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Diffstat (limited to 'compute/ARMComputeEx/src/core/CL/cl_kernels/instance_normalization_ex.cl')
| -rw-r--r-- | compute/ARMComputeEx/src/core/CL/cl_kernels/instance_normalization_ex.cl | 251 |
1 files changed, 251 insertions, 0 deletions
diff --git a/compute/ARMComputeEx/src/core/CL/cl_kernels/instance_normalization_ex.cl b/compute/ARMComputeEx/src/core/CL/cl_kernels/instance_normalization_ex.cl new file mode 100644 index 000000000..1d96150f8 --- /dev/null +++ b/compute/ARMComputeEx/src/core/CL/cl_kernels/instance_normalization_ex.cl @@ -0,0 +1,251 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * 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(DIM_X) && \ + defined(DIM_Y) && defined(DIM_Z) +/** 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=data_type compile flag, e.g. + * -DDATA_TYPE=float + * @attention Normalization epsilon parameter should be given as a preprocessor argument with + * -DEPSILON=value. e.g. -DEPSILON=0.001f + * @attention Dimensions X, Y, and Z should be given as a preprocessor argument with -DDIM_X=value, + * -DDIM_Y=value, -DDIM_Z=value. e.g. -DDIM_X=6, -DDIM_Y=2, -DDIM_Z=7 + * + * @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 (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_stride_z (Optional) Stride of the destination tensor in Z + * dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements + * along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in + * the destination tensor + * @param[in] gamma_ptr (Optional) Pointer to the gamma tensor. + * Supported data types: same as @p input_ptr + * @param[in] gamma_stride_x (Optional) Stride of the gamma tensor in X + * dimension (in bytes) + * @param[in] gamma_step_x (Optional) output_stride_x * number of elements + * along X processed per workitem(in bytes) + * @param[in] gamma_offset_first_element_in_bytes (Optional) The offset of the first element in + * the gamma tensor + * @param[in] beta_ptr (Optional) Pointer to the beta tensor. Supported + * data types: same as @p input_ptr + * @param[in] beta_stride_x (Optional) Stride of the beta tensor in X + * dimension (in bytes) + * @param[in] beta_step_x (Optional) output_stride_x * number of elements + * along X processed per workitem(in bytes) + * @param[in] beta_offset_first_element_in_bytes (Optional) The offset of the first element in + * the beta tensor + */ +__kernel void instance_normalization_ex(TENSOR4D_DECLARATION(input), +#ifndef IN_PLACE + TENSOR4D_DECLARATION(output) +#endif /* IN_PLACE */ +#ifdef GAMMA + , + VECTOR_DECLARATION(gamma) +#endif // GAMMA +#ifdef BETA + , + VECTOR_DECLARATION(beta) +#endif // BETA + ) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0); +#ifndef IN_PLACE + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(output, 0); +#endif /* IN_PLACE */ + + float sum = 0.f; + float sum_sq = 0.f; + +#if defined(NHWC) + + const int ch = get_global_id(0); // Current channel + const int batch = get_global_id(2); // Current batch + const int elements_plane = DIM_Y * DIM_Z; + + for (int i_w = 0; i_w < DIM_Y; ++i_w) + { + for (int i_h = 0; i_h < DIM_Z; ++i_h) + { + float data = (float)*((__global DATA_TYPE *)tensor4D_offset(&in, ch, i_w, i_h, batch)); + sum += data; + sum_sq += data * data; + } + } + +#else // !defined(NHWC) + const int ch = get_global_id(2) % DIM_Z; // Current channel + const int batch = get_global_id(2) / DIM_Z; // Current batch + const int elements_plane = DIM_X * DIM_Y; + + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + