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/*
* Copyright (c) 2017-2021 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"
#define ADD_OP(a, b) ((a) + (b))
#define SUB_OP(a, b) ((a) - (b))
#define MUL_OP(a, b) ((a) * (b))
#define INVSQRT_OP(a) rsqrt((a))
#define SQCVT_SAT(a) (a)
#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(ACTIVATION_TYPE)
#include "activation_float_helpers.h"
/** Apply batch normalization on tensors with NHWC format.
*
* @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu
* @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively
*
* @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_stride_z Stride of the first source tensor in Z dimension (in bytes)
* @param[in] input_step_z input_stride_z * number of elements along Z 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 Pointer to the destination tensor. Supported data types: same as @p input_ptr
* @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p input_ptr
* @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
* @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
* @param[in] var_ptr Pointer to the var tensor. Supported data types: same as @p input_ptr
* @param[in] var_stride_x Stride of the var tensor in X dimension (in bytes)
* @param[in] var_step_x var_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] var_offset_first_element_in_bytes The offset of the first element in the var source tensor
* @param[in] beta_ptr Pointer to the beta source tensor. Supported data types: same as @p input_ptr
* @param[in] beta_stride_x Stride of the beta source tensor in X dimension (in bytes)
* @param[in] beta_step_x beta_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] beta_offset_first_element_in_bytes The offset of the first element in the beta source tensor
* @param[in] gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p input_ptr
* @param[in] gamma_stride_x Stride of the gamma source tensor in X dimension (in bytes)
* @param[in] gamma_step_x gamma_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] gamma_offset_first_element_in_bytes The offset of the first element in the gamma source tensor
* @param[in] epsilon Epsilon parameter in the batch normalization equation
*/
__kernel void batchnormalization_layer_nhwc(TENSOR3D_DECLARATION(input),
#ifndef IN_PLACE
TENSOR3D_DECLARATION(output),
#endif /* not IN_PLACE */
VECTOR_DECLARATION(mean),
VECTOR_DECLARATION(var),
#ifndef USE_DEFAULT_BETA
VECTOR_DECLARATION(beta),
#endif /* USE_DEFAULT_BETA */
#ifndef USE_DEFAULT_GAMMA
VECTOR_DECLARATION(gamma),
#endif /* USE_DEFAULT_GAMMA */
float epsilon)
{
uint x_offs = max((int)(get_global_id(0) * VEC_SIZE * sizeof(DATA_TYPE) - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE * sizeof(DATA_TYPE)), 0);
__global uchar *input_addr = input_ptr + input_offset_first_element_in_bytes + x_offs + get_global_id(1) * input_stride_y + get_global_id(2) * input_stride_z;
#ifdef IN_PLACE
__global uchar *output_addr = input_ptr;
#else /* IN_PLACE */
__global uchar *output_addr = output_ptr + output_offset_first_element_in_bytes + x_offs + get_global_id(1) * output_stride_y + get_global_id(2) * output_stride_z;
#endif /* IN_PLACE */
__global uchar *mean_addr = mean_ptr + mean_offset_first_element_in_bytes + x_offs;
__global uchar *var_addr = var_ptr + var_offset_first_element_in_bytes + x_offs;
#ifndef USE_DEFAULT_BETA
__global uchar *beta_addr = beta_ptr + beta_offset_first_element_in_bytes + x_offs;
#endif /* USE_DEFAULT_BETA */
#ifndef USE_DEFAULT_GAMMA
__global uchar *gamma_addr = gamma_ptr + gamma_offset_first_element_in_bytes + x_offs;
#endif /* USE_DEFAULT_GAMMA */
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
data = 0;
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
denominator = 0;
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
numerator = 0;
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
x_bar = 0;
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
res0 = 0;
data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input_addr);
denominator = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)var_addr);
denominator = INVSQRT_OP(ADD_OP(denominator, ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(epsilon))));
// Calculate x bar and store results
numerator = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)mean_addr);
numerator = SUB_OP(data, numerator);
x_bar = MUL_OP(numerator, denominator);
#ifndef USE_DEFAULT_GAMMA
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
gamma_vec = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)gamma_addr);
res0 = MUL_OP(gamma_vec, x_bar);
#else /* USE_DEFAULT_GAMMA */
// gamma is equal to 1, no need to perform multiplications
res0 = x_bar;
#endif /* USE_DEFAULT_GAMMA */
#ifndef USE_DEFAULT_BETA
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
beta_vec = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)beta_addr);
// beta is not zero, hence we need to perform the addition
res0 = ADD_OP(res0, beta_vec);
#endif /* USE_DEFAULT_BETA */
res0 = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, res0, A_VAL, B_VAL);
STORE_VECTOR_SELECT(res, DATA_TYPE, output_addr, VEC_SIZE, VEC_SIZE_LEFTOVER, VEC_SIZE_LEFTOVER != 0 && get_global_id(0) == 0)
}
#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DATA_TYPE)*/
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