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 | |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 28 | #ifdef BIAS |
| 29 | // This function performs in-place bias addition for float/half datatype when bias is enabled. |
| 30 | // 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 (e.g. -DN0=8, -DM0=4). |
| 31 | inline void perform_bias_addition(uchar *bias_ptr, uint bias_offset_first_element_in_bytes, TILE(DATA_TYPE, M0, N0, acc), uint x) |
| 32 | { |
| 33 | TILE(DATA_TYPE, 1, N0, bias_tile); |
| 34 | |
| 35 | // below expands to use bias_ptr and bias_offset_first_element_in_bytes |
| 36 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, x, 0, 1, 0, bias_tile); |
| 37 | |
| 38 | // c = c + bias[broadcasted] |
| 39 | T_ELTWISE_BROADCAST_ADD_X(DATA_TYPE, M0, N0, acc, bias_tile, acc); |
| 40 | } |
| 41 | #endif // defined(BIAS) |
| 42 | |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 43 | #if defined(MAT_MUL_NATIVE_NT_NT) |
| 44 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS non-transposed - buffer only |
| 45 | * |
| 46 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 47 | * 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] | 48 | * @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] | 49 | * @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] | 50 | * @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] | 51 | * @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] | 52 | * @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] | 53 | * @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] | 54 | * @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] | 55 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 56 | * - M0 > 0 |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 57 | * - 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] | 58 | * - K0 = 1, 2, 3, 4, 8, 16 |
| 59 | * @note Values > 8 for M0 are not expected to be efficient |
| 60 | * |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 61 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 62 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 63 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 64 | * @param[in] lhs_w The width of the lhs tensor |
| 65 | * @param[in] lhs_h The height of the lhs tensor |
| 66 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 67 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| 68 | * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE |
| 69 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 70 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 71 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 72 | * @param[in] rhs_w The width of the rhs tensor |
| 73 | * @param[in] rhs_h The height of the rhs tensor |
| 74 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 75 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 76 | * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr |
| 77 | * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) |
| 78 | * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) |
| 79 | * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor |
| 80 | * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor |
| 81 | * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor |
| 82 | * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor |
| 83 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 84 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 85 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 86 | * @param[in] dst_w The width of the dst tensor |
| 87 | * @param[in] dst_h The height of the dst tensor |
| 88 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 89 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 90 | */ |
| 91 | __kernel void mat_mul_native_nt_nt( |
| 92 | TENSOR3D_T(lhs, BUFFER), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 93 | TENSOR3D_T(rhs, RHS_TENSOR_TYPE), |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 94 | #ifdef BIAS |
| 95 | TENSOR3D_T(bias, BUFFER), |
| 96 | #endif // defined(BIAS) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 97 | TENSOR3D_T(dst, BUFFER)) |
| 98 | { |
| 99 | const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| 100 | const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| 101 | const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| 102 | |
| 103 | // Compute LHS/RHS/DST matrix address |
| 104 | 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] | 105 | dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| 106 | |
| 107 | // Initialize the accumulators |
| 108 | TILE(DATA_TYPE, M0, N0, acc); |
| 109 | |
| 110 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 111 | { |
| 112 | acc[i].v = 0.f; |
| 113 | }) |
| 114 | |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 115 | const int rhs_z = z * rhs_h; |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 116 | int k; |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 117 | for(k = 0; k <= K - K0; k += K0) |
| 118 | { |
| 119 | TILE(DATA_TYPE, M0, K0, a); |
| 120 | TILE(DATA_TYPE, K0, N0, b); |
| 121 | |
| 122 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 123 | { |
| 124 | a[i].v = 0.f; |