part_sum = 0.f; + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + part_sum_sq = 0.f; + // Calculate partial sum + for (int y = 0; y < DIM_Y; ++y) + { + int x = 0; + for (; x <= (DIM_X - VEC_SIZE); x += VEC_SIZE) + { + // Load data + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch)); + part_sum += data; + part_sum_sq += data * data; + } + // Left-overs loop + for (; x < DIM_X; ++x) + { + DATA_TYPE data = *((__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch)); + part_sum.s0 += data; + part_sum_sq.s0 += data * data; + } + } +// Perform reduction +#if VEC_SIZE > 8 + part_sum.s01234567 += part_sum.s89abcdef; + part_sum_sq.s01234567 += part_sum_sq.s89abcdef; +#endif // VEC_SIZE > 8 +#if VEC_SIZE > 4 + part_sum.s0123 += part_sum.s4567; + part_sum_sq.s0123 += part_sum_sq.s4567; +#endif // VEC_SIZE > 4 +#if VEC_SIZE > 2 + part_sum.s01 += part_sum.s23; + part_sum_sq.s01 += part_sum_sq.s23; +#endif // VEC_SIZE > 2 + part_sum.s0 += part_sum.s1; + part_sum_sq.s0 += part_sum_sq.s1; + + sum = (float)part_sum.s0; + sum_sq = (float)part_sum_sq.s0; + +#endif // defined(NHWC) + + const float mean_float = (sum / elements_plane); + const DATA_TYPE mean = (DATA_TYPE)mean_float; + const float var_float = (sum_sq / elements_plane) - (mean_float * mean_float); +#if defined(GAMMA) + const float multip_float = *((__global DATA_TYPE *)gamma_ptr + ch) / sqrt(var_float + EPSILON); + const DATA_TYPE multip = (DATA_TYPE)multip_float; +#else // !defined(GAMMA) + const DATA_TYPE multip = (DATA_TYPE)0; +#endif // defined(GAMMA) +#if defined(BETA) + const DATA_TYPE beta = *((__global DATA_TYPE *)beta_ptr + ch); +#else // !defined(BETA) + const DATA_TYPE beta = 0; +#endif // defined(BETA) + +#if defined(NHWC) + + for (int i_w = 0; i_w < DIM_Y; ++i_w) + { + for (int i_h = 0; i_h < DIM_Z; ++i_h) + { + __global DATA_TYPE *input_address = + (__global DATA_TYPE *)tensor4D_offset(&in, ch, i_w, i_h, batch); +#ifdef IN_PLACE + __global DATA_TYPE *output_address = input_address; +#else /* !IN_PLACE */ + __global DATA_TYPE *output_address = + (__global DATA_TYPE *)tensor4D_offset(&out, ch, i_w, i_h, batch); +#endif /* IN_PLACE */ + *(output_address) = (*(input_address)-mean) * multip + beta; + } + } + +#else // !defined(NHWC) + for (int y = 0; y < DIM_Y; ++y) + { + int x = 0; + for (; x <= (DIM_X - VEC_SIZE); x += VEC_SIZE) + { + __global DATA_TYPE *input_address = + (__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch); +#ifdef IN_PLACE + __global DATA_TYPE *output_address = input_address; +#else /* !IN_PLACE */ + __global DATA_TYPE *output_address = + (__global DATA_TYPE *)tensor4D_offset(&out, x, y, ch, batch); +#endif /* IN_PLACE */ + + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + data = VLOAD(VEC_SIZE)(0, input_address); + + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + res = (data - mean) * multip + beta; + VSTORE(VEC_SIZE) + (res, 0, output_address); + } + // Left-overs loop + for (; x < DIM_X; ++x) + { + __global DATA_TYPE *input_address = + (__global DATA_TYPE *)tensor4D_offset(&in, x, y, ch, batch); +#ifdef IN_PLACE + __global DATA_TYPE *output_address = input_address; +#else /* !IN_PLACE */ + __global DATA_TYPE *output_address = + (__global DATA_TYPE *)tensor4D_offset(&out, x, y, ch, batch); +#endif /* IN_PLACE */ + *(output_address) = (*(input_address)-mean) * multip + beta; + } + } +#endif // defined(NHWC) +} +#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(EPSILON) && defined(DIM_X) && \ + defined(DIM_Y) && defined(DIM_Z) */ |