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|
/*
* Copyright (c) 2021-2024 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 <typename T, bool IS_LOG>
void neon_softmax_x_quantized(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window)
{
ARM_COMPUTE_UNUSED(axis);
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 input_width = in->info()->valid_region().shape.x();
const float scale_beta = -beta * in->info()->quantization_info().uniform().scale;
const float32x4_t scale_beta_vec = vdupq_n_f32(scale_beta);
Iterator in_it(in, window);
Iterator out_it(out, window);
constexpr int vec_size = 16;
#ifndef __aarch64__
const int sum_stages = log2(vec_size >> 1);
#endif // __aarch64__
using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
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());
float *tmp_ptr = reinterpret_cast<float *>(tmp);
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
float sum_transformed{};
/* Compute exponentials and sum */
{
/* Get max value */
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);
float32x4x4_t 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 float32x4_t 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]));
float sum;
#ifdef __aarch64__
sum = wrapper::vaddv(sum_16_byte);
#else // __aarch64__
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);
#endif // __aarch64__
/* 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_transformed = 256.f / sum;
}
else
{
sum_transformed = std::log(sum);
}
} // Compute exponentials and sum
/* Normalize exponentials */
{
constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value;
const float32x4_t sum_vec = vdupq_n_f32(sum_transformed);
/* 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], sum_vec),
vsubq_f32(vec_in.val[1], sum_vec),
vsubq_f32(vec_in.val[2], sum_vec),
vsubq_f32(vec_in.val[3], sum_vec),
};
normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(sub);
}
else
{
float32x4x4_t mul = {
vmulq_f32(vec_in.val[0], sum_vec),
vmulq_f32(vec_in.val[1], sum_vec),
vmulq_f32(vec_in.val[2], sum_vec),
vmulq_f32(vec_in.val[3], sum_vec),
};
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_transformed);
}
else
{
out_ptr[x] = utils::cast::saturate_cast<T>((tmp_ptr[x] * sum_transformed) -
(is_qasymm8_signed ? 128.f : 0));
}
}
} // Normalize exponentials
},
in_it, out_it);
}
template <typename T, bool IS_LOG>
void neon_softmax_non_x_quantized(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, 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 float scale_beta = -beta * in->info()->quantization_info().uniform().scale;
const float32x4_t scale_beta_vec = vdupq_n_f32(scale_beta);
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;
const ITensorInfo *in_info = in->info();
const ITensorInfo *out_info = out->info();
const int x_width = in_info->valid_region().shape.x();
const int in_axis_stride = in_info->strides_in_bytes()[axis];
const int out_axis_stride = out_info->strides_in_bytes()[axis];
const int tmp_axis_stride = in_axis_stride;
const int axis_width = in_info->dimension(axis);
const int end_actual = std::min(window[0].end(), x_width);
execute_window_loop(
window,
[&](const Coordinates &winCoords)
{
const bool vector_exceeds_bounds = ((winCoords[0] + vec_size) > end_actual);
int num_remaining = (end_actual - winCoords[0]);
int num_remaining_full = num_remaining / 4;
int num_remaining_partial = num_remaining % 4;
/* Get pointers */
const uint8_t *in_ptr = in_it.ptr();
uint8_t *out_ptr = out_it.ptr();
uint8_t *tmp_ptr = reinterpret_cast<uint8_t *>(tmp);
auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
/* Compute Max */
{
if (!vector_exceeds_bounds)
{
int i = 0;
for (; i < axis_width; ++i)
{
const auto current_value =
wrapper::vloadq((i * in_axis_stride) + reinterpret_cast<const T *>(in_ptr));
vec_max = wrapper::vmax(vec_max, current_value);
}
}
else
{
int i = 0;
for (; i < axis_width; ++i)
{
const T *const base_ptr_in = ((i * in_axis_stride) + reinterpret_cast<const T *>(in_ptr));
int j = 0;
for (; j < num_remaining; ++j)
{
const T current_value = *(base_ptr_in + j);
vec_max[j] = std::max(vec_max[j], current_value);
}
}
}
} // Compute Max
float32x4x4_t vec_sum_transformed = {
vdupq_n_f32(0.f),
vdupq_n_f32(0.f),
vdupq_n_f32(0.f),
vdupq_n_f32(0.f),
};
/* Compute exponentials and sum */
{
/* Init sum to zero */
float32x4x4_t vec_sum = vec_sum_transformed;
auto vec_elements = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
float32x4x4_t vec_elements_flt;
if (!vector_exceeds_bounds)
{
int i = 0;
for (; i < axis_width; ++i)
{
vec_elements = wrapper::vloadq((i * in_axis_stride) + reinterpret_cast<const T *>(in_ptr));
vec_elements = wrapper::vqsub(vec_max, vec_elements);
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((i * tmp_axis_stride) + reinterpret_cast<float *>(tmp_ptr), vec_elements_flt);
}
auto vec_256 = wrapper::vdup_n(static_cast<float32_t>(256.f), ExactTagType{});
if (!IS_LOG)
{
vec_sum_transformed.val[0] = wrapper::vdiv(vec_256, vec_sum.val[0]);
vec_sum_transformed.val[1] = wrapper::vdiv(vec_256, vec_sum.val[1]);
vec_sum_transformed.val[2] = wrapper::vdiv(vec_256, vec_sum.val[2]);
vec_sum_transformed.val[3] = wrapper::vdiv(vec_256, vec_sum.val[3]);
}
else
{
vec_sum_transformed.val[0] = wrapper::vlog(vec_sum.val[0]);
vec_sum_transformed.val[1] = wrapper::vlog(vec_sum.val[1]);
vec_sum_transformed.val[2] = wrapper::vlog(vec_sum.val[2]);
vec_sum_transformed.val[3] = wrapper::vlog(vec_sum.val[3]);
}
}
else
{
int i = 0;
for (; i < axis_width; ++i)
{
const T *const base_ptr_in = (i * in_axis_stride) + reinterpret_cast<const T *>(in_ptr);
auto vec_elements = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
//vec_els is functionally redundant but is needed as a workaround for a toolchain bug.
std::vector<T> vec_els(16);
for (int k = 0; k < num_remaining_full; ++k)
{
for (int j = 0; j < 4; ++j)
{
vec_els[k * 4 + j] = *(base_ptr_in + (4 * k + j));
}
}
for (int j = 0; j < num_remaining_partial; ++j)
{
vec_els[num_remaining_full * 4 + j] = *(base_ptr_in + (4 * num_remaining_full + j));
}
for (int q = 0; q < 16; q++)
{
vec_elements[q] = vec_els[q];
}
vec_elements = wrapper::vqsub(vec_max, vec_elements);
float32x4x4_t 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]);
}
float *const base_ptr_tmp = (i * tmp_axis_stride) + reinterpret_cast<float *>(tmp_ptr);
for (int k = 0; k < num_remaining_full; ++k)
{
for (int j = 0; j < 4; ++j)
{
*(base_ptr_tmp + (4 * k + j)) = vec_elements_flt.val[k][j];
}
}
for (int j = 0; j < num_remaining_partial; ++j)
{
*(base_ptr_tmp + (4 * num_remaining_full + j)) =
vec_elements_flt.val[num_remaining_full][j];
}
}
auto vec_256 = wrapper::vdup_n(static_cast<float32_t>(256), ExactTagType{});
if (!IS_LOG)
{
vec_sum_transformed.val[0] = wrapper::vdiv(vec_256, vec_sum.val[0]);
vec_sum_transformed.val[1] = wrapper::vdiv(vec_256, vec_sum.val[1]);
vec_sum_transformed.val[2] = wrapper::vdiv(vec_256, vec_sum.val[2]);
vec_sum_transformed.val[3] = wrapper::vdiv(vec_256, vec_sum.val[3]);
}
else
{
vec_sum_transformed.val[0] = wrapper::vlog(vec_sum.val[0]);