| 125 | }) |
| 126 | |
| 127 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 128 | { |
| 129 | b[i].v = 0.f; |
| 130 | }) |
| 131 | |
| 132 | // Load tile from the lhs/rhs tensors |
| 133 | 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] | 134 | 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] | 135 | |
| 136 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, a, b, acc); |
| 137 | |
| 138 | lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 139 | } |
| 140 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 141 | #if K % K0 != 0 |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 142 | /* Leftover Loop */ |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 143 | for(; k < K; ++k) |
| 144 | { |
| 145 | TILE(DATA_TYPE, M0, 1, a); |
| 146 | TILE(DATA_TYPE, 1, N0, b); |
| 147 | |
| 148 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 149 | { |
| 150 | a[i].v = 0.f; |
| 151 | }) |
| 152 | |
| 153 | LOOP_UNROLLING(int, i, 0, 1, 1, |
| 154 | { |
| 155 | b[i].v = 0.f; |
| 156 | }) |
| 157 | |
| 158 | // Load tile from the lhs/rhs tensors |
| 159 | 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] | 160 | 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] | 161 | |
| 162 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, a, b, acc); |
| 163 | |
| 164 | lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 165 | } |
| 166 | #endif // K % K0 != 0 |
| 167 | |
| 168 | const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| 169 | const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| 170 | |
| 171 | TILE(int, M0, 1, indirect_buffer); |
| 172 | LOOP_UNROLLING(int, _i, 0, 1, M0, |
| 173 | { |
| 174 | indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| 175 | }); |
| 176 | |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 177 | #ifdef BIAS |
| 178 | perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x); |
| 179 | #endif // defined(BIAS) |
| 180 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 181 | T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc); |
| 182 | |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 183 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| 184 | } |
| 185 | #endif // defined(MAT_MUL_NATIVE_NT_NT) |
| 186 | |
| 187 | #if defined(MAT_MUL_NATIVE_NT_T) |
| 188 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS transposed - buffer only |
| 189 | * |
| 190 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 191 | * 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] | 192 | * @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] | 193 | * @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] | 194 | * @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] | 195 | * @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] | 196 | * @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] | 197 | * @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] | 198 | * @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] | 199 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 200 | * - M0 > 0 |
| 201 | * - N0 = 1, 2, 3, 4, 8, 16 |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 202 | * - 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] | 203 | * @note Values > 8 for M0, N0 and K0 are not expected to be efficient |
| 204 | * |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 205 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 206 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 207 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 208 | * @param[in] lhs_w The width of the lhs tensor |
| 209 | * @param[in] lhs_h The height of the lhs tensor |
| 210 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 211 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| 212 | * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE |
| 213 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 214 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 215 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 216 | * @param[in] rhs_w The width of the rhs tensor |
| 217 | * @param[in] rhs_h The height of the rhs tensor |
| 218 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 219 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 220 | * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr |
| 221 | * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) |
| 222 | * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) |
| 223 | * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor |
| 224 | * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor |
| 225 | * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor |
| 226 | * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor |
| 227 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 228 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 229 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 230 | * @param[in] dst_w The width of the dst tensor |
| 231 | * @param[in] dst_h The height of the dst tensor |
| 232 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 233 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 234 | */ |
