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review.mlplatform.org / ml / ComputeLibrary / ec320d9fc418e2d95a3a38ce87233397535f467d / . / src / core / CL / cl_kernels / tile_helpers.h
blob: 1e4dddd2db2c63bf3d9423b0042de597247d9b44 [file] [log] [blame]
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1/*
Viet-Hoa Dob3077fb2023-01-03 17:59:14 +0000[diff] [blame]2 * Copyright (c) 2021-2023 Arm Limited.
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
Ramy Elgammalec320d92022-12-14 09:20:09 +0000[diff] [blame^]24#ifndef SRC_CORE_CL_CL_KERNELS_TILE_HELPERS
25#define SRC_CORE_CL_CL_KERNELS_TILE_HELPERS
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]26
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]27// *INDENT-OFF*
28// clang-format off
29
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]30#define TILE_VECTOR_SIZE1 1
31#define TILE_VECTOR_SIZE2 2
32#define TILE_VECTOR_SIZE3 3
33#define TILE_VECTOR_SIZE4 4
34#define TILE_VECTOR_SIZE5 8
35#define TILE_VECTOR_SIZE6 8
36#define TILE_VECTOR_SIZE7 8
37#define TILE_VECTOR_SIZE8 8
38#define TILE_VECTOR_SIZE9 16
39#define TILE_VECTOR_SIZE10 16
40#define TILE_VECTOR_SIZE11 16
41#define TILE_VECTOR_SIZE12 16
42#define TILE_VECTOR_SIZE13 16
43#define TILE_VECTOR_SIZE14 16
44#define TILE_VECTOR_SIZE15 16
45#define TILE_VECTOR_SIZE16 16
46
47#define TILE_VECTOR_TYPE1(DATA_TYPE) DATA_TYPE##1
48#define TILE_VECTOR_TYPE2(DATA_TYPE) DATA_TYPE##2
49#define TILE_VECTOR_TYPE3(DATA_TYPE) DATA_TYPE##3
50#define TILE_VECTOR_TYPE4(DATA_TYPE) DATA_TYPE##4
51#define TILE_VECTOR_TYPE5(DATA_TYPE) DATA_TYPE##8
52#define TILE_VECTOR_TYPE6(DATA_TYPE) DATA_TYPE##8
53#define TILE_VECTOR_TYPE7(DATA_TYPE) DATA_TYPE##8
54#define TILE_VECTOR_TYPE8(DATA_TYPE) DATA_TYPE##8
55#define TILE_VECTOR_TYPE9(DATA_TYPE) DATA_TYPE##16
56#define TILE_VECTOR_TYPE10(DATA_TYPE) DATA_TYPE##16
57#define TILE_VECTOR_TYPE11(DATA_TYPE) DATA_TYPE##16
58#define TILE_VECTOR_TYPE12(DATA_TYPE) DATA_TYPE##16
59#define TILE_VECTOR_TYPE13(DATA_TYPE) DATA_TYPE##16
60#define TILE_VECTOR_TYPE14(DATA_TYPE) DATA_TYPE##16
61#define TILE_VECTOR_TYPE15(DATA_TYPE) DATA_TYPE##16
62#define TILE_VECTOR_TYPE16(DATA_TYPE) DATA_TYPE##16
63
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]64/** Tile object
65 * A tile object is a 2D memory block and can be accessed using the following syntax:
66 * -# a[m0].v = access the the vector at row "m0" (OpenCL vector)
Ramy Elgammal404462a2022-11-08 02:14:46 +0000[diff] [blame]67 * -# dst[m0].s[n0] = access the scalar element at row "m0" and column "n0" (scalar access)
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]68 *
69 * @param[in] DATA_TYPE Data type of the tile
70 * @param[in] H Number of tile rows
71 * @param[in] W Number of tile colums
72 * @param[in] BASENAME Tile's name
73 */
74#define TILE(DATA_TYPE, H, W, BASENAME) TILE_STR(DATA_TYPE, H, W, BASENAME)
75#define TILE_STR(DATA_TYPE, H, W, BASENAME) \
76 union { \
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]77 DATA_TYPE s[TILE_VECTOR_SIZE##W]; \
78 TILE_VECTOR_TYPE##W(DATA_TYPE) v; \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]79 } BASENAME[H]
80
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]81#define TENSOR4D_IMAGE(name) \
82 __read_only image2d_t name##_img, \
83 __global uchar *name##_ptr, \
84 uint name##_stride_x, \
85 uint name##_step_x, \
86 uint name##_stride_y, \
87 uint name##_step_y, \
88 uint name##_stride_z, \
89 uint name##_step_z, \
90 uint name##_stride_w, \
91 uint name##_step_w, \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]92 uint name##_offset_first_element_in_bytes
93
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]94#define TENSOR4D_BUFFER(name) \
95 __global uchar *name##_ptr, \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]96 uint name##_stride_x, \
97 uint name##_step_x, \
98 uint name##_stride_y, \
99 uint name##_step_y, \
100 uint name##_stride_z, \
101 uint name##_step_z, \
102 uint name##_stride_w, \
103 uint name##_step_w, \
104 uint name##_offset_first_element_in_bytes
105
106#define TENSOR4D_STR(name, type) TENSOR4D_##type(name)
107#define TENSOR4D(name, type) TENSOR4D_STR(name, type)
108
Adnan AlSinan17975a62021-11-08 17:46:39 +0000[diff] [blame]109#define TENSOR4D_T_IMAGE(name) \
110 __read_only image2d_t name##_img, \
111 __global uchar *name##_ptr, \
112 uint name##_stride_y, \
113 uint name##_stride_z, \
114 uint name##_stride_w, \
115 uint name##_c, \
116 uint name##_w, \
117 uint name##_h, \
118 uint name##_n, \
119 uint name##_offset_first_element_in_bytes
120
121#define TENSOR4D_T_BUFFER(name) \
122 __global uchar *name##_ptr, \
123 uint name##_stride_y, \
124 uint name##_stride_z, \
125 uint name##_stride_w, \
126 uint name##_c, \
127 uint name##_w, \
128 uint name##_h, \
129 uint name##_n, \
130 uint name##_offset_first_element_in_bytes
131
132#define TENSOR4D_T_STR(name, type) TENSOR4D_T_##type(name)
Gian Marco Iodice3cce35d2022-12-30 16:07:45 +0000[diff] [blame]133
134/** Legacy tensor 4D arguments
135 *
136 * @param[in] name Tensor name. The tensor name is the prefix of the tensor components
137 * @param[in] type Tensor type (BUFFER or IMAGE)
138 */
Adnan AlSinan17975a62021-11-08 17:46:39 +0000[diff] [blame]139#define TENSOR4D_T(name, type) TENSOR4D_T_STR(name, type)
140
Gian Marco Iodice3cce35d2022-12-30 16:07:45 +0000[diff] [blame]141#define TENSOR4D_RO_T_IMAGE(name) \
142 __read_only image2d_t name##_img, \
143 TENSOR4D_T_BUFFER(name)
144
145#define TENSOR4D_RO_T_BUFFER(name) TENSOR4D_T_BUFFER(name)
146
147#define TENSOR4D_RO_T_STR(name, type) TENSOR4D_RO_T_##type(name)
148
149/** Read-Only (RO) tensor 4D.
150 *
151 * @param[in] name Tensor name. The tensor name is the prefix of the tensor components
152 * @param[in] type Tensor type (BUFFER or IMAGE)
153 */
154#define TENSOR4D_RO_T(name, type) TENSOR4D_RO_T_STR(name, type)
155
156#define TENSOR4D_WO_T_IMAGE(name) \
157 __write_only image2d_t name##_img, \
158 TENSOR4D_T_BUFFER(name)
159
160#define TENSOR4D_WO_T_BUFFER(name) TENSOR4D_T_BUFFER(name)
161
162#define TENSOR4D_WO_T_STR(name, type) TENSOR4D_WO_T_##type(name)
163
164/** Write-Only (WO) tensor 4D.
