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/*
* 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
}
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