/* * Copyright (c) 2021-2023 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" // *INDENT-OFF* // clang-format off #define CALCULATE_WEIGHTS_OFFSET_CORRECTION(A_DATA_TYPE, B_DATA_TYPE) CALCULATE_WEIGHTS_OFFSET_CORRECTION_STR(A_DATA_TYPE, B_DATA_TYPE) #define CALCULATE_WEIGHTS_OFFSET_CORRECTION_STR(A_DATA_TYPE, B_DATA_TYPE) CALCULATE_WEIGHTS_OFFSET_CORRECTION_##A_DATA_TYPE##_##B_DATA_TYPE #define CALCULATE_WEIGHTS_OFFSET_CORRECTION_char_char (0) #define CALCULATE_WEIGHTS_OFFSET_CORRECTION_uchar_uchar (0) #define CALCULATE_WEIGHTS_OFFSET_CORRECTION_uchar_char (128) #define CALCULATE_WEIGHTS_OFFSET_CORRECTION_char_uchar (-128) #define T_LOAD_MULTIPLIERS_SHIFT_PER_TENSOR() \ ({}) #define T_LOAD_MULTIPLIERS_SHIFT_PER_CHANNEL() \ TILE(DST_MULTIPLIERS_DATA_TYPE, 1, N0, multipliers); \ TILE(DST_SHIFTS_DATA_TYPE, 1, N0, shifts); \ T_LOAD(DST_MULTIPLIERS_DATA_TYPE, 1, N0, BUFFER, dst_multipliers, cout, 0, 0, 0, multipliers); \ T_LOAD(DST_SHIFTS_DATA_TYPE, 1, N0, BUFFER, dst_shifts, cout, 0, 0, 0, shifts); #define T_LOAD_MULTIPLIERS_SHIFT(QUANTIZATION_TYPE) T_LOAD_MULTIPLIERS_SHIFT_STR(QUANTIZATION_TYPE) #define T_LOAD_MULTIPLIERS_SHIFT_STR(QUANTIZATION_TYPE) T_LOAD_MULTIPLIERS_SHIFT_##QUANTIZATION_TYPE() #if defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) //! @cond Doxygen_Suppress /** OpenCL kernel to compute the depthwise convolution for quantized data types * * @note Data layout supported: NHWC * @note Data type supported: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL * @note The 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 convolution strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2) * @note The convolution dilations must be passed at compile time using -DDILATION_X and -DDILATION_Y (e.g. -DDILATION_X=2, -DDILATION_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 tensor type ("BUFFER" or "IMAGE") of the source tensor must be passed at compile time using -DSRC_TENSOR_TYPE (e.g. -DSRC_TENSOR_TYPE=BUFFER) * @note The tensor type ("BUFFER" or "IMAGE") of the weights tensor must be passed at compile time using -DWEI_TENSOR_TYPE (e.g. -DWEI_TENSOR_TYPE=BUFFER) * @note The tensor type ("BUFFER" or "IMAGE") 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=int8) * @note The data type of the weights tensor must be passed at compile time using -DWEI_DATA_TYPE (e.g. -DWEI_DATA_TYPE=int8) * @note The data type of the destination tensor must be passed at compile time using -DDST_DATA_TYPE (e.g. -DDST_DATA_TYPE=int8) * @note The data type of the accumulators must be passed at compile time using -DACC_DATA_TYPE (e.g. -DACC_DATA_TYPE=int) * @note The number of M0 rows (width) 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 size of the partial store block in the first dimension must be passed at compile time using -DPARTIAL_N0 (e.g. -DPARTIAL_N0=1) * @note The activation type must be passed at compile using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu * @note The A and B variables required by some activation functions must be passed at compile time using -DA_VAL= and -DB_VAL= respectively * @note The quantization offset used for both the per-tensor and per-channel quantization must be passed at compile using -DDST_OFFSET (e.g., -DDST_OFFSET=3) * @note The quantization shift for the per-tensor quantization must be passed at compile time using -DDST_SHIFT (e.g., -DDST_SHIFT=1) * @note The quantization multiplier for the per-tensor quantization must be passed at compile using -DDST_MULTIPLIER (e.g., -DDST_MULTIPLER=121432) * @note Only the following configurations