From 7891a73ef36f4ad7b71069b3c57694f85bb79454 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 20 Aug 2021 21:39:25 +0100 Subject: 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 Change-Id: I69545765fe7a730368105cdbd067d3135ec7a174 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6155 Comments-Addressed: Arm Jenkins Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins --- src/cpu/kernels/pool2d/neon/fp16.cpp | 317 +++++++++ src/cpu/kernels/pool2d/neon/fp32.cpp | 314 +++++++++ src/cpu/kernels/pool2d/neon/list.h | 97 +++ src/cpu/kernels/pool2d/neon/nchw/all.cpp | 700 ++++++++++++++++++++ src/cpu/kernels/pool2d/neon/qasymm8.cpp | 41 ++ src/cpu/kernels/pool2d/neon/qasymm8_signed.cpp | 41 ++ src/cpu/kernels/pool2d/neon/quantized.h | 863 +++++++++++++++++++++++++ 7 files changed, 2373 insertions(+) create mode 100644 src/cpu/kernels/pool2d/neon/fp16.cpp create mode 100644 src/cpu/kernels/pool2d/neon/fp32.cpp create mode 100644 src/cpu/kernels/pool2d/neon/list.h create mode 100644 src/cpu/kernels/pool2d/neon/nchw/all.cpp create mode 100644 src/cpu/kernels/pool2d/neon/qasymm8.cpp create mode 100644 src/cpu/kernels/pool2d/neon/qasymm8_signed.cpp create mode 100644 src/cpu/kernels/pool2d/neon/quantized.h (limited to 'src/cpu/kernels/pool2d') diff --git a/src/cpu/kernels/pool2d/neon/fp16.cpp b/src/cpu/kernels/pool2d/neon/fp16.cpp new file mode 100644 index 0000000000..534d24ab49 --- /dev/null +++ b/src/cpu/kernels/pool2d/neon/fp16.cpp @@ -0,0 +1,317 @@ +/* + * 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. + */ +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/pool2d/neon/list.h" + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) + +namespace arm_compute +{ +namespace cpu +{ +namespace +{ +void pooling2_f16_maxpool_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 8; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(src, window_src); + Iterator out(dst0, window_out); + Iterator indices(dst1, window_out); + + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + + const int pad_right = src->info()->padding().right; + const int pad_left = src->info()->padding().left; + const int pad_horizontal = pad_right + pad_left; + const int in_stride_y = static_cast(src->info()->strides_in_bytes().y()); + const int in_stride_z = static_cast(src->info()->strides_in_bytes().z()); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast(src->info()->strides_in_bytes().z()); + const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z()); + const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z()); + const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z()); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + const auto in_x0_ptr = reinterpret_cast(in.ptr() + in_x0_offset) + x_off; + const auto in_x1_ptr = reinterpret_cast(in.ptr() + in_x1_offset) + x_off; + const auto in_x2_ptr = reinterpret_cast(in.ptr() + in_x2_offset) + x_off; + const auto in_x3_ptr = reinterpret_cast(in.ptr() + in_x3_offset) + x_off; + const auto v_x0 = vld1q_f16(in_x0_ptr); + const auto v_x1 = vld1q_f16(in_x1_ptr); + const auto v_x2 = vld1q_f16(in_x2_ptr); + const auto v_x3 = vld1q_f16(in_x3_ptr); + float16x8_t vres = vmaxq_f16(vmaxq_f16(v_x2, v_x3), vmaxq_f16(v_x0, v_x1)); + // Store result + vst1q_f16(reinterpret_cast(out.ptr()) + x_off, vres); + + const uint32_t offset_base = offset_no_padding(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_horizontal; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_horizontal * src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_horizontal; + const uint32x4_t voffset_x0_0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 }; + const uint32x4_t voffset_x0_1 = { offset_x0 + 4, offset_x0 + 5, offset_x0 + 6, offset_x0 + 7 }; + const uint16x8_t voffset_x0 = vcombine_u16(vmovn_u32(voffset_x0_0), vmovn_u32(voffset_x0_1)); + const uint32x4_t voffset_x1_0 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 }; + const uint32x4_t voffset_x1_1 = { offset_x1 + 4, offset_x1 + 5, offset_x1 + 6, offset_x1 + 7 }; + const uint16x8_t voffset_x1 = vcombine_u16(vmovn_u32(voffset_x1_0), vmovn_u32(voffset_x1_1)); + const uint32x4_t voffset_x2_0 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 }; + const uint32x4_t voffset_x2_1 = { offset_x2 + 4, offset_x2 + 5, offset_x2 + 6, offset_x2 + 7 }; + const uint16x8_t voffset_x2 = vcombine_u16(vmovn_u32(voffset_x2_0), vmovn_u32(voffset_x2_1)); + const uint32x4_t voffset_x3_0 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3 }; + const uint32x4_t voffset_x3_1 = { offset_x3 + 4, offset_x3 + 5, offset_x3 + 6, offset_x3 + 7 }; + const uint16x8_t voffset_x3 = vcombine_u16(vmovn_u32(voffset_x3_0), vmovn_u32(voffset_x3_1)); + const uint16x8_t tmp_indices0 = vbslq_u16(vcgeq_f16(v_x0, v_x1), voffset_x0, voffset_x1); + const uint16x8_t tmp_indices1 = vbslq_u16(vcgeq_f16(v_x2, v_x3), voffset_x2, voffset_x3); + const uint16x8_t tmp_indices2 = vbslq_u16(vcgeq_f16(vmaxq_f16(v_x0, v_x1), vmaxq_f16(v_x2, v_x3)), tmp_indices0, tmp_indices1); + const uint32x4_t tmp_indeces3_0 = vmovl_u16(vget_low_u16(tmp_indices2)); + const uint32x4_t tmp_indeces3_1 = vmovl_u16(vget_high_u16(tmp_indices2)); + // Store indicies + vst1q_u32(reinterpret_cast(indices.ptr()) + x_off, tmp_indeces3_0); + vst1q_u32(reinterpret_cast(indices.ptr() + 16) + x_off, tmp_indeces3_1); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + const auto x0 = *(reinterpret_cast(in.ptr() + in_x0_offset) + x_off); + const auto x1 = *(reinterpret_cast(in.ptr() + in_x1_offset) + x_off); + const auto x2 = *(reinterpret_cast(in.ptr() + in_x2_offset) + x_off); + const auto x3 = *(reinterpret_cast(in.ptr() + in_x3_offset) + x_off); + float16_t res = std::max(std::max(x2, x3), std::max(x0, x1)); + + // Store result + *(reinterpret_cast(out.ptr()) + x_off) = res; + + const uint32_t offset_base = offset_no_padding(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_horizontal; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_horizontal * src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_horizontal; + const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1; + const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3; + const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1; + + // Store indices + *(reinterpret_cast(indices.ptr()) + x_off) = tmp_idx2; + } + }, + in, out, indices); +} +} + +void poolingMxN_fp16_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + if(pool_info.pool_size == Size2D(2, 2) && pool_info.pool_type == PoolingType::MAX && dst1) + { + pooling2_f16_maxpool_indices(src, dst0, dst1, pool_info, window_src, window); + } + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 8; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(src, window_src); + Iterator out(dst0, window_out); + + const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width; + const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + float16x8_t vres; + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float16x8_t scale_v = vdupq_n_f16(scale); + + // Perform pooling + vres = vdupq_n_f16(0.0f); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float16x8_t data = vld1q_f16(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pool_info.pool_type == PoolingType::L2) + { + vres = vaddq_f16(vres, vmulq_f16(data, data)); + } + else + { + vres = vaddq_f16(vres, data); + } + } + } + // Divide by scale + vres = vmulq_f16(vres, scale_v); + } + else + { + vres = vdupq_n_f16(std::numeric_limits::lowest()); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float16x8_t data = vld1q_f16(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + vres = vmaxq_f16(vres, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + float16x8_t sqrt_reciprocal = vrsqrteq_f16(vres); + vres = vmulq_f16(vres, vmulq_f16(vrsqrtsq_f16(vmulq_f16(vres, sqrt_reciprocal), sqrt_reciprocal), sqrt_reciprocal)); + } + + // Store result + vst1q_f16(reinterpret_cast(out.ptr()) + x_off, vres); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + float16_t res = 0.0f; + + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float16_t scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pool_info.pool_type == PoolingType::L2) + { + res += data * data; + } + else + { + res += data; + } + } + } + + // Divide by scale + res *= scale; + } + else + { + res = std::numeric_limits::lowest(); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float16_t data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + res = std::max(res, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast(out.ptr()) + x_off) = res; + } + }, + in, out); +} +} // namespace cpu +} // namespace arm_compute + +#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */ \ No newline at end of file diff --git a/src/cpu/kernels/pool2d/neon/fp32.cpp b/src/cpu/kernels/pool2d/neon/fp32.cpp new file mode 100644 index 0000000000..26a32ed9d4 --- /dev/null +++ b/src/cpu/kernels/pool2d/neon/fp32.cpp @@ -0,0 +1,314 @@ +/* + * 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. + */ +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/pool2d/neon/list.h" + +namespace arm_compute +{ +namespace cpu +{ +namespace +{ +void pooling2_f32_maxpool_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 4; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(src, window_src); + Iterator out(dst0, window_out); + Iterator indices(dst1, window_out); + + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + + float32x4_t vres; + float res; + + const int pad_right = src->info()->padding().right; + const int pad_left = src->info()->padding().left; + const int pad_horizontal = pad_right + pad_left; + const int in_stride_y = static_cast(src->info()->strides_in_bytes().y()); + const int in_stride_z = static_cast(src->info()->strides_in_bytes().z()); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + + const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast(src->info()->strides_in_bytes().z()); + const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z()); + const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z()); + const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z()); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + const auto in_x0_ptr = reinterpret_cast(in.ptr() + in_x0_offset); + const auto in_x1_ptr = reinterpret_cast(in.ptr() + in_x1_offset); + const auto in_x2_ptr = reinterpret_cast(in.ptr() + in_x2_offset); + const auto in_x3_ptr = reinterpret_cast(in.ptr() + in_x3_offset); + const auto v_x0 = vld1q_f32(in_x0_ptr + x_off); + const auto v_x1 = vld1q_f32(in_x1_ptr + x_off); + const auto v_x2 = vld1q_f32(in_x2_ptr + x_off); + const auto v_x3 = vld1q_f32(in_x3_ptr + x_off); + vres = vmaxq_f32(vmaxq_f32(v_x2, v_x3), vmaxq_f32(v_x0, v_x1)); + // Store result + vst1q_f32(reinterpret_cast(out.ptr()) + x_off, vres); + + const uint32_t offset_base = offset_no_padding(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_horizontal; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_horizontal * src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_horizontal; + const uint32x4_t voffset_x0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 }; + const uint32x4_t voffset_x1 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 }; + const uint32x4_t voffset_x2 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 }; + const uint32x4_t voffset_x3 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3 }; + const uint32x4_t tmp_indices0 = vbslq_u32(vcgeq_f32(v_x0, v_x1), voffset_x0, voffset_x1); + const uint32x4_t tmp_indices1 = vbslq_u32(vcgeq_f32(v_x2, v_x3), voffset_x2, voffset_x3); + const uint32x4_t tmp_indices2 = vbslq_u32(vcgeq_f32(vmaxq_f32(v_x0, v_x1), vmaxq_f32(v_x2, v_x3)), tmp_indices0, tmp_indices1); + + // Store indices + vst1q_u32(reinterpret_cast(indices.ptr()) + x_off, tmp_indices2); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + const auto x0 = *(reinterpret_cast(in.ptr() + in_x0_offset) + x_off); + const auto x1 = *(reinterpret_cast(in.ptr() + in_x1_offset) + x_off); + const auto x2 = *(reinterpret_cast(in.ptr() + in_x2_offset) + x_off); + const auto x3 = *(reinterpret_cast(in.ptr() + in_x3_offset) + x_off); + res = std::max(std::max(x2, x3), std::max(x0, x1)); + + // Store result + *(reinterpret_cast(out.ptr()) + x_off) = res; + + const uint32_t offset_base = offset_no_padding(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_horizontal; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_horizontal * src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_horizontal; + const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1; + const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3; + const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1; + + // Store indices + *(reinterpret_cast(indices.ptr()) + x_off) = tmp_idx2; + } + }, + in, out, indices); +} +} + +void poolingMxN_fp32_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + if(pool_info.pool_size == Size2D(2, 2) && pool_info.pool_type == PoolingType::MAX && dst1) + { + pooling2_f32_maxpool_indices(src, dst0, dst1, pool_info, window_src, window); + } + else + { + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 4; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(src, window_src); + Iterator out(dst0, window_out); + + const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width; + const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + float32x4_t vres; + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float32x4_t scale_v = vdupq_n_f32(scale); + + // Perform pooling + vres = vdupq_n_f32(0.0f); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float32x4_t data = vld1q_f32(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pool_info.pool_type == PoolingType::L2) + { + vres = vmlaq_f32(vres, data, data); + } + else + { + vres = vaddq_f32(vres, data); + } + } + } + // Divide by scale + vres = vmulq_f32(vres, scale_v); + } + else + { + vres = vdupq_n_f32(std::numeric_limits::lowest()); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float32x4_t data = vld1q_f32(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + vres = vmaxq_f32(vres, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + float32x4_t l2_res = { static_cast(sqrt(vgetq_lane_f32(vres, 0))), + static_cast(sqrt(vgetq_lane_f32(vres, 1))), + static_cast(sqrt(vgetq_lane_f32(vres, 2))), + static_cast(sqrt(vgetq_lane_f32(vres, 3))) + }; + vres = l2_res; + } + + // Store result + vst1q_f32(reinterpret_cast(out.ptr()) + x_off, vres); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + float res = 0.0f; + + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pool_info.pool_type == PoolingType::L2) + { + res += data * data; + } + else + { + res += data; + } + } + } + + // Divide by scale + res *= scale; + } + else + { + res = std::numeric_limits::lowest(); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + res = std::max(res, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast(out.ptr()) + x_off) = res; + } + }, + in, out); + } +} +} // namespace cpu +} // namespace arm_compute \ No newline at end of file diff --git a/src/cpu/kernels/pool2d/neon/list.h b/src/cpu/kernels/pool2d/neon/list.h new file mode 100644 index 0000000000..b79323213e --- /dev/null +++ b/src/cpu/kernels/pool2d/neon/list.h @@ -0,0 +1,97 @@ +/* + * 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_POOLING_LIST_H +#define SRC_CORE_NEON_KERNELS_POOLING_LIST_H + +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "src/cpu/kernels/pool2d/neon/quantized.h" +#include + +namespace arm_compute +{ +namespace cpu +{ +#define DECLARE_POOLING_KERNEL(func_name) \ + void func_name(const ITensor *src0, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &, const Window &window_src, const Window &window) + +DECLARE_POOLING_KERNEL(poolingMxN_qasymm8_neon_nhwc); +DECLARE_POOLING_KERNEL(poolingMxN_qasymm8_signed_neon_nhwc); +DECLARE_POOLING_KERNEL(poolingMxN_fp16_neon_nhwc); +DECLARE_POOLING_KERNEL(poolingMxN_fp32_neon_nhwc); + +#if defined(ENABLE_NCHW_KERNELS) + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) +DECLARE_POOLING_KERNEL(pooling2_fp16_neon_nchw); +DECLARE_POOLING_KERNEL(pooling3_fp16_neon_nchw); +DECLARE_POOLING_KERNEL(poolingMxN_fp16_neon_nchw); +#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */ + +DECLARE_POOLING_KERNEL(pooling2_fp32_neon_nchw); +DECLARE_POOLING_KERNEL(pooling3_fp32_neon_nchw); +DECLARE_POOLING_KERNEL(pooling7_fp32_neon_nchw); +DECLARE_POOLING_KERNEL(poolingMxN_fp32_neon_nchw); +#endif /* defined(ENABLE_NCHW_KERNELS) */ + +#undef DECLARE_POOLING_KERNEL + +template +inline uint32_t offset_no_padding(uint32_t padded_offset, const Coordinates &id, const ITensorInfo &info, int pool_stride_x, int pool_stride_y, DataLayout data_layout) +{ + const int pad_left = info.padding().left; + const int pad_right = info.padding().right; + const int pad_top = info.padding().top; + const int pad_bottom = info.padding().bottom; + const int in_stride_y = static_cast(info.strides_in_bytes().y()); + const int in_stride_w = static_cast(info.strides_in_bytes()[3]); + const int pad_horiz = pad_left + pad_right; + const int pad_vert = pad_top + pad_bottom; + + if(data_layout == DataLayout::NCHW) + { + const uint32_t offset_base = padded_offset + - sizeof(T) * pad_horiz * id.y() * pool_stride_y /* subtract padding elems per row */ + - pad_top * sizeof(T) /* top padding */ + - sizeof(T) * pad_horiz * info.tensor_shape()[1] * id.z() - pad_vert * in_stride_y * id.z() /* for each Z plane there are height*pad_right padding elems */ + - in_stride_w * id[3]; + + return offset_base; + } + else + { + const uint32_t offset_base = padded_offset + - sizeof(T) * pad_horiz * id.y() * pool_stride_x // subtract padding elems per row + - pad_top * sizeof(T) // top padding + - sizeof(T) * pad_horiz * info.tensor_shape()[1] * id.z() * pool_stride_y // for each Z plane there are width*pad_right padding elems + - in_stride_w * id[3]; + + return offset_base; + } +} +} // namespace cpu +} // namespace arm_compute + +#endif // SRC_CORE_NEON_KERNELS_POOLING_LIST_H \ No newline at end of file diff --git a/src/cpu/kernels/pool2d/neon/nchw/all.cpp b/src/cpu/kernels/pool2d/neon/nchw/all.cpp new file mode 100644 index 0000000000..3ca7701087 --- /dev/null +++ b/src/cpu/kernels/pool2d/neon/nchw/all.cpp @@ -0,0 +1,700 @@ +/* + * 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. + */ +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/pool2d/neon/list.h" + +#ifdef ENABLE_NCHW_KERNELS +namespace arm_compute +{ +namespace cpu +{ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +void pooling3_fp16_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + ARM_COMPUTE_UNUSED(pool_info.pool_type); + ARM_COMPUTE_UNUSED(pool_info.exclude_padding); + + Iterator in(src, window_src); + Iterator out(dst0, window); + + constexpr const int pool_size = 3; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + const unsigned char *const src_top_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top))); + const unsigned char *const src_middle_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1)); + const unsigned char *const src_bottom_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 2)); + + execute_window_loop(window, [&](const Coordinates & id) + { + float16x4_t top_data = vld1_f16(reinterpret_cast(src_top_ptr + in.offset())); + float16x4_t middle_data = vld1_f16(reinterpret_cast(src_middle_ptr + in.offset())); + float16x4_t bottom_data = vld1_f16(reinterpret_cast(src_bottom_ptr + in.offset())); + float16x4_t res = {}; + + // Get power of 2 in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + top_data = vmul_f16(top_data, top_data); + middle_data = vmul_f16(middle_data, middle_data); + bottom_data = vmul_f16(bottom_data, bottom_data); + } + + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float16x4_t scale_v = vdup_n_f16(scale); + // Perform pooling + const float16x4_t sum_data = vadd_f16(vadd_f16(top_data, bottom_data), middle_data); + res = vpadd_f16(vset_lane_f16(0.f, sum_data, 3), sum_data); + res = vmul_f16(vpadd_f16(res, res), scale_v); + } + else + { + const float16x4_t max_data = vmax_f16(vmax_f16(top_data, bottom_data), middle_data); + res = vpmax_f16(vset_lane_f16(-std::numeric_limits::max(), max_data, 3), max_data); + res = vpmax_f16(res, res); + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + res = vinv_f16(vinvsqrt_f16(res)); + } + + *(reinterpret_cast(out.ptr())) = vget_lane_f16(res, 0); + }, + in, out); +} + +template +inline typename std::enable_if::value, float32x2_t>::type +f16_to_f32(float16x4_t in) +{ + float32x2_t out = { static_cast(vget_lane_f16(in, 0)), static_cast(vget_lane_f16(in, 1)) }; + return out; +} +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +template +inline typename std::enable_if::value, float32x2_t>::type +f16_to_f32(float32x2_t in) +{ + return in; +} + +template +void pooling2_nchw_maxpool_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + Iterator in(src, window_src); + Iterator out(dst0, window); + Iterator indices(dst1, window); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const uint8_t *const src_top_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top))); + const uint8_t *const src_bottom_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1)); + const int pad_left = src->info()->padding().left; + const int pad_right = src->info()->padding().right; + const int in_stride_y = static_cast(src->info()->strides_in_bytes().y()); + + execute_window_loop(window, [&](const Coordinates & id) + { + auto top_data = wrapper::vload(reinterpret_cast(src_top_ptr + in.offset())); + auto bottom_data = wrapper::vload(reinterpret_cast(src_bottom_ptr + in.offset())); + float32x2_t top_data_f32 = f16_to_f32(top_data); + float32x2_t bottom_data_f32 = f16_to_f32(bottom_data); + + // Calculate max data, compare top first, then bottom, to make sue the first max is recorded. + const float32x2_t max_data_top = vpmax_f32(top_data_f32, top_data_f32); + const float32x2_t max_data_bottom = vpmax_f32(bottom_data_f32, bottom_data_f32); + const float32x2_t max_data = vmax_f32(max_data_top, max_data_bottom); + *(reinterpret_cast(out.ptr())) = static_cast(vget_lane_f32(max_data, 0)); + + // Calculate max data indice, which will be used in max unpool. + const uint32_t offset_base = offset_no_padding(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NCHW); + const uint32_t offset_top = (uint32_t)(offset_base / sizeof(T)); + const uint32_t offset_bottom = offset_top + in_stride_y / sizeof(T) - pad_right - pad_left; + const uint32x2_t voffset_top = { offset_top, offset_top + 1u }; + const uint32x2_t voffset_bottom = { offset_bottom, offset_bottom + 1u }; + const uint32x2_t tmp_indices_top = vbsl_u32(vcge_f32(top_data_f32, vrev64_f32(top_data_f32)), voffset_top, vrev64_u32(voffset_top)); + const uint32x2_t tmp_indices_bottom = vbsl_u32(vcge_f32(bottom_data_f32, vrev64_f32(bottom_data_f32)), voffset_bottom, vrev64_u32(voffset_bottom)); + *(reinterpret_cast(indices.ptr())) = vget_lane_u32(vbsl_u32(vcge_f32(max_data_top, max_data_bottom), tmp_indices_top, tmp_indices_bottom), 0); + }, + in, out, indices); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +void pooling2_fp16_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + if(pool_info.pool_type == PoolingType::MAX && dst1) + { + pooling2_nchw_maxpool_indices(src, dst0, dst1, pool_info, window_src, window); + } + else + { + Iterator in(src, window_src); + Iterator out(dst0, window); + constexpr int pool_size = 2; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x, pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + const unsigned char *const src_top_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top))); + const unsigned char *const src_bottom_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1)); + + execute_window_loop(window, [&](const Coordinates & id) + { + float16x4_t top_data = vld1_f16(reinterpret_cast(src_top_ptr + in.offset())); + float16x4_t bottom_data = vld1_f16(reinterpret_cast(src_bottom_ptr + in.offset())); + float16x4_t res = {}; + + // Get power of 2 in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + top_data = vmul_f16(top_data, top_data); + bottom_data = vmul_f16(bottom_data, bottom_data); + } + + if(pool_info.pool_type != PoolingType::MAX) + { + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float16x4_t scale_v = vdup_n_f16(scale); + + const float16x4_t sum_data = vadd_f16(top_data, bottom_data); + res = vmul_f16(vpadd_f16(sum_data, sum_data), scale_v); + } + else + { + const float16x4_t max_data = vmax_f16(top_data, bottom_data); + res = vpmax_f16(max_data, max_data); + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + res = vinv_f16(vinvsqrt_f16(res)); + } + + // Store result + *(reinterpret_cast(out.ptr())) = vget_lane_f16(res, 0); + }, + in, out); + } +} + +void poolingMxN_fp16_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + Iterator in(src, window_src); + Iterator out(dst0, window); + + const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().x() : pool_info.pool_size.width; + const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.height; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + execute_window_loop(window, [&](const Coordinates & id) + { + float16_t res = 0.0f; + float16x8_t vres = vdupq_n_f16(0.0f); + + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + // Perform pooling + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 8); x += 8) + { + const float16x8_t data = vld1q_f16(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + + // Get power of 2 in case of l2 pooling and accumulate + if(pool_info.pool_type == PoolingType::L2) + { + vres = vaddq_f16(vres, vmulq_f16(data, data)); + } + else + { + vres = vaddq_f16(vres, data); + } + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + float16_t data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + + (y - pool_pad_top) * static_cast(src->info()->strides_in_bytes().y()))); + + // Get power of 2 in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + data *= data; + } + + res += data; + } + } + + // Reduction + float16x4_t tmp = vpadd_f16(vget_high_f16(vres), vget_low_f16(vres)); + res += vget_lane_f16(tmp, 0); + res += vget_lane_f16(tmp, 1); + res += vget_lane_f16(tmp, 2); + res += vget_lane_f16(tmp, 3); + + // Divide by scale + res *= scale; + } + else + { + float16x8_t vres = vdupq_n_f16(std::numeric_limits::lowest()); + res = std::numeric_limits::lowest(); + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 8); x += 8) + { + const float16x8_t data = vld1q_f16(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + vres = vmaxq_f16(vres, data); + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + const float16_t data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + + (y - pool_pad_top) * static_cast(src->info()->strides_in_bytes().y()))); + res = std::max(res, data); + } + } + + float16x4_t tmp = vpmax_f16(vget_high_f16(vres), vget_low_f16(vres)); + res = std::max(res, vget_lane_f16(tmp, 0)); + res = std::max(res, vget_lane_f16(tmp, 1)); + res = std::max(res, vget_lane_f16(tmp, 2)); + res = std::max(res, vget_lane_f16(tmp, 3)); + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast(out.ptr())) = res; + }, + in, out); +} +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +void poolingMxN_fp32_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + Iterator in(src, window_src); + Iterator out(dst0, window); + + const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().x() : pool_info.pool_size.width; + const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.height; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + execute_window_loop(window, [&](const Coordinates & id) + { + float res = 0.0f; + + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + // Perform pooling + float32x4_t vres = vdupq_n_f32(0.0f); + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 4); x += 4) + { + const float32x4_t data = vld1q_f32(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + + // Get power of 2 in case of l2 pooling and accumulate + if(pool_info.pool_type == PoolingType::L2) + { + vres = vmlaq_f32(vres, data, data); + } + else + { + vres = vaddq_f32(vres, data); + } + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + float data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + + // Get power of 2 in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + data *= data; + } + + res += data; + } + } + +#if defined(__aarch64__) + // Reduction operation available on 64 bit architectures only + res += vaddvq_f32(vres); +#else // __aarch64__ + // Reduction + float32x2_t tmp = vpadd_f32(vget_high_f32(vres), vget_low_f32(vres)); + tmp = vpadd_f32(tmp, tmp); + + res += vget_lane_f32(tmp, 0); +#endif // __aarch64__ + // Divide by scale + res *= scale; + } + else + { + float32x4_t vres = vdupq_n_f32(std::numeric_limits::lowest()); + res = std::numeric_limits::lowest(); + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 4); x += 4) + { + const float32x4_t data = vld1q_f32(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + vres = vmaxq_f32(vres, data); + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + const float data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + res = std::max(res, data); + } + } +#if defined(__aarch64__) + // Reduction operation available on 64 bit architectures only + res = std::max(vmaxvq_f32(vres), res); +#else // __aarch64__ + float32x2_t tmp = vpmax_f32(vget_high_f32(vres), vget_low_f32(vres)); + tmp = vpmax_f32(tmp, tmp); + + res = std::max(res, vget_lane_f32(tmp, 0)); +#endif // __aarch64__ + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast(out.ptr())) = res; + }, + in, out); +} + +void pooling2_fp32_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + if(pool_info.pool_type == PoolingType::MAX && dst1) + { + pooling2_nchw_maxpool_indices(src, dst0, dst1, pool_info, window_src, window); + } + else + { + Iterator in(src, window_src); + Iterator out(dst0, window); + constexpr int pool_size = 2; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + const uint8_t *const src_top_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top))); + const uint8_t *const src_bottom_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1)); + + execute_window_loop(window, [&](const Coordinates & id) + { + const auto in_top_ptr = reinterpret_cast(src_top_ptr + in.offset()); + const auto in_bottom_ptr = reinterpret_cast(src_bottom_ptr + in.offset()); + float32x2_t top_data = vld1_f32(in_top_ptr); + float32x2_t bottom_data = vld1_f32(in_bottom_ptr); + float32x2_t res = {}; + float final_res = 0; + // Get power of 2 in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + top_data = vmul_f32(top_data, top_data); + bottom_data = vmul_f32(bottom_data, bottom_data); + } + + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float32x2_t scale_v = vdup_n_f32(scale); + + // Perform pooling + const float32x2_t sum_data = vadd_f32(top_data, bottom_data); + res = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v); + } + else + { + const float32x2_t max_data = vmax_f32(top_data, bottom_data); + res = vpmax_f32(max_data, max_data); + } + final_res = vget_lane_f32(res, 0); + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + final_res = sqrt(final_res); + } + + // Store result + *(reinterpret_cast(out.ptr())) = final_res; + }, + in, out); + } +} + +void pooling3_fp32_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + Iterator in(src, window_src); + Iterator out(dst0, window); + + constexpr const int pool_size = 3; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + const uint8_t *const src_top_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top))); + const uint8_t *const src_middle_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1)); + const uint8_t *const src_bottom_ptr = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 2)); + + execute_window_loop(window, [&](const Coordinates & id) + { + float32x4_t top_data = vld1q_f32(reinterpret_cast(src_top_ptr + in.offset())); + float32x4_t middle_data = vld1q_f32(reinterpret_cast(src_middle_ptr + in.offset())); + float32x4_t bottom_data = vld1q_f32(reinterpret_cast(src_bottom_ptr + in.offset())); + float32x2_t res = {}; + float final_res = 0; + + // Get power of 2 in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + top_data = vmulq_f32(top_data, top_data); + middle_data = vmulq_f32(middle_data, middle_data); + bottom_data = vmulq_f32(bottom_data, bottom_data); + } + + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float32x2_t scale_v = vdup_n_f32(scale); + + // Perform pooling + const float32x4_t sum_data = vaddq_f32(vaddq_f32(top_data, bottom_data), middle_data); + res = vpadd_f32(vget_high_f32(vsetq_lane_f32(0.f, sum_data, 3)), vget_low_f32(sum_data)); + res = vmul_f32(vpadd_f32(res, res), scale_v); + } + else + { + const float32x4_t max_data = vmaxq_f32(vmaxq_f32(top_data, bottom_data), middle_data); + res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits::max(), max_data, 3)), vget_low_f32(max_data)); + res = vpmax_f32(res, res); + } + final_res = vget_lane_f32(res, 0); + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + final_res = sqrt(final_res); + } + + // Store result + *(reinterpret_cast(out.ptr())) = final_res; + }, + in, out); +} + +void pooling7_fp32_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + Iterator in(src, window_src); + Iterator out(dst0, window); + + constexpr const int pool_size = 7; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + std::array src_ptrs{ {} }; + for(int i = 0; i < pool_size; ++i) + { + src_ptrs[i] = src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + i)); + } + + execute_window_loop(window, [&](const Coordinates & id) + { + float32x2_t res = {}; + float final_res = 0.f; + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float32x2_t scale_v = vdup_n_f32(scale); + + // Perform pooling + float32x4x2_t data = vld2q_f32(reinterpret_cast(src_ptrs[0] + in.offset())); + // Get power of 2 in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + data.val[0] = vmulq_f32(data.val[0], data.val[0]); + data.val[1] = vmulq_f32(data.val[1], data.val[1]); + } + float32x4_t sum_data = vaddq_f32(data.val[0], vsetq_lane_f32(0.f, data.val[1], 3)); + for(int i = 1; i < pool_size; ++i) + { + data = vld2q_f32(reinterpret_cast(src_ptrs[i] + in.offset())); + // Get power of 2 in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + data.val[0] = vmulq_f32(data.val[0], data.val[0]); + data.val[1] = vmulq_f32(data.val[1], data.val[1]); + } + sum_data = vaddq_f32(sum_data, data.val[0]); + sum_data = vaddq_f32(sum_data, vsetq_lane_f32(0.f, data.val[1], 3)); + } + res = vpadd_f32(vget_high_f32(sum_data), vget_low_f32(sum_data)); + res = vmul_f32(vpadd_f32(res, res), scale_v); + } + else + { + float32x4x2_t max_data = vld2q_f32(reinterpret_cast(src_ptrs[0] + in.offset())); + for(int i = 1; i < pool_size; ++i) + { + const float32x4x2_t data = vld2q_f32(reinterpret_cast(src_ptrs[i] + in.offset())); + max_data = vmax2q_f32(max_data, data); + } + res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits::max(), max_data.val[1], 3)), vget_low_f32(max_data.val[1])); + res = vpmax_f32(res, vpmax_f32(vget_high_f32(max_data.val[0]), vget_low_f32(max_data.val[0]))); + res = vpmax_f32(res, res); + } + final_res = vget_lane_f32(res, 0); + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + final_res = sqrt(final_res); + } + + // Store result + *(reinterpret_cast(out.ptr())) = final_res; + }, + in, out); +} +} // namespace cpu +} // namespace arm_compute + +#endif // ENABLE_NCHW_KERNELS \ No newline at end of file diff --git a/src/cpu/kernels/pool2d/neon/qasymm8.cpp b/src/cpu/kernels/pool2d/neon/qasymm8.cpp new file mode 100644 index 0000000000..7f8841edd8 --- /dev/null +++ b/src/cpu/kernels/pool2d/neon/qasymm8.cpp @@ -0,0 +1,41 @@ +/* + * 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. + */ +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/pool2d/neon/list.h" + +namespace arm_compute +{ +namespace cpu +{ +void poolingMxN_qasymm8_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + poolingMxN_q8_neon_nhwc(src, dst0, dst1, pool_info, window_src, window); +} +} // namespace cpu +} // namespace arm_compute \ No newline at end of file diff --git a/src/cpu/kernels/pool2d/neon/qasymm8_signed.cpp b/src/cpu/kernels/pool2d/neon/qasymm8_signed.cpp new file mode 100644 index 0000000000..8643651f27 --- /dev/null +++ b/src/cpu/kernels/pool2d/neon/qasymm8_signed.cpp @@ -0,0 +1,41 @@ +/* + * 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. + */ +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/pool2d/neon/list.h" + +namespace arm_compute +{ +namespace cpu +{ +void poolingMxN_qasymm8_signed_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + poolingMxN_q8_neon_nhwc(src, dst0, dst1, pool_info, window_src, window); +} +} // namespace cpu +} // namespace arm_compute \ No newline at end of file diff --git a/src/cpu/kernels/pool2d/neon/quantized.h b/src/cpu/kernels/pool2d/neon/quantized.h new file mode 100644 index 0000000000..a16960a205 --- /dev/null +++ b/src/cpu/kernels/pool2d/neon/quantized.h @@ -0,0 +1,863 @@ +/* + * 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_QUANTIZED_H +#define SRC_CORE_NEON_KERNELS_QUANTIZED_H + +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/NEAsymm.h" +#include "src/core/NEON/NEFixedPoint.h" +#include "src/core/NEON/NEMath.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include + +namespace arm_compute +{ +namespace cpu +{ +template +inline typename std::enable_if::value, int8_t>::type +quantize(float val, const UniformQuantizationInfo &info) +{ + return quantize_qasymm8_signed(val, info); +} + +template +inline typename std::enable_if::value, uint8_t>::type +quantize(float val, const UniformQuantizationInfo &info) +{ + return quantize_qasymm8(val, info); +} + +template +inline T vcvtq_q32_f32(float32x4_t values); + +template <> +inline uint32x4_t vcvtq_q32_f32(float32x4_t values) +{ + return vcvtq_u32_f32(values); +} + +template <> +inline int32x4_t vcvtq_q32_f32(float32x4_t values) +{ + return vcvtq_s32_f32(values); +} + +template +inline float32x4_t vcvtq_f32_q32(T values); + +template <> +inline float32x4_t vcvtq_f32_q32(uint32x4_t values) +{ + return vcvtq_f32_u32(values); +} + +template <> +inline float32x4_t vcvtq_f32_q32(int32x4_t values) +{ + return vcvtq_f32_s32(values); +} + +template +inline Tout vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset); + +template <> +inline uint8x16_t vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset) +{ + const float new_scale = quant_rescale / scale_pooling; + return vquantize(acc, UniformQuantizationInfo(new_scale, new_offset)); +} + +template <> +inline int8x16_t vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset) +{ + const float new_scale = quant_rescale / scale_pooling; + return vquantize_signed(acc, UniformQuantizationInfo(new_scale, new_offset)); +} + +template +inline Tout vrequantize_pooling(Tin vec1, Tin vec2, const UniformQuantizationInfo &requant_qinfo); + +template <> +inline uint8x16_t vrequantize_pooling(uint8x8_t vec1, uint8x8_t vec2, const UniformQuantizationInfo &requant_qinfo) +{ + const float32x4x4_t acc = + { + { + vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec1))))), + vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec1))))), + vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec2))))), + vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec2))))), + } + }; + return vquantize(acc, requant_qinfo); +} + +template <> +inline int8x16_t vrequantize_pooling(int8x8_t vec1, int8x8_t vec2, const UniformQuantizationInfo &requant_qinfo) +{ + const float32x4x4_t acc = + { + { + vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec1))))), + vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec1))))), + vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec2))))), + vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec2))))), + } + }; + return vquantize_signed(acc, requant_qinfo); +} + +template +inline T vrequantize_pooling(T &vec, const UniformQuantizationInfo &requant_qinfo); + +template <> +inline uint8x8_t vrequantize_pooling(uint8x8_t &vec, const UniformQuantizationInfo &requant_qinfo) +{ + const float32x4x2_t acc = + { + { + vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec))))), + vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec))))), + } + }; + return vquantize(acc, requant_qinfo); +} + +template <> +inline int8x8_t vrequantize_pooling(int8x8_t &vec, const UniformQuantizationInfo &requant_qinfo) +{ + const float32x4x2_t acc = + { + { + vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec))))), + vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec))))), + } + }; + return vquantize_signed(acc, requant_qinfo); +} + +inline float calculate_avg_scale(bool exclude_padding, DataLayout data_layout, const Coordinates &id, const int pool_size_x, const int pool_size_y, const int upper_bound_w, const int upper_bound_h, + const int pad_x, const int pad_y, const int stride_x, const int stride_y) +{ + const unsigned int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const unsigned int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + + int start_x = id[idx_width] * stride_x - pad_x; + int start_y = id[idx_height] * stride_y - pad_y; + + const int end_x = std::min(start_x + pool_size_x, upper_bound_w); + const int end_y = std::min(start_y + pool_size_y, upper_bound_h); + if(exclude_padding) + { + start_x = std::max(0, start_x); + start_y = std::max(0, start_y); + } + return 1.f / ((end_y - start_y) * (end_x - start_x)); +} + +template +void poolingMxN_q8_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 16; + const int window_half_step_x = window_step_x / 2; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(src, window_src); + Iterator out(dst0, window_out); + + using q8x8_t = typename wrapper::traits::neon_vector::type; + using q8x16_t = typename wrapper::traits::neon_vector::type; + using q16_t = typename wrapper::traits::promote_t; + using q16x8_t = typename wrapper::traits::neon_vector::type; + using q32_t = typename wrapper::traits::promote_t; + using q32x4_t = typename wrapper::traits::neon_vector::type; + + const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width; + const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + const float32x4_t half_scale_v = vdupq_n_f32(0.5f); + const UniformQuantizationInfo src_qinfo = src->info()->quantization_info().uniform(); + const UniformQuantizationInfo dst_qinfo = dst0->info()->quantization_info().uniform(); + + const float quant_rescale = dst_qinfo.scale / src_qinfo.scale; + // "new_offset" doesn't have to consider the "half_scale_v" in its computation + // With a requantization performed in a single step there won't be uncertainties introduced + const int32_t new_offset = dst_qinfo.offset - static_cast(static_cast(src_qinfo.offset) / quant_rescale); + + const float requant_scale = dst_qinfo.scale / src_qinfo.scale; + const int32_t requant_offset = dst_qinfo.offset - static_cast(static_cast(src_qinfo.offset) / requant_scale); + const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + if(pool_info.pool_type != PoolingType::MAX) + { + q32x4_t vres1 = wrapper::vdup_n(static_cast(0.f), wrapper::traits::vector_128_tag{}); + q32x4_t vres2 = wrapper::vdup_n(static_cast(0.f), wrapper::traits::vector_128_tag{}); + q32x4_t vres3 = wrapper::vdup_n(static_cast(0.f), wrapper::traits::vector_128_tag{}); + q32x4_t vres4 = wrapper::vdup_n(static_cast(0.f), wrapper::traits::vector_128_tag{}); + + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + // Perform pooling + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const q8x16_t data = wrapper::vloadq(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + + const q16x8_t data_q16 = wrapper::vmovl(wrapper::vgetlow(data)); + const q16x8_t data2_q16 = wrapper::vmovl(wrapper::vgethigh(data)); + vres1 = wrapper::vadd(vres1, wrapper::vmovl(wrapper::vgetlow(data_q16))); + vres2 = wrapper::vadd(vres2, wrapper::vmovl(wrapper::vgethigh(data_q16))); + vres3 = wrapper::vadd(vres3, wrapper::vmovl(wrapper::vgetlow(data2_q16))); + vres4 = wrapper::vadd(vres4, wrapper::vmovl(wrapper::vgethigh(data2_q16))); + } + } + + if(src_qinfo != dst_qinfo) + { + const float32x4x4_t vres = + { + { + vcvtq_f32_q32(vres1), + vcvtq_f32_q32(vres2), + vcvtq_f32_q32(vres3), + vcvtq_f32_q32(vres4), + } + }; + const auto requantized_dst = vrequantize_pooling_with_scale(vres, quant_rescale, scale, new_offset); + // Store result + wrapper::vstore(reinterpret_cast(out.ptr()) + x_off, wrapper::vgetlow(requantized_dst)); + wrapper::vstore(reinterpret_cast(out.ptr()) + x_off + 8, wrapper::vgethigh(requantized_dst)); + } + else + { + const float32x4_t scale_v = vdupq_n_f32(scale); + // Divide by scale and add 0.5f to round to nearest instead of rounding towards zero + vres1 = vcvtq_q32_f32(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres1), scale_v)); + vres2 = vcvtq_q32_f32(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres2), scale_v)); + vres3 = vcvtq_q32_f32(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres3), scale_v)); + vres4 = vcvtq_q32_f32(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres4), scale_v)); + + const q8x8_t res1 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres1), wrapper::vmovn(vres2))); + const q8x8_t res2 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres3), wrapper::vmovn(vres4))); + // Store result + wrapper::vstore(reinterpret_cast(out.ptr()) + x_off, res1); + wrapper::vstore(reinterpret_cast(out.ptr()) + x_off + 8, res2); + } + } + else + { + q8x16_t vres = wrapper::vdup_n(std::numeric_limits::min(), wrapper::traits::vector_128_tag{}); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const q8x16_t data = wrapper::vloadq(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + vres = wrapper::vmax(vres, data); + } + } + + // Store result + wrapper::vstore(reinterpret_cast(out.ptr()) + x_off, (src_qinfo != dst_qinfo) ? vrequantize_pooling(wrapper::vgetlow(vres), wrapper::vgethigh(vres), + requant_qinfo) : + vres); + } + } + + if(pool_info.pool_type == PoolingType::MAX) + { + for(; x_off <= (window_end_x - window_half_step_x); x_off += window_half_step_x) + { + q8x8_t vres = wrapper::vdup_n(std::numeric_limits::min(), wrapper::traits::vector_64_tag{}); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const q8x8_t data = wrapper::vload(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + vres = wrapper::vmax(vres, data); + } + } + + // Store result + wrapper::vstore(reinterpret_cast(out.ptr()) + x_off, + (src_qinfo != dst_qinfo) ? vrequantize_pooling(vres, requant_qinfo) : vres); + } + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + if(pool_info.pool_type != PoolingType::MAX) + { + q32_t res = static_cast(0.f); + + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + // Perform pooling + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const T data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + res += data; + } + } + + if(src_qinfo != dst_qinfo) + { + const float res_f = static_cast(res); + const float new_scale = quant_rescale / scale; + const auto requantized_dst = quantize(res_f, UniformQuantizationInfo(new_scale, new_offset)); + + // Store result + *(reinterpret_cast(out.ptr()) + x_off) = requantized_dst; + } + else + { + // Divide by scale and add 0.5f to round to nearest instead of rounding towards zero + res = static_cast(0.5f + static_cast(res) * scale); + + // Store result + *(reinterpret_cast(out.ptr()) + x_off) = res; + } + } + else + { + T res = std::numeric_limits::min(); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const T data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().z())) + x_off); + res = std::max(res, data); + } + } + + // Store result + if(src_qinfo != dst_qinfo) + { + const float res_f = static_cast(res); + *(reinterpret_cast(out.ptr()) + x_off) = quantize(res_f, requant_qinfo); + } + else + { + *(reinterpret_cast(out.ptr()) + x_off) = res; + } + } + } + + }, + in, out); +} + +#if defined(ENABLE_NCHW_KERNELS) +template +inline void scale_vector_q16x8(bool exclude_padding, TVec &v, const Coordinates &id, int id_offset, int step, + const int pool_size, const int upper_bound_w, const int upper_bound_h, + const int pad_x, const int pad_y, const int stride_x, const int stride_y) +{ + int start_x = (id.x() + id_offset) * stride_x - pad_x; + int start_y = id.y() * stride_y - pad_y; + const int end_y = std::min(start_y + pool_size, upper_bound_h); + if(exclude_padding) + { + start_y = std::max(0, start_y); + } + + std::array elems = + { + { + wrapper::vgetlane(v, 0), + wrapper::vgetlane(v, 1), + wrapper::vgetlane(v, 2), + wrapper::vgetlane(v, 3), + wrapper::vgetlane(v, 4), + wrapper::vgetlane(v, 5), + wrapper::vgetlane(v, 6), + wrapper::vgetlane(v, 7), + } + }; + + for(auto &el : elems) + { + int c_start_x = start_x; + const int end_x = std::min(c_start_x + pool_size, upper_bound_w); + if(exclude_padding) + { + c_start_x = std::max(0, c_start_x); + } + float scale = 1.f / ((end_y - start_y) * (end_x - c_start_x)); + el *= scale; + start_x += step * stride_x; + } + + v = wrapper::vsetlane(elems[0], v, 0); + v = wrapper::vsetlane(elems[1], v, 1); + v = wrapper::vsetlane(elems[2], v, 2); + v = wrapper::vsetlane(elems[3], v, 3); + v = wrapper::vsetlane(elems[4], v, 4); + v = wrapper::vsetlane(elems[5], v, 5); + v = wrapper::vsetlane(elems[6], v, 6); + v = wrapper::vsetlane(elems[7], v, 7); +} + +template +void pooling2_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + Iterator in(src, window_src); + Iterator out(dst0, window); + + /** SIMD vector types */ + using q8x8_t = typename wrapper::traits::neon_vector::type; + using q8x16_t = typename wrapper::traits::neon_vector::type; + using q8x8x2_t = typename std::conditional::value, uint8x8x2_t, int8x8x2_t>::type; + using q16_t = typename wrapper::traits::promote_t; + using q16x4_t = typename wrapper::traits::neon_vector::type; + using q16x8_t = typename wrapper::traits::neon_vector::type; + using q16x8x2_t = typename wrapper::traits::neon_vector::type; + + constexpr int pool_size = 2; + int pool_stride_x = 0; + int pool_stride_y = 0; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + const T *const src_top_ptr = reinterpret_cast(src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top)))); + const T *const src_bottom_ptr = reinterpret_cast(src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1))); + + const int scale_step_x = (pool_stride_x == 1) ? 2 : 1; + + const UniformQuantizationInfo src_qinfo = src->info()->quantization_info().uniform(); + const UniformQuantizationInfo dst_qinfo = dst0->info()->quantization_info().uniform(); + const bool have_different_qinfo = src_qinfo != dst_qinfo; + + const float requant_scale = dst_qinfo.scale / src_qinfo.scale; + const int32_t requant_offset = dst_qinfo.offset - static_cast(static_cast(src_qinfo.offset) / requant_scale); + const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); + + execute_window_loop(window, [&](const Coordinates & id) + { + const auto top_data = wrapper::vloadq(src_top_ptr + in.offset()); + const auto bottom_data = wrapper::vloadq(src_bottom_ptr + in.offset()); + q8x8_t lower_res = {}; + q8x8_t upper_res = {}; + + if(pool_info.pool_type != PoolingType::MAX) + { + const q16x8x2_t top_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(top_data)), wrapper::vmovl(wrapper::vgethigh(top_data)) } }; + const q16x8x2_t bottom_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(bottom_data)), wrapper::vmovl(wrapper::vgethigh(bottom_data)) } }; + + // Add rows + const q16x8x2_t vrsum = + { + { + wrapper::vadd(top_data_q16.val[0], bottom_data_q16.val[0]), + wrapper::vadd(top_data_q16.val[1], bottom_data_q16.val[1]), + } + }; + + // Pair-wise add row data + const q16x4_t vpsum_1 = wrapper::vpadd(wrapper::vgetlow(vrsum.val[0]), wrapper::vgethigh(vrsum.val[0])); + const q16x4_t vpsum_2 = wrapper::vpadd(wrapper::vgetlow(vrsum.val[1]), wrapper::vgethigh(vrsum.val[1])); + + q16x8_t res_lower = wrapper::vcombine(vpsum_1, vpsum_2); + + // Scale lower result + scale_vector_q16x8(pool_info.exclude_padding, res_lower, id, 0, scale_step_x, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + lower_res = wrapper::vmovn(res_lower); + + // Compute upper result for stride_x == 1 + if(pool_stride_x == 1) + { + // Shifted row sum + const q16x8x2_t vrsum_shifted = + { + { + wrapper::vext_1(vrsum.val[0], vrsum.val[1]), + wrapper::vext_1(vrsum.val[1], vrsum.val[1]) + } + }; + + // Pair-wise add shifted row + q16x8_t res_upper = wrapper::vcombine( + wrapper::vpadd(wrapper::vgetlow(vrsum_shifted.val[0]), wrapper::vgethigh(vrsum_shifted.val[0])), + wrapper::vpadd(wrapper::vgetlow(vrsum_shifted.val[1]), wrapper::vgethigh(vrsum_shifted.val[1]))); + + // Scale upper result + scale_vector_q16x8(pool_info.exclude_padding, res_upper, id, 1, 2, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + upper_res = wrapper::vmovn(res_upper); + } + } + else + { + const q8x16_t max_data = wrapper::vmax(top_data, bottom_data); + lower_res = wrapper::vpmax(wrapper::vgetlow(max_data), wrapper::vgethigh(max_data)); + if(pool_stride_x == 1) + { + const q8x16_t max_data_shifted = wrapper::vext_1(max_data, max_data); + upper_res = wrapper::vpmax(wrapper::vgetlow(max_data_shifted), wrapper::vgethigh(max_data_shifted)); + } + } + + if(have_different_qinfo) + { + const auto requantized_dst = vrequantize_pooling(lower_res, upper_res, requant_qinfo); + lower_res = wrapper::vgetlow(requantized_dst); + upper_res = wrapper::vgethigh(requantized_dst); + } + + // Store result + if(pool_stride_x == 1) + { + const q8x8x2_t res = { { lower_res, upper_res } }; + wrapper::vstore(reinterpret_cast(out.ptr()), res); + } + else + { + wrapper::vstore(reinterpret_cast(out.ptr()), lower_res); + } + }, + in, out); +} + +template +void pooling3_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + Iterator in(src, window_src); + Iterator out(dst0, window); + + /** SIMD vector types */ + using q8x8_t = typename wrapper::traits::neon_vector::type; + using q8x16_t = typename wrapper::traits::neon_vector::type; + using q8x8x2_t = typename std::conditional::value, uint8x8x2_t, int8x8x2_t>::type; + using