SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2023 Arm Limited. |
| 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 | */ |
| 24 | #include "helpers.h" |
| 25 | #include "tile_helpers.h" |
| 26 | |
| 27 | #if defined(MAT_MUL_NATIVE_MMUL_NT_NT) |
| 28 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul) using MMUL: LHS non-transposed, RHS non-transposed - buffer only |
| 29 | * |
| 30 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 31 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| 32 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
| 33 | * @note The tile's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=1). |
| 34 | * @note The number of leftover outputs rows/columns must be passed using -DN0_LEFTOVER and -DM0_LEFTOVER (e.g. -DN0_LEFTOVER=2, -DM0_LEFTOVER=3) |
| 35 | * @note The MMUL block dimension (MMUL_M0, MMUL_N0, MMUL_K0) must be passed at compile time using -DMMUL_M0, -DMMUL_N0 and -DMMUL_K0 (e.g. -DMMUL_M0=4, -DMMUL_N0=4, -DMMUL_K0=4). |
SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 36 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_MMUL_NT_NT) |
| 37 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 38 | * - M0 > 0 |
| 39 | * - N0 = 1, 2, 3, 4, 8, 16 |
| 40 | * - K0 = 1 |
| 41 | * @note Values > 8 for M0 are not expected to be efficient |
| 42 | * |
| 43 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 44 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 45 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 46 | * @param[in] lhs_w The width of the lhs tensor |
| 47 | * @param[in] lhs_h The height of the lhs tensor |
| 48 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 49 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| 50 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 51 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 52 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 53 | * @param[in] rhs_w The width of the rhs tensor |
| 54 | * @param[in] rhs_h The height of the rhs tensor |
| 55 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 56 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 57 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 58 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 59 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 60 | * @param[in] dst_w The width of the dst tensor |
| 61 | * @param[in] dst_h The height of the dst tensor |
| 62 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 63 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| 64 | * @param[in] M Number of rows in LHS matrix |
| 65 | * @param[in] N Number of columns in RHS matrix |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 66 | * @param[in] K Number of columns in LHS matrix and rows in RHS matrix, which is multiple of MMUL_K0. |
SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 67 | */ |
| 68 | __kernel void mat_mul_native_mmul_nt_nt( |
| 69 | TENSOR3D_T(lhs, BUFFER), |
| 70 | TENSOR3D_T(rhs, BUFFER), |
| 71 | TENSOR3D_T(dst, BUFFER), |
| 72 | const int M, |
Ramy Elgammal | c952596 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 73 | const int N, |
| 74 | const int K) |
SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 75 | { |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 76 | #define MMUL_BLOCK_SIZE (MMUL_M0 * MMUL_N0) // MMUL block size for the output matrix |
SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 77 | |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 78 | // The output/destination matrix is divided into "sections". Each section is filled by a group of |
| 79 | // threads of size MMUL_BLOCK_SIZE, bundled together according to GWS_x. |
| 80 | // Each thread writes to a tile of M0 x N0 (the usual output block size for a thread) in the output matrix. |
| 81 | // Therefore, the section dimensions are (MMUL_M0 x M0) x (MMUL_N0 x N0). |
| 82 | |
| 83 | // The GWS is constructed in such a way that the y global id is the y section coordinate, |
| 84 | // and the x global id is a transformed thread id: MMUL_BLOCK_SIZE number of consecutive threads |
| 85 | // in the x dimension corresponding to a section. |
| 86 | // This can be visualized as first obtaining the coordinates of all the sections: |
| 87 | // x = [0, (N / N0) / MMUL_N0) --> (N / N0) / MMUL_N0 is the number of sections in x dimension |
| 88 | // y = [0, (M / M0) / MMUL_M0) --> (M / M0) / MMUL_M0 is the number of sections in y dimension |
| 89 | // Then multiply the x coordinates with MMUL_SECTION_NUM_THREADS to get the consecutive thread ids in the x dimension |
| 90 | // x = [0, ((N / N0) / MMUL_N0) * MMUL_N0 * MMUL_M0) |
| 91 | // x = [0, (N / N0) * MMUL_MO) |
| 92 | const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0) |
| 93 | // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE) |
| 94 | const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0) |
SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 95 | const uint z = get_global_id(2); // Batch |
| 96 | |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 97 | // Get section coordinates |
