| /* |
| * Copyright (c) 2017-2019 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 "arm_compute/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <arm_neon.h> |
| #include <cstddef> |
| #include <cstdint> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| void inline vector_matrix_multiply_u8(Iterator &ina, Iterator &inb, Iterator &out, int width_a, int width_b, size_t stride_b, const Window &window) |
| { |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| if(id.x() > width_b) |
| { |
| return; |
| } |
| |
| // Note: Since the input are all positives, we can use uint32_t |
| // Accumulators for the block 0 |
| uint32x4x4_t c0 = |
| { |
| { |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0) |
| } |
| }; |
| |
| auto vec_a = reinterpret_cast<const uint8_t *>(ina.ptr()); |
| auto matrix_b = reinterpret_cast<const uint8_t *>(inb.ptr()); |
| auto vec_a_end_addr = vec_a + width_a; |
| |
| // This for loop performs 8 accumulations |
| for(; vec_a <= (vec_a_end_addr - 8);) |
| { |
| const uint8x8_t a00_u8 = vld1_u8(vec_a); |
| const uint8x16_t b00_u8 = vld1q_u8(matrix_b + 0 * stride_b); |
| const uint8x16_t b10_u8 = vld1q_u8(matrix_b + 1 * stride_b); |
| const uint8x16_t b20_u8 = vld1q_u8(matrix_b + 2 * stride_b); |
| const uint8x16_t b30_u8 = vld1q_u8(matrix_b + 3 * stride_b); |
| const uint8x16_t b40_u8 = vld1q_u8(matrix_b + 4 * stride_b); |
| const uint8x16_t b50_u8 = vld1q_u8(matrix_b + 5 * stride_b); |
| const uint8x16_t b60_u8 = vld1q_u8(matrix_b + 6 * stride_b); |
| const uint8x16_t b70_u8 = vld1q_u8(matrix_b + 7 * stride_b); |
| |
| // Convert a00_u8 to uint16_t and get the lower part |
| const uint16x4x2_t a00_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(a00_u8)), |
| vget_high_u16(vmovl_u8(a00_u8)) |
| } |
| }; |
| |
| const uint16x4x4_t b00_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b00_u8))) |
| } |
| }; |
| |
| const uint16x4x4_t b10_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b10_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b10_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b10_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b10_u8))) |
| } |
| }; |
| |
| const uint16x4x4_t b20_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b20_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b20_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b20_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b20_u8))) |
| } |
| }; |
| |
| const uint16x4x4_t b30_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b30_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b30_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b30_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b30_u8))) |
| } |
| }; |
| |
| const uint16x4x4_t b40_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b40_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b40_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b40_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b40_u8))) |
| } |
| }; |
| |
| const uint16x4x4_t b50_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b50_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b50_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b50_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b50_u8))) |
| } |
| }; |
| |
| const uint16x4x4_t b60_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b60_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b60_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b60_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b60_u8))) |
| } |
| }; |
| |
| const uint16x4x4_t b70_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b70_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b70_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b70_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b70_u8))) |
| } |
| }; |
| |
| // Accumulate 0: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16.val[0], 0); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16.val[0], 0); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16.val[0], 0); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16.val[0], 0); |
| |
| // Accumulate 1: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b10_u16.val[0], a00_u16.val[0], 1); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b10_u16.val[1], a00_u16.val[0], 1); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b10_u16.val[2], a00_u16.val[0], 1); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b10_u16.val[3], a00_u16.val[0], 1); |