165 *
166 * @param[in] name Tensor name. The tensor name is the prefix of the tensor components
167 * @param[in] type Tensor type (BUFFER or IMAGE)
168 */
169#define TENSOR4D_WO_T(name, type) TENSOR4D_WO_T_STR(name, type)
170
Gian Marco Iodice4fb56702021-11-10 11:18:50 +0000[diff] [blame]171#define TENSOR3D_T_IMAGE(name) \
172 __read_only image2d_t name##_img, \
173 __global uchar *name##_ptr, \
174 uint name##_stride_y, \
175 uint name##_stride_z, \
176 uint name##_w, \
177 uint name##_h, \
178 uint name##_n, \
179 uint name##_offset_first_element_in_bytes
180
181#define TENSOR3D_T_BUFFER(name) \
182 __global uchar *name##_ptr, \
183 uint name##_stride_y, \
184 uint name##_stride_z, \
185 uint name##_w, \
186 uint name##_h, \
187 uint name##_n, \
188 uint name##_offset_first_element_in_bytes
189
190#define TENSOR3D_T_STR(name, type) TENSOR3D_T_##type(name)
191#define TENSOR3D_T(name, type) TENSOR3D_T_STR(name, type)
192
Giorgio Arenaea8d2662021-05-20 11:36:56 +0100[diff] [blame]193#if !defined(UNROLL_WITH_PRAGMA)
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]194#define UNROLL_INCR(idx, step, macro) idx += (step); (macro)
195
196#define LOOP_UNROLLING_1(idx, step, macro) (macro)
197#define LOOP_UNROLLING_2(idx, step, macro) LOOP_UNROLLING_1(idx, step, macro); UNROLL_INCR(idx, step, macro)
198#define LOOP_UNROLLING_3(idx, step, macro) LOOP_UNROLLING_2(idx, step, macro); UNROLL_INCR(idx, step, macro)
199#define LOOP_UNROLLING_4(idx, step, macro) LOOP_UNROLLING_3(idx, step, macro); UNROLL_INCR(idx, step, macro)
200#define LOOP_UNROLLING_5(idx, step, macro) LOOP_UNROLLING_4(idx, step, macro); UNROLL_INCR(idx, step, macro)
201#define LOOP_UNROLLING_6(idx, step, macro) LOOP_UNROLLING_5(idx, step, macro); UNROLL_INCR(idx, step, macro)
202#define LOOP_UNROLLING_7(idx, step, macro) LOOP_UNROLLING_6(idx, step, macro); UNROLL_INCR(idx, step, macro)
203#define LOOP_UNROLLING_8(idx, step, macro) LOOP_UNROLLING_7(idx, step, macro); UNROLL_INCR(idx, step, macro)
204#define LOOP_UNROLLING_9(idx, step, macro) LOOP_UNROLLING_8(idx, step, macro); UNROLL_INCR(idx, step, macro)
205#define LOOP_UNROLLING_10(idx, step, macro) LOOP_UNROLLING_9(idx, step, macro); UNROLL_INCR(idx, step, macro)
206#define LOOP_UNROLLING_11(idx, step, macro) LOOP_UNROLLING_10(idx, step, macro); UNROLL_INCR(idx, step, macro)
207#define LOOP_UNROLLING_12(idx, step, macro) LOOP_UNROLLING_11(idx, step, macro); UNROLL_INCR(idx, step, macro)
208#define LOOP_UNROLLING_13(idx, step, macro) LOOP_UNROLLING_12(idx, step, macro); UNROLL_INCR(idx, step, macro)
209#define LOOP_UNROLLING_14(idx, step, macro) LOOP_UNROLLING_13(idx, step, macro); UNROLL_INCR(idx, step, macro)
210#define LOOP_UNROLLING_15(idx, step, macro) LOOP_UNROLLING_14(idx, step, macro); UNROLL_INCR(idx, step, macro)
211#define LOOP_UNROLLING_16(idx, step, macro) LOOP_UNROLLING_15(idx, step, macro); UNROLL_INCR(idx, step, macro)
212#define LOOP_UNROLLING_17(idx, step, macro) LOOP_UNROLLING_16(idx, step, macro); UNROLL_INCR(idx, step, macro)
213#define LOOP_UNROLLING_18(idx, step, macro) LOOP_UNROLLING_17(idx, step, macro); UNROLL_INCR(idx, step, macro)
214#define LOOP_UNROLLING_19(idx, step, macro) LOOP_UNROLLING_18(idx, step, macro); UNROLL_INCR(idx, step, macro)
215#define LOOP_UNROLLING_20(idx, step, macro) LOOP_UNROLLING_19(idx, step, macro); UNROLL_INCR(idx, step, macro)
216#define LOOP_UNROLLING_21(idx, step, macro) LOOP_UNROLLING_20(idx, step, macro); UNROLL_INCR(idx, step, macro)
217#define LOOP_UNROLLING_22(idx, step, macro) LOOP_UNROLLING_21(idx, step, macro); UNROLL_INCR(idx, step, macro)
218#define LOOP_UNROLLING_23(idx, step, macro) LOOP_UNROLLING_22(idx, step, macro); UNROLL_INCR(idx, step, macro)
219#define LOOP_UNROLLING_24(idx, step, macro) LOOP_UNROLLING_23(idx, step, macro); UNROLL_INCR(idx, step, macro)
220#define LOOP_UNROLLING_25(idx, step, macro) LOOP_UNROLLING_24(idx, step, macro); UNROLL_INCR(idx, step, macro)
221#define LOOP_UNROLLING_26(idx, step, macro) LOOP_UNROLLING_25(idx, step, macro); UNROLL_INCR(idx, step, macro)
222#define LOOP_UNROLLING_27(idx, step, macro) LOOP_UNROLLING_26(idx, step, macro); UNROLL_INCR(idx, step, macro)
223#define LOOP_UNROLLING_28(idx, step, macro) LOOP_UNROLLING_27(idx, step, macro); UNROLL_INCR(idx, step, macro)
224#define LOOP_UNROLLING_29(idx, step, macro) LOOP_UNROLLING_28(idx, step, macro); UNROLL_INCR(idx, step, macro)
225#define LOOP_UNROLLING_30(idx, step, macro) LOOP_UNROLLING_29(idx, step, macro); UNROLL_INCR(idx, step, macro)
226#define LOOP_UNROLLING_31(idx, step, macro) LOOP_UNROLLING_30(idx, step, macro); UNROLL_INCR(idx, step, macro)
227#define LOOP_UNROLLING_32(idx, step, macro) LOOP_UNROLLING_31(idx, step, macro); UNROLL_INCR(idx, step, macro)
228#define LOOP_UNROLLING_33(idx, step, macro) LOOP_UNROLLING_32(idx, step, macro); UNROLL_INCR(idx, step, macro)
229#define LOOP_UNROLLING_34(idx, step, macro) LOOP_UNROLLING_33(idx, step, macro); UNROLL_INCR(idx, step, macro)
230#define LOOP_UNROLLING_35(idx, step, macro) LOOP_UNROLLING_34(idx, step, macro); UNROLL_INCR(idx, step, macro)
231#define LOOP_UNROLLING_36(idx, step, macro) LOOP_UNROLLING_35(idx, step, macro); UNROLL_INCR(idx, step, macro)
232#define LOOP_UNROLLING_37(idx, step, macro) LOOP_UNROLLING_36(idx, step, macro); UNROLL_INCR(idx, step, macro)
233#define LOOP_UNROLLING_38(idx, step, macro) LOOP_UNROLLING_37(idx, step, macro); UNROLL_INCR(idx, step, macro)
234#define LOOP_UNROLLING_39(idx, step, macro) LOOP_UNROLLING_38(idx, step, macro); UNROLL_INCR(idx, step, macro)
235#define LOOP_UNROLLING_40(idx, step, macro) LOOP_UNROLLING_39(idx, step, macro); UNROLL_INCR(idx, step, macro)
236#define LOOP_UNROLLING_41(idx, step, macro) LOOP_UNROLLING_40(idx, step, macro); UNROLL_INCR(idx, step, macro)
237#define LOOP_UNROLLING_42(idx, step, macro) LOOP_UNROLLING_41(idx, step, macro); UNROLL_INCR(idx, step, macro)
238#define LOOP_UNROLLING_43(idx, step, macro) LOOP_UNROLLING_42(idx, step, macro); UNROLL_INCR(idx, step, macro)
239#define LOOP_UNROLLING_44(idx, step, macro) LOOP_UNROLLING_43(idx, step, macro); UNROLL_INCR(idx, step, macro)
240#define LOOP_UNROLLING_45(idx, step, macro) LOOP_UNROLLING_44(idx, step, macro); UNROLL_INCR(idx, step, macro)
241#define LOOP_UNROLLING_46(idx, step, macro) LOOP_UNROLLING_45(idx, step, macro); UNROLL_INCR(idx, step, macro)
242#define LOOP_UNROLLING_47(idx, step, macro) LOOP_UNROLLING_46(idx, step, macro); UNROLL_INCR(idx, step, macro)
243#define LOOP_UNROLLING_48(idx, step, macro) LOOP_UNROLLING_47(idx, step, macro); UNROLL_INCR(idx, step, macro)
244#define LOOP_UNROLLING_49(idx, step, macro) LOOP_UNROLLING_48(idx, step, macro); UNROLL_INCR(idx, step, macro)
245#define LOOP_UNROLLING_50(idx, step, macro) LOOP_UNROLLING_49(idx, step, macro); UNROLL_INCR(idx, step, macro)
246#define LOOP_UNROLLING_51(idx, step, macro) LOOP_UNROLLING_50(idx, step, macro); UNROLL_INCR(idx, step, macro)
247#define LOOP_UNROLLING_52(idx, step, macro) LOOP_UNROLLING_51(idx, step, macro); UNROLL_INCR(idx, step, macro)
248#define LOOP_UNROLLING_53(idx, step, macro) LOOP_UNROLLING_52(idx, step, macro); UNROLL_INCR(idx, step, macro)
249#define LOOP_UNROLLING_54(idx, step, macro) LOOP_UNROLLING_53(idx, step, macro); UNROLL_INCR(idx, step, macro)
250#define LOOP_UNROLLING_55(idx, step, macro) LOOP_UNROLLING_54(idx, step, macro); UNROLL_INCR(idx, step, macro)
251#define LOOP_UNROLLING_56(idx, step, macro) LOOP_UNROLLING_55(idx, step, macro); UNROLL_INCR(idx, step, macro)
252#define LOOP_UNROLLING_57(idx, step, macro) LOOP_UNROLLING_56(idx, step, macro); UNROLL_INCR(idx, step, macro)
253#define LOOP_UNROLLING_58(idx, step, macro) LOOP_UNROLLING_57(idx, step, macro); UNROLL_INCR(idx, step, macro)
254#define LOOP_UNROLLING_59(idx, step, macro) LOOP_UNROLLING_58(idx, step, macro); UNROLL_INCR(idx, step, macro)
255#define LOOP_UNROLLING_60(idx, step, macro) LOOP_UNROLLING_59(idx, step, macro); UNROLL_INCR(idx, step, macro)
256#define LOOP_UNROLLING_61(idx, step, macro) LOOP_UNROLLING_60(idx, step, macro); UNROLL_INCR(idx, step, macro)
257#define LOOP_UNROLLING_62(idx, step, macro) LOOP_UNROLLING_61(idx, step, macro); UNROLL_INCR(idx, step, macro)
258#define LOOP_UNROLLING_63(idx, step, macro) LOOP_UNROLLING_62(idx, step, macro); UNROLL_INCR(idx, step, macro)
