PARTIAL(WIDTH0, WIDTH1) \ (CONVERT(src[HEIGHT - 1 - _i].v, VEC_DATA_TYPE(DATA_TYPE, WIDTH0)), 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ }) \ } \ else \ { \ LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \ { \ VSTORE(WIDTH0) \ (CONVERT(src[HEIGHT - 1 - _i].v, VEC_DATA_TYPE(DATA_TYPE, WIDTH0)), 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ }) \ } \ }) /** Offset correction for the QASYMM8 computation * * @param[in] ACC_DATA_TYPE Accumulator data type * @param[in] M0 Number of src/dst rows * @param[in] N0 Number of src/dst columns * @param[in] K0 Number of src columns * @param[in] SRC_OFFSET Source quantization offset * @param[in] WEI_OFFSET Weights quantization shift * @param[in] lhs LHS tile * @param[in] rhs RHS tile * @param[out] dst DST tile */ #define T_OFFSET_CORRECTION(ACC_DATA_TYPE, M0, N0, K0, SRC_OFFSET, WEI_OFFSET, lhs, rhs, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ ACC_DATA_TYPE _tm = 0; \ LOOP_UNROLLING(int, _k0, 0, 1, K0, \ { \ _tm += ((ACC_DATA_TYPE)lhs[_m0].s[_k0] * (ACC_DATA_TYPE)WEI_OFFSET); \ }) \ LOOP_UNROLLING(int, _n0, 0, 1, N0, \ { \ dst[_m0].s[_n0] += _tm; \ LOOP_UNROLLING(int, _k0, 0, 1, K0, \ { \ dst[_m0].s[_n0] += ((ACC_DATA_TYPE)rhs[_n0].s[_k0] * (ACC_DATA_TYPE)SRC_OFFSET); \ }) \ }) \ }) \ }) /** 8-bit quantization with fixed-point scale * * @param[in] SRC_DATA_TYPE SRC data type * @param[in] DST_DATA_TYPE DST data type * @param[in] QUANTIZATION_TYPE Quantization type (PER_TENSOR or PER_CHANNEL) * @param[in] M0 Number of src/dst rows * @param[in] N0 Number of src/dst columns * @param[in] DST_OFFSET Quantization offset used for both the per-tensor and per-channel quantization * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization * @param[in] src Input tile * @param[in] dst_multipliers Output multipliers tile for the per-channel quantization * @param[in] dst_shifts Output shift tile for the per-channel quantization * @param[out] dst Output tile */ #define T_QUANTIZE8(SRC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) T_QUANTIZE8_STR(SRC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) #define T_QUANTIZE8_STR(SRC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) T_QUANTIZE8_##QUANTIZATION_TYPE(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) /** 8-bit per-tensor quantization with fixed-point scale * * @param[in] SRC_DATA_TYPE SRC data type * @param[in] DST_DATA_TYPE DST data type * @param[in] M0 Number of src/dst rows * @param[in] N0 Number of src/dst columns * @param[in] DST_OFFSET Quantization offset * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization * @param[in] src Input tile * @param[in] dst_multipliers (unused) * @param[in] dst_shifts (unused) * @param[out] dst Output tile */ #define T_QUANTIZE8_PER_TENSOR(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ LOOP_UNROLLING(int, _n0, 0, 1, N0, \ { \ SRC_DATA_TYPE _tmp = 0; \ SRC_DATA_TYPE _src = src[_m0].s[_n0]; \ _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-DST_SHIFT)), ((SRC_DATA_TYPE)DST_SHIFT < (SRC_DATA_TYPE)0)); \ SRC_DATA_TYPE overflow = _src == DST_MULTIPLIER && _src == INT_MIN; \ long a_64 = (long)(_src); \ long b_64 = (long)(DST_MULTIPLIER); \ long ab_64 = a_64 * b_64; \ long mask1 = 1 << 30; \ long mask2 = 1 - (1 << 30); \ long is_positive_or_zero = ab_64 >= 0; \ long nudge = select(mask2, mask1, is_positive_or_zero); \ SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \ _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \ if(DST_SHIFT >= 0) \ { \ long mask = ((((int)1) << DST_SHIFT) - (long)1); \ long threshold = _tmp < (int)0 ? (mask >> 1) + (long)1 : (mask >> 1) + 0; \ _tmp = (_tmp & mask) > threshold ? (_tmp >> DST_SHIFT) + (int)1 : (_tmp >> DST_SHIFT); \ } \ _tmp += DST_OFFSET; \ dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \ }) \ }) \ }) /** 8-bit per-channel quantization with fixed-point scale * * @param[in] SRC_DATA_TYPE SRC data type * @param[in] DST_DATA_TYPE DST data type * @param[in] M0 Number of src/dst rows * @param[in] N0 Number of src/dst columns * @param[in] DST_OFFSET Quantization offset * @param[in] DST_SHIFT (unused) * @param[in] DST_MULTIPLIER (unused) * @param[in] src Input tile * @param[in] dst_multipliers Output multipliers tile for the per-channel quantization * @param[in] dst_shifts Output shift tile for the per-channel quantization * @param[out] dst Output tile */ #define