Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [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 | */ |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 24 | #include "activation_float_helpers.h" |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 25 | #include "helpers.h" |
| 26 | #include "tile_helpers.h" |
| 27 | |
| 28 | #if defined(MAT_MUL_NATIVE_NT_NT) |
| 29 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS non-transposed - buffer only |
| 30 | * |
| 31 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 32 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 33 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 34 | * @note The block'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=4). |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 35 | * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions. |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 36 | * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 37 | * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 38 | * @note The tensor type ("BUFFER" or "IMAGE") of the rhs tensor must be passed at compile time using -DRHS_TENSOR_TYPE (e.g. -DRHS_TENSOR_TYPE=BUFFER) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 39 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_NT_NT) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 40 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 41 | * - M0 > 0 |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 42 | * - N0 = 1, 2, 3, 4, 8, 16 (only 4, 8, 16 if RHS_TENSOR_TYPE=IMAGE) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 43 | * - K0 = 1, 2, 3, 4, 8, 16 |
| 44 | * @note Values > 8 for M0 are not expected to be efficient |
| 45 | * |
| 46 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 47 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 48 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 49 | * @param[in] lhs_w The width of the lhs tensor |
| 50 | * @param[in] lhs_h The height of the lhs tensor |
| 51 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 52 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 53 | * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 54 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 55 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 56 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 57 | * @param[in] rhs_w The width of the rhs tensor |
| 58 | * @param[in] rhs_h The height of the rhs tensor |
| 59 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 60 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 61 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 62 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 63 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 64 | * @param[in] dst_w The width of the dst tensor |
| 65 | * @param[in] dst_h The height of the dst tensor |
| 66 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 67 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| 68 | */ |
| 69 | __kernel void mat_mul_native_nt_nt( |
| 70 | TENSOR3D_T(lhs, BUFFER), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 71 | TENSOR3D_T(rhs, RHS_TENSOR_TYPE), |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 72 | TENSOR3D_T(dst, BUFFER)) |
| 73 | { |
| 74 | const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| 75 | const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| 76 | const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| 77 | |
| 78 | // Compute LHS/RHS/DST matrix address |
| 79 | lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z; |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 80 | dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| 81 | |
| 82 | // Initialize the accumulators |
| 83 | TILE(DATA_TYPE, M0, N0, acc); |
| 84 | |
| 85 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 86 | { |
| 87 | acc[i].v = 0.f; |
| 88 | }) |
| 89 | |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 90 | const int rhs_z = z * rhs_h; |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 91 | int k; |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 92 | for(k = 0; k <= K - K0; k += K0) |
| 93 | { |
| 94 | TILE(DATA_TYPE, M0, K0, a); |
| 95 | TILE(DATA_TYPE, K0, N0, b); |
| 96 | |
| 97 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 98 | { |
| 99 | a[i].v = 0.f; |
| 100 | }) |
| 101 | |
| 102 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 103 | { |
| 104 | b[i].v = 0.f; |
| 105 | }) |
| 106 | |
| 107 | // Load tile from the lhs/rhs tensors |
| 108 | T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 109 | T_LOAD(DATA_TYPE, K0, N0, RHS_TENSOR_TYPE, rhs, x, k + rhs_z, 1, rhs_stride_y, b); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 110 | |
| 111 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, a, b, acc); |
| 112 | |
| 113 | lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 114 | } |
| 115 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 116 | #if K % K0 != 0 |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 117 | /* Leftover Loop */ |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 118 | for(; k < K; ++k) |
| 119 | { |
| 120 | TILE(DATA_TYPE, M0, 1, a); |
| 121 | TILE(DATA_TYPE, 1, N0, b); |
| 122 | |
| 123 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 124 | { |
| 125 | a[i].v = 0.f; |
| 126 | }) |
| 127 | |
| 128 | LOOP_UNROLLING(int, i, 0, 1, 1, |
| 129 | { |
| 130 | b[i].v = 0.f; |
| 131 | }) |
| 132 | |
| 133 | // Load tile from the lhs/rhs tensors |
| 134 | T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 135 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, x, k + rhs_z, 1, rhs_stride_y, b); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 136 | |
| 137 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, a, b, acc); |
| 138 | |
| 139 | lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 140 | } |
