/* * Copyright (c) 2022-2023 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" //! @cond Doxygen_Suppress /** OpenCL kernel to compute the transposed convolution. * * @note Data layout supported: NHWC * @note Data type supported: F32/F16/QASYMM8/QASYMM8_SIGNED * @note The transposed convolution padding (left and top) must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (e.g. -DPAD_LEFT=2, -DPAD_TOP=2) * @note The transposed convolution strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2) * @note The spatial dimensions of the weights must be passed at compile time using -DWEI_WIDTH and -DWEI_HEIGHT (e.g. -DWEI_WIDTH=9, -DWEI_HEIGHT=9) * @note The spatial dimensions of the source tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT (e.g. -DSRC_WIDTH=96, -DSRC_HEIGHT=64) * @note The spatial dimensions of the destination tensor must be passed at compile time using -DDST_WIDTH and -DDST_HEIGHT (e.g. -DDST_WIDTH=96, -DDST_HEIGHT=64) * @note The channels of the source tensor must be passed at compile time using -DSRC_CHANNELS (e.g. -DSRC_CHANNELS=64) * @note The channels of the destination tensor must be passed at compile time using -DDST_CHANNELS (e.g. -DDST_CHANNELS=64) * @note The tensor type (currently only "BUFFER" is supported) of the source tensor must be passed at compile time using -DSRC_TENSOR_TYPE (e.g. -DSRC_TENSOR_TYPE=BUFFER) * @note The tensor type (currently only "BUFFER" is supported) of the weights tensor must be passed at compile time using -DWEI_TENSOR_TYPE (e.g. -DWEI_TENSOR_TYPE=BUFFER) * @note The tensor type (currently only "BUFFER" is supported) of the destination tensor must be passed at compile time using -DDST_TENSOR_TYPE (e.g. -DDST_TENSOR_TYPE=BUFFER) * @note The data type of the source tensor must be passed at compile time using -DSRC_DATA_TYPE (e.g. -DSRC_DATA_TYPE=float) * @note The data type of the weights tensor must be passed at compile time using -DWEI_DATA_TYPE (e.g. -DWEI_DATA_TYPE=float) * @note The data type of the destination tensor must be passed at compile time using -DDST_DATA_TYPE (e.g. -DDST_DATA_TYPE=float) * @note The data type of the destination tensor must be passed at compile time using -DBIA_DATA_TYPE (e.g. -DBIA_DATA_TYPE=float) * @note The data type of the accumulators must be passed at compile time using -DACC_DATA_TYPE (e.g. -DACC_DATA_TYPE=float) * @note The number of M0 rows (width*height) to process must be passed at compile time using -DM0 (e.g. -DM0=2) * @note The number of N0 output channels to process must be passed at compile time using -DN0 (e.g. -DN0=2) * @note The number of K0 inner accumulations must be passed at compile time using -DK0 (e.g. -DK0=2) * @note The size of the partial store block in x must be passed at compile time using -DPARTIAL_N0 (e.g. -DPARTIAL_N0=1) * @note If bias exists, the compile time argument -DHAS_BIAS should be passed * @note Only the following configurations of M0, N0 and K0 are currently supported: * - M0 = 1 * - N0 = 1, 2, 3, 4, 8, 16 * - K0 = 1, 2, 3, 4, 8, 16 * * @note In case of QASYMM8/QASYMM8_SIGNED, the following extra information must be passed at compile time: * - -DIS_QUANTIZED * - The destination quantization multiplier e.g. -DDST_MULTIPLIER=1234 * - The destination quantization shift e.g. -DDST_SHIFT=4 * - The destination offset e.g. -DDST_OFFSET=4 * - The source offset e.g. -DSRC_OFFSET=4 * - The weights offset e.g. -DWEI_OFFSET=4 * - The quantized zero value e.g. -DZERO_VALUE=4 * * @param[in] src_img (Not supported) Read only cl_image object for the source tensor. Included when SRC_TENSOR_TYPE=IMAGE * @param[in] src_ptr Pointer to the source tensor. Supported data type: F16/F32 * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) * @param[in] src_c The size of the channels (IFM) dimension of the source tensor * @param[in] src_w The size of the width dimension of the source tensor * @param[in] src_h The size of the height dimension of the source tensor * @param[in] src_n The size of the batches dimension of the source tensor * @param[out] dst_img (Not supported) Write only cl_image object for the destination tensor. Included when DST_TENSOR_TYPE=IMAGE * @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p src_ptr * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) * @param[in] dst_c The size of the channels (OFM) dimension of the destination tensor * @param[in] dst_w The size of the width dimension of the destination tensor * @param[in] dst_h The size of the height dimension of the destination tensor * @param[in] dst_n The size of the batches dimension of the destination tensor * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[in] wei_img (Not supported) Read only cl_image object for the weights tensor. Included when WEI_TENSOR_TYPE=IMAGE * @param[in] wei_ptr Pointer to the weights tensor. Supported data type: same as @p src_ptr * @param[in] wei_stride_y Stride of the weights tensor in Y dimension (in bytes) * @param[in] wei_stride_z Stride of the weights tensor in Z dimension (in bytes) * @param[in] wei_stride_w Stride of the weights tensor in W dimension (in bytes) * @param[in] wei_c The size of the channels (IFM) dimension of the weights tensor * @param[in] wei_w The size of the width dimension of the weights tensor * @param[in] wei_h The size of the height dimension of the weights tensor * @param[in] wei_n The size of the batches (OFM) dimension of the weights tensor * @param[in] wei_offset_first_element_in_bytes The offset of the first element in the bias matrix * @param[in] bia_ptr (Optional) Pointer to the bias tensor Supported data type: same as @p src_ptr (if F32/F16) * @param[in] bia_stride_x (Optional) Stride of the bias tensor in X dimension (in bytes) * @param[in] bia_step_x (Optional) bia_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias matrix */ //! @endcond __kernel void transposed_convolution_nhwc( TENSOR4D_RO_T(src, SRC_TENSOR_TYPE), TENSOR4D_WO_T(dst, DST_TENSOR_TYPE), TENSOR4D_RO_T(wei, WEI_TENSOR_TYPE) #if defined(HAS_BIAS) , VECTOR_DECLARATION(bia) #endif // defined(HAS_BIAS) ) { // All the tensor dimensions are passed at compile time. // In case of dynamic tensor support, the following dimensions should be passed as function argument. #define _IWEI_WIDTH WEI_WIDTH #define _IWEI_HEIGHT WEI_HEIGHT #define _ISRC_WIDTH SRC_WIDTH #define _ISRC_HEIGHT SRC_HEIGHT #define _ISRC_CHANNELS SRC_CHANNELS #define _IDST_WIDTH DST_WIDTH #define _IDST_HEIGHT DST_HEIGHT #define _IDST_CHANNELS DST_CHANNELS #define _IY_MULTIPLIER (_IWEI_WIDTH * _IWEI_HEIGHT) #if defined(IS_QUANTIZED) #define _IOUTPUT_TILE cq #else // defined(IS_QUANTIZED) #define _IOUTPUT_TILE c #endif // defined(IS_QUANTIZED) const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM const int mout = GET_SPATIAL_IDX(1, M0, 0); // WIDTH x HEIGHT const int bout = GET_SPATIAL_IDX(2, 1, 0); // BATCH SIZE IDX // .v = access the whole vector (OpenCL vector) // .s[x] = access the vector element at position x (scalar access) TILE(int, 1, M0, xi); TILE(int, 1, M0, yi); TILE(int, 1, M0, xu); TILE(int, 1, M0, yu); // Convert the linear index to coordinate LOOP_UNROLLING(int, i, 0, 1, M0, { xu[0].s[i] = ((mout + i) % _IDST_WIDTH) - PAD_LEFT; yu[0].s[i] = ((mout + i) / _IDST_WIDTH) - PAD_TOP; xi[0].s[i] = ceil(xu[0].s[i] / (float)STRIDE_X); yi[0].s[i] = ceil(yu[0].s[i] / (float)STRIDE_Y); }) // Initialize the accumulators TILE(ACC_DATA_TYPE, M0, N0, c); LOOP_UNROLLING(int, i, 0, 1, M0, { c[i].v = 0; }) // Flipped indices const int x_start = _IWEI_WIDTH - (xi[0].s[0] * STRIDE_X - xu[0].s[0]) - 1; const int y_start = _IWEI_HEIGHT - (yi[0].s[0] * STRIDE_Y - yu[0].s[0]) - 1; for(int yk = y_start, yi_step = 0; yk >= 0; yk -= STRIDE_Y, ++yi_step) { for(int xk = x_start, xi_step = 0; xk >= 0; xk -= STRIDE_X, ++xi_step) { const int weights_y = cout * _IY_MULTIPLIER + yk * _IWEI_WIDTH + xk; TILE(int, 1, M0, my); LOOP_UNROLLING(int, i, 0, 1, M0, { int