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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"
/** This kernel performs a direct convolution to convolve the low three dimensions.
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32
* @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1
* @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
* @note The output quantization multiplier must be passed at compile time using -DOUTPUT_MULTIPLIER e.g. -DOUTPUT_MULTIPLIER=1234
* @note The output quantization shift must be passed at compile time using -DOUTPUT_SHIFT e.g. -DOUTPUT_SHIFT=4
* @note The input offset quantization parameter must be passed at compile time using -DINPUT_OFFSET e.g. -DINPUT_OFFSET=3
* @note The weights offset quantization parameter must be passed at compile time using -DWEIGHTS_OFFSET e.g. -DWEIGHTS_OFFSET=3
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: 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_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 types: 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 Z 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_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
* @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
* @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
* @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
* @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
* @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
* @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
* @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
* @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
* @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
*/
__kernel void direct_convolution_nchw(
TENSOR3D_DECLARATION(src),
TENSOR3D_DECLARATION(dst),
TENSOR3D_DECLARATION(weights),
#ifdef HAS_BIAS
VECTOR_DECLARATION(biases),
#endif /* defined(HAS_BIAS) */
unsigned int weights_stride_w)
{
const int id0 = get_global_id(0);
const int id1 = get_global_id(1);
const int id2 = get_global_id(2);
const int x_coords = (id0 * STRIDE_X) - PAD_LEFT;
const int y_coords = (id1 * STRIDE_Y) - PAD_TOP;
const int x_offs = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE), 0) * sizeof(DATA_TYPE);
__global uchar *src_addr = (__global uchar *)(src_ptr + src_offset_first_element_in_bytes);
__global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + id2 * weights_stride_w);
__global uchar *dst_addr = (__global uchar *)dst_ptr + dst_offset_first_element_in_bytes + x_offs + id1 * dst_stride_y + id2 * dst_stride_z;
#ifdef IS_QUANTIZED
int acc_value = 0;
#else /* IS_QUANTIZED */
DATA_TYPE acc_value = 0;
#endif /* IS_QUANTIZED */
for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
{
for(int y = 0; y < WEI_HEIGHT; ++y)
{
for(int x = 0; x < WEI_WIDTH; ++x)
{
const int idx_x = (x_coords + x);
const int idx_y = (y_coords + y);
if((idx_x >= 0 && idx_x < SRC_WIDTH) && (idx_y >= 0 && idx_y < SRC_HEIGHT))
{
const int weight_offset = x + (WEI_HEIGHT * y);
const int input_offset = idx_x + SRC_WIDTH * idx_y;
#ifdef IS_QUANTIZED
int weight = convert_int(*((__global DATA_TYPE *)weights_addr + weight_offset));
int input = convert_int(*((__global DATA_TYPE *)src_addr + input_offset));
acc_value += (input + INPUT_OFFSET) * (weight + WEIGHTS_OFFSET);
#else /* IS_QUANTIZED */
DATA_TYPE weight = *((__global DATA_TYPE *)weights_addr + weight_offset);
DATA_TYPE input = *((__global DATA_TYPE *)src_addr + input_offset);
acc_value += input * weight;
#endif /* IS_QUANTIZED */
}
}
}
src_addr += src_stride_z;
weights_addr += weights_stride_z;
}
#ifdef HAS_BIAS
Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
#ifdef IS_QUANTIZED
int bias = *((__global int *)(vector_offset(&biases, id2)));
#else /* IS_QUANTIZED */
DATA_TYPE bias = *((__global DATA_TYPE *)(vector_offset(&biases, id2)));
#endif /* IS_QUANTIZED */
acc_value += bias;
#endif /* defined(HAS_BIAS) */
#ifdef IS_QUANTIZED
#if OUTPUT_SHIFT < 0
acc_value = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(acc_value, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 1);
#else // OUTPUT_SHIFT < 0
acc_value = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(acc_value, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 1);
#endif // OUTPUT_SHIFT < 0
acc_value = acc_value + OUTPUT_OFFSET;
#endif /* IS_QUANTIZED */
*(__global DATA_TYPE *)dst_addr = CONVERT_SAT(acc_value, DATA_TYPE);
}
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