| 141 | #endif // K % K0 != 0 |
| 142 | |
| 143 | const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| 144 | const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| 145 | |
| 146 | TILE(int, M0, 1, indirect_buffer); |
| 147 | LOOP_UNROLLING(int, _i, 0, 1, M0, |
| 148 | { |
| 149 | indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| 150 | }); |
| 151 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 152 | T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc); |
| 153 | |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 154 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| 155 | } |
| 156 | #endif // defined(MAT_MUL_NATIVE_NT_NT) |
| 157 | |
| 158 | #if defined(MAT_MUL_NATIVE_NT_T) |
| 159 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS transposed - buffer only |
| 160 | * |
| 161 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 162 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 163 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 164 | * @note The block'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=4). |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 165 | * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions. |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 166 | * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 167 | * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 168 | * @note The tensor type ("BUFFER" or "IMAGE") of the rhs tensor must be passed at compile time using -DRHS_TENSOR_TYPE (e.g. -DRHS_TENSOR_TYPE=BUFFER) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 169 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_NT_T) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 170 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 171 | * - M0 > 0 |
| 172 | * - N0 = 1, 2, 3, 4, 8, 16 |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 173 | * - K0 = 1, 2, 3, 4, 8, 16 (only 4, 8, 16 if RHS_TENSOR_TYPE=IMAGE) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 174 | * @note Values > 8 for M0, N0 and K0 are not expected to be efficient |
| 175 | * |
| 176 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 177 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 178 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 179 | * @param[in] lhs_w The width of the lhs tensor |
| 180 | * @param[in] lhs_h The height of the lhs tensor |
| 181 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 182 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 183 | * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 184 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 185 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 186 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 187 | * @param[in] rhs_w The width of the rhs tensor |
| 188 | * @param[in] rhs_h The height of the rhs tensor |
| 189 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 190 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 191 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 192 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 193 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 194 | * @param[in] dst_w The width of the dst tensor |
| 195 | * @param[in] dst_h The height of the dst tensor |
| 196 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 197 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| 198 | */ |
| 199 | __kernel void mat_mul_native_nt_t(TENSOR3D_T(lhs, BUFFER), |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 200 | TENSOR3D_T(rhs, RHS_TENSOR_TYPE), |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 201 | TENSOR3D_T(dst, BUFFER)) |
| 202 | |
| 203 | { |
| 204 | const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| 205 | const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| 206 | const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| 207 | |
| 208 | // Compute LHS/RHS/DST matrix address |
| 209 | lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z; |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 210 | dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| 211 | |
| 212 | // Initialize the accumulators |
| 213 | TILE(DATA_TYPE, M0, N0, acc); |
| 214 | |
| 215 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 216 | { |
| 217 | acc[i].v = 0.f; |
| 218 | }) |
| 219 | |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 220 | const int rhs_z = z * rhs_h; |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 221 | int k; |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 222 | for(k = 0; k <= K - K0; k += K0) |
| 223 | { |
| 224 | TILE(DATA_TYPE, M0, K0, a); |
| 225 | TILE(DATA_TYPE, N0, K0, b); |
| 226 | |
| 227 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 228 | { |
| 229 | a[i].v = 0.f; |
| 230 | }) |
| 231 | |
| 232 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 233 | { |
| 234 | b[i].v = 0.f; |
| 235 | }) |
| 236 | |
| 237 | // Load tile from the lhs/rhs tensors |
| 238 | T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 239 | T_LOAD(DATA_TYPE, N0, K0, RHS_TENSOR_TYPE, rhs, k, x + rhs_z, 1, rhs_stride_y, b); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 240 | |
| 241 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 242 | // This part is written to decrease the number of loop unrollings caused |
| 243 | // by T_MMUL. The NT/NT version is partly vectorized and uses less number |
| 244 | // of loop unrollings, and code behaves as expected. Although this is not |
| 245 | // a performant solution for the specified architecture, it is necessary |
| 246 | // to overcome some limitations. |
| 247 | TILE(DATA_TYPE, K0, N0, bt); |
| 248 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 249 | { |
| 250 | LOOP_UNROLLING(int, j, 0, 1, K0, |
| 251 | { |
| 252 | bt[j].s[i] = b[i].s[j]; |
| 253 | }) |
| 254 | }) |
