| /* |
| * 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. |
| */ |
| |
| #include "helpers.h" |
| #include "tile_helpers.h" |
| |
| //! @cond Doxygen_Suppress |
| /** OpenCL kernel to compute the direct convolution. |
| * |
| * @note Data layout supported: NDHWC |
| * @note Data type supported: F32/F16 |
| * @note The accumulation data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE_PROMOTED=half) |
| * @note The convolution padding (left, top and front) must be passed at compile time using -DPAD_LEFT, -DPAD_TOP and -DPAD_FRONT (e.g. -DPAD_LEFT=2, -DPAD_TOP=2, -DPAD_FRONT=2) |
| * @note The convolution strides must be passed at compile time using -DSTRIDE_X, -DSTRIDE_Y and -DSTRIDE_Z (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2, -DSTRIDE_Z=2) |
| * @note The spatial dimensions of the weights must be passed at compile time using -DWEI_WIDTH, -DWEI_HEIGHT and -DWEI_DEPTH (e.g. -DWEI_WIDTH=9, -DWEI_HEIGHT=9, -DWEI_DEPTH=9) |
| * @note The spatial dimensions of the source tensor must be passed at compile time using -DSRC_WIDTH, -DSRC_HEIGHT and -DSRC_DEPTH (e.g. -DSRC_WIDTH=96, -DSRC_HEIGHT=64, -DSRC_DEPTH=32) |
| * @note The spatial dimensions of the destination tensor must be passed at compile time using -DDST_WIDTH, -DDST_HEIGHT and -DDST_DEPTH (e.g. -DDST_WIDTH=96, -DDST_HEIGHT=64, -DDST_DEPTH=32) |
| * @note The channels of the source tensor must be passed at compile time using -DSRC_CHANNELS (e.g. -DSRC_CHANNELS=64) |
| * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_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 Only the following configurations of M0, N0 and K0 are currently supported: |
| * - M0 = 1, 2, 3, 4, 5, .... n |
| * - N0 = 2, 3, 4, 8, 16 |
| * - K0 = 2, 3, 4, 8, 16 |
| * |
| * @note If biases are used then -DHAS_BIAS has to be passed at compile time |
| * |
| * @param[in] src_ptr Pointer to the source tensor. Supported data type: F16/F32 |
| * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| * @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p src_ptr |
| * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) |
| * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) |
| * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| * @param[in] wei_ptr Pointer to the weights tensor. Supported data type: same as @p src_ptr |
| * @param[in] wei_stride_x Stride of the weights tensor in X dimension (in bytes) |
| * @param[in] wei_step_x wei_stride_x * number of elements along X processed per workitem(in bytes) |
| * @param[in] wei_stride_y Stride of the weights tensor in Y dimension (in bytes) |
| * @param[in] wei_step_y wei_stride_y * number of elements along Y processed per workitem(in bytes) |
| * @param[in] wei_stride_z Stride of the weights tensor in Z dimension (in bytes) |
| * @param[in] wei_step_z wei_stride_z * number of elements along Z processed per workitem(in bytes) |
| * @param[in] wei_stride_w Stride of the weights tensor in W dimension (in bytes) |
| * @param[in] wei_step_w wei_stride_w * number of elements along W processed per workitem(in bytes) |
| * @param[in] wei_offset_first_element_in_bytes The offset of the first element in the weights matrix |
| * @param[in] bia_ptr (Optional) Pointer to the bias tensor Supported data type: same as @p src_ptr |
| * @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 direct_convolution3d_ndhwc( |
| TENSOR4D(src, BUFFER), |
| TENSOR4D(dst, BUFFER), |
| TENSOR4D(wei, BUFFER) |
| #if defined(HAS_BIAS) |
| , |
| VECTOR_DECLARATION(bia) |
| #endif // defined(HAS_BIAS) |
| ) |
| { |
| #define _IWEI_WIDTH WEI_WIDTH |
| #define _IWEI_HEIGHT WEI_HEIGHT |
| #define _IWEI_DEPTH WEI_DEPTH |
| #define _ISRC_WIDTH SRC_WIDTH |
| #define _ISRC_HEIGHT SRC_HEIGHT |
| #define _ISRC_DEPTH SRC_DEPTH |
| #define _ISRC_CHANNELS SRC_CHANNELS |
| #define _IDST_WIDTH DST_WIDTH |
| #define _IDST_HEIGHT DST_HEIGHT |
| #define _IDST_DEPTH DST_DEPTH |
| #define _IDST_CHANNELS DST_CHANNELS |
| #define _IY_MULTIPLIER (_IWEI_WIDTH * _IWEI_HEIGHT * _IWEI_DEPTH) |
| |
| const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM |
| const int mout = GET_SPATIAL_IDX(1, M0, 0); // WIDTH x HEIGHT x DEPTH |
| const int bout = GET_SPATIAL_IDX(2, 1, 0); // BATCH SIZE IDX |
| |
| TILE(int, M0, 1, xi); |
| TILE(int, M0, 1, yi); |
| TILE(int, M0, 1, zi); |
| |
| // Convert the linear index to coordinate |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| xi[i].v = ((mout + i) % _IDST_WIDTH) * STRIDE_X; |
| yi[i].v = (((mout + i) / _IDST_WIDTH) % _IDST_HEIGHT) * STRIDE_Y; |
| zi[i].v = (((mout + i) / (_IDST_WIDTH * _IDST_HEIGHT)) % _IDST_DEPTH) * STRIDE_Z; |
| |
| xi[i].v -= PAD_LEFT; |
| yi[i].v -= PAD_TOP; |
| zi[i].v -= PAD_FRONT; |
| }) |
| |
| // Initialize the accumulators |
| TILE(ACC_DATA_TYPE, M0, N0, c); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| c[i].v = (ACC_DATA_TYPE)0; |
| }) |
| |
| for(int i = 0; i < _IY_MULTIPLIER; ++i) |
| { |
| int ck = 0; |
| int xk = i % _IWEI_WIDTH; |
| int yk = (i / _IWEI_WIDTH) % _IWEI_HEIGHT; |
| int zk = i / (_IWEI_WIDTH * _IWEI_HEIGHT); |
| |
| __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes; |
| |
| int k = 0; |
| for(; k <= (_ISRC_CHANNELS - K0); k += K0) |
| { |
| TILE(DATA_TYPE, M0, K0, a); |
| TILE(DATA_TYPE, N0, K0, b); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = (DATA_TYPE)0; |
| }) |
| |
| // Load tile from the src tensor |
| T_LOAD_NDHWC_INDIRECT(DATA_TYPE, M0, K0, BUFFER, src, bout, zk, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, _ISRC_DEPTH, src_stride_y, xi, yi, zi, a); |
| |
| // Load tile from the weights tensor |
| const int b_offs = k + (xk * _ISRC_CHANNELS) + (yk * _ISRC_CHANNELS * _IWEI_WIDTH) + (zk * _ISRC_CHANNELS * _IWEI_WIDTH * _IWEI_HEIGHT); |
| LOOP_UNROLLING(int, i, 0, 1, N0, |
| { |
| if((cout + i) < _IDST_CHANNELS) |
| { |
| LOOP_UNROLLING(int, j, 0, 1, K0, |
| { |
| b[i].s[j] = *(__global DATA_TYPE *)(wei_ptr + wei_offset_first_element_in_bytes + (cout + i) * sizeof(DATA_TYPE) + j * wei_stride_y + b_offs * wei_stride_y); |
| }) |
| } |
| }) |
| |
| // Compute the matrix multiplication between two tiles |
| T_MMUL(DATA_TYPE, DATA_TYPE, ACC_DATA_TYPE, M0, N0, K0, NT, T, a, b, c); |
| |
| ck += K0; |
| } |
| |
| #if((_ISRC_CHANNELS % K0) != 0) |
| // Left-over accumulations |
| for(; k < _ISRC_CHANNELS; ++k) |
| { |
| TILE(DATA_TYPE, M0, 1, a); |
| TILE(DATA_TYPE, N0, 1, b); |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = (DATA_TYPE)0; |
| }) |
| |
| // Load tile from the src tensor |
| T_LOAD_NDHWC_INDIRECT(DATA_TYPE, M0, 1, BUFFER, src, bout, zk, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, _ISRC_DEPTH, src_stride_y, xi, yi, zi, a); |
| |
| // Load tile from the weights tensor |
| const int b_offs = k + (xk * _ISRC_CHANNELS) + (yk * _ISRC_CHANNELS * _IWEI_WIDTH) + (zk * _ISRC_CHANNELS * _IWEI_WIDTH * _IWEI_HEIGHT); |
| LOOP_UNROLLING(int, i, 0, 1, N0, |
| { |
| if((cout + i) < _IDST_CHANNELS) |
| { |
| b[i].v = *(__global DATA_TYPE *)(wei_ptr + wei_offset_first_element_in_bytes + (cout + i) * sizeof(DATA_TYPE) + b_offs * wei_stride_y); |
| } |
| }) |
| |
| // // Compute the matrix multiplication between two tiles |
| T_MMUL(DATA_TYPE, DATA_TYPE, ACC_DATA_TYPE, M0, N0, 1, NT, T, a, b, c); |
| |
| ++ck; |
| } |
| #endif // ((_ISRC_CHANNELS % K0) != 0) |
| } |
| |
| #if defined(HAS_BIAS) |
| TILE(DATA_TYPE, 1, N0, bias0); |
| |
| if((cout + N0) <= _IDST_CHANNELS) |
| { |
| bias0[0].v = VLOAD(N0)(0, (__global DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + cout * sizeof(DATA_TYPE))); |
| } |
| else |
| { |
| VLOAD_PARTIAL(N0, PARTIAL_N0) |
| (bias0[0].v, 0, (__global DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + cout * sizeof(DATA_TYPE))); |
| } |
| |
| // c = c + bias[broadcasted] |
| T_ADD_BROADCAST_X(ACC_DATA_TYPE, M0, N0, c, bias0, c); |
| |
| #endif // HAS_BIAS |
| |
| 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 * _IDST_DEPTH) - 1); |
| dst_indirect_y[i].v += bout * (int)(_IDST_WIDTH *_IDST_HEIGHT * _IDST_DEPTH); |
| }) |
| |
| 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(DATA_TYPE, M0, N0, PARTIAL_N0, BUFFER, dst, cout, dst_stride_y, x_cond, c, dst_indirect_y); |
| } |