| |
| // Accumulate 2: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b20_u16.val[0], a00_u16.val[0], 2); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b20_u16.val[1], a00_u16.val[0], 2); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b20_u16.val[2], a00_u16.val[0], 2); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b20_u16.val[3], a00_u16.val[0], 2); |
| |
| // Accumulate 3: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b30_u16.val[0], a00_u16.val[0], 3); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b30_u16.val[1], a00_u16.val[0], 3); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b30_u16.val[2], a00_u16.val[0], 3); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b30_u16.val[3], a00_u16.val[0], 3); |
| |
| // Accumulate 4: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b40_u16.val[0], a00_u16.val[1], 0); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b40_u16.val[1], a00_u16.val[1], 0); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b40_u16.val[2], a00_u16.val[1], 0); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b40_u16.val[3], a00_u16.val[1], 0); |
| |
| // Accumulate 5: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b50_u16.val[0], a00_u16.val[1], 1); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b50_u16.val[1], a00_u16.val[1], 1); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b50_u16.val[2], a00_u16.val[1], 1); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b50_u16.val[3], a00_u16.val[1], 1); |
| |
| // Accumulate 6: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b60_u16.val[0], a00_u16.val[1], 2); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b60_u16.val[1], a00_u16.val[1], 2); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b60_u16.val[2], a00_u16.val[1], 2); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b60_u16.val[3], a00_u16.val[1], 2); |
| |
| // Accumulate 7: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b70_u16.val[0], a00_u16.val[1], 3); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b70_u16.val[1], a00_u16.val[1], 3); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b70_u16.val[2], a00_u16.val[1], 3); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b70_u16.val[3], a00_u16.val[1], 3); |
| |
| vec_a += 8; |
| matrix_b += 8 * stride_b; |
| } |
| |
| // This for loop performs the left-over accumulations |
| for(; vec_a < vec_a_end_addr;) |
| { |
| const uint8x8_t a00_u8 = vld1_dup_u8(vec_a); |
| const uint8x16_t b00_u8 = vld1q_u8(matrix_b); |
| |
| const uint16x4x4_t b00_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b00_u8))) |
| } |
| }; |
| |
| // Convert a00_u8 to uint16_t and get the lower part |
| const uint16x4_t a00_u16 = vget_low_u16(vmovl_u8(a00_u8)); |
| |
| // Accumulate 0: |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16, 0); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16, 0); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16, 0); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16, 0); |
| |
| vec_a += 1; |
| matrix_b += stride_b; |
| } |
| |
| auto vec_out = reinterpret_cast<int32_t *>(out.ptr()); |
| vst1q_s32(vec_out + 0, vreinterpretq_s32_u32(c0.val[0])); |
| vst1q_s32(vec_out + 4, vreinterpretq_s32_u32(c0.val[1])); |
| vst1q_s32(vec_out + 8, vreinterpretq_s32_u32(c0.val[2])); |
| vst1q_s32(vec_out + 12, vreinterpretq_s32_u32(c0.val[3])); |
| }, |
| ina, inb, out); |
| } |
| |
| void inline vector_matrix_multiply_s8(Iterator &ina, Iterator &inb, Iterator &out, int width_a, int width_b, size_t stride_b, const Window &window) |
| { |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| if(id.x() > width_b) |
| { |
| return; |
| } |
| |
| // Accumulators for the block 0 |
| int32x4x4_t c0 = |
| { |
| { |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0) |
| } |
| }; |
| |
| auto vec_a = reinterpret_cast<const int8_t *>(ina.ptr()); |
| auto matrix_b = reinterpret_cast<const int8_t *>(inb.ptr()); |
| auto vec_a_end_addr = vec_a + width_a; |
| |
| // This for loop performs 8 accumulations |
| for(; vec_a <= (vec_a_end_addr - 8);) |
| { |
| const int8x8_t a00_s8 = vld1_s8(vec_a); |
