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/*
* 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 "helpers_asymm.h"
#include "tile_helpers.h"
#if defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_WIDTH) && defined(DST_HEIGHT) && 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 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. -DDDST_CHANNELS=64)
* @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)
*
* @param[in] src_ptr Pointer to the source tensor. Supported data type: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL
* @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 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(src, SRC_TENSOR_TYPE),
TENSOR4D(dst, DST_TENSOR_TYPE),
TENSOR4D(wei, WEI_TENSOR_TYPE),
VECTOR_DECLARATION(dst_multipliers),
VECTOR_DECLARATION(dst_shifts)
#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 _IDST_WIDTH DST_WIDTH
#define _IDST_HEIGHT DST_HEIGHT
#define _IDST_CHANNELS DST_CHANNELS
#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 // Cols tile A
#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) % _IDST_HEIGHT; // HEIGHT
const int bout = GET_SPATIAL_IDX(2, 1, 0) / _IDST_HEIGHT; // 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;
int d = 0;
#if DEPTH_MULTIPLIER != 1
for(; d < DEPTH_MULTIPLIER; d++)
#endif // DEPTH_MULTIPLIER != 1
{
TILE(ACC_DATA_TYPE, M0, N0, c);
// Reset accumulators
LOOP_UNROLLING(int, i, 0, 1, M0,
{
c[i].v = 0;
})
LOOP_UNROLLING(int, yk, 0, 1, _IWEI_HEIGHT,
{
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, _ISRC_WIDTH, _ISRC_HEIGHT, DILATION_X, 1, src_stride_y, _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 * DEPTH_MULTIPLIER + d, 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
// Optimized path for the dot instruction
TILE(SRC_DATA_TYPE, 1, _IWEI_WIDTH, x0);
TILE(WEI_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];
})
DOT_PRODUCT_INTEGER8(SRC_DATA_TYPE, WEI_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, y0[0].v, c[m0].s[n0]);
REDUCE_INTEGER8(SRC_DATA_TYPE, WEI_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, offset_a);
REDUCE_INTEGER8(SRC_DATA_TYPE, WEI_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, y0[0].v, offset_b);
c[m0].s[n0] += offset_a * (ACC_DATA_TYPE)WEI_OFFSET + 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_WIDTH <= 16
T_ADD_CONSTANT(ACC_DATA_TYPE, M0, N0, c, (_IWEI_WIDTH * _IWEI_HEIGHT * SRC_OFFSET * WEI_OFFSET), 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 * DEPTH_MULTIPLIER + d, 0, 0, 0, bias0);
// c = c + bias[broadcasted]
T_ADD_BROADCAST_X(ACC_DATA_TYPE, M0, N0, c, bias0, c);
#endif // HAS_BIAS
#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()
#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 *DEPTH_MULTIPLIER + d, 0, 0, 0, multipliers); \
T_LOAD(DST_SHIFTS_DATA_TYPE, 1, N0, BUFFER, dst_shifts, cout *DEPTH_MULTIPLIER + d, 0, 0, 0, shifts);
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);
TILE(uint, M0, 1, dst_indirect_y);
bool x_cond = PARTIAL_N0 != 0 && get_global_id(0) == 0;
// Calculate the destination indirect Y
LOOP_UNROLLING(int, i, 0, 1, M0,
{
dst_indirect_y[i].v = min(xo + i, (int)(_IDST_WIDTH) - 1) + yo *_IDST_WIDTH;
dst_indirect_y[i].v += bout * (int)(_IDST_WIDTH * _IDST_HEIGHT);
})
// 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 * DEPTH_MULTIPLIER + d, dst_stride_y, x_cond, cq, dst_indirect_y);
}
}
#endif // defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_WIDTH) && defined(DST_HEIGHT) && 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)
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