| 235 | __kernel void mat_mul_native_nt_t(TENSOR3D_T(lhs, BUFFER), |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 236 | TENSOR3D_T(rhs, RHS_TENSOR_TYPE), |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 237 | #ifdef BIAS |
| 238 | TENSOR3D_T(bias, BUFFER), |
| 239 | #endif // defined(BIAS) |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 240 | TENSOR3D_T(dst, BUFFER)) |
| 241 | |
| 242 | { |
| 243 | const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| 244 | const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| 245 | const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| 246 | |
| 247 | // Compute LHS/RHS/DST matrix address |
| 248 | 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] | 249 | dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| 250 | |
| 251 | // Initialize the accumulators |
| 252 | TILE(DATA_TYPE, M0, N0, acc); |
| 253 | |
| 254 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 255 | { |
| 256 | acc[i].v = 0.f; |
| 257 | }) |
| 258 | |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 259 | const int rhs_z = z * rhs_h; |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 260 | int k; |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 261 | for(k = 0; k <= K - K0; k += K0) |
| 262 | { |
| 263 | TILE(DATA_TYPE, M0, K0, a); |
| 264 | TILE(DATA_TYPE, N0, K0, b); |
| 265 | |
| 266 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 267 | { |
| 268 | a[i].v = 0.f; |
| 269 | }) |
| 270 | |
| 271 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 272 | { |
| 273 | b[i].v = 0.f; |
| 274 | }) |
| 275 | |
| 276 | // Load tile from the lhs/rhs tensors |
| 277 | 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] | 278 | 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] | 279 | |
| 280 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 281 | // This part is written to decrease the number of loop unrollings caused |
| 282 | // by T_MMUL. The NT/NT version is partly vectorized and uses less number |
| 283 | // of loop unrollings, and code behaves as expected. Although this is not |
| 284 | // a performant solution for the specified architecture, it is necessary |
| 285 | // to overcome some limitations. |
| 286 | TILE(DATA_TYPE, K0, N0, bt); |
| 287 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 288 | { |
| 289 | LOOP_UNROLLING(int, j, 0, 1, K0, |
| 290 | { |
| 291 | bt[j].s[i] = b[i].s[j]; |
| 292 | }) |
| 293 | }) |
| 294 | 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] | 295 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 296 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, T, a, b, acc); |
| 297 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 298 | |
| 299 | lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 300 | } |
| 301 | |
| 302 | #if K % K0 != 0 |
| 303 | /* Leftover Loop */ |
| 304 | for(; k < K; ++k) |
| 305 | { |
| 306 | TILE(DATA_TYPE, M0, 1, a); |
| 307 | TILE(DATA_TYPE, N0, 1, b); |
| 308 | |
| 309 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 310 | { |
| 311 | a[i].v = 0.f; |
| 312 | }) |
| 313 | |
| 314 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 315 | { |
| 316 | b[i].v = 0.f; |
| 317 | }) |
| 318 | |
| 319 | // Load tile from the lhs/rhs tensors |
| 320 | 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] | 321 | 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] | 322 | |
| 323 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 324 | // See the main loop for the explanation of this part |
| 325 | TILE(DATA_TYPE, 1, N0, bt); |
| 326 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 327 | { |
| 328 | bt[0].s[i] = b[i].s[0]; |
| 329 | }) |
| 330 | 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] | 331 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 332 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, T, a, b, acc); |
| 333 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 334 | |
| 335 | lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 336 | } |
| 337 | #endif // K % K0 != 0 |
| 338 | |
| 339 | const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| 340 | const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| 341 | |
| 342 | TILE(int, M0, 1, indirect_buffer); |
| 343 | LOOP_UNROLLING(int, _i, 0, 1, M0, |
| 344 | { |
| 345 | indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| 346 | }); |
| 347 | |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 348 | #ifdef BIAS |
| 349 | perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x); |
| 350 | #endif // defined(BIAS) |
| 351 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 352 | T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc); |
| 353 | |
Ramy Elgammal | 2b6ebfe | 2023-03-09 21:15:37 +0000 | [diff] [blame] | 354 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| 355 | } |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 356 | #endif // defined(MAT_MUL_NATIVE_NT_T) |
| 357 | |
| 358 | #if defined(MAT_MUL_NATIVE_T_NT) |
| 359 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS non-transposed - buffer only |
| 360 | * |
| 361 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 362 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| 363 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