| const int8x16_t b00_s8 = vld1q_s8(matrix_b + 0 * stride_b); |
| const int8x16_t b10_s8 = vld1q_s8(matrix_b + 1 * stride_b); |
| const int8x16_t b20_s8 = vld1q_s8(matrix_b + 2 * stride_b); |
| const int8x16_t b30_s8 = vld1q_s8(matrix_b + 3 * stride_b); |
| const int8x16_t b40_s8 = vld1q_s8(matrix_b + 4 * stride_b); |
| const int8x16_t b50_s8 = vld1q_s8(matrix_b + 5 * stride_b); |
| const int8x16_t b60_s8 = vld1q_s8(matrix_b + 6 * stride_b); |
| const int8x16_t b70_s8 = vld1q_s8(matrix_b + 7 * stride_b); |
| |
| // Convert a00_s8 to int16_t and get the lower part |
| const int16x4x2_t a00_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(a00_s8)), |
| vget_high_s16(vmovl_s8(a00_s8)) |
| } |
| }; |
| |
| const int16x4x4_t b00_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b00_s8))) |
| } |
| }; |
| |
| const int16x4x4_t b10_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b10_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b10_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b10_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b10_s8))) |
| } |
| }; |
| |
| const int16x4x4_t b20_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b20_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b20_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b20_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b20_s8))) |
| } |
| }; |
| |
| const int16x4x4_t b30_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b30_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b30_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b30_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b30_s8))) |
| } |
| }; |
| |
| const int16x4x4_t b40_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b40_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b40_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b40_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b40_s8))) |
| } |
| }; |
| |
| const int16x4x4_t b50_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b50_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b50_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b50_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b50_s8))) |
| } |
| }; |
| |
| const int16x4x4_t b60_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b60_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b60_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b60_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b60_s8))) |
| } |
| }; |
| |
| const int16x4x4_t b70_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b70_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b70_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b70_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b70_s8))) |
| } |
| }; |
| |
| // Accumulate 0: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16.val[0], 0); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16.val[0], 0); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16.val[0], 0); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16.val[0], 0); |
| |
| // Accumulate 1: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b10_s16.val[0], a00_s16.val[0], 1); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b10_s16.val[1], a00_s16.val[0], 1); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b10_s16.val[2], a00_s16.val[0], 1); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b10_s16.val[3], a00_s16.val[0], 1); |
| |
| // Accumulate 2: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b20_s16.val[0], a00_s16.val[0], 2); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b20_s16.val[1], a00_s16.val[0], 2); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b20_s16.val[2], a00_s16.val[0], 2); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b20_s16.val[3], a00_s16.val[0], 2); |
| |
| // Accumulate 3: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b30_s16.val[0], a00_s16.val[0], 3); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b30_s16.val[1], a00_s16.val[0], 3); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b30_s16.val[2], a00_s16.val[0], 3); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b30_s16.val[3], a00_s16.val[0], 3); |
| |
