| /* |
| * Copyright (c) 2023 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "helpers.h" |
| #include "tile_helpers.h" |
| |
| #if defined(MAT_MUL_NATIVE_NT_NT) |
| /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS non-transposed - buffer only |
| * |
| * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| * @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). |
| * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) |
| * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| * - M0 > 0 |
| * - N0 = 1, 2, 3, 4, 8, 16 |
| * - K0 = 1, 2, 3, 4, 8, 16 |
| * @note Values > 8 for M0 are not expected to be efficient |
| * |
| * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| * @param[in] lhs_w The width of the lhs tensor |
| * @param[in] lhs_h The height of the lhs tensor |
| * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: F32/F16 |
| * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| * @param[in] rhs_w The width of the rhs tensor |
| * @param[in] rhs_h The height of the rhs tensor |
| * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: F32/F16 |
| * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| * @param[in] dst_w The width of the dst tensor |
| * @param[in] dst_h The height of the dst tensor |
| * @param[in] dst_n Number of the matrices (buffers) in the batch |
| * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| */ |
| __kernel void mat_mul_native_nt_nt( |
| TENSOR3D_T(lhs, BUFFER), |
| TENSOR3D_T(rhs, BUFFER), |
| TENSOR3D_T(dst, BUFFER)) |
| { |
| const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| |
| // Compute LHS/RHS/DST matrix address |
| lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z; |
| rhs_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + z * rhs_stride_z; |
| dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| |
| // Initialize the accumulators |
| TILE(DATA_TYPE, M0, N0, acc); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| acc[i].v = 0.f; |
| }) |
| |
| int k; |
| for(k = 0; k <= K - K0; k += K0) |
| { |
| TILE(DATA_TYPE, M0, K0, a); |
| TILE(DATA_TYPE, K0, N0, b); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = 0.f; |
| }) |
| |
| LOOP_UNROLLING(int, i, 0, 1, K0, |
| { |
| b[i].v = 0.f; |
| }) |
| |
| // Load tile from the lhs/rhs tensors |
| T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
| T_LOAD(DATA_TYPE, K0, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); |
| |
| T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, a, b, acc); |
| |
| lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); |
| rhs_offset_first_element_in_bytes += K0 * rhs_stride_y; |
| } |
| |
| #ifdef K % K0 != 0 |
| for(; k < K; ++k) |
| { |
| TILE(DATA_TYPE, M0, 1, a); |
| TILE(DATA_TYPE, 1, N0, b); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = 0.f; |
| }) |
| |
| LOOP_UNROLLING(int, i, 0, 1, 1, |
| { |
| b[i].v = 0.f; |
| }) |
| |
| // Load tile from the lhs/rhs tensors |
| T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
| T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); |
| |
| T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, a, b, acc); |
| |
| lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); |
| rhs_offset_first_element_in_bytes += 1 * rhs_stride_y; |
| } |
| #endif // K % K0 != 0 |
| |
| const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| |
| TILE(int, M0, 1, indirect_buffer); |
| LOOP_UNROLLING(int, _i, 0, 1, M0, |
| { |
| indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| }); |
| |
| T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| } |
| #endif // defined(MAT_MUL_NATIVE_NT_NT) |
| |
| #if defined(MAT_MUL_NATIVE_NT_T) |
| /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS transposed - buffer only |
| * |
| * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it |
| * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension |
| * @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). |
| * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) |
| * @note Only the following configurations of M0, N0 and K0 are currently supported: |
| * - M0 > 0 |
| * - N0 = 1, 2, 3, 4, 8, 16 |
| * - K0 = 1, 2, 3, 4, 8, 16 |
| * @note Values > 8 for M0, N0 and K0 are not expected to be efficient |
| * |
| * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 |
| * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) |
| * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) |
| * @param[in] lhs_w The width of the lhs tensor |
| * @param[in] lhs_h The height of the lhs tensor |