259#define LOOP_UNROLLING_64(idx, step, macro) LOOP_UNROLLING_63(idx, step, macro); UNROLL_INCR(idx, step, macro)
260#define LOOP_UNROLLING_65(idx, step, macro) LOOP_UNROLLING_64(idx, step, macro); UNROLL_INCR(idx, step, macro)
261#define LOOP_UNROLLING_66(idx, step, macro) LOOP_UNROLLING_65(idx, step, macro); UNROLL_INCR(idx, step, macro)
262#define LOOP_UNROLLING_67(idx, step, macro) LOOP_UNROLLING_66(idx, step, macro); UNROLL_INCR(idx, step, macro)
263#define LOOP_UNROLLING_68(idx, step, macro) LOOP_UNROLLING_67(idx, step, macro); UNROLL_INCR(idx, step, macro)
264#define LOOP_UNROLLING_69(idx, step, macro) LOOP_UNROLLING_68(idx, step, macro); UNROLL_INCR(idx, step, macro)
265#define LOOP_UNROLLING_70(idx, step, macro) LOOP_UNROLLING_69(idx, step, macro); UNROLL_INCR(idx, step, macro)
266#define LOOP_UNROLLING_71(idx, step, macro) LOOP_UNROLLING_70(idx, step, macro); UNROLL_INCR(idx, step, macro)
267#define LOOP_UNROLLING_72(idx, step, macro) LOOP_UNROLLING_71(idx, step, macro); UNROLL_INCR(idx, step, macro)
268#define LOOP_UNROLLING_73(idx, step, macro) LOOP_UNROLLING_72(idx, step, macro); UNROLL_INCR(idx, step, macro)
269#define LOOP_UNROLLING_74(idx, step, macro) LOOP_UNROLLING_73(idx, step, macro); UNROLL_INCR(idx, step, macro)
270#define LOOP_UNROLLING_75(idx, step, macro) LOOP_UNROLLING_74(idx, step, macro); UNROLL_INCR(idx, step, macro)
271#define LOOP_UNROLLING_76(idx, step, macro) LOOP_UNROLLING_75(idx, step, macro); UNROLL_INCR(idx, step, macro)
272#define LOOP_UNROLLING_77(idx, step, macro) LOOP_UNROLLING_76(idx, step, macro); UNROLL_INCR(idx, step, macro)
273#define LOOP_UNROLLING_78(idx, step, macro) LOOP_UNROLLING_77(idx, step, macro); UNROLL_INCR(idx, step, macro)
274#define LOOP_UNROLLING_79(idx, step, macro) LOOP_UNROLLING_78(idx, step, macro); UNROLL_INCR(idx, step, macro)
275#define LOOP_UNROLLING_80(idx, step, macro) LOOP_UNROLLING_79(idx, step, macro); UNROLL_INCR(idx, step, macro)
276#define LOOP_UNROLLING_81(idx, step, macro) LOOP_UNROLLING_80(idx, step, macro); UNROLL_INCR(idx, step, macro)
277#define LOOP_UNROLLING_82(idx, step, macro) LOOP_UNROLLING_81(idx, step, macro); UNROLL_INCR(idx, step, macro)
278#define LOOP_UNROLLING_83(idx, step, macro) LOOP_UNROLLING_82(idx, step, macro); UNROLL_INCR(idx, step, macro)
279#define LOOP_UNROLLING_84(idx, step, macro) LOOP_UNROLLING_83(idx, step, macro); UNROLL_INCR(idx, step, macro)
280#define LOOP_UNROLLING_85(idx, step, macro) LOOP_UNROLLING_84(idx, step, macro); UNROLL_INCR(idx, step, macro)
281#define LOOP_UNROLLING_86(idx, step, macro) LOOP_UNROLLING_85(idx, step, macro); UNROLL_INCR(idx, step, macro)
282#define LOOP_UNROLLING_87(idx, step, macro) LOOP_UNROLLING_86(idx, step, macro); UNROLL_INCR(idx, step, macro)
283#define LOOP_UNROLLING_88(idx, step, macro) LOOP_UNROLLING_87(idx, step, macro); UNROLL_INCR(idx, step, macro)
284#define LOOP_UNROLLING_89(idx, step, macro) LOOP_UNROLLING_88(idx, step, macro); UNROLL_INCR(idx, step, macro)
285#define LOOP_UNROLLING_90(idx, step, macro) LOOP_UNROLLING_89(idx, step, macro); UNROLL_INCR(idx, step, macro)
286#define LOOP_UNROLLING_91(idx, step, macro) LOOP_UNROLLING_90(idx, step, macro); UNROLL_INCR(idx, step, macro)
287#define LOOP_UNROLLING_92(idx, step, macro) LOOP_UNROLLING_91(idx, step, macro); UNROLL_INCR(idx, step, macro)
288#define LOOP_UNROLLING_93(idx, step, macro) LOOP_UNROLLING_92(idx, step, macro); UNROLL_INCR(idx, step, macro)
289#define LOOP_UNROLLING_94(idx, step, macro) LOOP_UNROLLING_93(idx, step, macro); UNROLL_INCR(idx, step, macro)
290#define LOOP_UNROLLING_95(idx, step, macro) LOOP_UNROLLING_94(idx, step, macro); UNROLL_INCR(idx, step, macro)
291#define LOOP_UNROLLING_96(idx, step, macro) LOOP_UNROLLING_95(idx, step, macro); UNROLL_INCR(idx, step, macro)
292#define LOOP_UNROLLING_97(idx, step, macro) LOOP_UNROLLING_96(idx, step, macro); UNROLL_INCR(idx, step, macro)
293#define LOOP_UNROLLING_98(idx, step, macro) LOOP_UNROLLING_97(idx, step, macro); UNROLL_INCR(idx, step, macro)
294#define LOOP_UNROLLING_99(idx, step, macro) LOOP_UNROLLING_98(idx, step, macro); UNROLL_INCR(idx, step, macro)
295#define LOOP_UNROLLING_100(idx, step, macro) LOOP_UNROLLING_99(idx, step, macro); UNROLL_INCR(idx, step, macro)
296#define LOOP_UNROLLING_101(idx, step, macro) LOOP_UNROLLING_100(idx, step, macro); UNROLL_INCR(idx, step, macro)
297#define LOOP_UNROLLING_102(idx, step, macro) LOOP_UNROLLING_101(idx, step, macro); UNROLL_INCR(idx, step, macro)
298#define LOOP_UNROLLING_103(idx, step, macro) LOOP_UNROLLING_102(idx, step, macro); UNROLL_INCR(idx, step, macro)
299#define LOOP_UNROLLING_104(idx, step, macro) LOOP_UNROLLING_103(idx, step, macro); UNROLL_INCR(idx, step, macro)
300#define LOOP_UNROLLING_105(idx, step, macro) LOOP_UNROLLING_104(idx, step, macro); UNROLL_INCR(idx, step, macro)
301#define LOOP_UNROLLING_106(idx, step, macro) LOOP_UNROLLING_105(idx, step, macro); UNROLL_INCR(idx, step, macro)
302#define LOOP_UNROLLING_107(idx, step, macro) LOOP_UNROLLING_106(idx, step, macro); UNROLL_INCR(idx, step, macro)
303#define LOOP_UNROLLING_108(idx, step, macro) LOOP_UNROLLING_107(idx, step, macro); UNROLL_INCR(idx, step, macro)
304#define LOOP_UNROLLING_109(idx, step, macro) LOOP_UNROLLING_108(idx, step, macro); UNROLL_INCR(idx, step, macro)
305#define LOOP_UNROLLING_110(idx, step, macro) LOOP_UNROLLING_109(idx, step, macro); UNROLL_INCR(idx, step, macro)
306#define LOOP_UNROLLING_111(idx, step, macro) LOOP_UNROLLING_110(idx, step, macro); UNROLL_INCR(idx, step, macro)
307#define LOOP_UNROLLING_112(idx, step, macro) LOOP_UNROLLING_111(idx, step, macro); UNROLL_INCR(idx, step, macro)
308#define LOOP_UNROLLING_113(idx, step, macro) LOOP_UNROLLING_112(idx, step, macro); UNROLL_INCR(idx, step, macro)
309#define LOOP_UNROLLING_114(idx, step, macro) LOOP_UNROLLING_113(idx, step, macro); UNROLL_INCR(idx, step, macro)
310#define LOOP_UNROLLING_115(idx, step, macro) LOOP_UNROLLING_114(idx, step, macro); UNROLL_INCR(idx, step, macro)
311#define LOOP_UNROLLING_116(idx, step, macro) LOOP_UNROLLING_115(idx, step, macro); UNROLL_INCR(idx, step, macro)
312#define LOOP_UNROLLING_117(idx, step, macro) LOOP_UNROLLING_116(idx, step, macro); UNROLL_INCR(idx, step, macro)
313#define LOOP_UNROLLING_118(idx, step, macro) LOOP_UNROLLING_117(idx, step, macro); UNROLL_INCR(idx, step, macro)
314#define LOOP_UNROLLING_119(idx, step, macro) LOOP_UNROLLING_118(idx, step, macro); UNROLL_INCR(idx, step, macro)
315#define LOOP_UNROLLING_120(idx, step, macro) LOOP_UNROLLING_119(idx, step, macro); UNROLL_INCR(idx, step, macro)
316#define LOOP_UNROLLING_121(idx, step, macro) LOOP_UNROLLING_120(idx, step, macro); UNROLL_INCR(idx, step, macro)
317#define LOOP_UNROLLING_122(idx, step, macro) LOOP_UNROLLING_121(idx, step, macro); UNROLL_INCR(idx, step, macro)
318#define LOOP_UNROLLING_123(idx, step, macro) LOOP_UNROLLING_122(idx, step, macro); UNROLL_INCR(idx, step, macro)
319#define LOOP_UNROLLING_124(idx, step, macro) LOOP_UNROLLING_123(idx, step, macro); UNROLL_INCR(idx, step, macro)
320#define LOOP_UNROLLING_125(idx, step, macro) LOOP_UNROLLING_124(idx, step, macro); UNROLL_INCR(idx, step, macro)
321#define LOOP_UNROLLING_126(idx, step, macro) LOOP_UNROLLING_125(idx, step, macro); UNROLL_INCR(idx, step, macro)
322#define LOOP_UNROLLING_127(idx, step, macro) LOOP_UNROLLING_126(idx, step, macro); UNROLL_INCR(idx, step, macro)
323#define LOOP_UNROLLING_128(idx, step, macro) LOOP_UNROLLING_127(idx, step, macro); UNROLL_INCR(idx, step, macro)
324
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]325#define LOOP_UNROLLING_STR(type, idx, start, step, num, macro) \
326 { \
327 type idx = start; \
328 LOOP_UNROLLING_##num(idx, step, macro); \
329 }
Giorgio Arenaea8d2662021-05-20 11:36:56 +0100[diff] [blame]330#else // !defined(UNROLL_WITH_PRAGMA)
331#define LOOP_UNROLLING_STR(type, idx, start, step, num, macro) \
332 { \
333 _Pragma("unroll") \
334 for(type idx = start; idx < (num * step); idx += step) \
335 { \
336 (macro); \
337 } \
338 }
339#endif // !defined(UNROLL_WITH_PRAGMA)
340#define LOOP_UNROLLING(type, idx, start, step, num, macro) LOOP_UNROLLING_STR(type, idx, start, step, num, macro)
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]341
342/** 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
343 * to avoid out-of-bound read/write
344 *
345 * @note PARTIAL_N0 is used for get_global_id(n) = 0.