| // Accumulate 4: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b40_s16.val[0], a00_s16.val[1], 0); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b40_s16.val[1], a00_s16.val[1], 0); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b40_s16.val[2], a00_s16.val[1], 0); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b40_s16.val[3], a00_s16.val[1], 0); |
| |
| // Accumulate 5: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b50_s16.val[0], a00_s16.val[1], 1); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b50_s16.val[1], a00_s16.val[1], 1); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b50_s16.val[2], a00_s16.val[1], 1); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b50_s16.val[3], a00_s16.val[1], 1); |
| |
| // Accumulate 6: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b60_s16.val[0], a00_s16.val[1], 2); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b60_s16.val[1], a00_s16.val[1], 2); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b60_s16.val[2], a00_s16.val[1], 2); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b60_s16.val[3], a00_s16.val[1], 2); |
| |
| // Accumulate 7: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b70_s16.val[0], a00_s16.val[1], 3); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b70_s16.val[1], a00_s16.val[1], 3); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b70_s16.val[2], a00_s16.val[1], 3); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b70_s16.val[3], a00_s16.val[1], 3); |
| |
| vec_a += 8; |
| matrix_b += 8 * stride_b; |
| } |
| |
| // This for loop performs the left-over accumulations |
| for(; vec_a < vec_a_end_addr;) |
| { |
| const int8x8_t a00_s8 = vld1_dup_s8(vec_a); |
| const int8x16_t b00_s8 = vld1q_s8(matrix_b); |
| |
| const int16x4x4_t b00_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b00_s8))) |
| } |
| }; |
| |
| // Convert a00_s8 to uint16_t and get the lower part |
| const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8)); |
| |
| // Accumulate 0: |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0); |
| |
| vec_a += 1; |
| matrix_b += stride_b; |
| } |
| |
| auto vec_out = reinterpret_cast<int32_t *>(out.ptr()); |
| vst1q_s32(vec_out + 0, c0.val[0]); |
| vst1q_s32(vec_out + 4, c0.val[1]); |
| vst1q_s32(vec_out + 8, c0.val[2]); |
| vst1q_s32(vec_out + 12, c0.val[3]); |
| }, |
| ina, inb, out); |
| } |
| |
| void inline matrix_multiply_u8(Iterator &ina, Iterator &inb, Iterator &out, int width_b, size_t out_stride, const Window &window) |
| { |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8_t *mtx_a0 = ina.ptr(); |
| const uint8_t *mtx_b0 = inb.ptr(); |
| |
| // Note: Since the input are all positives, we can use uint32_t |
| // Accumulators for the block 0 |
| uint32x4x4_t c0 = |
| { |
| { |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0) |
| } |
| }; |
| |
| // Accumulators for the block 1 |
| uint32x4x4_t c1 = |
| { |
| { |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0) |
| } |
| }; |
| |
| // Accumulators for the block 2 |
| uint32x4x4_t c2 = |
| { |
| { |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0) |
| } |
| }; |
| |
| // Accumulators for the block 3 |
| uint32x4x4_t c3 = |
| { |
| { |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0), |
| vdupq_n_u32(0) |
| } |
| }; |
| |
| for(int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16) |
| { |
| const uint8x8_t a00_u8 = vld1_u8(mtx_a0); |
| const uint8x16_t b00_u8 = vld1q_u8(mtx_b0); |
| |
| // Convert a00_u8 to uint16_t and get the lower part |
| const uint16x4_t a00_u16 = vget_low_u16(vmovl_u8(a00_u8)); |
| |
| // Convert b00_s8 to uint16_t |
| const uint16x4x4_t b00_u16 = |
| { |
| { |
| vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), |
| vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))), |
| vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), |
| vget_high_u16(vmovl_u8(vget_high_u8(b00_u8))) |
| } |
| }; |
| |
| // 4x4 block 0 |
| c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16, 0); |
| c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16, 0); |
| c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16, 0); |
| c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16, 0); |
| |
| // 4x4 block 1 |
| c1.val[0] = vmlal_lane_u16(c1.val[0], b00_u16.val[0], a00_u16, 1); |
| c1.val[1] = vmlal_lane_u16(c1.val[1], b00_u16.val[1], a00_u16, 1); |
| c1.val[2] = vmlal_lane_u16(c1.val[2], b00_u16.val[2], a00_u16, 1); |
