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diff --git a/src/core/CL/cl_kernels/nhwc/dwc_native_quantized_nhwc.cl b/src/core/CL/cl_kernels/nhwc/dwc_native_quantized_nhwc.cl
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+/*
+ * 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