346 *
347 * @param[in] IDX get_global_id index (0,1 and 2 only)
348 * @param[in] N0 Number of elements read/written on the IDX direction
349 * @param[in] PARTIAL_N0 Number of elements read/written on the IDX direction for get_global_id(IDX) = 0. If zero,
350 * the Number of elements read/written on the IDX direction for get_global_id(IDX) = 0 is N0
351 */
352#define GET_SPATIAL_IDX(IDX, N0, PARTIAL_N0) (max((int)(get_global_id(IDX) * N0 - (N0 - PARTIAL_N0) % N0), 0))
353
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]354/** Dot product integet 8bit function
355 *
356 * @note Performs: c += dot(a, b)
357 *
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]358 * @param[in] A_DATA_TYPE A (lhs) data type
359 * @param[in] B_DATA_TYPE B (rhs) data type
360 * @param[in] C_DATA_TYPE C (accumulator) data type
361 * @param[in] K0 Number of accumulations
362 * @param[in] a OpenCL vector a
363 * @param[in] b OpenCL vector b
364 * @param[in] c Scalar variable c
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]365 */
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]366#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)
367#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)
368#define DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]369 ({ \
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]370 c += (C_DATA_TYPE)(a) * (C_DATA_TYPE)(b); \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]371 })
Viet-Hoa Do82169b32022-05-26 16:50:21 +0100[diff] [blame]372#if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_khr_integer_dot_product)
373#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)));
374#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));
375#define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += dot((a), (b));
376#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)
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]377#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));
378#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));
379#define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c = arm_dot_acc((a), (b), (c));
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]380#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)
Michalis Spyrouc38ca382021-07-14 13:30:28 +0100[diff] [blame]381#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)));
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]382#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));
383#define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) c += arm_dot((a), (b));
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]384#else // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8)
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]385#define DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
386 ({ \
387 c += (C_DATA_TYPE)(a).s0 * (C_DATA_TYPE)(b).s0; \
388 c += (C_DATA_TYPE)(a).s1 * (C_DATA_TYPE)(b).s1; \
389 })
390#define DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
391 ({ \
392 DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c); \
393 c += (C_DATA_TYPE)(a).s2 * (C_DATA_TYPE)(b).s2; \
394 })
395#define DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, x, y, val) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]396 ({ \
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]397 val += (C_DATA_TYPE)(x).s0 * (C_DATA_TYPE)(y).s0; \
398 val += (C_DATA_TYPE)(x).s1 * (C_DATA_TYPE)(y).s1; \
399 val += (C_DATA_TYPE)(x).s2 * (C_DATA_TYPE)(y).s2; \
400 val += (C_DATA_TYPE)(x).s3 * (C_DATA_TYPE)(y).s3; \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]401 })
402#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8)
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]403#define DOT_PRODUCT5_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
404 ({ \
405 DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \
406 DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s4), ((b).s4), c); \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]407 })
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]408#define DOT_PRODUCT6_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
409 ({ \
410 DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \
411 DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s45), ((b).s45), c); \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]412 })
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]413#define DOT_PRODUCT7_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
414 ({ \
415 DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s0123), ((b).s0123), c); \
416 DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s456), ((b).s456), c); \
417 })
418#define DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
419 ({ \
420 DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).lo), ((b).lo), c); \
421 DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).hi), ((b).hi), c); \
422 })
423#define DOT_PRODUCT9_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
424 ({ \
425 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \
426 DOT_PRODUCT1_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s8), ((b).s8), c); \
427 })
428#define DOT_PRODUCT10_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
429 ({ \
430 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \
431 DOT_PRODUCT2_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89), ((b).s89), c); \
432 })
433#define DOT_PRODUCT11_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
434 ({ \
435 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \
436 DOT_PRODUCT3_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89A), ((b).s89A), c); \
437 })
438#define DOT_PRODUCT12_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
439 ({ \
440 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \
441 DOT_PRODUCT4_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89AB), ((b).s89AB), c); \
442 })
443#define DOT_PRODUCT13_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
444 ({ \
445 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \
446 DOT_PRODUCT5_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABC), ((b).s89ABC), c); \
447 })
448#define DOT_PRODUCT14_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
449 ({ \
450 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \
451 DOT_PRODUCT6_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABCD), ((b).s89ABCD), c); \
452 })
453#define DOT_PRODUCT15_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
454 ({ \
455 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s01234567), ((b).s01234567), c); \
456 DOT_PRODUCT7_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).s89ABCDE), ((b).s89ABCDE), c); \
457 })
458#define DOT_PRODUCT16_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, a, b, c) \
459 ({ \
460 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).lo), ((b).lo), c); \
461 DOT_PRODUCT8_INTEGER8(A_DATA_TYPE, B_DATA_TYPE, C_DATA_TYPE, ((a).hi), ((b).hi), c); \
462 })
463
464/** Dot product integet 8bit function
465 *
466 * @note Performs: c += dot(a, b)
467 *
468 * @param[in] A_DATA_TYPE A (lhs) data type
469 * @param[in] B_DATA_TYPE B (rhs) data type
470 * @param[in] C_DATA_TYPE C (accumulator) data type
471 * @param[in] K0 Number of accumulations
472 * @param[in] a OpenCL vector a
473 * @param[in] c Scalar variable c
474 */
475#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)
476#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)
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]477
478/** Load a vector from global memory (tensor)
479 *
480 * @param[in] DATA_TYPE Data type
481 * @param[in] WIDTH Number of dst columns
482 * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image).
483 * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16)
484 * @param[in] TENSOR Tensor basename
485 * @param[in] X Starting X position
486 * @param[in] Y Starting Y position
487 * @param[in] STRIDE_Y Stride Y (in bytes)
488 */
489#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)
490#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)
491#define V_LOAD_BUFFER(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) \
492 VLOAD(WIDTH) \
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]493 (0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (Y) * (STRIDE_Y)))
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]494#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))
495
Gian Marco Iodice3cce35d2022-12-30 16:07:45 +0000[diff] [blame]496/** Store a vector in global memory (tensor)
497 *
498 * @param[in] DATA_TYPE Data type
499 * @param[in] WIDTH Number of dst columns
500 * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image).
501 * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16)
502 * @param[in] TENSOR Tensor basename
503 * @param[in] X Starting X position
504 * @param[in] Y Starting Y position
505 * @param[in] STRIDE_Y Stride Y (in bytes)
506 * @param[in] VALUES Values to store in memory
507 */
508#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)
509#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)
510#define V_STORE_BUFFER(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y, VALUES) \
511 VSTORE(WIDTH) \
512 (VALUES, 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (Y) * (STRIDE_Y)))
513#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)
514
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]515/** Load a tile from global memory (tensor)
516 *
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]517 * @param[in] DATA_TYPE Data type
518 * @param[in] HEIGHT Number of dst rows
519 * @param[in] WIDTH Number of dst columns
520 * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image).