q16_t = typename wrapper::traits::promote_t; + using q16x8_t = typename wrapper::traits::neon_vector::type; + using q16x8x2_t = typename wrapper::traits::neon_vector::type; + + constexpr int pool_size = 3; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + const UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform(); + const UniformQuantizationInfo &dst_qinfo = dst0->info()->quantization_info().uniform(); + + const float requant_scale = dst_qinfo.scale / src_qinfo.scale; + const int32_t requant_offset = dst_qinfo.offset - static_cast(static_cast(src_qinfo.offset) / requant_scale); + const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); + + const T *const src_top_ptr = reinterpret_cast(src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top)))); + const T *const src_middle_ptr = reinterpret_cast(src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1))); + const T *const src_bottom_ptr = reinterpret_cast(src->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 2))); + + execute_window_loop(window, [&](const Coordinates & id) + { + const auto top_data = wrapper::vloadq(src_top_ptr + in.offset()); + const auto middle_data = wrapper::vloadq(src_middle_ptr + in.offset()); + const auto bottom_data = wrapper::vloadq(src_bottom_ptr + in.offset()); + q8x8_t fres = {}; + q8x16_t fqres = {}; + + if(pool_info.pool_type == PoolingType::AVG) + { + // Convert data to u16 + const q16x8x2_t top_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(top_data)), wrapper::vmovl(wrapper::vgethigh(top_data)) } }; + const q16x8x2_t middle_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(middle_data)), wrapper::vmovl(wrapper::vgethigh(middle_data)) } }; + const q16x8x2_t bottom_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(bottom_data)), wrapper::vmovl(wrapper::vgethigh(bottom_data)) } }; + + // Calculate row sums + const q16x8x2_t vrsum = + { + { + wrapper::vadd(wrapper::vadd(top_data_q16.val[0], bottom_data_q16.val[0]), middle_data_q16.val[0]), + wrapper::vadd(wrapper::vadd(top_data_q16.val[1], bottom_data_q16.val[1]), middle_data_q16.val[1]), + } + }; + const q16x8x2_t vrsum_shifted_1 = + { + { + wrapper::vext_1(vrsum.val[0], vrsum.val[1]), + wrapper::vext_1(vrsum.val[1], vrsum.val[1]) + } + }; + const q16x8x2_t vrsum_shifted_2 = + { + { + wrapper::vext_2(vrsum.val[0], vrsum.val[1]), + wrapper::vext_2(vrsum.val[1], vrsum.val[1]) + } + }; + // Calculate final sum + q16x8x2_t final_sum = + { + { + wrapper::vadd(wrapper::vadd(vrsum.val[0], vrsum_shifted_1.val[0]), vrsum_shifted_2.val[0]), + wrapper::vadd(wrapper::vadd(vrsum.val[1], vrsum_shifted_1.val[1]), vrsum_shifted_2.val[1]), + } + }; + if(pool_stride_x == 2) + { + q16x8_t res = + { + wrapper::vgetlane(final_sum.val[0], 0), + wrapper::vgetlane(final_sum.val[0], 2), + wrapper::vgetlane(final_sum.val[0], 4), + wrapper::vgetlane(final_sum.val[0], 6), + wrapper::vgetlane(final_sum.val[1], 0), + wrapper::vgetlane(final_sum.val[1], 2), + wrapper::vgetlane(final_sum.val[1], 4), + wrapper::vgetlane(final_sum.val[1], 6), + }; + + scale_vector_q16x8(pool_info.exclude_padding, res, id, 0, 1, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + fres = wrapper::vmovn(res); + } + else + { + // Scale lower result + scale_vector_q16x8(pool_info.exclude_padding, final_sum.val[0], id, 0, 1, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + // Scale lower result + scale_vector_q16x8(pool_info.exclude_padding, final_sum.val[1], id, 8, 1, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + fqres = wrapper::vcombine(wrapper::vmovn(final_sum.val[0]), wrapper::vmovn(final_sum.val[1])); + } + } + else + { + const q8x16_t max_data = wrapper::vmax(wrapper::vmax(top_data, bottom_data), middle_data); + const q8x16_t max_data_shift1 = wrapper::vext_1(max_data, max_data); + const q8x16_t max_data_shift2 = wrapper::vext_2(max_data, max_data); + const q8x16_t final_max = wrapper::vmax(wrapper::vmax(max_data, max_data_shift1), max_data_shift2); + + if(pool_stride_x == 2) + { + const q8x8x2_t table = { { wrapper::vgetlow(final_max), wrapper::vgethigh(final_max) } }; + static const q8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 }; + fres = wrapper::vtbl(table, lookup_val); + } + else + { + fqres = final_max; + } + } + + // Store result + if(pool_stride_x == 1) + { + if(src_qinfo != dst_qinfo) + { + fqres = vrequantize_pooling(wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), requant_qinfo); + } + wrapper::vstore(reinterpret_cast(out.ptr()), fqres); + } + else + { + if(src_qinfo != dst_qinfo) + { + fres = vrequantize_pooling(fres, requant_qinfo); + } + wrapper::vstore(reinterpret_cast(out.ptr()), fres); + } + }, + in, out); +} + +template +void poolingMxN_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + ARM_COMPUTE_UNUSED(dst1); + Iterator in(src, window_src); + Iterator out(dst0, window); + + /** SIMD vector types */ + using q8x8_t = typename wrapper::traits::neon_vector::type; + using q16_t = typename wrapper::traits::promote_t; + using q16x8_t = typename wrapper::traits::neon_vector::type; + using q32_t = typename wrapper::traits::promote_t; + using q32x4_t = typename wrapper::traits::neon_vector::type; + + const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().x() : pool_info.pool_size.width; + const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.height; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + const UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform(); + const UniformQuantizationInfo &dst_qinfo = dst0->info()->quantization_info().uniform(); + + execute_window_loop(window, [&](const Coordinates & id) + { + T res = std::numeric_limits::min(); + + if(pool_info.pool_type != PoolingType::MAX) + { + q32x4_t vres = wrapper::vdup_n(static_cast(0.f), wrapper::traits::vector_128_tag{}); + q32_t sres = 0; + + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + // Perform pooling + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 8); x += 8) + { + const q8x8_t data = wrapper::vload(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + + const q16x8_t data_q16 = wrapper::vmovl(data); + vres = wrapper::vadd(vres, wrapper::vaddl(wrapper::vgethigh(data_q16), wrapper::vgetlow(data_q16))); + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + T data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + sres += data; + } + } + + // Reduction + const auto tmp = wrapper::vpadd(wrapper::vgethigh(vres), wrapper::vgetlow(vres)); + sres += wrapper::vgetlane(tmp, 0) + wrapper::vgetlane(tmp, 1); + + // Divide by scale + res = static_cast(support::cpp11::round(sres * scale)); + } + else + { + q8x8_t vres = wrapper::vdup_n(std::numeric_limits::min(), wrapper::traits::vector_64_tag{}); + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 8); x += 8) + { + const q8x8_t data = wrapper::vload(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + vres = wrapper::vmax(vres, data); + } + // Leftover for loop + for(; x < pool_size_x; ++x) + { + const T data = *(reinterpret_cast(in.ptr() + (x - pool_pad_left) * static_cast(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast + (src->info()->strides_in_bytes().y()))); + res = std::max(res, data); + } + } + + // Reduce max + vres = wrapper::vpmax(vres, vres); + vres = wrapper::vpmax(vres, vres); + vres = wrapper::vpmax(vres, vres); + + // Get max value + res = std::max(res, wrapper::vgetlane(vres, 0)); + } + // Store result + res = (src_qinfo != dst_qinfo) ? Qasymm8QuantizationHelper::quantize(Qasymm8QuantizationHelper::dequantize(res, src_qinfo), dst_qinfo) : res; + *(reinterpret_cast(out.ptr())) = res; + }, + in, out); +} +#endif /* defined(ENABLE_NCHW_KERNELS) */ +} // namespace cpu +} // namespace arm_compute + +#endif // SRC_CORE_NEON_KERNELS_QUANTIZED_H -- cgit v1.2.1