| c1.val[3] = vmlal_lane_u16(c1.val[3], b00_u16.val[3], a00_u16, 1); |
| |
| // 4x4 block 2 |
| c2.val[0] = vmlal_lane_u16(c2.val[0], b00_u16.val[0], a00_u16, 2); |
| c2.val[1] = vmlal_lane_u16(c2.val[1], b00_u16.val[1], a00_u16, 2); |
| c2.val[2] = vmlal_lane_u16(c2.val[2], b00_u16.val[2], a00_u16, 2); |
| c2.val[3] = vmlal_lane_u16(c2.val[3], b00_u16.val[3], a00_u16, 2); |
| |
| // 4x4 block 3 |
| c3.val[0] = vmlal_lane_u16(c3.val[0], b00_u16.val[0], a00_u16, 3); |
| c3.val[1] = vmlal_lane_u16(c3.val[1], b00_u16.val[1], a00_u16, 3); |
| c3.val[2] = vmlal_lane_u16(c3.val[2], b00_u16.val[2], a00_u16, 3); |
| c3.val[3] = vmlal_lane_u16(c3.val[3], b00_u16.val[3], a00_u16, 3); |
| } |
| |
| auto mtx_out = reinterpret_cast<int32_t *>(out.ptr()); |
| vst1q_s32(mtx_out + 0 * out_stride + 0, vreinterpretq_s32_u32(c0.val[0])); |
| vst1q_s32(mtx_out + 0 * out_stride + 4, vreinterpretq_s32_u32(c0.val[1])); |
| vst1q_s32(mtx_out + 0 * out_stride + 8, vreinterpretq_s32_u32(c0.val[2])); |
| vst1q_s32(mtx_out + 0 * out_stride + 12, vreinterpretq_s32_u32(c0.val[3])); |
| vst1q_s32(mtx_out + 1 * out_stride + 0, vreinterpretq_s32_u32(c1.val[0])); |
| vst1q_s32(mtx_out + 1 * out_stride + 4, vreinterpretq_s32_u32(c1.val[1])); |
| vst1q_s32(mtx_out + 1 * out_stride + 8, vreinterpretq_s32_u32(c1.val[2])); |
| vst1q_s32(mtx_out + 1 * out_stride + 12, vreinterpretq_s32_u32(c1.val[3])); |
| vst1q_s32(mtx_out + 2 * out_stride + 0, vreinterpretq_s32_u32(c2.val[0])); |
| vst1q_s32(mtx_out + 2 * out_stride + 4, vreinterpretq_s32_u32(c2.val[1])); |
| vst1q_s32(mtx_out + 2 * out_stride + 8, vreinterpretq_s32_u32(c2.val[2])); |
| vst1q_s32(mtx_out + 2 * out_stride + 12, vreinterpretq_s32_u32(c2.val[3])); |
| vst1q_s32(mtx_out + 3 * out_stride + 0, vreinterpretq_s32_u32(c3.val[0])); |
| vst1q_s32(mtx_out + 3 * out_stride + 4, vreinterpretq_s32_u32(c3.val[1])); |
| vst1q_s32(mtx_out + 3 * out_stride + 8, vreinterpretq_s32_u32(c3.val[2])); |
| vst1q_s32(mtx_out + 3 * out_stride + 12, vreinterpretq_s32_u32(c3.val[3])); |
| }, |
| ina, inb, out); |
| } |
| |
| void inline matrix_multiply_s8(Iterator &ina, Iterator &inb, Iterator &out, int width_b, size_t out_stride, const Window &window) |
| { |
| // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW |
| // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration |
| // All the values needed for computing a single 4x4 block will be read from consecutive memory positions |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| auto *mtx_a0 = reinterpret_cast<const int8_t *>(ina.ptr()); |
| auto *mtx_b0 = reinterpret_cast<const int8_t *>(inb.ptr()); |
| |
| // Note: Since the input are all positives, we can use uint32_t |
| // Accumulators for the block 0 |
| int32x4x4_t c0 = |
| { |
| { |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0) |
| } |
| }; |
| |
| // Accumulators for the block 1 |
| int32x4x4_t c1 = |
| { |
| { |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0) |
| } |
| }; |
| |
| // Accumulators for the block 2 |
| int32x4x4_t c2 = |
| { |
| { |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0) |
| } |
| }; |
| |
| // Accumulators for the block 3 |
| int32x4x4_t c3 = |
| { |
| { |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0), |
| vdupq_n_s32(0) |
| } |
| }; |
| |
| for(int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16) |
| { |
| const int8x8_t a00_s8 = vld1_s8(mtx_a0); |
| const int8x16_t b00_s8 = vld1q_s8(mtx_b0); |
| |
| // Convert a00_s8 to uint16_t and get the lower part |
| const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8)); |
| |
| // Convert b00_s8 to int16_t |
| const int16x4x4_t b00_s16 = |
| { |
| { |
| vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), |
| vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))), |
| vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), |
| vget_high_s16(vmovl_s8(vget_high_s8(b00_s8))) |
| } |
| }; |
| |
| // 4x4 block 0 |
| c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0); |
| c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0); |
| c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0); |
| c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0); |
| |
| // 4x4 block 1 |
| c1.val[0] = vmlal_lane_s16(c1.val[0], b00_s16.val[0], a00_s16, 1); |
| c1.val[1] = vmlal_lane_s16(c1.val[1], b00_s16.val[1], a00_s16, 1); |