521 * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16)
522 * @param[in] TENSOR Tensor basename
523 * @param[in] X Starting X position
524 * @param[in] Y Starting Y position
525 * @param[in] YI_MULTIPLIER Parameter used to multiply the internal row increment (_i).
526 * 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).
527 * In this case the address calculation is performed as: (Y + _i * Y_MULTIPLIER) * STRIDE_Y
528 * @param[in] STRIDE_Y Stride Y (in bytes) used to load each row.
529 * @param[out] dst Output tile
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]530 */
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]531#define T_LOAD(DATA_TYPE, HEIGHT, WIDTH, TENSOR_TYPE, TENSOR, X, Y, YI_MULTIPLIER, STRIDE_Y, dst) \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]532 ({ \
533 LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \
534 { \
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]535 dst[_i].v = V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, ((Y) + _i * (int)(YI_MULTIPLIER)), STRIDE_Y); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]536 }) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]537 })
538
539/** Load a tile from global memory (tensor) using an indirect Y index tile
540 *
541 * @param[in] DATA_TYPE Data type
542 * @param[in] HEIGHT Number of dst rows
543 * @param[in] WIDTH Number of dst columns
544 * @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
545 * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16)
546 * @param[in] TENSOR Tensor basename
547 * @param[in] X Starting X position
548 * @param[in] STRIDE_Y Stride Y (in bytes)
549 * @param[in] indirect_y Indirect Y index tile
550 * @param[out] dst Output tile
551 */
552#define T_LOAD_INDIRECT(DATA_TYPE, HEIGHT, WIDTH, TENSOR_TYPE, TENSOR, X, STRIDE_Y, indirect_y, dst) \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]553 ({ \
554 LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \
555 { \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]556 dst[_i].v = V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, (indirect_y[_i].v), STRIDE_Y); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]557 }) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]558 })
559
Adnan AlSinan3e155a52021-12-10 12:34:02 +0000[diff] [blame]560/** Load a tile from global memory (tensor) using an indirect Y index tile and conditionally use a different length for the load
561 *
562 * @note If WIDTH1_CONDITION is true, the load will use the WIDTH1 length for the store
563 * @note The vectors are stored in reverse order so the invalid rows are overwritten by the valid ones
564 *
565 * @param[in] DATA_TYPE Data type
566 * @param[in] HEIGHT Number of dst rows
567 * @param[in] WIDTH0 Store width to use if WIDTH1_CONDITION = false
568 * @param[in] WIDTH1 Store width to use if WIDTH1_CONDITION = true
569 * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image).
570 * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16)
571 * @param[in] TENSOR Tensor basename
572 * @param[in] X Starting X position
573 * @param[in] STRIDE_Y Stride Y (in bytes) used to load each row.
574 * @param[in] WIDTH1_CONDITION Condition to select the WIDTH1 store
575 * @param[out] dst Output tile
576 * @param[out] indirect_y Indirect Y index tile
577 */
578#define T_LOAD_INDIRECT_WIDTH_SELECT(DATA_TYPE, HEIGHT, WIDTH0, WIDTH1, TENSOR_TYPE, TENSOR, X, STRIDE_Y, WIDTH1_CONDITION, dst, indirect_y) \
579 ({ \
580 if(WIDTH1_CONDITION) \
581 { \
582 LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \
583 { \
584 VLOAD_PARTIAL(WIDTH0, WIDTH1) \
585 (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)); \
586 }) \
587 } \
588 else \
589 { \
590 LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \
591 { \
592 dst[HEIGHT - 1 - _i].v = V_LOAD(DATA_TYPE, WIDTH0, TENSOR_TYPE, TENSOR, X, (indirect_y[HEIGHT - 1 - _i].v), STRIDE_Y); \
593 }) \
594 } \
595 })
Gian Marco Iodice534b8892021-04-01 16:17:16 +0100[diff] [blame]596/** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout
597 *
598 * @param[in] DATA_TYPE Data type
599 * @param[in] TILE_HEIGHT Number of elements to load from Y (height) dimension
600 * @param[in] TILE_WIDTH Number of elements to load from X (width) dimension
601 * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension
602 * @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
603 * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16)
604 * @param[in] TENSOR Tensor basename
605 * @param[in] B Starting batch index
606 * @param[in] Y Starting Y index
607 * @param[in] X Starting X index
608 * @param[in] C Starting C index
609 * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension
610 * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension
611 * @param[in] STRIDE_Y Stride Y (in bytes)
612 * @param[out] dst Output tile
613 */
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]614#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) \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]615 ({ \
616 LOOP_UNROLLING(int, _yk, 0, 1, TILE_HEIGHT, \
617 { \
618 LOOP_UNROLLING(int, _xk, 0, 1, TILE_WIDTH, \
619 { \
620 int _src_y = (X) + _xk + ((Y) + _yk) * (TENSOR_WIDTH); \
621 _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT); \
Gian Marco Iodice534b8892021-04-01 16:17:16 +0100[diff] [blame]622 int _src_valid_y = (((X) + _xk) >= 0 && ((X) + _xk) < (int)(TENSOR_WIDTH) && ((Y) + _yk) >= 0 && ((Y) + _yk) < (int)(TENSOR_HEIGHT)); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]623 if(_src_valid_y != 0) \
624 { \
625 dst[_xk + _yk * (TILE_WIDTH)].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, STRIDE_Y); \
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]626 } \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]627 }) \
628 }) \
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]629 })
630
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]631/** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout with dilation for the X and Y increments
632 *
633 * @param[in] DATA_TYPE Data type
634 * @param[in] TILE_HEIGHT Number of elements to load from Y (height) dimension
635 * @param[in] TILE_WIDTH Number of elements to load from X (width) dimension
636 * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension
637 * @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
638 * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16)
639 * @param[in] TENSOR Tensor basename
640 * @param[in] B Starting batch index
641 * @param[in] Y Starting Y index
642 * @param[in] X Starting X index
643 * @param[in] C Starting C index
644 * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension
645 * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension
646 * @param[in] DILATION_X Dilation for the X increment
647 * @param[in] DILATION_Y Dilation for the Y increment
648 * @param[in] BOUNDARY_CHECK Boundary check flag. If true, it checks for any out-of-bound reads
649 * @param[out] dst Output tile
650 */
651#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) \
652 ({ \
653 LOOP_UNROLLING(int, _yk, 0, 1, TILE_HEIGHT, \
654 { \
655 LOOP_UNROLLING(int, _xk, 0, 1, TILE_WIDTH, \
656 { \
657 int _src_y = (X) + _xk * (DILATION_X); \
658 int _src_z = ((Y) + _yk * (DILATION_Y)); \
659 int _src_w = (B); \
660 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)); \
661 if(!(BOUNDARY_CHECK)) \
662 { \
663 dst[_xk + _yk * (TILE_WIDTH)].v = VLOAD(TILE_CHANNELS) \
664 (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))); \
665 } \
666 else \
667 { \
668 if(_src_valid_y) \
669 { \
670 dst[_xk + _yk * (TILE_WIDTH)].v = VLOAD(TILE_CHANNELS) \
671 (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))); \
672 } \
673 } \
674 }) \
675 }) \
676 })
677
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]678/** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout using indirect X and Y coordinates
679 *
680 * @param[in] DATA_TYPE Data type
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]681 * @param[in] TILE_AREA Number of elements to load from Y (height) dimension * Number of elements to load from X (width) dimension
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]682 * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension
683 * @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
684 * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16)
685 * @param[in] TENSOR Tensor basename
686 * @param[in] B Starting batch index
687 * @param[in] Y Starting Y index
688 * @param[in] X Starting X index
689 * @param[in] C Starting C index
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]690 * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension
Giorgio Arena945ae9e2021-10-13 11:13:04 +0100[diff] [blame]691 * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]692 * @param[in] STRIDE_Y Stride Y (in bytes)
693 * @param[out] xi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect X coordinate
694 * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate
695 * @param[out] dst Output tile
696 */
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]697#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) \
698 ({ \
699 LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \
700 { \
701 int _src_y = (X) + xi[_i].v + ((Y) + yi[_i].v) * (TENSOR_WIDTH); \
702 _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT); \
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]703 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)); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]704 if(_src_valid_y != 0) \
705 { \
706 dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, STRIDE_Y); \
Gian Marco Iodice0b76f7d2021-04-08 17:20:00 +0100[diff] [blame]707 } \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]708 }) \
Gian Marco Iodice534b8892021-04-01 16:17:16 +0100[diff] [blame]709 })
710
Gian Marco Iodice76335eb2022-11-17 11:03:39 +0000[diff] [blame]711/** Load a tile from global memory (tensor) using an indirect buffer for the Y coordinates
712 *
713 * @param[in] DATA_TYPE Data type
714 * @param[in] TILE_AREA Number of elements to load from Y (height) dimension * Number of elements to load from X (width) dimension
715 * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension
Gian Marco Iodice3cce35d2022-12-30 16:07:45 +0000[diff] [blame]716 * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image).
717 * When TENSOR_TYPE=IMAGE, the if condition for the out-of-bound check can be skipped
Gian Marco Iodice76335eb2022-11-17 11:03:39 +0000[diff] [blame]718 * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16)
719 * @param[in] TENSOR Tensor basename
720 * @param[in] C Starting C index
721 * @param[in] STRIDE_Y Stride Y (in bytes)
722 * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate
723 * 16 is the maximum indirect buffer size.