| 255 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, a, bt, acc); |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 256 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 257 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, T, a, b, acc); |
| 258 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 259 | |
| 260 | lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 261 | } |
| 262 | |
| 263 | #if K % K0 != 0 |
| 264 | /* Leftover Loop */ |
| 265 | for(; k < K; ++k) |
| 266 | { |
| 267 | TILE(DATA_TYPE, M0, 1, a); |
| 268 | TILE(DATA_TYPE, N0, 1, b); |
| 269 | |
| 270 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 271 | { |
| 272 | a[i].v = 0.f; |
| 273 | }) |
| 274 | |
| 275 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 276 | { |
| 277 | b[i].v = 0.f; |
| 278 | }) |
| 279 | |
| 280 | // Load tile from the lhs/rhs tensors |
| 281 | T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 282 | T_LOAD(DATA_TYPE, N0, 1, BUFFER, rhs, k, x + rhs_z, 1, rhs_stride_y, b); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 283 | |
| 284 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 285 | // See the main loop for the explanation of this part |
| 286 | TILE(DATA_TYPE, 1, N0, bt); |
| 287 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 288 | { |
| 289 | bt[0].s[i] = b[i].s[0]; |
| 290 | }) |
| 291 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, a, bt, acc); |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 292 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 293 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, T, a, b, acc); |
| 294 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 295 | |
| 296 | lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 297 | } |
| 298 | #endif // K % K0 != 0 |
| 299 | |
| 300 | const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| 301 | const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| 302 | |
| 303 | TILE(int, M0, 1, indirect_buffer); |
| 304 | LOOP_UNROLLING(int, _i, 0, 1, M0, |
| 305 | { |
| 306 | indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| 307 | }); |
| 308 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 309 | T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc); |
| 310 | |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 311 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| 312 | } |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 313 | #endif // defined(MAT_MUL_NATIVE_NT_T) |
| 314 | |
| 315 | #if defined(MAT_MUL_NATIVE_T_NT) |
| 316 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS non-transposed - buffer only |
| 317 | * |
| 318 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 319 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| 320 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
| 321 | * @note The block'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=4). |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 322 | * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions. |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 323 | * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3) |
| 324 | * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 325 | * @note The tensor type ("BUFFER" or "IMAGE") of the rhs tensor must be passed at compile time using -DRHS_TENSOR_TYPE (e.g. -DRHS_TENSOR_TYPE=BUFFER) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 326 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_T_NT) |
| 327 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 328 | * - M0 = 1, 2, 3, 4, 8, 16 |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 329 | * - N0 = 1, 2, 3, 4, 8, 16 (only 4, 8, 16 if RHS_TENSOR_TYPE=IMAGE) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 330 | * - K0 > 0 |
| 331 | * * @note Values > 8 for M0, and K0 are not expected to be efficient |
| 332 | * |
| 333 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 334 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 335 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 336 | * @param[in] lhs_w The width of the lhs tensor |
| 337 | * @param[in] lhs_h The height of the lhs tensor |
| 338 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 339 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 340 | * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 341 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 342 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 343 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 344 | * @param[in] rhs_w The width of the rhs tensor |
| 345 | * @param[in] rhs_h The height of the rhs tensor |
| 346 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 347 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 348 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 349 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 350 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 351 | * @param[in] dst_w The width of the dst tensor |
| 352 | * @param[in] dst_h The height of the dst tensor |
| 353 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 354 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| 355 | */ |
| 356 | __kernel void mat_mul_native_t_nt( |
| 357 | TENSOR3D_T(lhs, BUFFER), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 358 | TENSOR3D_T(rhs, RHS_TENSOR_TYPE), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 359 | TENSOR3D_T(dst, BUFFER)) |
| 360 | { |
| 361 | const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| 362 | const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| 363 | const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| 364 | |
| 365 | // Compute LHS/RHS/DST matrix address |
| 366 | lhs_offset_first_element_in_bytes += y * sizeof(DATA_TYPE) + z * lhs_stride_z; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 367 | dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| 368 | |
