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Diffstat (limited to 'src/core/cpu/kernels/CpuDirectConvolutionKernel.cpp')
| -rw-r--r-- | src/core/cpu/kernels/CpuDirectConvolutionKernel.cpp | 1385 |
1 files changed, 1385 insertions, 0 deletions
diff --git a/src/core/cpu/kernels/CpuDirectConvolutionKernel.cpp b/src/core/cpu/kernels/CpuDirectConvolutionKernel.cpp new file mode 100644 index 0000000000..4f46eb2bf6 --- /dev/null +++ b/src/core/cpu/kernels/CpuDirectConvolutionKernel.cpp @@ -0,0 +1,1385 @@ +/* + * Copyright (c) 2017-2021 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 "src/core/cpu/kernels/CpuDirectConvolutionKernel.h" + +#include "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" +#include "src/core/NEON/wrapper/wrapper.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/IAccessWindow.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "src/core/AccessWindowStatic.h" +#include "src/core/CPP/Validate.h" +#include "src/core/NEON/NEFixedPoint.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" + +#include <algorithm> + +using namespace arm_compute::detail; + +namespace arm_compute +{ +namespace cpu +{ +namespace kernels +{ +namespace +{ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template <unsigned int stridex> +float16x8_t internal_vld1q(const float16_t *in); + +template <> +float16x8_t internal_vld1q<1>(const float16_t *in) +{ + return vld1q_f16(in); +} + +template <> +float16x8_t internal_vld1q<2>(const float16_t *in) +{ + const float16x8x2_t tmp = vld2q_f16(in); + return tmp.val[0]; +} + +template <> +float16x8_t internal_vld1q<3>(const float16_t *in) +{ + const float16x8x3_t tmp = vld3q_f16(in); + return tmp.val[0]; +} + +inline float16x8_t internal_vdupq_n(float16_t v) +{ + return vdupq_n_f16(v); +} + +inline void internal_vst1q(float16_t *p, const float16x8_t &v) +{ + vst1q_f16(p, v); +} + +float16x8_t internal_vmull(const float16x8_t &x, const float16x8_t &y) +{ + return vmulq_f16(x, y); +} + +inline float16x8_t internal_vmlal(const float16x8_t &x, const float16x8_t &y, const float16x8_t &z) +{ + return vaddq_f16(x, vmulq_f16(y, z)); +} +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +template <unsigned int stridex> +float32x4_t internal_vld1q(const float *in); + +template <> +float32x4_t internal_vld1q<1>(const float *in) +{ + return vld1q_f32(in); +} + +template <> +float32x4_t internal_vld1q<2>(const float *in) +{ + const float32x4x2_t tmp = vld2q_f32(in); + return tmp.val[0]; +} + +template <> +float32x4_t internal_vld1q<3>(const float *in) +{ + const float32x4x3_t tmp = vld3q_f32(in); + return tmp.val[0]; +} + +inline float32x4_t internal_vdupq_n(float v) +{ + return vdupq_n_f32(v); +} + +inline void internal_vst1q(float *p, const float32x4_t &v) +{ + vst1q_f32(p, v); +} + +float32x4_t internal_vmull(const float32x4_t &x, const float32x4_t &y) +{ + return vmulq_f32(x, y); +} + +inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z) +{ + return vmlaq_f32(x, y, z); +} + +constexpr int small_tensor_size_optim = 8; +inline bool run_optim_small_tensor_info(const ITensorInfo *t) +{ + return t->dimension(Window::DimX) <= small_tensor_size_optim && t->dimension(Window::DimY) <= small_tensor_size_optim; +} + +inline bool run_optim_small_tensor(const ITensor *t) +{ + return run_optim_small_tensor_info(t->info()); +} + +// Optimized convolver for 1x1 kernels used only where input width and height are both <= 8 +// For big Z as in Input=7x7x832, this implementation is faster than the general code becuase it doesn't need to +// store intermidiate results in memory. Temporary results are stored in SIMD registers directly and then written to the output buffer. +template <unsigned int stridex> +class convolver_w1x1_i8x8_f32 +{ +public: + static void convolve(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) + { + ARM_COMPUTE_ERROR_ON(src->info()->dimension(Window::DimX) > small_tensor_size_optim); + ARM_COMPUTE_ERROR_ON(src->info()->dimension(Window::DimY) > small_tensor_size_optim); + + const int input_stride_x = src->info()->strides_in_bytes().x(); + const int input_stride_y = src->info()->strides_in_bytes().y(); + const int input_stride_z = src->info()->strides_in_bytes().z(); + const int output_stride_y = dst->info()->strides_in_bytes().y(); + const int output_stride_z = dst->info()->strides_in_bytes().z(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_h = dst->info()->dimension(1); + const int range_z = window.z().end() - window.z().start(); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); + + // setup output window for the iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, dst->info()->dimension(Window::DimX), dst->info()->dimension(Window::DimX))); + window_out.set(Window::DimY, Window::Dimension(0, dst->info()->dimension(Window::DimY), dst->info()->dimension(Window::DimY))); + window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), range_z)); + + // setup input window for the iterator + Window window_in = window; + // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0 + window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Window window_k = calculate_max_window(*weights->info(), Steps(1u)); + Iterator out(dst, window_out); + Iterator in(src, window_in); + Iterator k(weights, window_k); + + const uint8_t *k_ptr = k.ptr(); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const uint8_t *input_ptr = in.ptr() - conv_pad_left * input_stride_x - conv_pad_top * input_stride_y; + uint8_t *out_ptr = out.ptr(); + int ih = 0; + int oh = 0; + std::array<float32x4_t, 8> accum0 = { vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0) }; + std::array<float32x4_t, 8> accum1 = { vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0) }; + for(int oz = 0; oz < range_z; ++oz) + { + accum0[0] = accum0[1] = accum0[2] = accum0[3] = accum0[4] = accum0[5] = accum0[6] = accum0[7] = vdupq_n_f32(0.f); + accum1[0] = accum1[1] = accum1[2] = accum1[3] = accum1[4] = accum1[5] = accum1[6] = accum1[7] = vdupq_n_f32(0.f); + auto p_out_base = out_ptr + oz * output_stride_z; + for(int p = 0; p < kernel_depth; ++p) + { + const auto k_val = reinterpret_cast<const float *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w); + const auto vk0 = internal_vdupq_n(*k_val); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + const int offset_xy = ih * input_stride_y; + auto in_val = reinterpret_cast<const float *>(input_ptr + p * input_stride_z + offset_xy); + auto v_in0 = internal_vld1q<stridex>(in_val); + auto v_in1 = internal_vld1q<stridex>(in_val + 4); + accum0[oh] = vmlaq_f32(accum0[oh], vk0, v_in0); + accum1[oh] = vmlaq_f32(accum1[oh], vk0, v_in1); + } + } + for(oh = 0; oh < output_h; ++oh) + { + auto p_out = reinterpret_cast<float *>(p_out_base + oh * output_stride_y); + vst1q_f32(p_out, accum0[oh]); + vst1q_f32(p_out + 4, accum1[oh]); + } + } + }, + in, out); + } +}; + +template <typename T1, typename T2, unsigned int stridex> +class convolver_1x1 +{ +public: + static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) + { + const int input_stride_x = src->info()->strides_in_bytes().x(); + const int input_stride_y = src->info()->strides_in_bytes().y(); + const int input_stride_z = src->info()->strides_in_bytes().z(); + const int output_stride_y = dst->info()->strides_in_bytes().y(); + const int output_stride_z = dst->info()->strides_in_bytes().z(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_w = dst->info()->dimension(0); + const int output_h = dst->info()->dimension(1); + const int range_z = window.z().end() - window.z().start(); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); + + // setup output window for the iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, dst->info()->dimension(Window::DimX), dst->info()->dimension(Window::DimX))); + window_out.set(Window::DimY, Window::Dimension(0, dst->info()->dimension(Window::DimY), dst->info()->dimension(Window::DimY))); + window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), range_z)); + + // setup input window for the iterator + Window window_in = window; + // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0 + window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Window window_k = calculate_max_window(*weights->info(), Steps(1u)); + Iterator out(dst, window_out); + Iterator in(src, window_in); + Iterator k(weights, window_k); + + const uint8_t *k_ptr = k.ptr(); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + /* + For a detailed explanation on how the algorithm works refer to template <> class convolver_3x3<1> + */ + const uint8_t *input_ptr = in.ptr() - conv_pad_left * input_stride_x - conv_pad_top * input_stride_y; + uint8_t *out_ptr = out.ptr(); + int ih = 0; + int oh = 0; + for(int oz = 0; oz < range_z; ++oz) + { + auto p_out_base = out_ptr + oz * output_stride_z; + // Step 1 + { + const auto k_val = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w); + const auto vk = internal_vdupq_n(*k_val); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + const int offset_xy = ih * input_stride_y; + auto in_val = reinterpret_cast<const T1 *>(input_ptr + (0 * input_stride_z + offset_xy)); + auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration) + { + internal_vst1q(p_out, internal_vmull(vk, internal_vld1q<stridex>(in_val))); + } + } + } + + // Step 2 + for(int p = 1; p < kernel_depth; ++p) + { + const auto k_val = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w); + const auto vk = internal_vdupq_n(*k_val); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + const int offset_xy = ih * input_stride_y; + auto in_val = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + offset_xy); + auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration) + { + internal_vst1q(p_out, internal_vmlal(internal_vld1q<1>(p_out), vk, internal_vld1q<stridex>(in_val))); + } + } + } + } + }, + in, out); + } +}; + +template <unsigned int stridex> +float32x4x2_t