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diff --git a/src/core/cpu/kernels/softmax/impl/neon/list.h b/src/core/cpu/kernels/softmax/impl/neon/list.h
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-/*
- * Copyright (c) 2021 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 SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H
-#define SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H
-
-#include "src/core/NEON/NEFixedPoint.h"
-#include "src/core/NEON/NEMath.h"
-#include "src/core/NEON/wrapper/wrapper.h"
-#include "support/SaturateCast.h"
-
-namespace arm_compute
-{
-namespace cpu
-{
-template <typename T>
-void neon_logits_1d_max(const ITensor *in, ITensor *out, const Window &window)
-{
- /** SIMD vector tag type. */
- using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
-
- constexpr int window_step_x = 16 / sizeof(T);
- 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);
-
- const int sum_stages = log2(window_step_x / 2);
- execute_window_loop(win, [&](const Coordinates &)
- {
- // Get pointers
- const auto in_ptr = reinterpret_cast<const T *>(input.ptr());
- const auto out_ptr = reinterpret_cast<T *>(output.ptr());
-
- // Init max value
- auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
- int x = window_start_x;
-
- for(; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const auto current_value = wrapper::vloadq(in_ptr + x);
- vec_max = wrapper::vmax(vec_max, current_value);
- }
- 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);
- }
- T max_val = wrapper::vgetlane(carry_max, 0);
-
- // Compute left-over elements
- for(; x < window_end_x; ++x)
- {
- max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val;
- }
-
- *out_ptr = max_val;
- },
- input, output);
-}
-
-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 <typename T>
-void neon_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);
-
- /** 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 / 2);
-
- 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<T *>(tmp);
-
- T sum{};
- T 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, 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, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}));
- vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements));
- }
- else
- {
- vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{})));
- vec_sum = wrapper::vadd(vec_sum, vec_elements);
- }
- wrapper::vstore(tmp_ptr + x, vec_elements);
- }
-
- /* Reduce sum */
- 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);
-
- /* 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;
- }
- tmp_ptr[x] = element;
- }
-
- if(!is_log)
- {
- sum_inversed = T(1) / sum;
- }
- else
- {
- sum = static_cast<T>(std::log(sum));
- }
- }
-
- /* Normalize exponentials */
- {
- /* Loop over row and compute softmax */
- int x = 0;
- for(; x <= (input_width - vec_size); x += vec_size)
- {
- auto vec_in = wrapper::vloadq(tmp_ptr + x);
- auto normalized_value = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
- if(is_log)
- {
- normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast<T>(sum), ExactTagType{}));
- }
- else
- {
- normalized_value = wrapper::vmul(vec_in, wrapper::vdup_n(static_cast<T>(sum_inversed), ExactTagType{}));
- }
- wrapper::vstore(out_ptr + x, normalized_value);
- }
- /* Run remaining elements */
- for(; x < input_width; ++x)
- {
- if(is_log)
- {
- out_ptr[x] = tmp_ptr[x] - sum;
- }
- else
- {
- out_ptr[x] = tmp_ptr[x] * sum_inversed;
- }
- }
- }
- },
- in_it, max_it, out_it);
-}
-
-} // namespace cpu
-} // namespace arm_compute
-
-#endif /* SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H */
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