vec_sum_transformed.val[1] = wrapper::vlog(vec_sum.val[1]);
vec_sum_transformed.val[2] = wrapper::vlog(vec_sum.val[2]);
vec_sum_transformed.val[3] = wrapper::vlog(vec_sum.val[3]);
}
}
} // Compute exponentials and sum
/* Normalize exponentials */
{
constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value;
if (!vector_exceeds_bounds)
{
int i = 0;
for (; i < axis_width; ++i)
{
using int_vec_type = wrapper::traits::neon_vector_t<T, 16>;
float32x4x4_t vec_in = vld4q_f32((i * tmp_axis_stride) + reinterpret_cast<float *>(tmp_ptr));
int_vec_type normalized_value{};
if (IS_LOG)
{
const float32x4x4_t sub = {
vsubq_f32(vec_in.val[0], vec_sum_transformed.val[0]),
vsubq_f32(vec_in.val[1], vec_sum_transformed.val[1]),
vsubq_f32(vec_in.val[2], vec_sum_transformed.val[2]),
vsubq_f32(vec_in.val[3], vec_sum_transformed.val[3]),
};
normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(sub);
}
else
{
float32x4x4_t mul = {
vmulq_f32(vec_in.val[0], vec_sum_transformed.val[0]),
vmulq_f32(vec_in.val[1], vec_sum_transformed.val[1]),
vmulq_f32(vec_in.val[2], vec_sum_transformed.val[2]),
vmulq_f32(vec_in.val[3], vec_sum_transformed.val[3]),
};
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((i * out_axis_stride) + reinterpret_cast<T *>(out_ptr), normalized_value);
}
}
else
{
int i = 0;
for (; i < axis_width; ++i)
{
T *const base_ptr_out = (i * out_axis_stride) + reinterpret_cast<T *>(out_ptr);
float *const base_ptr_tmp = (i * tmp_axis_stride) + reinterpret_cast<float *>(tmp_ptr);
if (IS_LOG)
{
for (int k = 0; k < num_remaining_full; ++k)
{
for (int j = 0; j < 4; ++j)
{
*(base_ptr_out + (4 * k + j)) = utils::cast::saturate_cast<T>(
(*(base_ptr_tmp + (4 * k + j)) - vec_sum_transformed.val[k][j]));
}
}
for (int j = 0; j < num_remaining_partial; ++j)
{
*(base_ptr_out + (4 * num_remaining_full + j)) =
utils::cast::saturate_cast<T>(*(base_ptr_tmp + (4 * num_remaining_full + j)) -
vec_sum_transformed.val[num_remaining_full][j]);
}
}
else
{
for (int k = 0; k < num_remaining_full; ++k)
{
for (int j = 0; j < 4; ++j)
{
*(base_ptr_out + (4 * k + j)) = utils::cast::saturate_cast<T>(
*(base_ptr_tmp + (4 * k + j)) * vec_sum_transformed.val[k][j] -
(is_qasymm8_signed ? 128.f : 0));
}
}
for (int j = 0; j < num_remaining_partial; ++j)
{
*(base_ptr_out + (4 * num_remaining_full + j)) =
utils::cast::saturate_cast<T>(*(base_ptr_tmp + (4 * num_remaining_full + j)) *
vec_sum_transformed.val[num_remaining_full][j] -
(is_qasymm8_signed ? 128.f : 0));
}
}
}
}
} // Normalize exponentials
},
in_it, out_it);
}
template void neon_softmax_x_quantized<qasymm8_signed_t, true>(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
template void neon_softmax_x_quantized<qasymm8_signed_t, false>(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
template void neon_softmax_x_quantized<qasymm8_t, true>(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
template void neon_softmax_x_quantized<qasymm8_t, false>(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
template void neon_softmax_non_x_quantized<qasymm8_signed_t, true>(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
template void neon_softmax_non_x_quantized<qasymm8_signed_t, false>(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
template void neon_softmax_non_x_quantized<qasymm8_t, true>(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
template void neon_softmax_non_x_quantized<qasymm8_t, false>(
const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
} // namespace cpu
} // namespace arm_compute
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