724 * @param[out] dst Output tile
725 */
Gian Marco Iodice3cce35d2022-12-30 16:07:45 +0000[diff] [blame]726#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)
727#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)
728#define T_LOAD2D_INDIRECT_BUFFER(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, STRIDE_Y, yi, dst) \
729 ({ \
730 LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \
731 { \
732 if(yi[0].s[_i] >= 0) \
733 { \
734 dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, yi[0].s[_i], STRIDE_Y); \
735 } \
736 }) \
737 })
738
739#define T_LOAD2D_INDIRECT_IMAGE(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, STRIDE_Y, yi, dst) \
740 ({ \
741 LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \
742 { \
743 dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, yi[0].s[_i], STRIDE_Y); \
744 }) \
Gian Marco Iodice3394f3e2022-09-16 14:14:21 +0100[diff] [blame]745 })
746
Giorgio Arena945ae9e2021-10-13 11:13:04 +0100[diff] [blame]747/** Load a tile from global memory (tensor) when the tensor is stored using a NDHWC layout using indirect X, Y and Z coordinates
748 *
749 * @param[in] DATA_TYPE Data type
750 * @param[in] TILE_AREA Number of elements to load from Y (height) dimension * Number of elements to load from X (width) dimension
751 * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension
752 * @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
753 * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16)
754 * @param[in] TENSOR Tensor basename
755 * @param[in] B Starting batch index
756 * @param[in] Z Starting Z index
757 * @param[in] Y Starting Y index
758 * @param[in] X Starting X index
759 * @param[in] C Starting C index
760 * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension
761 * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension
762 * @param[in] TENSOR_DEPTH Number of elements to load from Z (depth) dimension
763 * @param[in] STRIDE_Y Stride Y (in bytes)
764 * @param[out] xi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect X coordinate
765 * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate
766 * @param[out] zi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Z coordinate
767 * @param[out] dst Output tile
768 */
769#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) \
770 ({ \
771 LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \
772 { \
773 int _src_y = (X) + xi[_i].v + ((Y) + yi[_i].v) * (TENSOR_WIDTH) + ((Z) + zi[_i].v) * (TENSOR_WIDTH * TENSOR_HEIGHT); \
774 _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT) * (int)(TENSOR_DEPTH); \
775 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) \
776 && ((Z) + zi[_i].v) >= 0 && ((Z) + zi[_i].v) < (int)(TENSOR_DEPTH)); \
777 if(_src_valid_y != 0) \
778 { \
779 dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, STRIDE_Y); \
780 } \
781 }) \
782 })
783
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]784/** Store a tile to global memory (tensor) using an indirect Y index tile and conditionally use a different length for the store
785 *
786 * @note If WIDTH1_CONDITION is true, the store will use the WIDTH1 length for the store
787 * @note The vectors are stored in reverse order so the invalid rows are overwritten by the valid ones
788 *
789 * @param[in] DATA_TYPE Data type
790 * @param[in] HEIGHT Number of src rows
791 * @param[in] WIDTH0 Store width to use if WIDTH1_CONDITION = false
792 * @param[in] WIDTH1 Store width to use if WIDTH1_CONDITION = true
793 * @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
794 * cl_image is not supported.
795 * @param[in] TENSOR Tensor basename
796 * @param[in] X Starting X position
797 * @param[in] STRIDE_Y Stride Y (in bytes)
798 * @param[in] WIDTH1_CONDITION Condition to select the WIDTH1 store
799 * @param[in] src Input tile
800 * @param[in] indirect_y Indirect Y index tile
801 */
Gian Marco Iodicea8903c82021-03-24 14:48:22 +0000[diff] [blame]802#define T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, HEIGHT, WIDTH0, WIDTH1, TENSOR_TYPE, TENSOR, X, STRIDE_Y, WIDTH1_CONDITION, src, indirect_y) \
803 ({ \
804 if(WIDTH1_CONDITION) \
805 { \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]806 LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \
Gian Marco Iodicea8903c82021-03-24 14:48:22 +0000[diff] [blame]807 { \
808 VSTORE_PARTIAL(WIDTH0, WIDTH1) \
Giorgio Arena945ae9e2021-10-13 11:13:04 +0100[diff] [blame]809 (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)); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]810 }) \
Gian Marco Iodicea8903c82021-03-24 14:48:22 +0000[diff] [blame]811 } \
812 else \
813 { \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]814 LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \
Gian Marco Iodicea8903c82021-03-24 14:48:22 +0000[diff] [blame]815 { \
816 VSTORE(WIDTH0) \
Giorgio Arena945ae9e2021-10-13 11:13:04 +0100[diff] [blame]817 (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)); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]818 }) \
Gian Marco Iodicea8903c82021-03-24 14:48:22 +0000[diff] [blame]819 } \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]820 })
821
822/** Offset correction for the QASYMM8 computation
823 *
824 * @param[in] ACC_DATA_TYPE Accumulator data type
825 * @param[in] M0 Number of src/dst rows
826 * @param[in] N0 Number of src/dst columns
827 * @param[in] K0 Number of src columns
828 * @param[in] SRC_OFFSET Source quantization offset
829 * @param[in] WEI_OFFSET Weights quantization shift
830 * @param[in] lhs LHS tile
831 * @param[in] rhs RHS tile
832 * @param[out] dst DST tile
833 */
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]834#define T_OFFSET_CORRECTION(ACC_DATA_TYPE, M0, N0, K0, SRC_OFFSET, WEI_OFFSET, lhs, rhs, dst) \
835 ({ \
836 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
837 { \
838 ACC_DATA_TYPE _tm = 0; \
839 LOOP_UNROLLING(int, _k0, 0, 1, K0, \
840 { \
841 _tm += ((ACC_DATA_TYPE)lhs[_m0].s[_k0] * (ACC_DATA_TYPE)WEI_OFFSET); \
842 }) \
843 LOOP_UNROLLING(int, _n0, 0, 1, N0, \
844 { \
845 dst[_m0].s[_n0] += _tm; \
846 LOOP_UNROLLING(int, _k0, 0, 1, K0, \
847 { \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]848 dst[_m0].s[_n0] += ((ACC_DATA_TYPE)rhs[_n0].s[_k0] * (ACC_DATA_TYPE)SRC_OFFSET); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]849 }) \
850 }) \
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]851 }) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]852 })
853
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]854/** 8-bit quantization with fixed-point scale
855 *
856 * @param[in] SRC_DATA_TYPE SRC data type
857 * @param[in] DST_DATA_TYPE DST data type
858 * @param[in] QUANTIZATION_TYPE Quantization type (PER_TENSOR or PER_CHANNEL)
859 * @param[in] M0 Number of src/dst rows
860 * @param[in] N0 Number of src/dst columns
861 * @param[in] DST_OFFSET Quantization offset used for both the per-tensor and per-channel quantization
862 * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization
863 * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization
864 * @param[in] src Input tile
865 * @param[in] dst_multipliers Output multipliers tile for the per-channel quantization
866 * @param[in] dst_shifts Output shift tile for the per-channel quantization
867 * @param[out] dst Output tile
868 */
869#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)
870#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)
871
872/** 8-bit per-tensor quantization with fixed-point scale
873 *
874 * @param[in] SRC_DATA_TYPE SRC data type
875 * @param[in] DST_DATA_TYPE DST data type
876 * @param[in] M0 Number of src/dst rows
877 * @param[in] N0 Number of src/dst columns
878 * @param[in] DST_OFFSET Quantization offset
879 * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization
880 * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization
881 * @param[in] src Input tile
882 * @param[in] dst_multipliers (unused)
883 * @param[in] dst_shifts (unused)
884 * @param[out] dst Output tile
885 */
886#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) \
887 ({ \
888 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
889 { \
890 LOOP_UNROLLING(int, _n0, 0, 1, N0, \
891 { \
892 SRC_DATA_TYPE _tmp = 0; \
893 SRC_DATA_TYPE _src = src[_m0].s[_n0]; \
894 _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-DST_SHIFT)), ((SRC_DATA_TYPE)DST_SHIFT < (SRC_DATA_TYPE)0)); \
895 SRC_DATA_TYPE overflow = _src == DST_MULTIPLIER && _src == INT_MIN; \
896 long a_64 = (long)(_src); \
897 long b_64 = (long)(DST_MULTIPLIER); \
898 long ab_64 = a_64 * b_64; \
899 long mask1 = 1 << 30; \
900 long mask2 = 1 - (1 << 30); \
901 long is_positive_or_zero = ab_64 >= 0; \
902 long nudge = select(mask2, mask1, is_positive_or_zero); \
903 SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \
904 _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \
905 if(DST_SHIFT >= 0) \
906 { \
Freddie Liardet767dbf92021-07-21 16:20:41 +0100[diff] [blame]907 long mask = ((((int)1) << DST_SHIFT) - (long)1); \
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]908 long threshold = _tmp < (int)0 ? (mask >> 1) + (long)1 : (mask >> 1) + 0; \
909 _tmp = (_tmp & mask) > threshold ? (_tmp >> DST_SHIFT) + (int)1 : (_tmp >> DST_SHIFT); \
910 } \
911 _tmp += DST_OFFSET; \
912 dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \
913 }) \
914 }) \
915 })
916
917/** 8-bit per-channel quantization with fixed-point scale
918 *
919 * @param[in] SRC_DATA_TYPE SRC data type
920 * @param[in] DST_DATA_TYPE DST data type
921 * @param[in] M0 Number of src/dst rows
922 * @param[in] N0 Number of src/dst columns
923 * @param[in] DST_OFFSET Quantization offset
924 * @param[in] DST_SHIFT (unused)
925 * @param[in] DST_MULTIPLIER (unused)
926 * @param[in] src Input tile
927 * @param[in] dst_multipliers Output multipliers tile for the per-channel quantization
928 * @param[in] dst_shifts Output shift tile for the per-channel quantization
929 * @param[out] dst Output tile
930 */
931#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) \
932 ({ \
933 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
934 { \
935 LOOP_UNROLLING(int, _n0, 0, 1, N0, \
936 { \
937 SRC_DATA_TYPE _tmp = 0; \
Pablo Marquez Tello96fb1942022-11-01 09:32:20 +0000[diff] [blame]938 SRC_DATA_TYPE _tmp2 = 0; \
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]939 SRC_DATA_TYPE _src = src[_m0].s[_n0]; \
940 SRC_DATA_TYPE _dst_multiplier = dst_multipliers[0].s[_n0]; \
941 SRC_DATA_TYPE _dst_shift = dst_shifts[0].s[_n0]; \
942 _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-_dst_shift)), ((SRC_DATA_TYPE)_dst_shift < (SRC_DATA_TYPE)0)); \
943 SRC_DATA_TYPE overflow = _src == _dst_multiplier && _src == INT_MIN; \
944 long a_64 = (long)(_src); \
945 long b_64 = (long)(_dst_multiplier); \
946 long ab_64 = a_64 * b_64; \
947 long mask1 = 1 << 30; \
948 long mask2 = 1 - (1 << 30); \
949 long is_positive_or_zero = ab_64 >= 0; \
950 long nudge = select(mask2, mask1, is_positive_or_zero); \
951 SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \
952 _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \
Pablo Marquez Tello96fb1942022-11-01 09:32:20 +0000[diff] [blame]953 long mask = ((((int)1) << _dst_shift) - (int)1); \
954 long threshold = (mask >> 1) + any(_tmp); \
955 _tmp2 = _tmp >> _dst_shift; \
956 _tmp2 += select(0, 1, (_tmp & mask) > threshold); \
957 _tmp = select(_tmp, _tmp2, _dst_shift >= 0); \
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]958 _tmp += DST_OFFSET; \
959 dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \
960 }) \
961 }) \
962 })
963
964/** Quantized the 8-bit tile with fixed-point scale for asymmetric
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]965 *
966 * @param[in] SRC_DATA_TYPE SRC data type
967 * @param[in] DST_DATA_TYPE DST data type
968 * @param[in] M0 Number of src/dst rows
969 * @param[in] N0 Number of src/dst columns
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]970 * @param[in] DST_OFFSET Quantization offset used for both the per-tensor and per-channel quantization
971 * @param[in] DST_SHIFT Quantization shift for the per-tensor quantization
972 * @param[in] DST_MULTIPLIER Quantization multiplier for the per-tensor quantization
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]973 * @param[in] src Input tile
974 * @param[out] dst Output tile
975 */
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]976#define T_QUANTIZE8_ASYMMETRIC(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst) \
977 ({ \
978 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
979 { \
980 LOOP_UNROLLING(int, _n0, 0, 1, N0, \
981 { \
982 SRC_DATA_TYPE _tmp = 0; \
983 SRC_DATA_TYPE _src = src[_m0].s[_n0]; \
984 _src *= select((SRC_DATA_TYPE)1, ((SRC_DATA_TYPE)1 << (SRC_DATA_TYPE)(-DST_SHIFT)), ((SRC_DATA_TYPE)DST_SHIFT < (SRC_DATA_TYPE)0)); \
985 SRC_DATA_TYPE overflow = _src == DST_MULTIPLIER && _src == INT_MIN; \
986 long a_64 = (long)(_src); \
987 long b_64 = (long)(DST_MULTIPLIER); \
988 long ab_64 = a_64 * b_64; \
989 long mask1 = 1 << 30; \
990 long mask2 = 1 - (1 << 30); \
991 long is_positive_or_zero = ab_64 >= 0; \
992 long nudge = select(mask2, mask1, is_positive_or_zero); \
993 SRC_DATA_TYPE ab_x2_high32 = CONVERT((ab_64 + nudge) / (long)(1ll << 31), SRC_DATA_TYPE); \
994 _tmp = select(ab_x2_high32, (SRC_DATA_TYPE)INT_MAX, overflow); \
995 if(DST_SHIFT >= 0) \
996 { \
997 long mask = ((((int)1) << DST_SHIFT) - (int)1); \
998 long threshold = _tmp < (int)0 ? (mask >> 1) + (long)1 : (mask >> 1) + 0; \
999 _tmp = (_tmp & mask) > threshold ? (_tmp >> DST_SHIFT) + (int)1 : (_tmp >> DST_SHIFT); \
1000 } \
1001 _tmp += DST_OFFSET; \
1002 dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \
1003 }) \
1004 }) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1005 })
1006
1007/** Conditional rowset (memset by row)
1008 *
1009 * @note Set the row to VALUE_TO_SET if the corresponding mask == 0
1010 *
1011 * @param[in] DATA_TYPE Data type
1012 * @param[in] M0 Number of LHS rows
1013 * @param[in] N0 Number of LHS columns
1014 * @param[in] VALUE_TO_SET Value to set the row
1015 * @param[in, out] a Input/output tile
1016 * @param[out] mask Mask to check for setting the row to VALUE_TO_SET
1017 */
1018#define T_ROWSET_MASK(DATA_TYPE, M0, N0, VALUE_TO_SET, a, mask) \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1019 ({ \
1020 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1021 { \
1022 LOOP_UNROLLING(int, _n0, 0, 1, N0, \
1023 { \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1024 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)); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1025 }) \
1026 }) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1027 })
1028
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]1029/** Element-wise activation for floating point types
Gian Marco Iodicea8903c82021-03-24 14:48:22 +0000[diff] [blame]1030 *
1031 * @note Performs: activation(LHS) = DST
1032 *
1033 * @param[in] DATA_TYPE SRC/DST data type
1034 * @param[in] M0 Number of SRC/DST rows
1035 * @param[in] N0 Number of SRC/DST columns
1036 * @param[in] ACTIVATION_TYPE Activation type
1037 * @param[in] A_VAL A value used for the activation (e.g. tanh_op, brelu,..)
1038 * @param[in] B_VAL B value used for the activation (e.g. tanh_op, brelu,..)
1039 * @param[out] src SRC tile
1040 * @param[out] dst DST tile
1041 */
1042#define T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, src, dst) \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1043 ({ \
1044 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1045 { \
Gian Marco Iodicea8903c82021-03-24 14:48:22 +0000[diff] [blame]1046 dst[_m0].v = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, N0, src[_m0].v, A_VAL, B_VAL); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1047 }) \
Gian Marco Iodicea8903c82021-03-24 14:48:22 +0000[diff] [blame]1048 })
1049
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]1050// RELU Activation
1051#define relu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (max((DATA_TYPE)ZERO_VALUE, x))
1052// Bounded RELU Activation
1053#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)))
1054// Lower Upper Bounded RELU Activation
1055#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))
1056// Hard Swish Activation
1057#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))
1058// Identity Activation
1059#define identity_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x)
1060
1061#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)
1062#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)
1063
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1064#define V_ADD(A_VAL, B_VAL) ((A_VAL) + (B_VAL))
Ramy Elgammalec320d92022-12-14 09:20:09 +0000[diff] [blame^]1065#define V_SUB(A_VAL, B_VAL) ((A_VAL) - (B_VAL))
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1066#define V_DIV(A_VAL, B_VAL) ((A_VAL) / (B_VAL))
Jakub Sujak7359a872023-01-05 14:24:13 +0000[diff] [blame]1067#define V_MUL(A_VAL, B_VAL) ((A_VAL) * (B_VAL))
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1068
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]1069/** Element-wise activation for quantized types
1070 *
1071 * @note Performs: activation(LHS) = DST
1072 *
1073 * @param[in] DATA_TYPE SRC/DST data type
1074 * @param[in] M0 Number of SRC/DST rows
1075 * @param[in] N0 Number of SRC/DST columns
1076 * @param[in] ACTIVATION_TYPE Activation type
1077 * @param[in] ZERO_VALUE The zero value to consider in the computation
1078 * @param[in] A_VAL A value used for the activation (e.g. tanh_op, brelu,..)
1079 * @param[in] B_VAL B value used for the activation (e.g. tanh_op, brelu,..)
