Enable fat binary support

Changes our build system to allow building both Neon(TM) and SVE
kernels and package them in the same binary. This will allow
runtime selection of the underlying architecture.

Adds new build option, fat_binary, for enabling this feature.

Change-Id: I8e8386149773ce28e071a2fb7ddd8c8ae0f28a4a
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5704
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 0 insertions, 388 deletions

diff --git a/src/core/cpu/kernels/softmax/impl/NEON/list.h b/src/core/cpu/kernels/softmax/impl/NEON/list.h
deleted file mode 100644
index 5ebee31272..0000000000
--- a/src/core/cpu/kernels/softmax/impl/NEON/list.h
+++ /dev/null
@@ -1,388 +0,0 @@
-/*
- * 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 */