| 364 | * @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] | 365 | * @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] | 366 | * @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) |
| 367 | * @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] | 368 | * @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] | 369 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_T_NT) |
| 370 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 371 | * - M0 = 1, 2, 3, 4, 8, 16 |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 372 | * - 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] | 373 | * - K0 > 0 |
| 374 | * * @note Values > 8 for M0, and K0 are not expected to be efficient |
| 375 | * |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 376 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 377 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 378 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 379 | * @param[in] lhs_w The width of the lhs tensor |
| 380 | * @param[in] lhs_h The height of the lhs tensor |
| 381 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 382 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| 383 | * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE |
| 384 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 385 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 386 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 387 | * @param[in] rhs_w The width of the rhs tensor |
| 388 | * @param[in] rhs_h The height of the rhs tensor |
| 389 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 390 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 391 | * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr |
| 392 | * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) |
| 393 | * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) |
| 394 | * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor |
| 395 | * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor |
| 396 | * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor |
| 397 | * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor |
| 398 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr |
| 399 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 400 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 401 | * @param[in] dst_w The width of the dst tensor |
| 402 | * @param[in] dst_h The height of the dst tensor |
| 403 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 404 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 405 | */ |
| 406 | __kernel void mat_mul_native_t_nt( |
| 407 | TENSOR3D_T(lhs, BUFFER), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 408 | TENSOR3D_T(rhs, RHS_TENSOR_TYPE), |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 409 | #ifdef BIAS |
| 410 | TENSOR3D_T(bias, BUFFER), |
| 411 | #endif // defined(BIAS) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 412 | TENSOR3D_T(dst, BUFFER)) |
| 413 | { |
| 414 | const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| 415 | const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| 416 | const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| 417 | |
| 418 | // Compute LHS/RHS/DST matrix address |
| 419 | 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] | 420 | dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| 421 | |
| 422 | // Initialize the accumulators |
| 423 | TILE(DATA_TYPE, M0, N0, acc); |
| 424 | |
| 425 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 426 | { |
| 427 | acc[i].v = 0.f; |
| 428 | }) |
| 429 | |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 430 | const int rhs_z = z * rhs_h; |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 431 | int k; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 432 | for(k = 0; k <= K - K0; k += K0) |
| 433 | { |
| 434 | TILE(DATA_TYPE, K0, M0, a); |
| 435 | TILE(DATA_TYPE, K0, N0, b); |
| 436 | |
| 437 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 438 | { |
| 439 | a[i].v = 0.f; |
| 440 | }) |
| 441 | |
| 442 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 443 | { |
| 444 | b[i].v = 0.f; |
| 445 | }) |
| 446 | |
| 447 | // Load tile from the lhs/rhs tensors |
| 448 | 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] | 449 | 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] | 450 | |
| 451 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 452 | // For explanation, see mat_mul_native_nt_t |
| 453 | TILE(DATA_TYPE, M0, K0, at); |
| 454 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 455 | { |
| 456 | LOOP_UNROLLING(int, j, 0, 1, M0, |
| 457 | { |
| 458 | at[j].s[i] = a[i].s[j]; |
| 459 | }) |
| 460 | }) |
| 461 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, at, b, acc); |
| 462 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| 463 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, T, NT, a, b, acc); |
| 464 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 465 | |
| 466 | lhs_offset_first_element_in_bytes += K0 * lhs_stride_y; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 467 | } |