1080 * @param[out] src SRC tile
1081 * @param[out] dst DST tile
1082 */
1083#define T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_VALUE, A_VAL, B_VAL, src, dst) \
1084 ({ \
1085 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1086 { \
1087 dst[_m0].v = ACTIVATION_QUANTIZED(ACTIVATION_TYPE, DATA_TYPE, N0, ZERO_VALUE, A_VAL, B_VAL, src[_m0].v); \
1088 }) \
1089 })
1090
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1091/** Element-wise addition between two tiles
1092 *
1093 * @note Performs: LHS + RHS = DST
1094 *
1095 * @param[in] DATA_TYPE LHS/RHS/DST data type
1096 * @param[in] M0 Number of LHS rows
1097 * @param[in] N0 Number of LHS columns
1098 * @param[in] lhs LHS tile
1099 * @param[in] rhs Constant RHS tile
1100 * @param[out] dst DST tile
1101 */
1102#define T_ADD(DATA_TYPE, M0, N0, lhs, rhs, dst) \
1103 ({ \
1104 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1105 { \
1106 dst[_m0].v = lhs[_m0].v + rhs[_m0].v; \
1107 }) \
1108 })
1109
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1110/** Element-wise addition with a constant value
1111 *
1112 * @note Performs: LHS + constant = DST
1113 *
1114 * @param[in] DATA_TYPE LHS/RHS/DST data type
1115 * @param[in] M0 Number of LHS rows
1116 * @param[in] N0 Number of LHS columns
1117 * @param[in] lhs LHS tile
1118 * @param[in] rhs_constant Constant value
1119 * @param[out] dst DST tile
1120 */
1121#define T_ADD_CONSTANT(DATA_TYPE, M0, N0, lhs, rhs_constant, dst) \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1122 ({ \
1123 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1124 { \
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1125 dst[_m0].v = lhs[_m0].v + (DATA_TYPE)rhs_constant; \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1126 }) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1127 })
1128
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1129#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)
Viet-Hoa Dob3077fb2023-01-03 17:59:14 +0000[diff] [blame]1130#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)
1131#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)
1132
Ramy Elgammalec320d92022-12-14 09:20:09 +0000[diff] [blame^]1133#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)
1134#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)
1135
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1136#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)
1137
Jakub Sujak7359a872023-01-05 14:24:13 +0000[diff] [blame]1138#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)
1139#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)
1140
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1141/** Element-wise scale with a constant value
1142 *
1143 * @note Performs: LHS * constant = DST
1144 *
1145 * @param[in] DATA_TYPE LHS/RHS/DST data type
1146 * @param[in] M0 Number of LHS rows
1147 * @param[in] N0 Number of LHS columns
1148 * @param[in] lhs LHS tile
1149 * @param[in] rhs_constant Constant value
1150 * @param[out] dst DST tile
1151 */
1152#define T_SCALE_CONSTANT(DATA_TYPE, M0, N0, lhs, rhs_constant, dst) \
1153 ({ \
1154 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1155 { \
1156 dst[_m0].v = lhs[_m0].v * (DATA_TYPE)rhs_constant; \
1157 }) \
1158 })
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1159
1160/** Element-wise operation with RHS broadcasted (RHS has the X dimension only)
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1161 *
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1162 * @note Performs: LHS OP RHS[broadcasted] = DST
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1163 * @note Both tiles must have same data type
1164 *
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1165 * @param[in] T_ELWISE_OP Elementwise operator to perform
Giorgio Arena945ae9e2021-10-13 11:13:04 +0100[diff] [blame]1166 * @param[in] DST_DATA_TYPE DST data type
1167 * @param[in] M0 Number of LHS rows
1168 * @param[in] N0 Number of LHS columns
1169 * @param[in] lhs LHS tile
1170 * @param[in] rhs RHS tile
1171 * @param[out] dst DST tile
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1172 */
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1173#define T_ELTWISE_BROADCAST_X(T_ELWISE_OP, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1174 ({ \
1175 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1176 { \
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1177 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))); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1178 }) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1179 })
1180
Viet-Hoa Dob3077fb2023-01-03 17:59:14 +0000[diff] [blame]1181/** Element-wise operation with LHS broadcasted (LHS has the X dimension only)
1182 *
1183 * @note Performs: LHS[broadcasted] OP RHS = DST
1184 * @note Both tiles must have same data type
1185 *
1186 * @param[in] T_ELWISE_OP Elementwise operator to perform
1187 * @param[in] DST_DATA_TYPE DST data type
1188 * @param[in] M0 Number of RHS rows
1189 * @param[in] N0 Number of RHS columns
1190 * @param[in] lhs LHS tile
1191 * @param[in] rhs RHS tile
1192 * @param[out] dst DST tile
1193 */
1194#define T_ELTWISE_BROADCAST_LHS_X(T_ELWISE_OP, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) \
1195 ({ \
1196 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1197 { \
1198 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))); \
1199 }) \
1200 })
1201
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1202#define T_ELTWISE_ADD(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
Ramy Elgammalec320d92022-12-14 09:20:09 +0000[diff] [blame^]1203#define T_ELTWISE_SUB(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_SUB, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
Gunes Bayir4bfc70e2021-12-10 16:17:56 +0000[diff] [blame]1204#define T_ELTWISE_DIV(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_DIV, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
Jakub Sujak7359a872023-01-05 14:24:13 +0000[diff] [blame]1205#define T_ELTWISE_MUL(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_MUL, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1206
1207/** Element-wise operation between two tiles (LHS and RHS)
Michalis Spyrou06adbc52022-05-06 17:06:21 +0100[diff] [blame]1208 *
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1209 * @note Performs: LHS OP RHS = DST
Michalis Spyrou06adbc52022-05-06 17:06:21 +0100[diff] [blame]1210 * @note Both tiles must have same data type
1211 *
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1212 * @param[in] T_ELWISE_OP Elementwise operator to perform
Michalis Spyrou06adbc52022-05-06 17:06:21 +0100[diff] [blame]1213 * @param[in] DST_DATA_TYPE DST data type
1214 * @param[in] M0 Number of LHS rows
1215 * @param[in] N0 Number of LHS columns
1216 * @param[in] lhs LHS tile
1217 * @param[in] rhs RHS tile
1218 * @param[out] dst DST tile
1219 */
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1220#define T_ELTWISE(T_ELWISE_OP, DST_DATA_TYPE, M0, N0, lhs, rhs, dst) \
Michalis Spyrou06adbc52022-05-06 17:06:21 +0100[diff] [blame]1221 ({ \
1222 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1223 { \
Michalis Spyroub1fcefd2022-06-15 19:02:28 +0100[diff] [blame]1224 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))); \
1225 }) \
1226 })
1227
1228/** Floor operation on a tile
1229 *
1230 * @note Performs: floor(SRC) = DST
1231 * @note Both tiles must have same data type
1232 *
1233 * @param[in] DST_DATA_TYPE DST data type
1234 * @param[in] M0 Number of SRC rows
1235 * @param[in] N0 Number of SRC columns
1236 * @param[in] src LHS tile
1237 * @param[out] dst DST tile
1238 */
1239#define T_FLOOR(DST_DATA_TYPE, M0, N0, src, dst) \
1240 ({ \
1241 LOOP_UNROLLING(int, _m0, 0, 1, M0, \
1242 { \
1243 dst[_m0].v = floor(CONVERT(src[_m0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0))); \
Michalis Spyrou06adbc52022-05-06 17:06:21 +0100[diff] [blame]1244 }) \
1245 })
1246
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1247/** Matrix multiplication
1248 *
1249 * @note Performs: LHS X RHS + DST = DST
1250 *
1251 * @param[in] LHS_DATA_TYPE LHS tile data type
1252 * @param[in] RHS_DATA_TYPE RHS tile data type
1253 * @param[in] DST_DATA_TYPE RHS tile data type
1254 * @param[in] M0 Number of LHS rows
1255 * @param[in] N0 Number of RHS columns
1256 * @param[in] K0 Number of LHS columns
1257 * @param[in] LHS_LAYOUT LHS layout (T= transposed, NT= not transposed)
1258 * @param[in] RHS_LAYOUT RHS layout (T= transposed, NT= not transposed)
1259 * @param[in] lhs LHS tile
1260 * @param[in] rhs RHS tile
1261 * @param[in, out] dst DST tile
1262 */
1263#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)
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]1264#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)
1265#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)
Giorgio Arena945ae9e2021-10-13 11:13:04 +0100[diff] [blame]1266#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)
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]1267#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)
1268#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)
1269#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)
1270#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)
1271#define T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1272 { \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1273 LOOP_UNROLLING(int, _m, 0, 1, M0, \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1274 { \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1275 LOOP_UNROLLING(int, _n, 0, 1, N0, \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1276 { \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1277 LOOP_UNROLLING(int, _k, 0, 1, K0, \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1278 { \
Giorgio Arena945ae9e2021-10-13 11:13:04 +0100[diff] [blame]1279 dst[_m].s[_n] = fma((DST_DATA_TYPE)(lhs[_m].s[_k]), (DST_DATA_TYPE)(rhs[_n].s[_k]), dst[_m].s[_n]); \
Giorgio Arenabdd16d12021-05-13 16:58:51 +0100[diff] [blame]1280 }) \
1281 }) \
1282 }) \
Gian Marco Iodice5c9eed82021-03-19 11:26:20 +0000[diff] [blame]1283 }
Gian Marco Iodice8155c022021-04-16 15:08:59 +0100[diff] [blame]1284
1285#define T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \
1286 ({ \
1287 LOOP_UNROLLING(int, _m, 0, 1, M0, \
1288 { \
1289 LOOP_UNROLLING(int, _n, 0, 1, N0, \
1290 { \
1291 DOT_PRODUCT_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, K0, (lhs[_m].v), (rhs[_n].v), dst[_m].s[_n]); \
1292 }) \
1293 }) \
Gian Marco Iodice561c1762021-04-16 15:08:59 +0100[diff] [blame]1294 })
SiCong Lica364df2022-04-13 15:48:19 +0100[diff] [blame]1295
Ramy Elgammalec320d92022-12-14 09:20:09 +0000[diff] [blame^]1296#endif /* SRC_CORE_CL_CL_KERNELS_TILE_HELPERS */