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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-20 21:39:25 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-25 16:23:15 +0000 |
commit | 7891a73ef36f4ad7b71069b3c57694f85bb79454 (patch) | |
tree | 5b08692989e28ce63de2937d8d92ea5176589dbe /src/core/cpu/kernels/softmax/impl/neon/list.h | |
parent | a46c9c98c2b1d70acc7c6eee00e2cdc2a1e209a6 (diff) | |
download | ComputeLibrary-7891a73ef36f4ad7b71069b3c57694f85bb79454.tar.gz |
Move CPU/GPU files from Core/Runtime to the respective backend folders
Legacy structure contained two libraries core/runtime with two backends
in each.
We reduce the core/runtime libraries to a single library thus merging
the backend files
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I69545765fe7a730368105cdbd067d3135ec7a174
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6155
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core/cpu/kernels/softmax/impl/neon/list.h')
-rw-r--r-- | src/core/cpu/kernels/softmax/impl/neon/list.h | 388 |
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 */ |