| c1.val[2] = vmlal_lane_s16(c1.val[2], b00_s16.val[2], a00_s16, 1); |
| c1.val[3] = vmlal_lane_s16(c1.val[3], b00_s16.val[3], a00_s16, 1); |
| |
| // 4x4 block 2 |
| c2.val[0] = vmlal_lane_s16(c2.val[0], b00_s16.val[0], a00_s16, 2); |
| c2.val[1] = vmlal_lane_s16(c2.val[1], b00_s16.val[1], a00_s16, 2); |
| c2.val[2] = vmlal_lane_s16(c2.val[2], b00_s16.val[2], a00_s16, 2); |
| c2.val[3] = vmlal_lane_s16(c2.val[3], b00_s16.val[3], a00_s16, 2); |
| |
| // 4x4 block 3 |
| c3.val[0] = vmlal_lane_s16(c3.val[0], b00_s16.val[0], a00_s16, 3); |
| c3.val[1] = vmlal_lane_s16(c3.val[1], b00_s16.val[1], a00_s16, 3); |
| c3.val[2] = vmlal_lane_s16(c3.val[2], b00_s16.val[2], a00_s16, 3); |
| c3.val[3] = vmlal_lane_s16(c3.val[3], b00_s16.val[3], a00_s16, 3); |
| } |
| |
| auto mtx_out = reinterpret_cast<int32_t *>(out.ptr()); |
| vst1q_s32(mtx_out + 0 * out_stride + 0, c0.val[0]); |
| vst1q_s32(mtx_out + 0 * out_stride + 4, c0.val[1]); |
| vst1q_s32(mtx_out + 0 * out_stride + 8, c0.val[2]); |
| vst1q_s32(mtx_out + 0 * out_stride + 12, c0.val[3]); |
| vst1q_s32(mtx_out + 1 * out_stride + 0, c1.val[0]); |
| vst1q_s32(mtx_out + 1 * out_stride + 4, c1.val[1]); |
| vst1q_s32(mtx_out + 1 * out_stride + 8, c1.val[2]); |
| vst1q_s32(mtx_out + 1 * out_stride + 12, c1.val[3]); |
| vst1q_s32(mtx_out + 2 * out_stride + 0, c2.val[0]); |
| vst1q_s32(mtx_out + 2 * out_stride + 4, c2.val[1]); |
| vst1q_s32(mtx_out + 2 * out_stride + 8, c2.val[2]); |
| vst1q_s32(mtx_out + 2 * out_stride + 12, c2.val[3]); |
| vst1q_s32(mtx_out + 3 * out_stride + 0, c3.val[0]); |
| vst1q_s32(mtx_out + 3 * out_stride + 4, c3.val[1]); |
| vst1q_s32(mtx_out + 3 * out_stride + 8, c3.val[2]); |
| vst1q_s32(mtx_out + 3 * out_stride + 12, c3.val[3]); |
| }, |
| ina, inb, out); |
| } |
| } // namespace |
| |
| class Coordinates; |
| } // namespace arm_compute |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::S8, DataType::U8); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); |
| |
| TensorShape in0_shape = input0->tensor_shape(); |
| TensorShape in1_shape = input1->tensor_shape(); |
| TensorShape out_shape = output->tensor_shape(); |
| |
| // Check vector-by-matrix case |
| if(out_shape[1] == 1) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(in0_shape[0] != in1_shape[1], "The number of input0's columns must be equal to input1's rows"); |
| } |
| else |
| { |
| in0_shape.collapse(2); |
| in1_shape.collapse(2); |
| out_shape.collapse(2); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(in0_shape[2] != out_shape[2], "Output tensor must have the same number of batches of input0 tensor"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_shape[2] != 1 && in0_shape[2] != in1_shape[2], "Input1 tensor must have the same number of batches of input0 or the number of batches must be set to 1"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_shape[0] % 16, "Input1's width must be a multiple of 16"); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output) |
| { |
| constexpr unsigned int num_elems_processed_per_iteration_x = 16; |
| constexpr unsigned int num_elems_processed_per_iteration_y = 4; |
| |
| Window win; |
| bool window_changed = false; |
| |
| // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication |
| if((output->dimension(1) == 1)) |
| { |
| // Configure kernel window |
| win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x)); |
| |
| // We cannot read out-of-bound elements from matrix A as we use the left-over for loop |
| AccessWindowStatic in0_access(input0, 0, 0, input0->tensor_shape().x(), 1); |
| AccessWindowHorizontal in1_access(input1, 0, num_elems_processed_per_iteration_x); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_x); |
| |
| window_changed = update_window_and_padding(win, in0_access, in1_access, output_access); |
| |
| Coordinates coord; |
| coord.set_num_dimensions(output->num_dimensions()); |
| output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape())); |
| } |
| else |
| { |
| win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); |
| |
| unsigned int num_k_iterations = ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x) / 16; |
| // For each iteration of "k" we increment the input pointer by 4, and we load 8 elements a the time: |
| AccessWindowStatic in0_access(input0, 0, 0, (num_k_iterations - 1) * 4 + 8, input0->dimension(1)); |
