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/*
* Copyright (c) 2021-2023 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 "src/cpu/kernels/softmax/generic/sve/impl.h"
#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
namespace arm_compute
{
namespace cpu
{
/// TODO: (COMPMID-6505) Similar to Neon(TM), this implementation be converted to
/// a single kernel that performs softmax operation. Leaving the SVE code here for
/// future references. Implementation for Neon(TM) is introduced in COMPMID-6500
template <typename ScalarType>
void sve_logits_1d_max(const ITensor *in, ITensor *out, const Window &window)
{
const auto all_true_pg = wrapper::svptrue<ScalarType>();
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
Window win{window};
win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input(in, win);
Iterator output(out, win);
execute_window_loop(
win,
[&](const Coordinates &)
{
// Get pointers
const auto in_ptr = reinterpret_cast<const ScalarType *>(input.ptr());
const auto out_ptr = reinterpret_cast<ScalarType *>(output.ptr());
// Init max value
auto vec_max = wrapper::svdup_n(support::cpp11::lowest<ScalarType>());
int x = window_start_x;
svbool_t pg = wrapper::svwhilelt<ScalarType>(x, window_end_x);
do
{
const auto current_value = svld1(pg, in_ptr + x);
vec_max = svmax_m(pg, vec_max, current_value);
x += wrapper::svcnt<ScalarType>();
pg = wrapper::svwhilelt<ScalarType>(x, window_end_x);
} while (svptest_any(all_true_pg, pg));
auto max_val = svmaxv(all_true_pg, vec_max);
*out_ptr = max_val;
},
input, output);
}
template <typename ScalarType>
void sve_softmax_logits_1d_float(const ITensor *in,
const ITensor *max,
void *const tmp,
ITensor *out,
const float beta,
bool is_log,
const Window &window)
{
const int start_x = in->info()->valid_region().anchor.x();
const int input_width = in->info()->valid_region().shape.x();
Iterator in_it(in, window);
Iterator max_it(max, window);
Iterator out_it(out, window);
const auto all_true_pg = wrapper::svptrue<ScalarType>();
execute_window_loop(
window,
[&](const Coordinates &)
{
/* Get pointers */
const auto in_ptr = reinterpret_cast<const ScalarType *>(in_it.ptr()) + start_x;
const auto out_ptr = reinterpret_cast<ScalarType *>(out_it.ptr()) + start_x;
const auto tmp_ptr = reinterpret_cast<ScalarType *>(tmp);
ScalarType sum{0};
/* Compute exponentials and sum */
{
/* Get max value */
const auto max_val = *reinterpret_cast<const ScalarType *>(max_it.ptr());
const auto vec_max = wrapper::svdup_n(max_val);
const auto vec_beta = wrapper::svdup_n(static_cast<ScalarType>(beta));
/* Init sum to zero */
auto vec_sum = wrapper::svdup_n(static_cast<ScalarType>(0));
/* Loop over row and compute exponentials and sum */
int x = 0;
svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
do
{
auto vec_elements = svld1(pg, in_ptr + x);
vec_elements = svmul_z(pg, svsub_z(pg, vec_elements, vec_max), vec_beta);
if (!is_log)
{
vec_elements = wrapper::svexp_z(pg, vec_elements);
vec_sum = svadd_m(pg, vec_sum, vec_elements);
}
svst1(pg, tmp_ptr + x, vec_elements);
if (is_log)
{
vec_sum = svadd_m(pg, vec_sum, wrapper::svexp_z(pg, vec_elements));
}
x += wrapper::svcnt<ScalarType>();
pg = wrapper::svwhilelt<ScalarType>(x, input_width);
} while (svptest_any(all_true_pg, pg));
/* Reduce sum */
sum = svaddv(all_true_pg, vec_sum);
if (is_log)
{
sum = static_cast<ScalarType>(std::log(sum));
}
else
{
sum = ScalarType(1) / sum;
}
}
/* Normalize exponentials */
{
/* Loop over row and compute softmax */
int x = 0;
svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
do
{
auto vec_in = svld1(pg, tmp_ptr + x);
auto normalized_value = wrapper::svdup_n(static_cast<ScalarType>(0));
if (is_log)
{
normalized_value = svsub_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum)));
}
else
{
normalized_value = svmul_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum)));
}
svst1(pg, out_ptr + x, normalized_value);
x += wrapper::svcnt<ScalarType>();
pg = wrapper::svwhilelt<ScalarType>(x, input_width);
} while (svptest_any(all_true_pg, pg));
}
},
in_it, max_it, out_it);
}
} // namespace cpu
} // namespace arm_compute
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