| 98 | const uint section_x = (x0 / MMUL_BLOCK_SIZE); |
| 99 | const uint section_y = y0; |
SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 100 | |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 101 | // Within these sections, each thread writes onto a small output block of size M0 x N0 |
| 102 | // in row major order. A section divided into tiles can be visualized as below. |
| 103 | // |
| 104 | // (Figure 1) |
| 105 | // A Section in the Output Matrix |
| 106 | // |
| 107 | // _____N0__________N0____________________N0____ |
| 108 | // | | | | | |
| 109 | // | | | | | |
| 110 | // M0 | Thread 1 | Thread 2 | ... | Thread | |
| 111 | // | | | | MMUL_N0 | |
| 112 | // |___________|__________|_________|___________| |
| 113 | // | | | | |
| 114 | // | | | | |
| 115 | // M0 | Thread | . | | |
| 116 | // | MMUL_N0+1 | . | | (M0 x MMUL_M0) |
| 117 | // |___________| . | | |
| 118 | // | . | | |
| 119 | // | . | | |
| 120 | // | . | | |
| 121 | // | |___________| |
| 122 | // | | | |
| 123 | // | | Thread | |
| 124 | // M0 | | MMUL_N0 x | |
| 125 | // | | MMUL_M0 | |
| 126 | // |________________________________|___________| |
| 127 | // N0 x MMUL_N0 |
| 128 | // |
| 129 | // The output matrix has several of these sections. As shown above, each section |
| 130 | // will be filled by a separate thread group of size MMUL_BLOCK_SIZE. The overall |
| 131 | // section layout of the output matrix is as below. For instance, S(1,1) will be filled |
| 132 | // by MMUL_BLOCK_SIZE (possibly equal to 16) threads, so as S(0,1) and others. |
| 133 | // |
| 134 | // (Figure 2) |
| 135 | // DST Matrix |
| 136 | // ____________________________________ |
| 137 | // | | | | | |
| 138 | // | S(0,0) | S(0,1) | ... | S(0, X) | |
| 139 | // |________|________|_______|_________| |
| 140 | // | | | | | |
| 141 | // | S(1,0) | S(1,1) | ... | S(1, X) | |
| 142 | // |________|________|_______|_________| |
| 143 | // | . | | | |
| 144 | // | . | | | Y = (M / M0) / MMUL_M0 - 1 (Max possible section y coordinate) |
| 145 | // | . | | | X = (N / N0) / MMUL_N0 - 1 (Max possible section x coordinate) |
| 146 | // |________|________|_________________| |
| 147 | // | | | | | S(y, x) denotes the section, and y and x are computed in |
| 148 | // | S(Y,0) | S(Y,1) | | S(Y, X) | section_y, section_x respectively. |
| 149 | // |________|________|_______|_________| |
| 150 | // |
| 151 | // |
| 152 | // |
| 153 | // |
| 154 | // A complete view involving the three matrices is given below. It examplifies how the section S(0,0) is computed. |
| 155 | // |
| 156 | // (Figure 3) |
| 157 | // Complete View |
| 158 | // |
| 159 | // LHS Matrix RHS Matrix DST Matrix |
| 160 | // |
| 161 | // ___MMUL_K0___________ __MMUL_N0 x N0____________ ___MMUL_N0 x N0____________________ |
| 162 | // /|xxxxxxxxxx| | /|xxxxxxxxxxxxxxx| | /|xxxxxxxxxxxxxxxxxxx| | |
| 163 | // / |xxxxxxxxxx| | MMUK_K0 ||xxxxxxxxxxxxxxx| | / |xxxxxxxxxxxxxxxxxxx| | |
| 164 | // MMUL_M0 | |xxxxxxxxxx| ---> | ||xxxxxxxxxxxxxxx| . . . | MMUL_M0 | |xxxxxxxxxxxxxxxxxxx| | |
| 165 | // x M0 | |xxxxxxxxxx| | \|_______________|_________| x M0 | |xxxxxxxxxxxxxxxxxxx| ... | |
| 166 | // | |xxxxxxxxxx| | | | | |xxxxxxxxxxxxxxxxxxx| | |
| 167 | // | |xxxxxxxxxx| | x | | | = \ |xxxxxxxxxxxxxxxxxxx| | |
| 168 | // \|__________|_________| | | | \|___________________| | |
| 169 | // | | | \/ | | | |
| 170 | // | , | |_________________________| | . | |
| 171 | // | , | | . | |
| 172 | // | , | | . | |
| 173 | // |____________________| |_________________________________| |
| 174 | // |
| 175 | // Horizontal and vertical arrows show the direction of K loop (main loop in the kernel). |
| 176 | // Each output section shown above is a zooomed out version of Figure 1. |
| 177 | // |
| 178 | // In each iteration of the main loop, LHS matrix traverses towards rightward, and RHS matrix traverses towards downward, |
| 179 | // the LHS section of (MMUL_M0 x M0) x MMUL_K0 and RHS section of MMUL_K0 x (MMUL_N0 x N0) is multiplied |
| 180 | // "cooperatively" using arm_matrix_multiply calls, and the result is accummulated over the output (DST) section |
| 181 | // of size (MMUL_M0 x M0) x (MMUL_N0 x N0) shown with 'x' signs. |
| 182 | // |
| 183 | // If it was a single thread, this multiplication would have been straightforward with a T_MMUL call. |
| 184 | // However, since it involves multiple threads working together using the aforementioned extension, it |
| 185 | // works slightly differently. |
| 186 | // |
| 187 | // Here is how threads access the LHS and RHS matrices: |
| 188 | // (Assume MMUL_K0 = MMUL_N0 = MMUL_M0 = 4 because the following diagram is heavily dependent on this) |
| 189 | // |
| 190 | // (Figure 4) |
| 191 | // Thread Access Layouts in LHS & RHS matrices |
| 192 | // |
| 193 | // LHS matrix RHS Matrix |
| 194 | // ___________________________ __________N0 times______N0 times____________________N0 times_______ |
| 195 | // |__T0__|__T1__|__T2__|__T3__| |__T0__| ... |__T0__|__T1__| ... |__T1__| ... |__T3__| ... |__T3__| |
| 196 | // |__T0__| ... | |__T4__| ... |__T4__|__T5__| ... |__T5__| ... |__T7__| ... |__T7__| |
