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Diffstat (limited to 'src/core/CL/cl_kernels/tile_helpers.h')
| -rw-r--r-- | src/core/CL/cl_kernels/tile_helpers.h | 420 |
1 files changed, 420 insertions, 0 deletions
diff --git a/src/core/CL/cl_kernels/tile_helpers.h b/src/core/CL/cl_kernels/tile_helpers.h new file mode 100644 index 0000000000..19241cf219 --- /dev/null +++ b/src/core/CL/cl_kernels/tile_helpers.h @@ -0,0 +1,420 @@ +/* + * Copyright (c) 2021 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. + */ + +/** Tile object + * A tile object is a 2D memory block and can be accessed using the following syntax: + * -# a[m0].v = access the the vector at row "m0" (OpenCL vector) + * -# a[m0].s[x] = access the scalar element at row "m0" and column "n0" (scalar access) + * + * @param[in] DATA_TYPE Data type of the tile + * @param[in] H Number of tile rows + * @param[in] W Number of tile colums + * @param[in] BASENAME Tile's name + */ +#define TILE(DATA_TYPE, H, W, BASENAME) TILE_STR(DATA_TYPE, H, W, BASENAME) +#define TILE_STR(DATA_TYPE, H, W, BASENAME) \ + union { \ + DATA_TYPE s[W]; \ + DATA_TYPE##W v; \ + } BASENAME[H] + +#define TENSOR4D_IMAGE(name) \ + __read_only image2d_t name##_img, \ + __global uchar *name##_ptr, \ + uint name##_stride_x, \ + uint name##_step_x, \ + uint name##_stride_y, \ + uint name##_step_y, \ + uint name##_stride_z, \ + uint name##_step_z, \ + uint name##_stride_w, \ + uint name##_step_w, \ + uint name##_offset_first_element_in_bytes + +#define TENSOR4D_BUFFER(name) \ + __global uchar *name##_ptr, \ + uint name##_stride_x, \ + uint name##_step_x, \ + uint name##_stride_y, \ + uint name##_step_y, \ + uint name##_stride_z, \ + uint name##_step_z, \ + uint name##_stride_w, \ + uint name##_step_w, \ + uint name##_offset_first_element_in_bytes + +#define TENSOR4D_STR(name, type) TENSOR4D_##type(name) +#define TENSOR4D(name, type) TENSOR4D_STR(name, type) + +/** Loop unrolling */ +#define LOOP_UNROLLING(DATA_TYPE, VAR, START_IDX, NUM_ITERATIONS, STEP) \ + _Pragma("unroll") for(DATA_TYPE VAR = START_IDX; VAR < NUM_ITERATIONS; VAR += STEP) + +/** Get the get_global_id with partial N0. This function is useful when the dimension is not multiple of N0 and we need to use a partial N0 + * to avoid out-of-bound read/write + * + * @note PARTIAL_N0 is used for get_global_id(n) = 0. + * + * @param[in] IDX get_global_id index (0,1 and 2 only) + * @param[in] N0 Number of elements read/written on the IDX direction + * @param[in] PARTIAL_N0 Number of elements read/written on the IDX direction for get_global_id(IDX) = 0. If zero, + * the Number of elements read/written on the IDX direction for get_global_id(IDX) = 0 is N0 + */ +#define GET_SPATIAL_IDX(IDX, N0, PARTIAL_N0) (max((int)(get_global_id(IDX) * N0 - (N0 - PARTIAL_N0) % N0), 0)) + +/** Offset (in bytes) calculation for a 1D BUFFER (cl_buffer) tensor */ +#define OFFSET1D(base, data_type, x) (base##_offset_first_element_in_bytes + x * sizeof(data_type)) + +/** Offset (in bytes) calculation for a 2D BUFFER (cl_buffer) tensor */ +#define OFFSET2D(base, data_type, x, y) (base##_offset_first_element_in_bytes + x * sizeof(data_type) + y * base##_stride_y) + +/** Offset (in bytes) calculation for a 3D BUFFER (cl_buffer) tensor */ +#define OFFSET3D(base, data_type, x, y, z) (base##_offset_first_element_in_bytes + x * sizeof(data_type) + y * base##_stride_y + z * base##_stride_z) + +/** Offset (in bytes) calculation for a 4D BUFFER (cl_buffer) tensor */ +#define OFFSET4D(base, data_type, x, y, z, w) (base##_offset_first_element_in_bytes + x * sizeof(data_type) + y * base##_stride_y + z * base##_stride_z + w * base##_stride_w) + +/** Dot product integet 8bit function + * + * @note Performs: c += dot(a, b) + * + * @param[in] DST_DATA_TYPE Accumulator data type + * @param[in] K0 Number of accumulations + * @param[in] a OpenCL vector a + * @param[in] b OpenCL vector b + * @param[in] c Scalar variable c + */ +#define DOT_PRODUCT_INTEGER8(DST_DATA_TYPE, K0, a, b, c) DOT_PRODUCT_INTEGER8_STR(DST_DATA_TYPE, K0, a, b, c) +#define DOT_PRODUCT_INTEGER8_STR(DST_DATA_TYPE, K0, a, b, c) DOT_PRODUCT##K0##_INTEGER8(DST_DATA_TYPE, a, b, c) +#define DOT_PRODUCT1_INTEGER8(DST_DATA_TYPE, a, b, c) \ + ({ \ + c += (DST_DATA_TYPE)a * (DST_DATA_TYPE)b; \ + }) +#define DOT_PRODUCT2_INTEGER8(DST_DATA_TYPE, a, b, c) \ + ({ \ + c += (DST_DATA_TYPE)a.s0 * (DST_DATA_TYPE)b.s0; \ + c += (DST_DATA_TYPE)a.s1 * (DST_DATA_TYPE)b.s1; \ + }) +#define DOT_PRODUCT3_INTEGER8(DST_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT2_INTEGER8(DST_DATA_TYPE, a, b, c); \ + c += (DST_DATA_TYPE)a.s2 * (DST_DATA_TYPE)b.s2; \ + }) +#if defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) +#define DOT_PRODUCT4_INTEGER8(DST_DATA_TYPE, x, y, val) val = arm_dot_acc((x), (y), (val)); +#elif defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) +#define DOT_PRODUCT4_INTEGER8(DST_DATA_TYPE, x, y, val) val += arm_dot((x), (y)); +#else // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) +#define DOT_PRODUCT4_INTEGER8(DST_DATA_TYPE, x, y, val) \ + ({ \ + val += (DST_DATA_TYPE)x.s0 * (DST_DATA_TYPE)y.s0; \ + val += (DST_DATA_TYPE)x.s1 * (DST_DATA_TYPE)y.s1; \ + val += (DST_DATA_TYPE)x.s2 * (DST_DATA_TYPE)y.s2; \ + val += (DST_DATA_TYPE)x.s3 * (DST_DATA_TYPE)y.s3; \ + }) +#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) +#define DOT_PRODUCT8_INTEGER8(DST_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT4_INTEGER8((a.lo), (b.lo), c); \ + DOT_PRODUCT4_INTEGER8((a.hi), (b.hi), c); \ + }) +#define DOT_PRODUCT16_INTEGER8(DST_DATA_TYPE, a, b, c) \ + ({ \ + DOT_PRODUCT8_INTEGER8((a.lo), (b.lo), c); \ + DOT_PRODUCT8_INTEGER8((a.hi), (b.hi), c); \ + }) + +/** Load a vector from global memory (tensor) + * + * @param[in] DATA_TYPE Data type + * @param[in] WIDTH Number of dst columns + * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). + * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16) + * @param[in] TENSOR Tensor basename + * @param[in] X Starting X position + * @param[in] Y Starting Y position + * @param[in] STRIDE_Y Stride Y (in bytes) + */ +#define V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y) V_LOAD_STR(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y) +#define