| 369 | // Initialize the accumulators |
| 370 | TILE(DATA_TYPE, M0, N0, acc); |
| 371 | |
| 372 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 373 | { |
| 374 | acc[i].v = 0.f; |
| 375 | }) |
| 376 | |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 377 | const int rhs_z = z * rhs_h; |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 378 | int k; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 379 | for(k = 0; k <= K - K0; k += K0) |
| 380 | { |
| 381 | TILE(DATA_TYPE, K0, M0, a); |
| 382 | TILE(DATA_TYPE, K0, N0, b); |
| 383 | |
| 384 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 385 | { |
| 386 | a[i].v = 0.f; |
| 387 | }) |
| 388 | |
| 389 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 390 | { |
| 391 | b[i].v = 0.f; |
| 392 | }) |
| 393 | |
| 394 | // Load tile from the lhs/rhs tensors |
| 395 | T_LOAD(DATA_TYPE, K0, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 396 | T_LOAD(DATA_TYPE, K0, N0, RHS_TENSOR_TYPE, rhs, x, k + rhs_z, 1, rhs_stride_y, b); |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 397 | |
| 398 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 399 | // For explanation, see mat_mul_native_nt_t |
| 400 | TILE(DATA_TYPE, M0, K0, at); |
| 401 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 402 | { |
| 403 | LOOP_UNROLLING(int, j, 0, 1, M0, |
| 404 | { |
| 405 | at[j].s[i] = a[i].s[j]; |
| 406 | }) |
| 407 | }) |
| 408 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, at, b, acc); |
| 409 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| 410 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, T, NT, a, b, acc); |
| 411 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 412 | |
| 413 | lhs_offset_first_element_in_bytes += K0 * lhs_stride_y; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 414 | } |
| 415 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 416 | #if K % K0 != 0 |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 417 | /* Leftover Loop */ |
| 418 | for(; k < K; ++k) |
| 419 | { |
| 420 | TILE(DATA_TYPE, 1, M0, a); |
| 421 | TILE(DATA_TYPE, 1, N0, b); |
| 422 | |
| 423 | LOOP_UNROLLING(int, i, 0, 1, 1, |
| 424 | { |
| 425 | a[i].v = 0.f; |
| 426 | }) |
| 427 | |
| 428 | LOOP_UNROLLING(int, i, 0, 1, 1, |
| 429 | { |
| 430 | b[i].v = 0.f; |
| 431 | }) |
| 432 | |
| 433 | // Load tile from the lhs/rhs tensors |
| 434 | T_LOAD(DATA_TYPE, 1, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 435 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, x, k + rhs_z, 1, rhs_stride_y, b); |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 436 | |
| 437 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 438 | // For explanation, see mat_mul_native_nt_t |
| 439 | TILE(DATA_TYPE, M0, 1, at); |
| 440 | LOOP_UNROLLING(int, j, 0, 1, M0, |
| 441 | { |
| 442 | at[j].s[0] = a[0].s[j]; |
| 443 | }) |
| 444 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, at, b, acc); |
| 445 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| 446 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, T, NT, a, b, acc); |
| 447 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 448 | |
| 449 | lhs_offset_first_element_in_bytes += 1 * lhs_stride_y; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 450 | } |
| 451 | #endif // K % K0 != 0 |
| 452 | |
| 453 | const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| 454 | const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| 455 | |
| 456 | TILE(int, M0, 1, indirect_buffer); |
| 457 | LOOP_UNROLLING(int, _i, 0, 1, M0, |
| 458 | { |
| 459 | indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| 460 | }); |
| 461 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 462 | T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc); |
| 463 | |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 464 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| 465 | } |
| 466 | #endif // defined(MAT_MUL_NATIVE_T_NT) |
| 467 | |
| 468 | #if defined(MAT_MUL_NATIVE_T_T) |
| 469 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS transposed - buffer only |
| 470 | * |
| 471 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 472 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| 473 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
| 474 | * @note The block'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=4). |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 475 | * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions. |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 476 | * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3) |
| 477 | * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 478 | * @note The tensor type ("BUFFER" or "IMAGE") of the rhs tensor must be passed at compile time using -DRHS_TENSOR_TYPE (e.g. -DRHS_TENSOR_TYPE=BUFFER) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 479 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_T_NT) |
| 480 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 481 | * - M0 = 1, 2, 3, 4, 8, 16 |
| 482 | * - N0 = 1, 2, 3, 4, 8, 16 |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 483 | * - K0 = 1, 2, 3, 4, 8, 16 (only 4, 8, 16 if RHS_TENSOR_TYPE=IMAGE) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 484 | * @note Values > 8 for M0, N0 and K0 are not expected to be efficient |
| 485 | * |
| 486 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 487 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 488 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 489 | * @param[in] lhs_w The width of the lhs tensor |
| 490 | * @param[in] lhs_h The height of the lhs tensor |
| 491 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 492 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 493 | * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 494 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 495 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 496 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 497 | * @param[in] rhs_w The width of the rhs tensor |