| 197 | // M0 | . . | |__T8__| ... |__T8__|__T9__| ... |__T9__| ... |__T11_| ... |__T11_| |
| 198 | // Times | . . | |__T12_|_____|__T12_|__T13_|______|__T13_|_____|__T15_|_____|__T15_| |
| 199 | // | . . | X |
| 200 | // |__T0__|__T1__|__T2__|__T3__| |
| 201 | // |__T4__|__T5__|__T6__|__T7__| |
| 202 | // |__T4__|__T5__|__T6__|__T7__| |
| 203 | // M0 | . . | |
| 204 | // Times | . . | |
| 205 | // | . . | |
| 206 | // |__T4__|__T5__|__T6__|__T7__| |
| 207 | // |__T8__|__T9__|__T10_|__T11_| |
| 208 | // M0 | . | |
| 209 | // Times | . | |
| 210 | // | . | |
| 211 | // |__T12_|__T13_|__T14_|__T15_| |
| 212 | // M0 | . | |
| 213 | // Times | . | |
| 214 | // | . | |
| 215 | // |__T12_|__T13_|__T14_|__T15_| |
| 216 | // |
| 217 | // |
| 218 | // This access layout is designed such that the threads access continuous elements of each matrix (in terms of row/column). |
| 219 | // To multiply these large sections, the arm_matrix_multiply call is made for each of the M0xN0 elements. So, for each |
| 220 | // combination of m0 and n0 (iterators of M0 and N0 from 0 to M0-1 and N0-1 respectively), one arm_matrix_multiply call is |
| 221 | // made, and MMUL_BLOCK_SIZE number of threads compute the result. |
| 222 | // |
| 223 | // The matrix multiplication taking place in this extension |
| 224 | // is an "interleaved" one, because, for example, if m0=0 and n0=0, i.e. the first iteration, we would use the first, |
| 225 | // M0-th, 2M0-th and 3M0-th rows of the LHS matrix. Similarly, we jump N0 steps in the RHS matrix. This is how we access |
| 226 | // one element for each thread in a single (m0, n0) loop. |
| 227 | // |
| 228 | // For example, if we have |
| 229 | // - a 8 x 4 LHS section |
| 230 | // - 4 x 8 RHS section |
| 231 | // - Each vector variable ai, bj represent a 4x1 vector |
| 232 | // - ^T (superscript T) denotes transpose |
| 233 | // - M0 = N0 = 2 |
| 234 | // - MMUL_N0 = MMUL_M0 = MMUL_K0 = 4 |
| 235 | // |
| 236 | // (Figure 5) |
| 237 | // Mathematical view of the Matrix Multiplication |
| 238 | // |
| 239 | // LHS RHS DST |
| 240 | // [ a1^T ] [ b1 b2 b3 b4 b5 b6 b7 ] [ a1^Tb1 a1^Tb2 a1^Tb3 ... a1^Tb7 ] |
| 241 | // [ a2^T ] 4 x 8 [ a2^Tb1 a2^Tb2 a2^Tb3 ... a2^Tb7 ] |
| 242 | // [ a3^T ] [ ] |
| 243 | // [ a4^T ] = [ . . ] |
| 244 | // [ a5^T ] X [ . . ] |
| 245 | // [ a6^T ] [ . . ] |
| 246 | // [ a7^T ] [ ] |
| 247 | // [ a8^T ] [ a7^Tb1 a7^Tb2 a7^Tb3 ... a7^Tb7 ] |
| 248 | // 8 x 4 8 x 8 |
| 249 | // |
| 250 | // |
| 251 | // For the first iteration, i.e. (m0, n0) = (0, 0), the arm_matrix_multiply would multiply the following matrices: |
| 252 | // |
| 253 | // [ a1^T ] [ b1 b3 b5 b7 ] [ a1^Tb1 a1^Tb3 a1^Tb5 a1^Tb7 ] |
| 254 | // [ a3^T ] x 4 x 4 = [ a3^Tb1 a1^Tb3 a1^Tb5 a1^Tb7 ] |
| 255 | // [ a5^T ] [ a5^Tb1 a1^Tb3 a1^Tb5 a1^Tb7 ] |
| 256 | // [ a7^T ] [ a7^Tb1 a7^Tb3 a7^Tb5 a7^Tb7 ] |
| 257 | // 4 x 4 4 x 4 |
| 258 | // The elements calculated in the 4x4 output block are the "interleaved" elements in the DST above. |
| 259 | // When we follow for each combination of (m0, n0), every element of the DST matrix "section" is filled. |
| 260 | // |
| 261 | |
| 262 | // Get thread coordinates within an mmul block (of size MMUL_BLOCK_SIZE) |
| 263 | // Since threads are grouped in x dimension, the modular of x-dim global id |
| 264 | // wrt the MMUL_BLOCK_SIZE is the thread id in the group, ranging from 0 to |
| 265 | // MMUL_BLOCK_SIZE-1. Because the thread numbering is in row-major order. |
SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 266 | const uint thread_id = (x0 % MMUL_BLOCK_SIZE); |
| 267 | const uint thread_x = thread_id % MMUL_N0; |
| 268 | const uint thread_y = (thread_id / MMUL_N0); |
| 269 | |
| 270 | // Starting destination coordinates |
| 271 | // Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication |
| 272 | // part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results |
| 273 | // Although we will never write out-of-bound, we still need this clamp to ensure that we do not read out-of-bound either. |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 274 | // The unclamped dst coordinates can be calculated easily from the output section coordinates and the thread coordinates (see above figure). |
| 275 | |
| 276 | // See Figure 1 & 2. Thread step size is N0 and M0, |
| 277 | // Section step size is N0 x MMUL_N0 and M0 x MMUL_M0 |
| 278 | // respectively for x, y dimensions. |
| 279 | const uint dst_x_unclamped = thread_x * N0 + section_x * N0 * MMUL_N0; |
| 280 | const uint dst_y_unclamped = thread_y * M0 + section_y * M0 * MMUL_M0; |
SiCong Li | a8d8058 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 281 | const uint dst_x = min(dst_x_unclamped, (uint)(N - N0)); |
| 282 | const uint dst_y = min(dst_y_unclamped, (uint)(M - M0)); |
| 283 | |
| 284 | // Starting LHS coordinates |
| 285 | const uint lhs_x = thread_x; |
| 286 | const uint lhs_y = dst_y; |
| 287 | |
| 288 | // Starting RHS coordinates |
| 289 | const uint rhs_x = dst_x; |
| 290 | const uint rhs_y = thread_y; |
| 291 | |
| 292 | // Compute LHS/RHS/DST matrix address |
| 293 | lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z; |