convolve_5x5(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, + const float *m0, const float *m1, const float *m2, const float *m3, const float *m4); + +inline float32x4x3_t load_matrix_hi(const float *const m0, const float *const m1, const float *const m2) +{ + const float32x4x3_t m00 = + { + { + vld1q_dup_f32(m0), + vld1q_dup_f32(m1), + vld1q_dup_f32(m2) + } + }; + return m00; +} + +inline float32x4x2_t load_matrix_lo(const float *const m3, const float *const m4) +{ + const float32x4x2_t m00 = + { + { + vld1q_dup_f32(m3), + vld1q_dup_f32(m4) + } + }; + return m00; +} + +inline float32x4x3_t load_input(const float *const in) +{ + const float32x4x3_t vin = + { + { + vld1q_f32(in), + vld1q_f32(in + 4), + vld1q_f32(in + 8) + } + }; + return vin; +} + +template <> +inline float32x4x2_t convolve_5x5<1>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, + const float *m0, const float *m1, const float *m2, const float *m3, const float *m4) +{ + const float32x4x3_t vin0 = load_input(in_0); + const float32x4x3_t vin1 = load_input(in_1); + const float32x4x3_t vin2 = load_input(in_2); + const float32x4x3_t vin3 = load_input(in_3); + const float32x4x3_t vin4 = load_input(in_4); + const float32x4x3_t m00 = load_matrix_hi(m0, 1 + m0, 2 + m0); + const float32x4x2_t m01 = load_matrix_lo(3 + m0, 4 + m0); + const float32x4x3_t m10 = load_matrix_hi(m1, 1 + m1, 2 + m1); + const float32x4x2_t m11 = load_matrix_lo(3 + m1, 4 + m1); + const float32x4x3_t m20 = load_matrix_hi(m2, 1 + m2, 2 + m2); + const float32x4x2_t m21 = load_matrix_lo(3 + m2, 4 + m2); + const float32x4x3_t m30 = load_matrix_hi(m3, 1 + m3, 2 + m3); + const float32x4x2_t m31 = load_matrix_lo(3 + m3, 4 + m3); + const float32x4x3_t m40 = load_matrix_hi(m4, 1 + m4, 2 + m4); + const float32x4x2_t m41 = load_matrix_lo(3 + m4, 4 + m4); + + float32x4x2_t out = + { + { + vmulq_f32(vin0.val[0], m00.val[0]), + vmulq_f32(vin0.val[1], m00.val[0]) + } + }; + + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin0.val[0], vin0.val[1], 1), m00.val[1]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin0.val[0], vin0.val[1], 2), m00.val[2]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin0.val[0], vin0.val[1], 3), m01.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vin0.val[1], m01.val[1]); + + out.val[0] = vmlaq_f32(out.val[0], vin1.val[0], m10.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin1.val[0], vin1.val[1], 1), m10.val[1]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin1.val[0], vin1.val[1], 2), m10.val[2]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin1.val[0], vin1.val[1], 3), m11.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vin1.val[1], m11.val[1]); + + out.val[0] = vmlaq_f32(out.val[0], vin2.val[0], m20.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin2.val[0], vin2.val[1], 1), m20.val[1]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin2.val[0], vin2.val[1], 2), m20.val[2]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin2.val[0], vin2.val[1], 3), m21.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vin2.val[1], m21.val[1]); + + out.val[0] = vmlaq_f32(out.val[0], vin3.val[0], m30.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin3.val[0], vin3.val[1], 1), m30.val[1]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin3.val[0], vin3.val[1], 2), m30.val[2]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin3.val[0], vin3.val[1], 3), m31.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vin3.val[1], m31.val[1]); + + out.val[0] = vmlaq_f32(out.val[0], vin4.val[0], m40.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin4.val[0], vin4.val[1], 1), m40.val[1]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin4.val[0], vin4.val[1], 2), m40.val[2]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vin4.val[0], vin4.val[1], 3), m41.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vin4.val[1], m41.val[1]); + + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin0.val[1], vin0.val[2], 1), m00.val[1]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin0.val[1], vin0.val[2], 2), m00.val[2]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin0.val[1], vin0.val[2], 3), m01.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vin0.val[2], m01.val[1]); + + out.val[1] = vmlaq_f32(out.val[1], vin1.val[1], m10.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin1.val[1], vin1.val[2], 1), m10.val[1]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin1.val[1], vin1.val[2], 2), m10.val[2]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin1.val[1], vin1.val[2], 3), m11.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vin1.val[2], m11.val[1]); + + out.val[1] = vmlaq_f32(out.val[1], vin2.val[1], m20.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin2.val[1], vin2.val[2], 1), m20.val[1]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin2.val[1], vin2.val[2], 2), m20.val[2]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin2.val[1], vin2.val[2], 3), m21.