of M0 and N0 are currently supported: * - M0 = 1, 2, 3, 4, 5, .... n (M0 != 1 with STRIDE_X == 1 && DILATION_X == 1 only) * - N0 = 2, 3, 4, 8, 16 * @note The number of rows to read from the src tensor must be passed at compile time using -DM0_A (e.g., -DM0_A=3). M0_A must be equal to WEI_WIDTH + (M0 - 1) * @note The number of columns to read from the src tensor must be passed at compile time using -DN0_A. It can either be 1 (for DEPTH_MULTIPLIER > 1) or N0 (for DEPTH_MULTIPLIER == 1) * * @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: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL * @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 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[in] src_offset_first_element_in_bytes The offset of the first element in 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 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 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 dimension of the weights tensor * @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 tensor * @param[in] dst_multipliers_ptr Pointer to the destination multipliers tensor for the per-channel quantization. Supported data type: S32 * @param[in] dst_multipliers_stride_x Stride of the destination multipliers tensor in X dimension (in bytes) * @param[in] dst_multipliers_step_x dst_multipliers_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_multipliers_offset_first_element_in_bytes The offset of the first element in the destination multipliers tensor * @param[in] dst_shifts_ptr Pointer to the destination shifts tensor for the per-channel quantization. Supported data type: S32 * @param[in] dst_shifts_stride_x Stride of the destination shifts tensor in X dimension (in bytes) * @param[in] dst_shifts_step_x dst_shifts_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_shifts_offset_first_element_in_bytes The offset of the first element in the destination shifts tensor * @param[in] bia_ptr (Optional) Pointer to the bias tensor Supported data type: S32 * @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 tensor */ //! @endcond __kernel void dwc_native_quantized_nhwc( TENSOR4D_RO_T(src, SRC_TENSOR_TYPE), TENSOR4D_WO_T(dst, DST_TENSOR_TYPE), TENSOR4D_RO_T(wei, WEI_TENSOR_TYPE), VECTOR_DECLARATION(dst_multipliers), VECTOR_DECLARATION(dst_shifts) #if defined(HAS_BIAS) , VECTOR_DECLARATION(bia) #endif // defined(HAS_BIAS) ) { // Only the weight 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 _IM0_A M0_A // _IWEI_WIDTH + (M0 - 1) Rows tile A (If M0 != 1, the tiles overlap of 1 element on the X dimension) #define _IN0_A N0_A // Cols tile A. It can be either 1 (for DEPTH_MULTIPLIER > 1) or N0 (for DEPTH_MULTIPLIER == 1) #define _IM0_B _IWEI_WIDTH // Rows tile B #define _IN0_B N0 // Cols tile B #define _IBOUNDARY_CHECK (!((WEI_WIDTH == 1 && WEI_HEIGHT == 1 && PAD_LEFT == 0 && PAD_TOP == 0 && M0 == 1))) const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM const int xo = GET_SPATIAL_IDX(1, M0, 0); // WIDTH #if defined(BATCHED_EXECUTION) const int yo = GET_SPATIAL_IDX(2, 1, 0) % dst_h; // HEIGHT const int bout = GET_SPATIAL_IDX(2, 1, 0) / dst_h; // BATCH SIZE IDX #else // defined(BATCHED_EXECUTION) const int yo = GET_SPATIAL_IDX(2, 1, 0); // HEIGHT const int bout = 0; // BATCH SIZE IDX #endif // defined(BATCHED_EXECUTION) int xi = xo * STRIDE_X; int yi = yo * STRIDE_Y; xi -= PAD_LEFT; yi -= PAD_TOP; TILE(ACC_DATA_TYPE, M0, N0, c); // Reset accumulators LOOP_UNROLLING(int, i, 0, 1, M0, { c[i].v = 0; }) #if _IWEI_HEIGHT <= 5 LOOP_UNROLLING(int, yk, 0, 