| 294 | rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z; |
| 295 | dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z; |
| 296 | |
| 297 | // Initialize the accumulators |
| 298 | // MMUL extension accumulate the result in F32 for both F32 and F16 |
| 299 | TILE(float, M0, N0, c_f32); |
| 300 | |
| 301 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 302 | { |
| 303 | c_f32[i].v = 0; |
| 304 | }) |
| 305 | |
| 306 | for(int k = 0; k < K; k += MMUL_K0) |
| 307 | { |
| 308 | // A tile of M0xK0 but K0 must be set to 1 |
| 309 | TILE(DATA_TYPE, M0, 1, a); |
| 310 | // A tile of K0xN0 but K0 must be set to 1 |
| 311 | TILE(DATA_TYPE, 1, N0, b); |
| 312 | |
| 313 | // Load tile from the lhs/rhs tensors |
| 314 | T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
| 315 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); |
| 316 | |
| 317 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 318 | { |
| 319 | LOOP_UNROLLING(int, n0, 0, 1, N0, |
| 320 | { |
| 321 | c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[0].s[n0], c_f32[m0].s[n0]); |
| 322 | }) |
| 323 | }) |
| 324 | |
| 325 | lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE); |
| 326 | rhs_offset_first_element_in_bytes += MMUL_K0 * rhs_stride_y; |
| 327 | } |
| 328 | |
| 329 | // For threads "outside" of the dst bound, we do not write but we have to "read" (arm_matrix_multiply). That's why this needs to happen after arm_matrix_multiply |
| 330 | if(dst_x_unclamped >= N || dst_y_unclamped >= M) |
| 331 | { |
| 332 | return; |
| 333 | } |
| 334 | |
| 335 | #if defined(HALF_PRECISION) |
| 336 | TILE(DATA_TYPE, M0, N0, c); |
| 337 | |
| 338 | // Conversion required for the half precision |
| 339 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 340 | { |
| 341 | LOOP_UNROLLING(int, n0, 0, 1, N0, |
| 342 | { |
| 343 | c[m0].s[n0] = c_f32[m0].s[n0]; |
| 344 | }) |
| 345 | }) |
| 346 | #else // defined(HALF_PRECISION) |
| 347 | #define c c_f32 |
| 348 | #endif // defined(HALF_PRECISION) |
| 349 | |
| 350 | if(dst_x + N0 <= N || N0_LEFTOVER == 0) |
| 351 | { |
| 352 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 353 | { |
| 354 | if(dst_y + m0 < M || M0_LEFTOVER == 0) |
| 355 | { |
| 356 | VSTORE(N0) |
| 357 | (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); |
| 358 | } |
| 359 | }) |
| 360 | } |
| 361 | else |
| 362 | { |
| 363 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 364 | { |
| 365 | if(dst_y + m0 < M || M0_LEFTOVER == 0) |
| 366 | { |
| 367 | VSTORE_PARTIAL(N0, N0_LEFTOVER) |
| 368 | (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); |
| 369 | } |
| 370 | }) |
| 371 | } |
| 372 | |
| 373 | #undef MMUL_BLOCK_SIZE |
| 374 | } |
| 375 | #endif // defined(MAT_MUL_NATIVE_MMUL_NT_NT) |
Ramy Elgammal | c952596 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 376 | |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 377 | #if defined(MAT_MUL_NATIVE_MMUL_T_NT) |
| 378 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul) using MMUL: LHS transposed, RHS non-transposed - buffer only |
| 379 | * |
| 380 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 381 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| 382 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
| 383 | * @note The tile's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=1). |
| 384 | * @note The number of leftover outputs rows/columns must be passed using -DN0_LEFTOVER and -DM0_LEFTOVER (e.g. -DN0_LEFTOVER=2, -DM0_LEFTOVER=3) |
| 385 | * @note The MMUL block dimension (MMUL_M0, MMUL_N0, MMUL_K0) must be passed at compile time using -DMMUL_M0, -DMMUL_N0 and -DMMUL_K0 (e.g. -DMMUL_M0=4, -DMMUL_N0=4, -DMMUL_K0=4). |
| 386 | * @note The dimension K must be passed at compile time using -DK (e.g. -DK=4). K must be a multiple of MMUL_K0 |
| 387 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_MMUL_T_NT) |
| 388 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 389 | * - M0 = 1, 2, 3, 4, 8, 16 |
| 390 | * - N0 = 1, 2, 3, 4, 8, 16 |
| 391 | * - K0 = 1 |
| 392 | * @note Values > 8 for M0 are not expected to be efficient |
| 393 | * |
| 394 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 395 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 396 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 397 | * @param[in] lhs_w The width of the lhs tensor |
| 398 | * @param[in] lhs_h The height of the lhs tensor |
| 399 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 400 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| 401 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 402 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 403 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 404 | * @param[in] rhs_w The width of the rhs tensor |
| 405 | * @param[in] rhs_h The height of the rhs tensor |
| 406 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 407 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 408 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 409 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 410 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 411 | * @param[in] dst_w The width of the dst tensor |
