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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/neon/impl.h"
#include "support/SaturateCast.h"
namespace arm_compute
{
namespace cpu
{
template void neon_logits_1d_max<qasymm8_signed_t>(const ITensor *in, ITensor *out, const Window &window);
template void neon_logits_1d_max<qasymm8_t>(const ITensor *in, ITensor *out, const Window &window);
template <typename T>
void neon_softmax_logits_1d_quantized(
const ITensor *in, const ITensor *max, void *const tmp, ITensor *out, float beta, bool is_log, const Window &window)
{
static_assert(std::is_same<T, qasymm8_t>::value || std::is_same<T, qasymm8_signed_t>::value,
"quantized type should be either qasymm8_t or qasymm8_signed_t.");
const int start_x = in->info()->valid_region().anchor.x();
const int input_width = in->info()->valid_region().shape.x();
const float scale_beta = -beta * in->info()->quantization_info().uniform().scale;
const auto scale_beta_vec = vdupq_n_f32(scale_beta);
Iterator in_it(in, window);
Iterator max_it(max, window);
Iterator out_it(out, window);
constexpr int vec_size = 16;
execute_window_loop(
window,
[&](const Coordinates &)
{
/* Get pointers */
const auto in_ptr = reinterpret_cast<const T *>(in_it.ptr()) + start_x;
const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x;
const auto tmp_ptr = reinterpret_cast<float *>(tmp);
float sum{};
float sum_inversed{};
/* Compute exponentials and sum */
{
/* Get max value */
const auto max_val = *reinterpret_cast<const T *>(max_it.ptr());
const auto vec_max = wrapper::vdup_n(max_val, wrapper::traits::vector_128_tag{});
/* Init sum to zero */
float32x4x4_t vec_sum = {
vdupq_n_f32(0.f),
vdupq_n_f32(0.f),
vdupq_n_f32(0.f),
vdupq_n_f32(0.f),
};
/* 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::vqsub(vec_max, vec_elements);
auto vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements);
if (is_log)
{
vec_elements_flt.val[0] = vmulq_f32(vec_elements_flt.val[0], scale_beta_vec);
vec_elements_flt.val[1] = vmulq_f32(vec_elements_flt.val[1], scale_beta_vec);
vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec);
vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec);
vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0]));
vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1]));
vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2]));
vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3]));
}
else
{
vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec));
vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec));
vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec));
vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec));
vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]);
vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]);
vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]);
vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]);
}
vst4q_f32(tmp_ptr + x, vec_elements_flt);
}
/* Reduce sum */
const auto sum_16_byte =
vaddq_f32(vaddq_f32(vec_sum.val[0], vec_sum.val[1]), vaddq_f32(vec_sum.val[2], vec_sum.val[3]));
auto sum_res = vpadd_f32(vget_high_f32(sum_16_byte), vget_low_f32(sum_16_byte));
sum_res = vpadd_f32(sum_res, sum_res);
sum = wrapper::vgetlane(sum_res, 0);
/* Run remaining elements */
for (; x < input_width; ++x)
{
float element{};
if (is_log)
{
element = (max_val - in_ptr[x]) * scale_beta;
sum += std::exp(element);
}
else
{
element = std::exp((max_val - in_ptr[x]) * scale_beta);
sum += element;
}
tmp_ptr[x] = element;
}
if (!is_log)
{
sum_inversed = 256.f / sum;
}
else
{
sum = std::log(sum);
}
}
/* Normalize exponentials */
{
constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value;
/* Loop over row and compute softmax */
int x = 0;
for (; x <= (input_width - vec_size); x += vec_size)
{
using int_vec_type = wrapper::traits::neon_vector_t<T, 16>;
float32x4x4_t vec_in = vld4q_f32(tmp_ptr + x);
int_vec_type normalized_value{};
if (is_log)
{
const float32x4x4_t sub = {
vsubq_f32(vec_in.val[0], vdupq_n_f32(sum)),
vsubq_f32(vec_in.val[1], vdupq_n_f32(sum)),
vsubq_f32(vec_in.val[2], vdupq_n_f32(sum)),
vsubq_f32(vec_in.val[3], vdupq_n_f32(sum)),
};
normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(sub);
}
else
{
float32x4x4_t mul = {
vmulq_f32(vec_in.val[0], vdupq_n_f32(sum_inversed)),
vmulq_f32(vec_in.val[1], vdupq_n_f32(sum_inversed)),
vmulq_f32(vec_in.val[2], vdupq_n_f32(sum_inversed)),
vmulq_f32(vec_in.val[3], vdupq_n_f32(sum_inversed)),
};
if (is_qasymm8_signed)
{
const auto offset_vec = wrapper::vdup_n(128.f, wrapper::traits::vector_128_tag{});
mul.val[0] = wrapper::vsub(mul.val[0], offset_vec);
mul.val[1] = wrapper::vsub(mul.val[1], offset_vec);
mul.val[2] = wrapper::vsub(mul.val[2], offset_vec);
mul.val[3] = wrapper::vsub(mul.val[3], offset_vec);
}
normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(mul);
}
wrapper::vstore(out_ptr + x, normalized_value);
}
/* Run remaining elements */
for (; x < input_width; ++x)
{
if (is_log)
{
out_ptr[x] = utils::cast::saturate_cast<T>(tmp_ptr[x] - sum);
}
else
{
out_ptr[x] = utils::cast::saturate_cast<T>((tmp_ptr[x] * sum_inversed) -
(is_qasymm8_signed ? 128.f : 0));
}
}
}
},
in_it, max_it, out_it);
}
template void neon_softmax_logits_1d_quantized<qasymm8_signed_t>(const ITensor *in,
const ITensor *max,
void *const tmp,
ITensor *out,
float beta,
bool is_log,
const Window &window);
template void neon_softmax_logits_1d_quantized<qasymm8_t>(const ITensor *in,
const ITensor *max,
void *const tmp,
ITensor *out,
float beta,
bool is_log,
const Window &window);
} // namespace cpu
} // namespace arm_compute
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