| AccessWindowStatic in1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1)); |
| AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); |
| |
| window_changed = update_window_and_padding(win, in0_access, in1_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| NEGEMMLowpMatrixMultiplyKernel::NEGEMMLowpMatrixMultiplyKernel() |
| : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true) |
| { |
| } |
| |
| void NEGEMMLowpMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info())); |
| |
| TensorShape in1_shape = input1->info()->tensor_shape(); |
| in1_shape.collapse(2); |
| |
| _input0 = input0; |
| _input1 = input1; |
| _output = output; |
| _slide_matrix_b = in1_shape[2] != 1; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()).first); |
| |
| return Status{}; |
| } |
| |
| void NEGEMMLowpMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication path |
| if((_output->info()->dimension(1) == 1)) |
| { |
| const auto width_matrix_a = static_cast<int>(_input0->info()->dimension(0)); |
| const auto width_matrix_b = static_cast<int>(_input1->info()->dimension(0)); |
| const auto in_b_stride = static_cast<int>(_input1->info()->strides_in_bytes()[1] / data_size_from_type(_input1->info()->data_type())); |
| |
| // The implementation computes 16 elements per iteration |
| const int window_start_x = 16 * info.thread_id; |
| const int window_step_x = 16 * info.num_threads; |
| // Make sure (window_end_x - window_start_x) is a multiple of window_step_x |
| const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x; |
| |
| Window win_out(window); |
| win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); |
| win_out.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| Window win_a(window); |
| win_a.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| win_a.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| |
| Window win_b; |
| // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2 |
| // This scenario can happen when the the matrix multiplication is used to perform a convolution operation |
| if(_input1->info()->num_dimensions() >= 3) |
| { |
| win_b = window; |
| } |
| win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); |
| win_b.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| Iterator ina(_input0, win_a); |
| Iterator inb(_input1, win_b); |
| Iterator out(_output, win_out); |
| |
| switch(_input0->info()->data_type()) |
| { |
| case DataType::S8: |
| { |
| vector_matrix_multiply_s8(ina, inb, out, width_matrix_a, width_matrix_b, in_b_stride, window); |
| break; |
| } |
| case DataType::U8: |
| case DataType::QASYMM8: |
| { |
| vector_matrix_multiply_u8(ina, inb, out, width_matrix_a, width_matrix_b, in_b_stride, window); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Not supported"); |
| break; |
| } |
| } |
| } |
| else |
| { |
| const size_t in_b_stride = _input1->info()->strides_in_bytes()[1]; |
| const size_t out_stride = _output->info()->strides_in_bytes()[1] / _output->info()->element_size(); |
| |
| // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix |
| Window win_a(window); |
| win_a.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, window.y().end() / 4, 1)); |
| |
| // Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the columns of the output matrix |
| Window win_b; |
| // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2 |
| // This scenario can happen when the the matrix multiplication is used to perform a convolution operation |
| if(_slide_matrix_b) |
| { |
| win_b = window; |
| } |
| win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, in_b_stride)); |
| win_b.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| |
| // The step x and step y for the output matrix has been already set using in configure() |
| Iterator ina(_input0, win_a); |
| Iterator inb(_input1, win_b); |
| Iterator out(_output, window); |
| |
| const int width_b = _input1->info()->dimension(0); |
| switch(_input0->info()->data_type()) |
| { |
| case DataType::S8: |
| { |
| matrix_multiply_s8(ina, inb, out, width_b, out_stride, window); |
| break; |
| } |
| case DataType::U8: |
| case DataType::QASYMM8: |
| { |
| matrix_multiply_u8(ina, inb, out, width_b, out_stride, window); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Not supported"); |
| break; |
| } |
| } |
| } |
| } |