| 412 | * @param[in] dst_h The height of the dst tensor |
| 413 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 414 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| 415 | * @param[in] M Number of rows in DST matrix |
| 416 | * @param[in] N Number of columns in DST matrix |
| 417 | * @param[in] K Number of rows in LHS and RHS matrices, which is multiple of MMUL_K0. |
| 418 | */ |
| 419 | __kernel void mat_mul_native_mmul_t_nt( |
| 420 | TENSOR3D_T(lhs, BUFFER), |
| 421 | TENSOR3D_T(rhs, BUFFER), |
| 422 | TENSOR3D_T(dst, BUFFER), |
| 423 | const int M, |
| 424 | const int N, |
| 425 | const int K) |
| 426 | { |
| 427 | #define MMUL_BLOCK_SIZE (MMUL_M0 * MMUL_N0) |
| 428 | // For explanations on how this kernel works, please refer to NT/NT kernel. This kernel makes little modifications to it. |
| 429 | |
| 430 | const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0) |
| 431 | // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE) |
| 432 | const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0) |
| 433 | const uint z = get_global_id(2); // Batch |
| 434 | |
| 435 | // Get section coordinates |
| 436 | const uint section_x = (x0 / MMUL_BLOCK_SIZE); |
| 437 | const uint section_y = y0; |
| 438 | |
| 439 | // Get thread coordinates |
| 440 | uint thread_id = (x0 % MMUL_BLOCK_SIZE); |
| 441 | uint thread_x = thread_id % MMUL_N0; |
| 442 | uint thread_y = (thread_id / MMUL_N0); |
| 443 | |
| 444 | // See Nt/Nt kernel for explanations |
| 445 | const uint dst_x_unclamped = thread_x * N0 + section_x * N0 * MMUL_N0; |
| 446 | const uint dst_y_unclamped = thread_y * M0 + section_y * M0 * MMUL_M0; |
| 447 | const uint dst_x = min(dst_x_unclamped, (uint)(N - N0)); |
| 448 | const uint dst_y = min(dst_y_unclamped, (uint)(M - M0)); |
| 449 | |
| 450 | // Starting LHS coordinates |
| 451 | uint lhs_x = dst_y; |
| 452 | uint lhs_y = thread_x; |
| 453 | |
| 454 | // Starting RHS coordinates |
| 455 | uint rhs_x = dst_x; |
| 456 | uint rhs_y = thread_y; |
| 457 | |
| 458 | // Compute LHS/RHS/DST matrix address |
| 459 | lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z; |
| 460 | rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z; |
| 461 | dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z; |
| 462 | |
| 463 | // Initialize the accumulators |
| 464 | // MMUL extension accumulate the result in F32 for both F32 and F16 |
| 465 | TILE(float, M0, N0, c_f32); |
| 466 | |
| 467 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 468 | { |
| 469 | c_f32[i].v = 0; |
| 470 | }) |
| 471 | |
| 472 | for(int k = 0; k < K; k += MMUL_K0) |
| 473 | { |
| 474 | TILE(DATA_TYPE, 1, M0, a); |
| 475 | TILE(DATA_TYPE, 1, N0, b); |
| 476 | |
| 477 | // Load tile from the lhs/rhs tensors |
| 478 | T_LOAD(DATA_TYPE, 1, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
| 479 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); |
| 480 | |
| 481 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 482 | { |
| 483 | LOOP_UNROLLING(int, n0, 0, 1, N0, |
| 484 | { |
| 485 | c_f32[m0].s[n0] = arm_matrix_multiply(a[0].s[m0], b[0].s[n0], c_f32[m0].s[n0]); |
| 486 | }) |
| 487 | }) |
| 488 | |
| 489 | lhs_offset_first_element_in_bytes += MMUL_K0 * lhs_stride_y; |
| 490 | rhs_offset_first_element_in_bytes += MMUL_K0 * rhs_stride_y; |
| 491 | } |
| 492 | |
| 493 | // For threads "outside" of the dst bound, we do not write but we have to "read" (arm_matrix_multiply). That's why this needs to happen after arm_matrix_multiply |
| 494 | if(dst_x_unclamped >= N || dst_y_unclamped >= M) |
| 495 | { |
| 496 | return; |
| 497 | } |
| 498 | |
| 499 | #if defined(HALF_PRECISION) |
| 500 | TILE(DATA_TYPE, M0, N0, c); |
| 501 | |
| 502 | // Conversion required for the half precision |
| 503 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 504 | { |
| 505 | LOOP_UNROLLING(int, n0, 0, 1, N0, |
| 506 | { |
| 507 | c[m0].s[n0] = c_f32[m0].s[n0]; |
| 508 | }) |
| 509 | }) |
| 510 | #else // defined(HALF_PRECISION) |
| 511 | #define c c_f32 |
| 512 | #endif // defined(HALF_PRECISION) |
| 513 | |
| 514 | if(dst_x + N0 <= N || N0_LEFTOVER == 0) |
| 515 | { |
| 516 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 517 | { |
| 518 | if(dst_y + m0 < M || M0_LEFTOVER == 0) |
| 519 | { |
| 520 | VSTORE(N0) |
| 521 | (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); |
| 522 | } |
| 523 | }) |
| 524 | } |
| 525 | else |
| 526 | { |
| 527 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 528 | { |
| 529 | if(dst_y + m0 < M || M0_LEFTOVER == 0) |
| 530 | { |
| 531 | VSTORE_PARTIAL(N0, N0_LEFTOVER) |
| 532 | (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); |
| 533 | } |
| 534 | }) |
| 535 | } |
| 536 | |
| 537 | #undef MMUL_BLOCK_SIZE |
| 538 | } |
| 539 | #endif // defined(MAT_MUL_NATIVE_MMUL_T_NT) |
| 540 | |
Ramy Elgammal | c952596 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 541 | #if defined(MAT_MUL_NATIVE_MMUL_NT_T) |
| 542 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul) using MMUL: LHS non-transposed, RHS transposed - buffer only |