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vin2.val[2], m21.val[1]); + + out.val[1] = vmlaq_f32(out.val[1], vin3.val[1], m30.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin3.val[1], vin3.val[2], 1), m30.val[1]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin3.val[1], vin3.val[2], 2), m30.val[2]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin3.val[1], vin3.val[2], 3), m31.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vin3.val[2], m31.val[1]); + + out.val[1] = vmlaq_f32(out.val[1], vin4.val[1], m40.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin4.val[1], vin4.val[2], 1), m40.val[1]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin4.val[1], vin4.val[2], 2), m40.val[2]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vin4.val[1], vin4.val[2], 3), m41.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vin4.val[2], m41.val[1]); + + return out; +} + +template <> +inline float32x4x2_t convolve_5x5<2>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, + const float *m0, const float *m1, const float *m2, const float *m3, const float *m4) +{ + float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4); + out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1); + out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2); + out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3); + return out; +} + +template <> +inline float32x4x2_t convolve_5x5<3>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, + const float *m0, const float *m1, const float *m2, const float *m3, const float *m4) +{ + float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4); + out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1); + return out; +} + +template <typename T1, typename T2, unsigned int stridex> +class convolver_3x3 +{ +public: + static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) + { + ARM_COMPUTE_UNUSED(num_elems_read_per_iteration); + const int input_stride_x = src->info()->strides_in_bytes().x(); + const int input_stride_y = src->info()->strides_in_bytes().y(); + const int input_stride_z = src->info()->strides_in_bytes().z(); + const int output_stride_y = dst->info()->strides_in_bytes().y(); + const int output_stride_z = dst->info()->strides_in_bytes().z(); + const int kernel_stride_x = weights->info()->strides_in_bytes().x(); + const int kernel_stride_y = weights->info()->strides_in_bytes().y(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_w = dst->info()->dimension(0); + const int output_h = dst->info()->dimension(1); + const int num_planes_z = window.z().end() - window.z().start(); + const int delta_input = get_input_num_elems_processed(num_elems_written_per_iteration, stridex); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); + + // setup output window for the iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, dst->info()->dimension(Window::DimX), dst->info()->dimension(Window::DimX))); + window_out.set(Window::DimY, Window::Dimension(0, dst->info()->dimension(Window::DimY), dst->info()->dimension(Window::DimY))); + window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), num_planes_z)); + + // setup input window for the iterator + Window window_in = window; + // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0 + window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Window window_k = calculate_max_window(*weights->info(), Steps(1u)); + + Iterator out(dst, window_out); + Iterator in(src, window_in); + Iterator k(weights, window_k); + + const uint8_t *k_ptr = k.ptr(); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const uint8_t *input_ptr = in.ptr() - conv_pad_left * input_stride_x - conv_pad_top * input_stride_y; + uint8_t *out_ptr = out.ptr(); + int ih = 0; + int oh = 0; + /* + Each thread executing this kernel computes one or more output's volume planes. + + Let's say the 3rd dimension of the output volume is 32, the first thread will compute the output for Z = [0,7], the second thread will compute the output for Z = [8,15], + the third thread [16,24] and the fourth thread [25,31]. + + The algorithm outer loop iterates over Z, P, Y, X where P is the depth/3rd dimension of each kernel. This order is not arbitrary, the main benefit of this + is that we setup the neon registers containing the kernel's values only once and then compute each XY using the preloaded registers as opposed as doing this for every XY value. + + The algorithm does not require allocating any additional memory amd computes the results directly in-place in two stages: + 1) Convolve plane 0 with kernel 0 and initialize the corresponding output plane with these values. + 2) Convolve the remaining planes and accumulate the results in the output's plane which has been initialized in step 1. + */ + for(int oz = 0; oz < num_planes_z; ++oz) + { + const int zoffset = id.z() + oz; + uint8_t *p_out_base = out_ptr + oz * output_stride_z; + // Step 1 + { + const auto ptr_k_r0 