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Diffstat (limited to 'src/core/CL/cl_kernels/nhwc/transposed_convolution.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/nhwc/transposed_convolution.cl | 297 |
1 files changed, 297 insertions, 0 deletions
diff --git a/src/core/CL/cl_kernels/nhwc/transposed_convolution.cl b/src/core/CL/cl_kernels/nhwc/transposed_convolution.cl new file mode 100644 index 0000000000..1393537283 --- /dev/null +++ b/src/core/CL/cl_kernels/nhwc/transposed_convolution.cl @@ -0,0 +1,297 @@ +/* + * 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 +} |