| 468 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 469 | #if K % K0 != 0 |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 470 | /* Leftover Loop */ |
| 471 | for(; k < K; ++k) |
| 472 | { |
| 473 | TILE(DATA_TYPE, 1, M0, a); |
| 474 | TILE(DATA_TYPE, 1, N0, b); |
| 475 | |
| 476 | LOOP_UNROLLING(int, i, 0, 1, 1, |
| 477 | { |
| 478 | a[i].v = 0.f; |
| 479 | }) |
| 480 | |
| 481 | LOOP_UNROLLING(int, i, 0, 1, 1, |
| 482 | { |
| 483 | b[i].v = 0.f; |
| 484 | }) |
| 485 | |
| 486 | // Load tile from the lhs/rhs tensors |
| 487 | 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] | 488 | 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] | 489 | |
| 490 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 491 | // For explanation, see mat_mul_native_nt_t |
| 492 | TILE(DATA_TYPE, M0, 1, at); |
| 493 | LOOP_UNROLLING(int, j, 0, 1, M0, |
| 494 | { |
| 495 | at[j].s[0] = a[0].s[j]; |
| 496 | }) |
| 497 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, at, b, acc); |
| 498 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| 499 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, T, NT, a, b, acc); |
| 500 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 501 | |
| 502 | lhs_offset_first_element_in_bytes += 1 * lhs_stride_y; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 503 | } |
| 504 | #endif // K % K0 != 0 |
| 505 | |
| 506 | const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| 507 | const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| 508 | |
| 509 | TILE(int, M0, 1, indirect_buffer); |
| 510 | LOOP_UNROLLING(int, _i, 0, 1, M0, |
| 511 | { |
| 512 | indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| 513 | }); |
| 514 | |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 515 | #ifdef BIAS |
| 516 | perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x); |
| 517 | #endif // defined(BIAS) |
| 518 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 519 | T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc); |
| 520 | |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 521 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| 522 | } |
| 523 | #endif // defined(MAT_MUL_NATIVE_T_NT) |
| 524 | |
| 525 | #if defined(MAT_MUL_NATIVE_T_T) |
| 526 | /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS transposed - buffer only |
| 527 | * |
| 528 | * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| 529 | * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| 530 | * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) |
| 531 | * @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] | 532 | * @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] | 533 | * @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) |
| 534 | * @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] | 535 | * @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] | 536 | * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_T_NT) |
| 537 | * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| 538 | * - M0 = 1, 2, 3, 4, 8, 16 |
| 539 | * - N0 = 1, 2, 3, 4, 8, 16 |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 540 | * - 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] | 541 | * @note Values > 8 for M0, N0 and K0 are not expected to be efficient |
| 542 | * |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 543 | * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| 544 | * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| 545 | * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| 546 | * @param[in] lhs_w The width of the lhs tensor |
| 547 | * @param[in] lhs_h The height of the lhs tensor |
| 548 | * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| 549 | * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| 550 | * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE |
| 551 | * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr |
| 552 | * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| 553 | * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| 554 | * @param[in] rhs_w The width of the rhs tensor |
| 555 | * @param[in] rhs_h The height of the rhs tensor |
| 556 | * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| 557 | * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| 558 | * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr |
| 559 | * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) |
| 560 | * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) |
| 561 | * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor |
| 562 | * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor |
| 563 | * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor |
| 564 | * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor |
| 565 | * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr, |
| 566 | * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| 567 | * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| 568 | * @param[in] dst_w The width of the dst tensor |
| 569 | * @param[in] dst_h The height of the dst tensor |
| 570 | * @param[in] dst_n Number of the matrices (buffers) in the batch |
| 571 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 572 | */ |
| 573 | __kernel void mat_mul_native_t_t( |