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + zoffset * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r1 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + zoffset * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r2 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + zoffset * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x); + const auto vk_r0 = load_matrix_row(ptr_k_r0); + const auto vk_r1 = load_matrix_row(ptr_k_r1); + const auto vk_r2 = load_matrix_row(ptr_k_r2); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + auto in_top = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 0) * input_stride_y); + auto in_mid = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 1) * input_stride_y); + auto in_low = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 2) * input_stride_y); + auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, + in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) + { + convolve_3x3<false>(in_top, in_mid, in_low, p_out, vk_r0, vk_r1, vk_r2, stridex); + } + } + } + // Step 2 + for(int p = 1; p < kernel_depth; ++p) + { + const uint8_t *ptr_k_base = k_ptr + p * kernel_stride_z + zoffset * kernel_stride_w; + const uint8_t *input_base = input_ptr + p * input_stride_z; + const auto ptr_k_r0 = reinterpret_cast<const T1 *>(ptr_k_base); + const auto ptr_k_r1 = reinterpret_cast<const T1 *>(ptr_k_base + kernel_stride_y); + const auto ptr_k_r2 = reinterpret_cast<const T1 *>(ptr_k_base + kernel_stride_y * 2); + const auto vk_r0 = load_matrix_row(ptr_k_r0); + const auto vk_r1 = load_matrix_row(ptr_k_r1); + const auto vk_r2 = load_matrix_row(ptr_k_r2); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + auto in_top = reinterpret_cast<const T1 *>(input_base + (ih + 0) * input_stride_y); + auto in_mid = reinterpret_cast<const T1 *>(input_base + (ih + 1) * input_stride_y); + auto in_low = reinterpret_cast<const T1 *>(input_base + (ih + 2) * input_stride_y); + auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, + in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) + { + convolve_3x3<true>(in_top, in_mid, in_low, p_out, vk_r0, vk_r1, vk_r2, stridex); + } + } + } + } + }, + in, out); + } +}; + +template <typename T1, typename T2, unsigned int stridex> +class convolver_5x5 +{ +public: + static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) + { + ARM_COMPUTE_UNUSED(num_elems_read_per_iteration); + const int input_stride_x = src->info()->strides_in_bytes().x(); + const int input_stride_y = src->info()->strides_in_bytes().y(); + const int input_stride_z = src->info()->strides_in_bytes().z(); + const int output_stride_y = dst->info()->strides_in_bytes().y(); + const int output_stride_z = dst->info()->strides_in_bytes().z(); + const int kernel_stride_x = weights->info()->strides_in_bytes().x(); + const int kernel_stride_y = weights->info()->strides_in_bytes().y(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_w = dst->info()->dimension(0); + const int output_h = dst->info()->dimension(1); + const int num_planes_z = window.z().end() - window.z().start(); + const int delta_input = get_input_num_elems_processed(num_elems_written_per_iteration, stridex); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); + + // setup output window for the iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, dst->info()->dimension(Window::DimX), dst->info()->dimension(Window::DimX))); + window_out.set(Window::DimY, Window::Dimension(0, dst->info()->dimension(Window::DimY), dst->info()->dimension(Window::DimY))); + window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), num_planes_z)); + + // setup input window for the iterator + Window window_in = window; + // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0 + window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Window window_k = calculate_max_window(*weights->info(), Steps(1u)); + + Iterator out(dst, window_out); + Iterator in(src, window_in); + Iterator k(weights, window_k); + + const uint8_t *k_ptr = k.ptr(); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const uint8_t *input_ptr = in.ptr() - conv_pad_left * input_stride_x - conv_pad_top * input_stride_y; + uint8_t *out_ptr = out.ptr(); + int ih = 0; + int oh = 0; + for(int oz = 0; oz < num_planes_z; ++oz) + { + const int zoffset = id.z() + oz; + uint8_t *p_out_base = out_ptr + oz * output_stride_z; + // Step 1 + { + const auto ptr_k_r0 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + zoffset * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r1 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + zoffset * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r2 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + zoffset * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r3 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + zoffset * kernel_stride_w + 3 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r4 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + zoffset * kernel_stride_w + 4 * kernel_stride_y + 0 * kernel_stride_x); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + auto in_0 = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 0) * input_stride_y); + auto in_1 = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 1) * input_stride_y); + auto in_2 = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 2) * input_stride_y); + auto in_3 = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 3) * input_stride_y); + auto in_4 = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 4) * input_stride_y); + auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, + in_0 += delta_input, in_1 += delta_input, in_2 += delta_input, in_3 += delta_input, in_4 += delta_input, p_out += num_elems_written_per_iteration) + { + auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4); + store_results<stridex>(p_out, vres); + } + } + } + // Step 2 + for(int p = 1; p < kernel_depth; ++p) + { + const auto ptr_k_r0 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + zoffset * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r1 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + zoffset * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r2 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + zoffset * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r3 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + zoffset * kernel_stride_w + 3 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r4 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + zoffset * kernel_stride_w + 4 * kernel_stride_y + 0 * kernel_stride_x); + + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + auto in_0 = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 0) * input_stride_y); + auto in_1 = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 1) * input_stride_y); + auto in_2 = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 2) * input_stride_y); + auto in_3 = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 3) * input_stride_y); + auto in_4 = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 4) * input_stride_y); + auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, + in_0 += delta_input, in_1 += delta_input, in_2 += delta_input, in_3 += delta_input, in_4 += delta_input, p_out += num_elems_written_per_iteration) + { + auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4); + accumulate_results<stridex>(p_out, vres); + } + } + } + } + }, + in, out); + } +}; + +float vreduce(const float32x4_t &v) +{ + auto v0 = wrapper::vgethigh(v); + auto v1 = wrapper::vgetlow(v); + auto v_out = wrapper::vadd(v0, v1); + + float a = wrapper::vgetlane(v_out, 0); + float b = wrapper::vgetlane(v_out, 1); + return a + b; +} + +template <typename T1, typename T2> +inline void convolve_1x1(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +{ + const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); + switch(conv_stride_x) + { + case 1: + convolver_1x1<T1, T2, 1>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + case 2: + convolver_1x1<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + case 3: + convolver_1x1<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + default: + ARM_COMPUTE_ERROR("Not implemented"); + } +} + +template <> +inline void convolve_1x1<float, float>(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +{ + const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); + if(run_optim_small_tensor(src)) + { + switch(conv_stride_x) + { + case 1: + convolver_w1x1_i8x8_f32<1>::convolve(window, src, weights, dst, conv_info); + break; + case 2: + convolver_w1x1_i8x8_f32<2>::convolve(window, src, weights, dst, conv_info); + break; + case 3: + convolver_w1x1_i8x8_f32<3>::convolve(window, src, weights, dst, conv_info); + break; + default: + ARM_COMPUTE_ERROR("Not implemented"); + } + } + else + { + switch(conv_stride_x) + { + case 1: + convolver_1x1<float, float, 1>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + case 2: + convolver_1x1<float, float, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + case 3: + convolver_1x1<float, float, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + default: + ARM_COMPUTE_ERROR("Not implemented"); + } + } +} + +template <typename T1, typename T2> +inline void convolve_3x3(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +{ + const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); + switch(conv_stride_x) + { + case 1: + convolver_3x3<T1, T2, 1>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + case 2: + convolver_3x3<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + case 3: + convolver_3x3<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + default: + ARM_COMPUTE_ERROR("Not implemented"); + } +} + +template <typename T1, typename T2> +inline void convolve_5x5(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +{ + const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); + switch(conv_stride_x) + { + case 1: + convolver_5x5<T1, T2, 