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/*
* Copyright (c) 2019 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"
#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(EPSILON) && defined(WIDTH)
/** This function normalizes the input 2D tensor across the first dimension with respect to mean and standard deviation of the same dimension.
*
* @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
* @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
* @attention Width of the input tensor should be passed using the -DWIDTH compile flag, e.g. -DWIDTH=16
* @attention Normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f
*
* @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes)
* @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor
* @param[out] output_ptr (Optional) Pointer to the destination tensor. Supported data types: same as @p input_ptr
* @param[in] output_stride_x (Optional) Stride of the destination tensor in X dimension (in bytes)
* @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] output_stride_y (Optional) Stride of the destination tensor in Y dimension (in bytes)
* @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination tensor
*/
__kernel void mean_stddev_normalization(
IMAGE_DECLARATION(input)
#ifndef IN_PLACE
,
IMAGE_DECLARATION(output)
#endif /* IN_PLACE */
)
{
// Get pixels pointer
Image in = CONVERT_TO_IMAGE_STRUCT(input);
#ifdef IN_PLACE
Image out = in;
#else /* IN_PLACE */
Image out = CONVERT_TO_IMAGE_STRUCT(output);
#endif /* IN_PLACE */
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
sum = 0.f;
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
sum_sq = 0.f;
// Calculate partial sum
int i = 0;
for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE)
{
// Load data
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&in, i, 0));
sum += data;
sum_sq += data * data;
}
// Perform reduction
#if VEC_SIZE > 8
sum.s01234567 += sum.s89abcdef;
sum_sq.s01234567 += sum_sq.s89abcdef;
#endif // VEC_SIZE > 8
#if VEC_SIZE > 4
sum.s0123 += sum.s4567;
sum_sq.s0123 += sum_sq.s4567;
#endif // VEC_SIZE > 4
#if VEC_SIZE > 2
sum.s01 += sum.s23;
sum_sq.s01 += sum_sq.s23;
#endif // VEC_SIZE > 2
sum.s0 += sum.s1;
sum_sq.s0 += sum_sq.s1;
// Left-overs loop
for(; i < WIDTH; ++i)
{
DATA_TYPE data = *((__global DATA_TYPE *)offset(&in, i, 0));
sum.s0 += data;
sum_sq.s0 += data * data;
}
DATA_TYPE mean = sum.s0 / WIDTH;
DATA_TYPE var = (sum_sq.s0 / WIDTH) - (mean * mean);
DATA_TYPE stddev_inv = 1.f / sqrt(var + EPSILON);
i = 0;
for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE)
{
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&in, i, 0));
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
res = (data - mean) * stddev_inv;
VSTORE(VEC_SIZE)
(res, 0, (__global DATA_TYPE *)offset(&out, i, 0));
}
for(; i < WIDTH; ++i)
{
DATA_TYPE data = *((__global DATA_TYPE *)offset(&in, i, 0));
*((__global DATA_TYPE *)offset(&out, i, 0)) = (data - mean) * stddev_inv;
}
}
#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(EPSILON) && defined(WIDTH) */
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