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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.
*/
#ifndef ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H
#define ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H
#include "arm_compute/core/Helpers.h"
#include "src/core/NEON/NEMath.h"
#include "src/core/NEON/wrapper/wrapper.h"
namespace arm_compute
{
namespace cpu
{
#ifdef __aarch64__
namespace
{
// These helper functions are added because vaddv does not exist for fp16,
// and, therefore, is not part of the wrapper::vaddv interface.
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
inline float16_t wrapper_vaddv(const float16x8_t &a, int sum_stages)
{
auto sum_res = wrapper::vpadd(wrapper::vgethigh(a), wrapper::vgetlow(a));
for (int i = 0; i < sum_stages; ++i)
{
sum_res = wrapper::vpadd(sum_res, sum_res);
}
return wrapper::vgetlane(sum_res, 0);
}
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
inline float wrapper_vaddv(const float32x4_t &a, int sum_stages)
{
ARM_COMPUTE_UNUSED(sum_stages);
return wrapper::vaddv(a);
}
} // namespace
#endif // __aarch64__
// The template implementation for float data types is stored in the header file because
// we need all fp16 instantiated code to live in fp16.cpp files.
template <typename T, bool IS_LOG>
void neon_softmax_float(const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window)
{
ARM_COMPUTE_UNUSED(tmp);
const int input_width = in->info()->valid_region().shape.x();
Iterator in_it(in, window);
Iterator out_it(out, window);
/** SIMD vector tag type. */
using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
constexpr int vec_size = 16 / sizeof(T);
const int sum_stages = log2(vec_size >> 1);
const auto beta_vec = wrapper::vdup_n(static_cast<T>(beta), ExactTagType{});
execute_window_loop(
window,
[&](const Coordinates &)
{
/* Get pointers */
const T *in_ptr = reinterpret_cast<const T *>(in_it.ptr());
T *out_ptr = reinterpret_cast<T *>(out_it.ptr());
T max_val;
/* Compute Max */
{
// Init max value
auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
int x = 0;
for (; x <= (input_width - vec_size); x += vec_size)
{
const auto current_value = wrapper::vloadq(in_ptr + x);
vec_max = wrapper::vmax(vec_max, current_value);
}
#ifdef __aarch64__
max_val = wrapper::vmaxv(vec_max);
#else // __aarch64__
auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max));
for (int i = 0; i < sum_stages; ++i)
{
carry_max = wrapper::vpmax(carry_max, carry_max);
}
max_val = wrapper::vgetlane(carry_max, 0);
#endif // __aarch64__
// Compute left-over elements
for (; x < input_width; ++x)
{
max_val = std::max(*(in_ptr + x), max_val);
}
} // compute max
T sum_transformed{};
/* Compute exponentials and sum */
{
/* Get max value */
const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{});
/* Init sum to zero */
auto vec_sum = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
/* Loop over row and compute exponentials and sum */
int x = 0;
for (; x <= (input_width - vec_size); x += vec_size)
{
auto vec_elements = wrapper::vloadq(in_ptr + x);
vec_elements = wrapper::vsub(vec_elements, vec_max);
if (IS_LOG)
{
vec_elements = wrapper::vmul(vec_elements, beta_vec);
vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements));
}
else
{
vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, beta_vec));
vec_sum = wrapper::vadd(vec_sum, vec_elements);
}
wrapper::vstore(out_ptr + x, vec_elements);
}
/* Reduce sum */
T sum{};
#ifdef __aarch64__
sum = wrapper_vaddv(vec_sum, sum_stages);
#else // __aarch64__
auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum));
for (int i = 0; i < sum_stages; ++i)
{
sum_res = wrapper::vpadd(sum_res, sum_res);
}
sum = wrapper::vgetlane(sum_res, 0);
#endif // __aarch64__
/* Run remaining elements */
for (; x < input_width; ++x)
{
T element{};
if (IS_LOG)
{
element = (in_ptr[x] - max_val) * beta;
sum += std::exp(element);
}
else
{
element = std::exp((in_ptr[x] - max_val) * beta);
sum += element;
}
out_ptr[x] = element;
}
if (!IS_LOG)
{
sum_transformed = T(1) / sum;
}
else
{
sum_transformed = static_cast<T>(std::log(sum));
}
} // Compute exponentials and sum
/* Normalize exponentials */
{
const auto sum_vec = wrapper::vdup_n(static_cast<T>(sum_transformed), ExactTagType{});
/* Loop over row and compute softmax */
int x = 0;
for (; x <= (input_width - vec_size); x += vec_size)
{
const auto vec_in = wrapper::vloadq(out_ptr + x);
if (IS_LOG)
{
wrapper::vstore(out_ptr + x, wrapper::vsub(vec_in, sum_vec));
}
else
{
wrapper::vstore(out_ptr + x, wrapper::vmul(vec_in, sum_vec));
}
}
/* Run remaining elements */
for (; x < input_width; ++x)
{
if (IS_LOG)
{
out_ptr[x] = out_ptr[x] - sum_transformed;
}
else
{
out_ptr[x] = out_ptr[x] * sum_transformed;
}
}
} // Normalize exponentials
},
in_it, out_it);
}
template <typename T, bool IS_LOG>
void neon_softmax_quantized(const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window);
} // namespace cpu
} // namespace arm_compute
#endif // ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H
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