| * @param[in] lhs_n Number of the matrices (buffers) in the batch |
| * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix |
| * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: F32/F16 |
| * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) |
| * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) |
| * @param[in] rhs_w The width of the rhs tensor |
| * @param[in] rhs_h The height of the rhs tensor |
| * @param[in] rhs_n Number of the matrices (buffers) in the batch |
| * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix |
| * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: F32/F16 |
| * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) |
| * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) |
| * @param[in] dst_w The width of the dst tensor |
| * @param[in] dst_h The height of the dst tensor |
| * @param[in] dst_n Number of the matrices (buffers) in the batch |
| * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix |
| */ |
| __kernel void mat_mul_native_nt_t(TENSOR3D_T(lhs, BUFFER), |
| TENSOR3D_T(rhs, BUFFER), |
| TENSOR3D_T(dst, BUFFER)) |
| |
| { |
| const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); |
| const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); |
| const uint z = GET_SPATIAL_IDX(2, 1, 0); |
| |
| // Compute LHS/RHS/DST matrix address |
| lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z; |
| rhs_offset_first_element_in_bytes += x * rhs_stride_y + z * rhs_stride_z; |
| dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; |
| |
| // Initialize the accumulators |
| TILE(DATA_TYPE, M0, N0, acc); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| acc[i].v = 0.f; |
| }) |
| |
| int k; |
| for(k = 0; k <= K - K0; k += K0) |
| { |
| TILE(DATA_TYPE, M0, K0, a); |
| TILE(DATA_TYPE, N0, K0, b); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = 0.f; |
| }) |
| |
| LOOP_UNROLLING(int, i, 0, 1, N0, |
| { |
| b[i].v = 0.f; |
| }) |
| |
| // Load tile from the lhs/rhs tensors |
| T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
| T_LOAD(DATA_TYPE, N0, K0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); |
| |
| #if GPU_ARCH == GPU_ARCH_MIDGARD |
| // This part is written to decrease the number of loop unrollings caused |
| // by T_MMUL. The NT/NT version is partly vectorized and uses less number |
| // of loop unrollings, and code behaves as expected. Although this is not |
| // a performant solution for the specified architecture, it is necessary |
| // to overcome some limitations. |
| TILE(DATA_TYPE, K0, N0, bt); |
| LOOP_UNROLLING(int, i, 0, 1, N0, |
| { |
| LOOP_UNROLLING(int, j, 0, 1, K0, |
| { |
| bt[j].s[i] = b[i].s[j]; |
| }) |
| }) |
| T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, a, bt, acc); |
| #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, T, a, b, acc); |
| #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| |
| lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); |
| rhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); |
| } |
| |
| #if K % K0 != 0 |
| /* Leftover Loop */ |
| for(; k < K; ++k) |
| { |
| TILE(DATA_TYPE, M0, 1, a); |
| TILE(DATA_TYPE, N0, 1, b); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = 0.f; |
| }) |
| |
| LOOP_UNROLLING(int, i, 0, 1, N0, |
| { |
| b[i].v = 0.f; |
| }) |
| |
| // Load tile from the lhs/rhs tensors |
| T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); |
| T_LOAD(DATA_TYPE, N0, 1, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); |
| |
| #if GPU_ARCH == GPU_ARCH_MIDGARD |
| // See the main loop for the explanation of this part |
| TILE(DATA_TYPE, 1, N0, bt); |
| LOOP_UNROLLING(int, i, 0, 1, N0, |
| { |
| bt[0].s[i] = b[i].s[0]; |
| }) |
| T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, a, bt, acc); |
| #else // GPU_ARCH == GPU_ARCH_MIDGARD |
| T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, T, a, b, acc); |
| #endif // GPU_ARCH == GPU_ARCH_MIDGARD |
| |
| lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); |
| rhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); |
| } |
| #endif // K % K0 != 0 |
| |
| const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; |
| const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; |
| |
| TILE(int, M0, 1, indirect_buffer); |
| LOOP_UNROLLING(int, _i, 0, 1, M0, |
| { |
| indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); |
| }); |
| |
| T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); |
| } |
| #endif // defined(MAT_MUL_NATIVE_NT_T) |