x_s = xi[0].s[i] + xi_step; int y_s = yi[0].s[i] + yi_step; my[0].s[i] = x_s + y_s *_ISRC_WIDTH; my[0].s[i] = my[0].s[i] + bout * (int)(_ISRC_WIDTH * _ISRC_HEIGHT); my[0].s[i] = select(-1, my[0].s[i], x_s >= 0); my[0].s[i] = select(-1, my[0].s[i], x_s < _ISRC_WIDTH); my[0].s[i] = select(-1, my[0].s[i], y_s >= 0); my[0].s[i] = select(-1, my[0].s[i], y_s < _ISRC_HEIGHT); }) int ck = 0; for(; ck <= (_ISRC_CHANNELS - K0); ck += K0) { TILE(SRC_DATA_TYPE, M0, K0, a); TILE(WEI_DATA_TYPE, N0, K0, b); // Initialize tiles LOOP_UNROLLING(int, i, 0, 1, M0, { a[i].v = ZERO_VALUE; }) LOOP_UNROLLING(int, i, 0, 1, N0, { b[i].v = ZERO_VALUE; }) // Load tile from the src tensor T_LOAD2D_INDIRECT(SRC_DATA_TYPE, M0, K0, SRC_TENSOR_TYPE, src, ck, src_stride_y, my, a); // Load tile from the weights tensor T_LOAD(WEI_DATA_TYPE, N0, K0, WEI_TENSOR_TYPE, wei, ck, weights_y, _IY_MULTIPLIER, wei_stride_y, b); // Compute the matrix multiplication between two tiles T_MMUL(SRC_DATA_TYPE, WEI_DATA_TYPE, ACC_DATA_TYPE, M0, N0, K0, NT, T, a, b, c); #if defined(IS_QUANTIZED) // Apply the offset correction (correction usually needed for asymmetric quantized computation) // The computation is not performed if both SRC_OFFSET and WEI_OFFSET are zero T_OFFSET_CORRECTION(ACC_DATA_TYPE, M0, N0, K0, SRC_OFFSET, WEI_OFFSET, a, b, c); #endif // defined(IS_QUANTIZED) } // This #if directive should be removed in case of dynamic tensor support #if defined(LEFTOVER_LOOP) // Left-over accumulations for(; ck < _ISRC_CHANNELS; ++ck) { TILE(SRC_DATA_TYPE, M0, 1, a); TILE(WEI_DATA_TYPE, N0, 1, b); // Initialize tiles LOOP_UNROLLING(int, i, 0, 1, M0, { a[i].v = ZERO_VALUE; }) // Load tile from the src tensor // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration T_LOAD2D_INDIRECT(SRC_DATA_TYPE, M0, 1, BUFFER, src, ck, src_stride_y, my, a); // Load tile from the weights tensor // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration T_LOAD(WEI_DATA_TYPE, N0, 1, BUFFER, wei, ck, weights_y, _IY_MULTIPLIER, wei_stride_y, b); // Compute the matrix multiplication between two tiles T_MMUL(SRC_DATA_TYPE, WEI_DATA_TYPE, ACC_DATA_TYPE, M0, N0, 1, NT, T, a, b, c); #if defined(IS_QUANTIZED) // Apply the offset correction (correction usually needed for asymmetric quantized computation) // The computation is not performed if both SRC_OFFSET and WEI_OFFSET are zero T_OFFSET_CORRECTION(ACC_DATA_TYPE, M0, N0, 1, SRC_OFFSET, WEI_OFFSET, a, b, c); #endif // defined(IS_QUANTIZED) } #endif // defined(LEFTOVER_LOOP) } } #if defined(IS_QUANTIZED) const int total_pixels = floor((1 + y_start / (float)STRIDE_Y)) * floor(1 + x_start / (float)STRIDE_X); T_ADD_CONSTANT(ACC_DATA_TYPE, M0, N0, c, (total_pixels * _ISRC_CHANNELS * SRC_OFFSET * WEI_OFFSET), c); #endif // defined(IS_QUANTIZED) #if defined(HAS_BIAS) TILE(BIA_DATA_TYPE, 1, N0, bias0); T_LOAD(BIA_DATA_TYPE, 1, N0, BUFFER, bia, cout, 0, 1, 0, bias0); // c = c + bias[broadcasted] T_ELTWISE_BROADCAST_ADD_X(ACC_DATA_TYPE, M0, N0, c, bias0, c); #endif // HAS_BIAS #if defined(IS_QUANTIZED) TILE(DST_DATA_TYPE, M0, N0, cq); // Quantize the tile T_QUANTIZE8_ASYMMETRIC(ACC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, c, cq); #endif // defined(IS_QUANTIZED) TILE(uint, M0, 1, dst_indirect_y); // Calculate the destination indirect Y LOOP_UNROLLING(int, i, 0, 1, M0, { dst_indirect_y[i].v = (uint)min(mout + i, (int)(_IDST_WIDTH * _IDST_HEIGHT) - 1); dst_indirect_y[i].v += bout * (int)(_IDST_WIDTH * _IDST_HEIGHT); }) bool x_cond = PARTIAL_N0 != 0 && get_global_id(0) == 0; // Store the tile in reverse order so the invalid values are overwritten with the valid ones T_STORE_INDIRECT_WIDTH_SELECT(DST_DATA_TYPE, M0, N0, PARTIAL_N0, DST_TENSOR_TYPE, dst, cout, dst_stride_y, x_cond, _IOUTPUT_TILE, dst_indirect_y); #undef _IWEI_WIDTH #undef _IWEI_HEIGHT #undef _ISRC_WIDTH #undef _ISRC_HEIGHT #undef _ISRC_CHANNELS #undef _IDST_WIDTH #undef _IDST_HEIGHT #undef _IDST_CHANNELS #undef _IY_MULTIPLIER }