| 543 | * |
| 544 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 545 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| 546 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
| 547 | * @note The tile's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=1). |
| 548 | * @note The number of leftover outputs rows/columns must be passed using -DN0_LEFTOVER and -DM0_LEFTOVER (e.g. -DN0_LEFTOVER=2, -DM0_LEFTOVER=3) |
| 549 | * @note The MMUL block dimension (MMUL_M0, MMUL_N0, MMUL_K0) must be passed at compile time using -DMMUL_M0, -DMMUL_N0 and -DMMUL_K0 (e.g. -DMMUL_M0=4, -DMMUL_N0=4, -DMMUL_K0=4). |
| 550 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_MMUL_NT_T) |
| 551 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 552 | * - M0 > 0 |
| 553 | * - N0 = 1, 2, 3, 4, 8, 16 |
| 554 | * - K0 = 1 |
| 555 | * @note Values > 8 for M0 are not expected to be efficient |
| 556 | * |
| 557 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 558 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 559 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 560 | * @param[in] lhs_w The width of the lhs tensor |
| 561 | * @param[in] lhs_h The height of the lhs tensor |
| 562 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 563 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| 564 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 565 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 566 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 567 | * @param[in] rhs_w The width of the rhs tensor |
| 568 | * @param[in] rhs_h The height of the rhs tensor |
| 569 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 570 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 571 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 572 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 573 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 574 | * @param[in] dst_w The width of the dst tensor |
| 575 | * @param[in] dst_h The height of the dst tensor |
| 576 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 577 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| 578 | * @param[in] M Number of rows in LHS matrix |
| 579 | * @param[in] N Number of columns in RHS matrix |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 580 | * @param[in] K Number of columns in LHS matrix and columns in RHS matrix, which is multiple of MMUL_K0. |
Ramy Elgammal | c952596 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 581 | */ |
| 582 | __kernel void mat_mul_native_mmul_nt_t( |
| 583 | TENSOR3D_T(lhs, BUFFER), |
| 584 | TENSOR3D_T(rhs, BUFFER), |
| 585 | TENSOR3D_T(dst, BUFFER), |
| 586 | const int M, |
| 587 | const int N, |
| 588 | const int K) |
| 589 | { |
| 590 | #define MMUL_BLOCK_SIZE (MMUL_M0 * MMUL_N0) |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 591 | // For explanations on how this kernel works, please refer to NT/NT kernel. This kernel makes little modifications to it. |
Ramy Elgammal | c952596 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 592 | |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 593 | const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0) |
| 594 | // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE) |
| 595 | const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0) |
Ramy Elgammal | c952596 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 596 | const uint z = get_global_id(2); // Batch |
| 597 | |
| 598 | // Get block coordinates |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 599 | const uint section_x = (x0 / MMUL_BLOCK_SIZE); |
| 600 | const uint section_y = y0; |
Ramy Elgammal | c952596 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 601 | |
| 602 | // Get thread coordinates within a block |
| 603 | const uint thread_id = (x0 % MMUL_BLOCK_SIZE); |
| 604 | const uint thread_x = thread_id % MMUL_N0; |
| 605 | const uint thread_y = (thread_id / MMUL_N0); |
| 606 | |
| 607 | // Starting destination coordinates |
| 608 | // Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication |
| 609 | // part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results |
| 610 | // Although we will never write out-of-bound, we still need this clamp to ensure that we do not read out-of-bound either. |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 611 | const uint dst_x_unclamped = thread_x * N0 + section_x * N0 * MMUL_N0; |
| 612 | const uint dst_y_unclamped = thread_y * M0 + section_y * M0 * MMUL_M0; |
Ramy Elgammal | c952596 | 2023-05-19 14:23:37 +0100 | [diff] [blame] | 613 | const uint dst_x = min(dst_x_unclamped, (uint)(N - N0)); |
| 614 | const uint dst_y = min(dst_y_unclamped, (uint)(M - M0)); |
| 615 | |
| 616 | // Starting LHS coordinates |
| 617 | const uint lhs_x = thread_x; |
| 618 | const uint lhs_y = dst_y; |
| 619 | |
| 620 | // Starting RHS coordinates |
| 621 | const uint rhs_x = thread_y; |
| 622 | const uint rhs_y = dst_x; |
| 623 | |
| 624 | // Compute LHS/RHS/DST matrix address |
| 625 | lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z; |
| 626 | rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z; |