| 574 | TENSOR3D_T(lhs, BUFFER), |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 575 | TENSOR3D_T(rhs, RHS_TENSOR_TYPE), |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 576 | #ifdef BIAS |
| 577 | TENSOR3D_T(bias, BUFFER), |
| 578 | #endif // defined(BIAS) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 579 | TENSOR3D_T(dst, BUFFER)) |
| 580 | { |
| 581 | const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| 582 | const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| 583 | const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| 584 | |
| 585 | // Compute LHS/RHS/DST matrix address |
| 586 | 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] | 587 | dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| 588 | |
| 589 | // Initialize the accumulators |
| 590 | TILE(DATA_TYPE, M0, N0, acc); |
| 591 | |
| 592 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 593 | { |
| 594 | acc[i].v = 0.f; |
| 595 | }) |
| 596 | |
Ramy Elgammal | b531b75 | 2023-03-20 10:19:10 +0000 | [diff] [blame] | 597 | const int rhs_z = z * rhs_h; |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 598 | int k; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 599 | for(k = 0; k <= K - K0; k += K0) |
| 600 | { |
| 601 | TILE(DATA_TYPE, K0, M0, a); |
| 602 | TILE(DATA_TYPE, N0, K0, b); |
| 603 | |
| 604 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 605 | { |
| 606 | a[i].v = 0.f; |
| 607 | }) |
| 608 | |
| 609 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 610 | { |
| 611 | b[i].v = 0.f; |
| 612 | }) |
| 613 | |
| 614 | // Load tile from the lhs/rhs tensors |
| 615 | 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] | 616 | 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] | 617 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 618 | // For explanation, see mat_mul_native_nt_t |
| 619 | TILE(DATA_TYPE, M0, K0, at); |
| 620 | TILE(DATA_TYPE, K0, N0, bt); |
| 621 | |
| 622 | LOOP_UNROLLING(int, i, 0, 1, K0, |
| 623 | { |
| 624 | LOOP_UNROLLING(int, j, 0, 1, M0, |
| 625 | { |
| 626 | at[j].s[i] = a[i].s[j]; |
| 627 | }) |
| 628 | }) |
| 629 | |
| 630 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 631 | { |
| 632 | LOOP_UNROLLING(int, j, 0, 1, K0, |
| 633 | { |
| 634 | bt[j].s[i] = b[i].s[j]; |
| 635 | }) |
| 636 | }) |
| 637 | |
| 638 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, at, bt, acc); |
| 639 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| 640 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, T, T, a, b, acc); |
| 641 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 642 | |
| 643 | lhs_offset_first_element_in_bytes += K0 * lhs_stride_y; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 644 | } |
| 645 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 646 | #if K % K0 != 0 |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 647 | /* Leftover Loop */ |
| 648 | for(; k < K; ++k) |
| 649 | { |
| 650 | TILE(DATA_TYPE, 1, M0, a); |
| 651 | TILE(DATA_TYPE, N0, 1, b); |
| 652 | |
| 653 | LOOP_UNROLLING(int, i, 0, 1, 1, |
| 654 | { |
| 655 | a[i].v = 0.f; |
| 656 | }) |
| 657 | |
| 658 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 659 | { |
| 660 | b[i].v = 0.f; |
| 661 | }) |
| 662 | |
| 663 | // Load tile from the lhs/rhs tensors |
| 664 | 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] | 665 | 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] | 666 | |
| 667 | #if GPU_ARCH == GPU_ARCH_MIDGARD |
| 668 | // For explanation, see mat_mul_native_nt_t |
| 669 | TILE(DATA_TYPE, M0, 1, at); |
| 670 | TILE(DATA_TYPE, 1, N0, bt); |
| 671 | |
| 672 | LOOP_UNROLLING(int, j, 0, 1, M0, |
| 673 | { |
| 674 | at[j].s[0] = a[0].s[j]; |
| 675 | }) |
| 676 | |
| 677 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 678 | { |
| 679 | bt[0].s[i] = b[i].s[0]; |
| 680 | }) |
| 681 | |
| 682 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, at, bt, acc); |
| 683 | #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| 684 | T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, T, T, a, b, acc); |
| 685 | #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| 686 | |
| 687 | lhs_offset_first_element_in_bytes += 1 * lhs_stride_y; |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 688 | } |
| 689 | #endif // K % K0 != 0 |
| 690 | |
| 691 | const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| 692 | const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| 693 | |
| 694 | TILE(int, M0, 1, indirect_buffer); |
| 695 | LOOP_UNROLLING(int, _i, 0, 1, M0, |
| 696 | { |
| 697 | indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| 698 | }); |
| 699 | |
Mohammed Suhail Munshi | 8e2dede | 2023-06-27 14:25:58 +0100 | [diff] [blame] | 700 | #ifdef BIAS |
| 701 | perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x); |
| 702 | #endif // defined(BIAS) |
| 703 | |
Mohammed Suhail Munshi | 94abde4 | 2023-05-25 16:48:43 +0100 | [diff] [blame] | 704 | T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc); |
| 705 | |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 706 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| 707 | } |
| 708 | #endif // defined(MAT_MUL_NATIVE_T_T) |