V_LOAD_STR(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y) V_LOAD_##TENSOR_TYPE(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) +#define V_LOAD_BUFFER(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) \ + VLOAD(WIDTH) \ + (0, (__global DATA_TYPE *)(TENSOR##_ptr + (X) * sizeof(DATA_TYPE) + (Y)*STRIDE_Y)) +#define V_LOAD_IMAGE(DATA_TYPE, WIDTH, TENSOR, X, Y, STRIDE_Y) READ_IMAGE2D(DATA_TYPE, CONVERT_VECTOR_SIZE_TO_PIXEL_UNIT(WIDTH), TENSOR##_img, (X) / 4, (Y)) + +/** Load a tile from global memory (tensor) + * + * @param[in] DATA_TYPE Data type + * @param[in] HEIGHT Number of dst rows + * @param[in] WIDTH Number of dst columns + * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). + * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16) + * @param[in] TENSOR Tensor basename + * @param[in] X Starting X position + * @param[in] Y Starting Y position + * @param[in] STRIDE_Y Stride Y (in bytes) + * @param[out] dst Output tile + */ +#define T_LOAD(DATA_TYPE, HEIGHT, WIDTH, TENSOR_TYPE, TENSOR, X, Y, STRIDE_Y, dst) \ + ({ \ + LOOP_UNROLLING(int, _i, 0, HEIGHT, 1) \ + { \ + dst[_i].v = V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, ((Y) + _i), STRIDE_Y); \ + } \ + }) + +/** Load a tile from global memory (tensor) using an indirect Y index tile + * + * @param[in] DATA_TYPE Data type + * @param[in] HEIGHT Number of dst rows + * @param[in] WIDTH Number of dst columns + * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported + * In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16) + * @param[in] TENSOR Tensor basename + * @param[in] X Starting X position + * @param[in] STRIDE_Y Stride Y (in bytes) + * @param[in] indirect_y Indirect Y index tile + * @param[out] dst Output tile + */ +#define T_LOAD_INDIRECT(DATA_TYPE, HEIGHT, WIDTH, TENSOR_TYPE, TENSOR, X, STRIDE_Y, indirect_y, dst) \ + ({ \ + LOOP_UNROLLING(int, _i, 0, HEIGHT, 1) \ + { \ + dst[_i].v = V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, (indirect_y[_i].v), STRIDE_Y); \ + } \ + }) + +/** Store a tile to global memory (tensor) using an indirect Y index tile and conditionally use a different length for the store + * + * @note If WIDTH1_CONDITION is true, the store will use the WIDTH1 length for the store + * @note The vectors are stored in reverse order so the invalid rows are overwritten by the valid ones + * + * @param[in] DATA_TYPE Data type + * @param[in] HEIGHT Number of src rows + * @param[in] WIDTH0 Store width to use if WIDTH1_CONDITION = false + * @param[in] WIDTH1 Store width to use if WIDTH1_CONDITION = true + * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported + * cl_image is not supported. + * @param[in] TENSOR Tensor basename + * @param[in] X Starting X position + * @param[in] STRIDE_Y Stride Y (in bytes) + * @param[in] WIDTH1_CONDITION Condition to select the WIDTH1 store + * @param[in] src Input tile + * @param[in] indirect_y Indirect Y index tile + */ +#define T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, HEIGHT, WIDTH0, WIDTH1, TENSOR_TYPE, TENSOR, X, STRIDE_Y, WIDTH1_CONDITION, src, indirect_y) \ + ({ \ + if(WIDTH1_CONDITION) \ + { \ + LOOP_UNROLLING(int, _i, 0, HEIGHT, 1) \ + { \ + VSTORE_PARTIAL(WIDTH0, WIDTH1) \ + (src[HEIGHT - 1 - _i].v, 0, (__global DATA_TYPE *)(TENSOR##_ptr + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ + } \ + } \ + else \ + { \ + LOOP_UNROLLING(int, _i, 0, HEIGHT, 1) \ + { \ + VSTORE(WIDTH0) \ + (src[HEIGHT - 1 - _i].v, 0, (__global DATA_TYPE *)(TENSOR##_ptr + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ + } \ + } \ + }) + +/** Offset correction for the QASYMM8 computation + * + * @param[in] ACC_DATA_TYPE Accumulator data type + * @param[in] M0 Number of src/dst rows + * @param[in] N0 Number of src/dst columns + * @param[in] K0 Number of src columns + * @param[in] SRC_OFFSET Source quantization offset + * @param[in] WEI_OFFSET Weights quantization shift + * @param[in] lhs LHS tile + * @param[in] rhs RHS tile + * @param[out] dst DST tile + */ +#define T_OFFSET_CORRECTION(ACC_DATA_TYPE, M0, N0, K0, SRC_OFFSET, WEI_OFFSET, lhs, rhs, dst) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, M0, 1) \ + { \ + ACC_DATA_TYPE _tm = 0; \ + LOOP_UNROLLING(int, _k0, 0, K0, 1) \ + { \ + _tm += ((ACC_DATA_TYPE)lhs[_m0].s[_k0] * (ACC_DATA_TYPE)WEI_OFFSET); \ + } \ + LOOP_UNROLLING(int, _n0, 0, N0, 1) \ + { \ + dst[_m0].s[_n0] += _tm; \ + LOOP_UNROLLING(int, _k0, 0, K0, 1) \ + { \ + dst[_m0].s[_n0] += ((ACC_DATA_TYPE)rhs[_n0].s[_k0] * (ACC_DATA_TYPE)SRC_OFFSET); \ + } \ + } \ + } \ + }) + +/** Quantized the tile (ASYMMETRIC) with fixed-point scale + * + * @param[in] SRC_DATA_TYPE SRC data type + * @param[in] DST_DATA_TYPE DST data type + * @param[in] M0 Number of src/dst rows + * @param[in] N0 Number of src/dst columns + * @param[in] DST_OFFSET Quantization offset + * @param[in] DST_SHIFT Quantization shift + * @param[in] DST_MULTIPLIER Quantization multiplier + * @param[in] src Input tile + * @param[out] dst Output tile + */ +#define T_QUANTIZE8_ASYMMETRIC(SRC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, src, dst) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, M0, 1) \ + { \ + LOOP_UNROLLING(int, _n0, 0, N0, 1) \ + { \ + SRC_DATA_TYPE _tmp = 0; \ + if(DST_SHIFT < 0) \ + { \ + _tmp = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(src[_m0].s[_n0], DST_MULTIPLIER, DST_SHIFT, 1); \ + } \ + else \ + { \ + _tmp = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(src[_m0].s[_n0], DST_MULTIPLIER, DST_SHIFT, 1); \ + } \ + _tmp += DST_OFFSET; \ + dst[_m0].s[_n0] = CONVERT_SAT(_tmp, DST_DATA_TYPE); \ + } \ + } \ + }) + +/** Conditional rowset (memset by row) + * + * @note Set the row to VALUE_TO_SET if the corresponding mask == 0 + * + * @param[in] DATA_TYPE Data type + * @param[in] M0 Number of LHS rows + * @param[in] N0 Number of LHS columns + * @param[in] VALUE_TO_SET Value to set the row + * @param[in, out] a Input/output tile + * @param[out] mask Mask to check for setting the row to VALUE_TO_SET + */ +#define T_ROWSET_MASK(DATA_TYPE, M0, N0, VALUE_TO_SET, a, mask) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, M0, 1) \ + { \ + LOOP_UNROLLING(int, _n0, 0, N0, 1) \ + { \ + a[_m0].s[_n0] = select((DATA_TYPE)(a[_m0].s[_n0]), (DATA_TYPE)(VALUE_TO_SET), (SELECT_DATA_TYPE(DATA_TYPE))(mask[_m0].v == (DATA_TYPE)0)); \ + } \ + } \ + }) + +/** Element-wise addition with a constant value + * + * @note Performs: LHS + constant = DST + * + * @param[in] DATA_TYPE LHS/RHS/DST data type + * @param[in] M0 Number of LHS rows + * @param[in] N0 Number of LHS columns + * @param[in] lhs LHS tile + * @param[in] rhs_constant Constant value + * @param[out] dst DST tile + */ +#define T_ADD_CONSTANT(DATA_TYPE, M0, N0, lhs, rhs_constant, dst) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, M0, 1) \ + { \ + LOOP_UNROLLING(int, _n0, 0, N0, 1) \ + { \ + dst[_m0].s[_n0] = lhs[_m0].s[_n0] + rhs_constant; \ + } \ + } \ + }) + +/** Element-wise addition with RHS broadcasted (RHS has the X dimension only) + * + * @note Performs: LHS + RHS[broadcasted] = DST + * @note Both tiles must have same data type + * + * @param[in] DATA_TYPE LHS/RHS/DST data type + * @param[in] M0 Number of LHS rows + * @param[in] N0 Number of LHS columns + * @param[in] lhs LHS tile + * @param[in] rhs RHS tile + * @param[out] dst DST tile + */ +#define T_ADD_BROADCAST_X(DATA_TYPE, M0, N0, lhs, rhs, dst) \ + ({ \ + LOOP_UNROLLING(int, _m0, 0, M0, 1) \ + { \ + dst[_m0].v = lhs[_m0].v + rhs[0].v; \ + } \ + }) + +/** Matrix multiplication + * + * @note Performs: LHS X RHS + DST = DST + * + * @param[in] LHS_DATA_TYPE LHS tile data type + * @param[in] RHS_DATA_TYPE RHS tile data type + * @param[in] DST_DATA_TYPE RHS tile data type + * @param[in] M0 Number of LHS rows + * @param[in] N0 Number of RHS columns + * @param[in] K0 Number of LHS columns + * @param[in] LHS_LAYOUT LHS layout (T= transposed, NT= not transposed) + * @param[in] RHS_LAYOUT RHS layout (T= transposed, NT= not transposed) + * @param[in] lhs LHS tile + * @param[in] rhs RHS tile + * @param[in, out] dst DST tile + */ +#define T_MMUL(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, LHS_LAYOUT, RHS_LAYOUT, lhs, rhs, dst) T_MMUL_##LHS_LAYOUT##_##RHS_LAYOUT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_##LHS_DATA_TYPE##_##RHS_DATA_TYPE##_##DST_DATA_TYPE(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_float_float_float(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_half_half_half(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_char_char_int(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_uchar_uchar_uint(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_uchar_uchar_int(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_FLOAT(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ + { \ + LOOP_UNROLLING(int, _m, 0, M0, 1) \ + { \ + LOOP_UNROLLING(int, _n, 0, N0, 1) \ + { \ + LOOP_UNROLLING(int, _k, 0, K0, 1) \ + { \ + dst[_m].s[_n] = fma((lhs[_m].s[_k]), (rhs[_n].s[_k]), dst[_m].s[_n]); \ + } \ + } \ + } \ + } +#define T_MMUL_NT_T_INTEGER8(DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ + ({ \ + LOOP_UNROLLING(int, _m, 0, M0, 1) \ + { \ + LOOP_UNROLLING(int, _n, 0, N0, 1) \ + { \ + DOT_PRODUCT_INTEGER8(DST_DATA_TYPE, K0, (lhs[_m].v), (rhs[_n].v), dst[_m].s[_n]); \ + } \ + } \ + })
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