| 627 | dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z; |
| 628 | |
| 629 | // Initialize the accumulators |
| 630 | // MMUL extension accumulate the result in F32 for both F32 and F16 |
| 631 | TILE(float, M0, N0, c_f32); |
| 632 | |
| 633 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 634 | { |
| 635 | c_f32[i].v = 0; |
| 636 | }) |
| 637 | |
| 638 | for(int k = 0; k < K; k += MMUL_K0) |
| 639 | { |
| 640 | // A tile of M0xK0 but K0 must be set to 1 |
| 641 | TILE(DATA_TYPE, M0, 1, a); |
| 642 | // A tile of N0xK0 but K0 must be set to 1 |
| 643 | TILE(DATA_TYPE, N0, 1, b); |
| 644 | |
| 645 | // Load tile from the lhs/rhs tensors |
| 646 | T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
| 647 | T_LOAD(DATA_TYPE, N0, 1, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); |
| 648 | |
| 649 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 650 | { |
| 651 | LOOP_UNROLLING(int, n0, 0, 1, N0, |
| 652 | { |
| 653 | c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[n0].s[0], c_f32[m0].s[n0]); |
| 654 | }) |
| 655 | }) |
| 656 | |
| 657 | lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE); |
| 658 | rhs_offset_first_element_in_bytes += MMUL_N0 * sizeof(DATA_TYPE); |
| 659 | } |
| 660 | |
| 661 | // For threads "outside" of the dst bound, we do not write but we have to "read" (arm_matrix_multiply). That's why this needs to happen after arm_matrix_multiply |
| 662 | if(dst_x_unclamped >= N || dst_y_unclamped >= M) |
| 663 | { |
| 664 | return; |
| 665 | } |
| 666 | |
| 667 | #if defined(HALF_PRECISION) |
| 668 | TILE(DATA_TYPE, M0, N0, c); |
| 669 | |
| 670 | // Conversion required for the half precision |
| 671 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 672 | { |
| 673 | LOOP_UNROLLING(int, n0, 0, 1, N0, |
| 674 | { |
| 675 | c[m0].s[n0] = c_f32[m0].s[n0]; |
| 676 | }) |
| 677 | }) |
| 678 | #else // defined(HALF_PRECISION) |
| 679 | #define c c_f32 |
| 680 | #endif // defined(HALF_PRECISION) |
| 681 | |
| 682 | if(dst_x + N0 <= N || N0_LEFTOVER == 0) |
| 683 | { |
| 684 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 685 | { |
| 686 | if(dst_y + m0 < M || M0_LEFTOVER == 0) |
| 687 | { |
| 688 | VSTORE(N0) |
| 689 | (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); |
| 690 | } |
| 691 | }) |
| 692 | } |
| 693 | else |
| 694 | { |
| 695 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 696 | { |
| 697 | if(dst_y + m0 < M || M0_LEFTOVER == 0) |
| 698 | { |
| 699 | VSTORE_PARTIAL(N0, N0_LEFTOVER) |
| 700 | (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); |
| 701 | } |
| 702 | }) |
| 703 | } |
| 704 | |
| 705 | #undef MMUL_BLOCK_SIZE |
| 706 | } |
| 707 | #endif // defined(MAT_MUL_NATIVE_MMUL_NT_T) |
Gunes Bayir | 00474e9 | 2023-06-19 21:33:51 +0100 | [diff] [blame^] | 708 | |
| 709 | #if defined(MAT_MUL_NATIVE_MMUL_T_T) |
| 710 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul) using MMUL: LHS non-transposed, RHS transposed - buffer only |
| 711 | * |
| 712 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 713 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| 714 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
| 715 | * @note The tile's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=1). |
| 716 | * @note The number of leftover outputs rows/columns must be passed using -DN0_LEFTOVER and -DM0_LEFTOVER (e.g. -DN0_LEFTOVER=2, -DM0_LEFTOVER=3) |
| 717 | * @note The MMUL block dimension (MMUL_M0, MMUL_N0, MMUL_K0) must be passed at compile time using -DMMUL_M0, -DMMUL_N0 and -DMMUL_K0 (e.g. -DMMUL_M0=4, -DMMUL_N0=4, -DMMUL_K0=4). |
| 718 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_MMUL_NT_T) |
| 719 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 720 | * - M0 = 1, 2, 3, 4, 8, 16 |
| 721 | * - N0 = 1, 2, 3, 4, 8, 16 |
| 722 | * - K0 = 1 |
| 723 | * @note Values > 8 for M0 are not expected to be efficient |
| 724 | * |
| 725 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 726 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 727 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 728 | * @param[in] lhs_w The width of the lhs tensor |
| 729 | * @param[in] lhs_h The height of the lhs tensor |
| 730 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 731 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| 732 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 733 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 734 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 735 | * @param[in] rhs_w The width of the rhs tensor |
| 736 | * @param[in] rhs_h The height of the rhs tensor |
| 737 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 738 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 739 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 740 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 741 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 742 | * @param[in] dst_w The width of the dst tensor |
| 743 | * @param[in] dst_h The height of the dst tensor |
| 744 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 745 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| 746 | * @param[in] M Number of rows in LHS matrix |