T_QUANTIZE8_PER_CHANNEL(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst_multipliers, dst_shifts, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ LOOP_UNROLLING(int, _n0, 0, 1, N0, \ { \ SRC_DATA_TYPE _tmp = 0; \ SRC_DATA_TYPE _tmp2 = 0; \ SRC_DATA_TYPE _src = src[_m0].s[_n0]; \ SRC_DATA_TYPE _dst_multiplier = dst_multipliers[0].s[_n0]; \ SRC_DATA_TYPE _dst_shift = dst_shifts[0].s[_n0]; \ _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-_dst_shift)), ((SRC_DATA_TYPE)_dst_shift < (SRC_DATA_TYPE)0)); \ SRC_DATA_TYPE overflow = _src == _dst_multiplier && _src == INT_MIN; \ long a_64 = (long)(_src); \ long b_64 = (long)(_dst_multiplier); \ long ab_64 = a_64 * b_64; \ long mask1 = 1 << 30; \ long mask2 = 1 - (1 << 30); \ long is_positive_or_zero = ab_64 >= 0; \ long nudge = select(mask2, mask1, is_positive_or_zero); \ SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \ _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \ long mask = ((((int)1) << _dst_shift) - (int)1); \ long threshold = (mask >> 1) + any(_tmp); \ _tmp2 = _tmp >> _dst_shift; \ _tmp2 += select(0, 1, (_tmp & mask) > threshold); \ _tmp = select(_tmp, _tmp2, _dst_shift >= 0); \ _tmp += DST_OFFSET; \ dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \ }) \ }) \ }) /** Quantized the 8-bit tile with fixed-point scale for asymmetric * * @param[in] SRC_DATA_TYPE SRC data type * @param[in] DST_DATA_TYPE DST data type * @param[in] M0 Number of src/dst rows * @param[in] N0 Number of src/dst columns * @param[in] DST_OFFSET Quantization offset used for both the per-tensor and per-channel quantization * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization * @param[in] src Input tile * @param[out] dst Output tile */ #define T_QUANTIZE8_ASYMMETRIC(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ LOOP_UNROLLING(int, _n0, 0, 1, N0, \ { \ SRC_DATA_TYPE _tmp = 0; \ SRC_DATA_TYPE _src = src[_m0].s[_n0]; \ _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-DST_SHIFT)), ((SRC_DATA_TYPE)DST_SHIFT < (SRC_DATA_TYPE)0)); \ SRC_DATA_TYPE overflow = _src == DST_MULTIPLIER && _src == INT_MIN; \ long a_64 = (long)(_src); \ long b_64 = (long)(DST_MULTIPLIER); \ long ab_64 = a_64 * b_64; \ long mask1 = 1 << 30; \ long mask2 = 1 - (1 << 30); \ long is_positive_or_zero = ab_64 >= 0; \ long nudge = select(mask2, mask1, is_positive_or_zero); \ SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \ _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \ if(DST_SHIFT >= 0) \ { \ long mask = ((((int)1) << DST_SHIFT) - (int)1); \ long threshold = _tmp < (int)0 ? (mask >> 1) + (long)1 : (mask >> 1) + 0; \ _tmp = (_tmp & mask) > threshold ? (_tmp >> DST_SHIFT) + (int)1 : (_tmp >> DST_SHIFT); \ } \ _tmp += DST_OFFSET; \ dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \ }) \ }) \ }) /** Conditional rowset (memset by row) * * @note Set the row to VALUE_TO_SET if the corresponding mask == 0 * * @param[in] DATA_TYPE Data type * @param[in] M0 Number of LHS rows * @param[in] N0 Number of LHS columns * @param[in] VALUE_TO_SET Value to set the row * @param[in, out] a Input/output tile * @param[out] mask Mask to check for setting the row to VALUE_TO_SET */ #define T_ROWSET_MASK(DATA_TYPE, M0, N0, VALUE_TO_SET, a, mask) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ LOOP_UNROLLING(int, _n0, 0, 1, N0, \ { \ a[_m0].s[_n0] = select((DATA_TYPE)(a[_m0].s[_n0]), (DATA_TYPE)(VALUE_TO_SET), (SELECT_DATA_TYPE(DATA_TYPE))(mask[_m0].v == (DATA_TYPE)0)); \ }) \ }) \ }) /** Element-wise activation for floating point types * * @note Performs: activation(LHS) = DST * * @param[in] DATA_TYPE SRC/DST data type * @param[in] M0 Number of SRC/DST rows * @param[in] N0 Number of SRC/DST columns * @param[in] ACTIVATION_TYPE Activation type * @param[in] A_VAL A value used for the activation (e.g. tanh_op, brelu,..) * @param[in] B_VAL B value used for the activation (e.g. tanh_op, brelu,..) * @param[out] src SRC tile * @param[out] dst DST tile */ #define T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, src, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, N0, src[_m0].v, A_VAL, B_VAL); \ }) \ }) // RELU Activation #define relu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (max((DATA_TYPE)ZERO_VALUE, x)) // Bounded RELU Activation #define brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (min((DATA_TYPE)A_VAL, max((DATA_TYPE)ZERO_VALUE, x))) // Lower Upper Bounded RELU Activation #define