| 498 | * @param[in] rhs_h The height of the rhs tensor |
| 499 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 500 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 501 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 502 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 503 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 504 | * @param[in] dst_w The width of the dst tensor |
| 505 | * @param[in] dst_h The height of the dst tensor |
| 506 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 507 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| 508 | */ |
| 509 | __kernel void mat_mul_native_t_t( |
| 510 | TENSOR3D_T(lhs, BUFFER), |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 511 | TENSOR3D_T(rhs, RHS_TENSOR_TYPE), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 512 | TENSOR3D_T(dst, BUFFER)) |
| 513 | { |
| 514 | const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| 515 | const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| 516 | const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| 517 | |
| 518 | // Compute LHS/RHS/DST matrix address |
| 519 | lhs_offset_first_element_in_bytes += y * sizeof(DATA_TYPE) + z * lhs_stride_z; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 520 | dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| 521 | |
| 522 | // Initialize the accumulators |
| 523 | TILE(DATA_TYPE, M0, N0, acc); |
| 524 | |
| 525 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 526 | { |
| 527 | acc[i].v = 0.f; |
| 528 | }) |
| 529 | |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 530 | const int rhs_z = z * rhs_h; |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 531 | int k; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 532 | for(k = 0; k <= K - K0; k += K0) |
| 533 | { |
| 534 | TILE(DATA_TYPE, K0, M0, a); |
| 535 | TILE(DATA_TYPE, N0, K0, b); |
| 536 | |
| 537 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 538 | { |
| 539 | a[i].v = 0.f; |
| 540 | }) |
| 541 | |
| 542 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 543 | { |
| 544 | b[i].v = 0.f; |
| 545 | }) |
| 546 | |
| 547 | // Load tile from the lhs/rhs tensors |
| 548 | T_LOAD(DATA_TYPE, K0, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 549 | T_LOAD(DATA_TYPE, N0, K0, RHS_TENSOR_TYPE, rhs, k, x + rhs_z, 1, rhs_stride_y, b); |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 550 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 551 | // For explanation, see mat_mul_native_nt_t |
| 552 | TILE(DATA_TYPE, M0, K0, at); |
| 553 | TILE(DATA_TYPE, K0, N0, bt); |
| 554 | |
| 555 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 556 | { |
| 557 | LOOP_UNROLLING(int, j, 0, 1, M0, |
| 558 | { |
| 559 | at[j].s[i] = a[i].s[j]; |
| 560 | }) |
| 561 | }) |
| 562 | |
| 563 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 564 | { |
| 565 | LOOP_UNROLLING(int, j, 0, 1, K0, |
| 566 | { |
| 567 | bt[j].s[i] = b[i].s[j]; |
| 568 | }) |
| 569 | }) |
| 570 | |
| 571 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, at, bt, acc); |
| 572 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| 573 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, T, T, a, b, acc); |
| 574 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 575 | |
| 576 | lhs_offset_first_element_in_bytes += K0 * lhs_stride_y; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 577 | } |
| 578 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 579 | #if K % K0 != 0 |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 580 | /* Leftover Loop */ |
| 581 | for(; k < K; ++k) |
| 582 | { |
| 583 | TILE(DATA_TYPE, 1, M0, a); |
| 584 | TILE(DATA_TYPE, N0, 1, b); |
| 585 | |
| 586 | LOOP_UNROLLING(int, i, 0, 1, 1, |
| 587 | { |
| 588 | a[i].v = 0.f; |
| 589 | }) |
| 590 | |
| 591 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 592 | { |
| 593 | b[i].v = 0.f; |
| 594 | }) |
| 595 | |
| 596 | // Load tile from the lhs/rhs tensors |
| 597 | T_LOAD(DATA_TYPE, 1, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 598 | T_LOAD(DATA_TYPE, N0, 1, BUFFER, rhs, k, x + rhs_z, 1, rhs_stride_y, b); |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 599 | |
| 600 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 601 | // For explanation, see mat_mul_native_nt_t |
| 602 | TILE(DATA_TYPE, M0, 1, at); |
| 603 | TILE(DATA_TYPE, 1, N0, bt); |
| 604 | |
| 605 | LOOP_UNROLLING(int, j, 0, 1, M0, |
| 606 | { |
| 607 | at[j].s[0] = a[0].s[j]; |
| 608 | }) |
| 609 | |
| 610 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 611 | { |
| 612 | bt[0].s[i] = b[i].s[0]; |
| 613 | }) |
| 614 | |
| 615 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, at, bt, acc); |
| 616 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| 617 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, T, T, a, b, acc); |
| 618 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 619 | |
| 620 | lhs_offset_first_element_in_bytes += 1 * lhs_stride_y; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 621 | } |
| 622 | #endif // K % K0 != 0 |
| 623 | |
| 624 | const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| 625 | const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| 626 | |
| 627 | TILE(int, M0, 1, indirect_buffer); |
| 628 | LOOP_UNROLLING(int, _i, 0, 1, M0, |
| 629 | { |
| 630 | indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| 631 | }); |
| 632 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame^] | 633 | T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc); |
| 634 | |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 635 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| 636 | } |
| 637 | #endif // defined(MAT_MUL_NATIVE_T_T) |