| 747 | * @param[in] N Number of columns in RHS matrix |
| 748 | * @param[in] K Number of rows in LHS matrix and columns in RHS matrix, which is multiple of MMUL_K0. |
| 749 | */ |
| 750 | __kernel void mat_mul_native_mmul_t_t( |
| 751 | TENSOR3D_T(lhs, BUFFER), |
| 752 | TENSOR3D_T(rhs, BUFFER), |
| 753 | TENSOR3D_T(dst, BUFFER), |
| 754 | const int M, |
| 755 | const int N, |
| 756 | const int K) |
| 757 | { |
| 758 | #define MMUL_BLOCK_SIZE (MMUL_M0 * MMUL_N0) |
| 759 | // For explanations on how this kernel works, please refer to NT/NT kernel. This kernel makes little modifications to it. |
| 760 | |
| 761 | const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0) |
| 762 | // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE) |
| 763 | const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0) |
| 764 | const uint z = get_global_id(2); // Batch |
| 765 | |
| 766 | // Get block coordinates |
| 767 | const uint section_x = (x0 / MMUL_BLOCK_SIZE); |
| 768 | const uint section_y = y0; |
| 769 | |
| 770 | // Get thread coordinates within a block |
| 771 | const uint thread_id = (x0 % MMUL_BLOCK_SIZE); |
| 772 | const uint thread_x = thread_id % MMUL_N0; |
| 773 | const uint thread_y = (thread_id / MMUL_N0); |
| 774 | |
| 775 | // Starting destination coordinates |
| 776 | // Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication |
| 777 | // part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results |
| 778 | // Although we will never write out-of-bound, we still need this clamp to ensure that we do not read out-of-bound either. |
| 779 | const uint dst_x_unclamped = thread_x * N0 + section_x * N0 * MMUL_N0; |
| 780 | const uint dst_y_unclamped = thread_y * M0 + section_y * M0 * MMUL_M0; |
| 781 | const uint dst_x = min(dst_x_unclamped, (uint)(N - N0)); |
| 782 | const uint dst_y = min(dst_y_unclamped, (uint)(M - M0)); |
| 783 | |
| 784 | // Starting LHS coordinates |
| 785 | const uint lhs_x = dst_y; |
| 786 | const uint lhs_y = thread_x; |
| 787 | |
| 788 | // Starting RHS coordinates |
| 789 | const uint rhs_x = thread_y; |
| 790 | const uint rhs_y = dst_x; |
| 791 | |
| 792 | // Compute LHS/RHS/DST matrix address |
| 793 | lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z; |
| 794 | rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z; |
| 795 | dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z; |
| 796 | |
| 797 | // Initialize the accumulators |
| 798 | // MMUL extension accumulate the result in F32 for both F32 and F16 |
| 799 | TILE(float, M0, N0, c_f32); |
| 800 | |
| 801 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 802 | { |
| 803 | c_f32[i].v = 0; |
| 804 | }) |
| 805 | |
| 806 | for(int k = 0; k < K; k += MMUL_K0) |
| 807 | { |
| 808 | // A tile of K0xM0 but K0 must be set to 1 |
| 809 | TILE(DATA_TYPE, 1, M0, a); |
| 810 | // A tile of N0xK0 but K0 must be set to 1 |
| 811 | TILE(DATA_TYPE, N0, 1, b); |
| 812 | |
| 813 | // Load tile from the lhs/rhs tensors |
| 814 | T_LOAD(DATA_TYPE, 1, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
| 815 | T_LOAD(DATA_TYPE, N0, 1, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); |
| 816 | |
| 817 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 818 | { |
| 819 | LOOP_UNROLLING(int, n0, 0, 1, N0, |
| 820 | { |
| 821 | c_f32[m0].s[n0] = arm_matrix_multiply(a[0].s[m0], b[n0].s[0], c_f32[m0].s[n0]); |
| 822 | }) |
| 823 | }) |
| 824 | |
| 825 | lhs_offset_first_element_in_bytes += MMUL_K0 * lhs_stride_y; |
| 826 | rhs_offset_first_element_in_bytes += MMUL_N0 * sizeof(DATA_TYPE); |
| 827 | } |
| 828 | |
| 829 | // For threads "outside" of the dst bound, we do not write but we have to "read" (arm_matrix_multiply). That's why this needs to happen after arm_matrix_multiply |
| 830 | if(dst_x_unclamped >= N || dst_y_unclamped >= M) |
| 831 | { |
| 832 | return; |
| 833 | } |
| 834 | |
| 835 | #if defined(HALF_PRECISION) |
| 836 | TILE(DATA_TYPE, M0, N0, c); |
| 837 | |
| 838 | // Conversion required for the half precision |
| 839 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 840 | { |
| 841 | LOOP_UNROLLING(int, n0, 0, 1, N0, |
| 842 | { |
| 843 | c[m0].s[n0] = c_f32[m0].s[n0]; |
| 844 | }) |
| 845 | }) |
| 846 | #else // defined(HALF_PRECISION) |
| 847 | #define c c_f32 |
| 848 | #endif // defined(HALF_PRECISION) |
| 849 | |
| 850 | if(dst_x + N0 <= N || N0_LEFTOVER == 0) |
| 851 | { |
| 852 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 853 | { |
| 854 | if(dst_y + m0 < M || M0_LEFTOVER == 0) |
| 855 | { |
| 856 | VSTORE(N0) |
| 857 | (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); |
| 858 | } |
| 859 | }) |
| 860 | } |
| 861 | else |
| 862 | { |
| 863 | LOOP_UNROLLING(int, m0, 0, 1, M0, |
| 864 | { |
| 865 | if(dst_y + m0 < M || M0_LEFTOVER == 0) |
| 866 | { |
| 867 | VSTORE_PARTIAL(N0, N0_LEFTOVER) |
| 868 | (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); |
| 869 | } |
| 870 | }) |
| 871 | } |
| 872 | |
| 873 | #undef MMUL_BLOCK_SIZE |
| 874 | } |
| 875 | #endif // defined(MAT_MUL_NATIVE_MMUL_T_T) |