lu_brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (min(max(x, (DATA_TYPE)B_VAL), (DATA_TYPE)A_VAL)) // Hard Swish Activation #define hard_swish_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x * ((min(max((DATA_TYPE)(x + (DATA_TYPE)3.f), (DATA_TYPE)0.f), (DATA_TYPE)6.f)) * (DATA_TYPE)0.166666667f)) // Identity Activation #define identity_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x) #define ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) op##_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) #define ACTIVATION_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) #define V_ADD(A_VAL, B_VAL) ((A_VAL) + (B_VAL)) #define V_SUB(A_VAL, B_VAL) ((A_VAL) - (B_VAL)) #define V_DIV(A_VAL, B_VAL) ((A_VAL) / (B_VAL)) #define V_MUL(A_VAL, B_VAL) ((A_VAL) * (B_VAL)) /** Element-wise activation for quantized types * * @note Performs: activation(LHS) = DST * * @param[in] DATA_TYPE SRC/DST data type * @param[in] M0 Number of SRC/DST rows * @param[in] N0 Number of SRC/DST columns * @param[in] ACTIVATION_TYPE Activation type * @param[in] ZERO_VALUE The zero value to consider in the computation * @param[in] A_VAL A value used for the activation (e.g. tanh_op, brelu,..) * @param[in] B_VAL B value used for the activation (e.g. tanh_op, brelu,..) * @param[out] src SRC tile * @param[out] dst DST tile */ #define T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_VALUE, A_VAL, B_VAL, src, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = ACTIVATION_QUANTIZED(ACTIVATION_TYPE, DATA_TYPE, N0, ZERO_VALUE, A_VAL, B_VAL, src[_m0].v); \ }) \ }) /** Element-wise addition between two tiles * * @note Performs: LHS + RHS = DST * * @param[in] DATA_TYPE LHS/RHS/DST data type * @param[in] M0 Number of LHS rows * @param[in] N0 Number of LHS columns * @param[in] lhs LHS tile * @param[in] rhs Constant RHS tile * @param[out] dst DST tile */ #define T_ADD(DATA_TYPE, M0, N0, lhs, rhs, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = lhs[_m0].v + rhs[_m0].v; \ }) \ }) /** Element-wise addition with a constant value * * @note Performs: LHS + constant = DST * * @param[in] DATA_TYPE LHS/RHS/DST data type * @param[in] M0 Number of LHS rows * @param[in] N0 Number of LHS columns * @param[in] lhs LHS tile * @param[in] rhs_constant Constant value * @param[out] dst DST tile */ #define T_ADD_CONSTANT(DATA_TYPE, M0, N0, lhs, rhs_constant, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = lhs[_m0].v + (DATA_TYPE)rhs_constant; \ }) \ }) #define T_ELTWISE_BROADCAST_ADD_X(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(V_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_BROADCAST_LHS_X_ADD(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_LHS_X(V_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_BROADCAST_RHS_X_ADD(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(V_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_BROADCAST_LHS_X_SUB(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_LHS_X(V_SUB, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_BROADCAST_RHS_X_SUB(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(V_SUB, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_BROADCAST_DIV_X(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(V_DIV, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_BROADCAST_LHS_X_MUL(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_LHS_X(V_MUL, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_BROADCAST_RHS_X_MUL(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(V_MUL, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) /** Element-wise scale with a constant value * * @note Performs: LHS * constant = DST * * @param[in] DATA_TYPE LHS/RHS/DST data type * @param[in] M0 Number of LHS rows * @param[in] N0 Number of LHS columns * @param[in] lhs LHS tile * @param[in] rhs_constant Constant value * @param[out] dst DST tile */ #define T_SCALE_CONSTANT(DATA_TYPE, M0, N0, lhs, rhs_constant, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = lhs[_m0].v * (DATA_TYPE)rhs_constant; \ }) \ }) /** Element-wise operation with RHS broadcasted (RHS has the X dimension only) * * @note Performs: LHS OP RHS[broadcasted] = DST * @note Both tiles must have same data type * * @param[in] T_ELWISE_OP Elementwise operator to perform * @param[in] DST_DATA_TYPE DST data type * @param[in] M0 Number of LHS rows * @param[in] N0 Number of LHS columns * @param[in] lhs LHS tile * @param[in] rhs RHS