1, _IWEI_HEIGHT, #else // _IWEI_HEIGHT <= 5 for(int yk = 0; yk < _IWEI_HEIGHT; yk++) #endif // _IWEI_HEIGHT <= 5 { TILE(SRC_DATA_TYPE, _IM0_A, _IN0_A, a); LOOP_UNROLLING(int, i, 0, 1, _IM0_A, { a[i].v = ZERO_VALUE; }) // Load tile from the src tensor (TILE A) T_LOAD_NHWC_WITH_DILATION(SRC_DATA_TYPE, 1, _IM0_A, _IN0_A, SRC_TENSOR_TYPE, src, bout, yi + yk * DILATION_Y, xi, (cout / DEPTH_MULTIPLIER), src_w, src_h, DILATION_X, 1, _IBOUNDARY_CHECK, a); TILE(WEI_DATA_TYPE, _IM0_B, _IN0_B, b); // Load tile from the weights tensor (TILE B) T_LOAD(WEI_DATA_TYPE, _IM0_B, _IN0_B, WEI_TENSOR_TYPE, wei, cout, yk * _IM0_B, 1, wei_stride_y, b); // Optimized path for STRIDE_X == 1 // If M0 != 1, we can skip the common loads between the two applied kernels on the X (WIDTH) dimension LOOP_UNROLLING(int, m0, 0, 1, M0, { LOOP_UNROLLING(int, n0, 0, 1, N0, { #if _IWEI_WIDTH <= 16 #define DOT_DATA_TYPE SRC_DATA_TYPE #define WEI_OFFSET_CORRECTION (CALCULATE_WEIGHTS_OFFSET_CORRECTION(SRC_DATA_TYPE, WEI_DATA_TYPE)) // Optimized path for the dot instruction TILE(DOT_DATA_TYPE, 1, _IWEI_WIDTH, x0); TILE(DOT_DATA_TYPE, 1, _IWEI_WIDTH, y0); ACC_DATA_TYPE offset_a = 0; ACC_DATA_TYPE offset_b = 0; LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH, { x0[0].s[xk] = a[xk + m0].s[n0]; y0[0].s[xk] = b[xk].s[n0] + (int)WEI_OFFSET_CORRECTION; }) DOT_PRODUCT_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, y0[0].v, c[m0].s[n0]); REDUCE_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, offset_a); REDUCE_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, y0[0].v, offset_b); c[m0].s[n0] += offset_a * (ACC_DATA_TYPE)(WEI_OFFSET - (ACC_DATA_TYPE)WEI_OFFSET_CORRECTION) + offset_b * (ACC_DATA_TYPE)SRC_OFFSET; #else // _IWEI_WIDTH <= 16 LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH, { c[m0].s[n0] += ((ACC_DATA_TYPE)a[xk + m0].s[n0] + (ACC_DATA_TYPE)(SRC_OFFSET)) * ((ACC_DATA_TYPE)b[xk].s[n0] + (ACC_DATA_TYPE)(WEI_OFFSET)); }) #endif // _IWEI_WIDTH <= 16 }) }) } #if _IWEI_HEIGHT <= 5 ) #endif // _IWEI_HEIGHT <= 5 #if _IWEI_WIDTH <= 16 T_ADD_CONSTANT(ACC_DATA_TYPE, M0, N0, c, (_IWEI_WIDTH * _IWEI_HEIGHT * SRC_OFFSET * (ACC_DATA_TYPE)(WEI_OFFSET - (ACC_DATA_TYPE)WEI_OFFSET_CORRECTION)), c); #endif // _IWEI_WIDTH <= 16 #if defined(HAS_BIAS) TILE(BIA_DATA_TYPE, 1, N0, bias0); // Load bias T_LOAD(BIA_DATA_TYPE, 1, N0, BUFFER, bia, cout, 0, 0, 0, bias0); // c = c + bias[broadcasted] T_ELTWISE_BROADCAST_ADD_X(ACC_DATA_TYPE, M0, N0, c, bias0, c); #endif // HAS_BIAS T_LOAD_MULTIPLIERS_SHIFT(QUANTIZATION_TYPE); // Quantize the tile TILE(DST_DATA_TYPE, M0, N0, cq); T_QUANTIZE8(ACC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, c, multipliers, shifts, cq); // Perform activation T_ACTIVATION_QUANTIZED(DST_DATA_TYPE, M0, N0, ACTIVATION_TYPE, DST_OFFSET, A_VAL, B_VAL, cq, cq); bool x_cond = PARTIAL_N0 != 0 && get_global_id(0) == 0; if(x_cond) { LOOP_UNROLLING(int, m0, 0, 1, M0, { int xi_out = min(xo + M0 - 1 - m0, (int)(dst_w) - 1); VSTORE_PARTIAL(N0, PARTIAL_N0) (cq[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + (uint)cout * sizeof(DST_DATA_TYPE) + (uint)xi_out * dst_stride_y + (uint)yo * dst_stride_z + (uint)bout * dst_stride_w)); }) } else { LOOP_UNROLLING(int, m0, 0, 1, M0, { int xi_out = min(xo + M0 - 1 - m0, (int)(dst_w) - 1); VSTORE(N0) (cq[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + (uint)cout * sizeof(DST_DATA_TYPE) + (uint)xi_out * dst_stride_y + (uint)yo * dst_stride_z + (uint)bout * dst_stride_w)); }) } } #endif // defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) // *INDENT-ON* // clang-format on