| /* |
| * 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)) |
| |
| /** 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 + TENSOR##_offset_first_element_in_bytes + (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] YI_MULTIPLIER Parameter used to multiply the internal row increment (_i). |
| * In common cases should be 1 but it becomes useful when we want to load rows which are multiple of STRIDE_Y. (e.g. loading the weights of convolution layer). |
| * In this case the address calculation is performed as: (Y + _i * Y_MULTIPLIER) * STRIDE_Y |
| * @param[in] STRIDE_Y Stride Y (in bytes) used to load each row. |
| * @param[out] dst Output tile |
| */ |
| #define T_LOAD(DATA_TYPE, HEIGHT, WIDTH, TENSOR_TYPE, TENSOR, X, Y, YI_MULTIPLIER, STRIDE_Y, dst) \ |
| ({ \ |
| LOOP_UNROLLING(int, _i, 0, HEIGHT, 1) \ |
| { \ |
| dst[_i].v = V_LOAD(DATA_TYPE, WIDTH, TENSOR_TYPE, TENSOR, X, ((Y) + _i * (int)(YI_MULTIPLIER)), 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); \ |
| } \ |
| }) |
| |
| /** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout |
| * |
| * @param[in] DATA_TYPE Data type |
| * @param[in] TILE_HEIGHT Number of elements to load from Y (height) dimension |
| * @param[in] TILE_WIDTH Number of elements to load from X (width) dimension |
| * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension |
| * @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 TILE_CHANNELS multiples of 4 are supported (4, 8, 16) |
| * @param[in] TENSOR Tensor basename |
| * @param[in] B Starting batch index |
| * @param[in] Y Starting Y index |
| * @param[in] X Starting X index |
| * @param[in] C Starting C index |
| * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension |
| * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension |
| * @param[in] STRIDE_Y Stride Y (in bytes) |
| * @param[out] dst Output tile |
| */ |
| #define T_LOAD_NHWC(DATA_TYPE, TILE_HEIGHT, TILE_WIDTH, TILE_CHANNELS, TENSOR_TYPE, TENSOR, B, Y, X, C, TENSOR_WIDTH, TENSOR_HEIGHT, STRIDE_Y, dst) \ |
| ({ \ |
| LOOP_UNROLLING(int, _yk, 0, (TILE_HEIGHT), 1) \ |
| { \ |
| LOOP_UNROLLING(int, _xk, 0, (TILE_WIDTH), 1) \ |
| { \ |
| int _src_y = (X) + _xk + ((Y) + _yk) * (TENSOR_WIDTH); \ |
| _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT); \ |
| int _src_valid_y = (((X) + _xk) >= 0 && ((X) + _xk) < (int)(TENSOR_WIDTH) && ((Y) + _yk) >= 0 && ((Y) + _yk) < (int)(TENSOR_HEIGHT)); \ |
| if(_src_valid_y != 0) \ |
| { \ |
| dst[_xk + _yk * (TILE_WIDTH)].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, STRIDE_Y); \ |
| } \ |
| } \ |
| } \ |
| }) |
| |
| /** Load a tile from global memory (tensor) when the tensor is stored using a NHWC layout using indirect X and Y coordinates |
| * |
| * @param[in] DATA_TYPE Data type |
| * @param[in] TILE_HEIGHT Number of elements to load from Y (height) dimension |
| * @param[in] TILE_WIDTH Number of elements to load from X (width) dimension |
| * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension |
| * @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 TILE_CHANNELS multiples of 4 are supported (4, 8, 16) |
| * @param[in] TENSOR Tensor basename |
| * @param[in] B Starting batch index |
| * @param[in] Y Starting Y index |
| * @param[in] X Starting X index |
| * @param[in] C Starting C index |
| * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension |
| * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension |
| * @param[in] STRIDE_Y Stride Y (in bytes) |
| * @param[out] xi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect X coordinate |
| * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate |
| * @param[out] dst Output tile |
| */ |
| #define T_LOAD_NHWC_INDIRECT(DATA_TYPE, TILE_HEIGHT, TILE_WIDTH, TILE_CHANNELS, TENSOR_TYPE, TENSOR, B, Y, X, C, TENSOR_WIDTH, TENSOR_HEIGHT, STRIDE_Y, xi, yi, dst) \ |
| ({ \ |
| LOOP_UNROLLING(int, _i, 0, (TILE_WIDTH * TILE_HEIGHT), 1) \ |
| { \ |
| int _src_y = (X) + xi[_i].v + ((Y) + yi[_i].v) * (TENSOR_WIDTH); \ |
| _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT); \ |
| int _src_valid_y = (((X) + xi[_i].v) >= 0 && ((X) + xi[_i].v) < (int)(TENSOR_WIDTH) && ((Y) + yi[_i].v) >= 0 && ((Y) + yi[_i].v) < (int)(TENSOR_HEIGHT)); \ |
| if(_src_valid_y != 0) \ |
| { \ |
| dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, 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 + TENSOR##_offset_first_element_in_bytes + (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 + TENSOR##_offset_first_element_in_bytes + (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 activation |
| * |
| * @note Performs: activation(LHS) = DST |
| * |
| * @param[in] DATA_TYPE SRC/DST data type |
| * @param[in] M0 Number of SRC/DST rows |
| * @param[in] N0 Number of SRC/DST columns |
| * @param[in] ACTIVATION_TYPE Activation type |
| * @param[in] A_VAL A value used for the activation (e.g. tanh_op, brelu,..) |
| * @param[in] B_VAL B value used for the activation (e.g. tanh_op, brelu,..) |
| * @param[out] src SRC tile |
| * @param[out] dst DST tile |
| */ |
| #define T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, src, dst) \ |
| ({ \ |
| LOOP_UNROLLING(int, _m0, 0, M0, 1) \ |
| { \ |
| dst[_m0].v = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, N0, src[_m0].v, A_VAL, B_VAL); \ |
| } \ |
| }) |
| |
| /** 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]); \ |
| } \ |
| } \ |
| }) |