tile * @param[out] dst DST tile */ #define T_ELTWISE_BROADCAST_X(T_ELWISE_OP, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = T_ELWISE_OP(CONVERT(lhs[_m0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0)), CONVERT(rhs[0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0))); \ }) \ }) /** Element-wise operation with LHS broadcasted (LHS has the X dimension only) * * @note Performs: LHS[broadcasted] OP RHS = DST * @note Both tiles must have same data type * * @param[in] T_ELWISE_OP Elementwise operator to perform * @param[in] DST_DATA_TYPE DST data type * @param[in] M0 Number of RHS rows * @param[in] N0 Number of RHS columns * @param[in] lhs LHS tile * @param[in] rhs RHS tile * @param[out] dst DST tile */ #define T_ELTWISE_BROADCAST_LHS_X(T_ELWISE_OP, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = T_ELWISE_OP(CONVERT(lhs[0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0)), CONVERT(rhs[_m0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0))); \ }) \ }) #define T_ELTWISE_ADD(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_SUB(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_SUB, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_DIV(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_DIV, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) #define T_ELTWISE_MUL(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_MUL, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) /** Element-wise operation between two tiles (LHS and RHS) * * @note Performs: LHS OP RHS = DST * @note Both tiles must have same data type * * @param[in] T_ELWISE_OP Elementwise operator to perform * @param[in] DST_DATA_TYPE DST data type * @param[in] M0 Number of LHS rows * @param[in] N0 Number of LHS columns * @param[in] lhs LHS tile * @param[in] rhs RHS tile * @param[out] dst DST tile */ #define T_ELTWISE(T_ELWISE_OP, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = T_ELWISE_OP(CONVERT(lhs[_m0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0)), CONVERT(rhs[_m0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0))); \ }) \ }) /** Floor operation on a tile * * @note Performs: floor(SRC) = DST * @note Both tiles must have same data type * * @param[in] DST_DATA_TYPE DST data type * @param[in] M0 Number of SRC rows * @param[in] N0 Number of SRC columns * @param[in] src LHS tile * @param[out] dst DST tile */ #define T_FLOOR(DST_DATA_TYPE, M0, N0, src, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ dst[_m0].v = floor(CONVERT(src[_m0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0))); \ }) \ }) /** Matrix multiplication * * @note Performs: LHS X RHS + DST = DST * * @param[in] LHS_DATA_TYPE LHS tile data type * @param[in] RHS_DATA_TYPE RHS tile data type * @param[in] DST_DATA_TYPE RHS tile data type * @param[in] M0 Number of LHS rows * @param[in] N0 Number of RHS columns * @param[in] K0 Number of LHS columns * @param[in] LHS_LAYOUT LHS layout (T= transposed, NT= not transposed) * @param[in] RHS_LAYOUT RHS layout (T= transposed, NT= not transposed) * @param[in] lhs LHS tile * @param[in] rhs RHS tile * @param[in, out] dst DST tile */ #define T_MMUL(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, LHS_LAYOUT, RHS_LAYOUT, lhs, rhs, dst) T_MMUL_##LHS_LAYOUT##_##RHS_LAYOUT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_##LHS_DATA_TYPE##_##RHS_DATA_TYPE##_##DST_DATA_TYPE(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_float_float_float(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_half_half_float(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_half_half_half(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_char_char_int(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_uchar_uchar_uint(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_uchar_uchar_int(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ { \ LOOP_UNROLLING(int, _m, 0, 1, M0, \ { \ LOOP_UNROLLING(int, _n, 0, 1, N0, \ { \ LOOP_UNROLLING(int, _k, 0, 1, K0, \ { \ dst[_m].s[_n] = fma((DST_DATA_TYPE)(lhs[_m].s[_k]), (DST_DATA_TYPE)(rhs[_n].s[_k]), dst[_m].s[_n]); \ }) \ }) \ }) \ } #define T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ ({ \ LOOP_UNROLLING(int, _m, 0, 1, M0, \ { \ LOOP_UNROLLING(int, _n, 0, 1, N0, \ { \ DOT_PRODUCT_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, K0, (lhs[_m].v), (rhs[_n].v), dst[_m].s[_n]); \ }) \ }) \ }) #endif /* SRC_CORE_CL_CL_KERNELS_TILE_HELPERS */