1>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + case 2: + convolver_5x5<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + case 3: + convolver_5x5<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, src, weights, dst, conv_info); + break; + default: + ARM_COMPUTE_ERROR("Not implemented"); + } +} + +Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); + ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN); + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); + + const DataLayout data_layout = src->data_layout(); + const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported."); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(channel_idx) != src->dimension(channel_idx)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != weights->dimension(height_idx)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); + ARM_COMPUTE_RETURN_ERROR_ON(data_layout == DataLayout::NHWC && src->data_type() != DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(width_idx) > 3) && (src->data_type() == DataType::F16)); + + // Checks performed when output is configured + if(dst->total_size() != 0) + { + TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); + + DataType data_type = src->data_type(); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), output_shape); + ARM_COMPUTE_RETURN_ERROR_ON(dst->data_type() != data_type); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *weights, ITensorInfo *dst, const PadStrideInfo &conv_info, unsigned int &num_weight_elems_read_per_row, + unsigned int &num_elems_read_per_iteration, unsigned int &num_elems_written_per_iteration, BorderSize &border_size) +{ + ARM_COMPUTE_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN); + + const DataLayout data_layout = src->data_layout(); + const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + + // Calculate right and bottom border + unsigned int kernel_size = weights->dimension(width_idx); + const int conv_stride_x = std::get<0>(conv_info.stride()); + const int conv_stride_y = std::get<1>(conv_info.stride()); + const int input_width = src->dimension(width_idx); + + Window win{}; + bool window_changed = false; + + if(data_layout == DataLayout::NCHW) + { + switch(kernel_size) + { + case 1: + { + switch(src->data_type()) + { +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F16: + num_elems_written_per_iteration = 8; + break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + case DataType::F32: + if(run_optim_small_tensor_info(src)) + { + num_elems_written_per_iteration = 8; + } + else + { + num_elems_written_per_iteration = 4; + } + break; + default: + ARM_COMPUTE_ERROR("Data type not supported."); + break; + } + num_weight_elems_read_per_row = kernel_size; + num_elems_read_per_iteration = conv_stride_x * num_elems_written_per_iteration; + break; + } + case 3: + switch(src->data_type()) + { + case DataType::F32: + num_weight_elems_read_per_row = 4 + kernel_size - 1; + num_elems_read_per_iteration = 12; + num_elems_written_per_iteration = 16 >> conv_stride_x; + break; +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F16: + num_weight_elems_read_per_row = 8 + kernel_size - 1; + num_elems_read_per_iteration = 24; + num_elems_written_per_iteration = 32 >> conv_stride_x; + break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + default: + ARM_COMPUTE_ERROR("Data type not supported."); + break; + } + break; + case 5: + { + switch(src->data_type()) + { + case DataType::F32: + num_weight_elems_read_per_row = 4 + kernel_size - 1; + num_elems_read_per_iteration = 12; + num_elems_written_per_iteration = 16 >> conv_stride_x; + break; + default: + ARM_COMPUTE_ERROR("Data type not supported."); + break; + } + } + break; + default: + { + ARM_COMPUTE_ERROR("Not implemented"); + break; + } + } + + // Calculate right pad + int start_x = kernel_size / 2 - static_cast<int>(conv_info.pad_left()); + int end_x = ceil_to_multiple(static_cast<int>(dst->dimension(0)), num_elems_written_per_iteration) * conv_stride_x; + int upper_bound_w = ceil_to_multiple(start_x + end_x, num_elems_read_per_iteration) - input_width; + + // Calculate border + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); + const unsigned int conv_pad_right = std::max(upper_bound_w, 0); + const unsigned int conv_pad_bottom = conv_info.pad_bottom(); + + border_size.left = conv_pad_left; + border_size.top = conv_pad_top; + border_size.right = conv_pad_right; + border_size.bottom = conv_pad_bottom; + + // Configure window + win = calculate_max_window(*dst, Steps(num_elems_written_per_iteration)); + + AccessWindowRectangle input_access(src, -conv_pad_left, -conv_pad_top, + num_elems_read_per_iteration, kernel_size, + conv_stride_x, conv_stride_y); + AccessWindowStatic weights_access(weights, 0, 0, num_weight_elems_read_per_row, kernel_size); + AccessWindowHorizontal output_access(dst, 0, num_elems_written_per_iteration); + window_changed = update_window_and_padding(win, input_access, weights_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), dst->tensor_shape())); + } + else + { + // Configure window NHWC without any padding + win = calculate_max_window(*dst, Steps()); + } + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} + +bool have_zero_x_internal_padding(ITensorInfo *src, ITensorInfo *weights) +{ + return (src->padding().left == 0 && weights->padding().left == 0 && src->padding().right == 0 && weights->padding().right == 0); +} + +} // namespace + +template <typename T> +void CpuDirectConvolutionKernel::convolve_nhwc_optimized(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst) +{ + // This function assumes that input and weights have not padding in channel + + // Declare useful types + using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>; + using vector_type = typename vtype::type; + using tag_type = typename vtype::tag_type; + + // Scalar quantities + const int element_size = src->info()->element_size(); + const int input_stride_w = src->info()->strides_in_bytes().y() / element_size; + const int input_stride_h = src->info()->strides_in_bytes().z() / element_size; + const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size; + const int input_dim_w = src->info()->dimension(1); + const int input_dim_h = src->info()->dimension(2); + + const int output_stride_c = dst->info()->strides_in_bytes().x(); + + const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().y() / element_size; + const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().z() / element_size; + const int kernel_dim_w = weights->info()->dimension(1); + const int kernel_dim_h = weights->info()->dimension(2); + + const int conv_pad_top = _conv_info.pad_top(); + const int conv_pad_left = _conv_info.pad_left(); + const int conv_stride_w = std::get<0>(_conv_info.stride()); + const int conv_stride_h = std::get<1>(_conv_info.stride()); + + // Setup input window for the output iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + // Setup input window for the weights iterator + Window window_w = calculate_max_window(*weights->info(), Steps()); + window_w.set(Window::DimX, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimY, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimZ, Window::Dimension(0, 1, 1)); + + Iterator out(dst, window_out); + Iterator wei(weights, window_w); + + constexpr int num_elems_read_per_iteration = 16 / sizeof(T); + /* + * This implementation parallelize the full WC plane of input and weights by + * treating them as series of elements. So for example, a 3x3 weights and + * floating point vector operations of 4 elements per time, the first 3 + * channel elements of the first row would be taken and additionally the first + * element of the second row. The 9 elements in each single WC weight plane + * would require 2 4-element vector operations and a last single element operation. + * + * This works since when we create the input vector to multiply with the weights, + * the exact required elements are loaded in the same order. Therefore the + * multiplication works on the correct input/weight elements. + */ + execute_window_loop(window_out, [&](const Coordinates & id) + { + /* + * In here we create theoretical indexes which then we validate for both + * inputs and weights. + * As a reminder, this loop take each output point in NHW, C is treated + * in the weights loop. + */ + // We are computing the theoretical starting input starting points + const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left; + const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top; + const int in_w_end_t = in_w_start_t + kernel_dim_w; + const int in_h_end_t = in_h_start_t + kernel_dim_h; + + // We are computing the valid initial and ending input points by checking the borders + const int in_w_start = std::max(in_w_start_t, 0); + const int in_h_start = std::max(in_h_start_t, 0); + const int in_w_end = std::min(in_w_end_t, input_dim_w); + const int in_h_end = std::min(in_h_end_t, input_dim_h); + + // We use the input points to select the valid weight points to use + const int index_wc_start = (in_w_start - in_w_start_t) * kernel_stride_w; + const int index_h_start = in_h_start - in_h_start_t; + const int index_wc_end = (kernel_dim_w - (in_w_end_t - in_w_end)) * kernel_stride_w; + const int index_h_end = kernel_dim_h - (in_h_end_t - in_h_end); + + execute_window_loop(window_w, [&](const Coordinates & id_w) + { + /* + * This is the loop in the weights, and it goes along N (the batches) + * As a reminder, the batches of the weights are translated into the + * channels of the output + */ + const T *in_ptr_row = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + + id[3] * input_stride_n + in_w_start * input_stride_w + in_h_start * input_stride_h; + const T *weights_ptr_row = reinterpret_cast<const T *>(wei.ptr()) + index_h_start * kernel_stride_h; + uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c; + + T out_temp = static_cast<T>(0); + for(int index_h = index_h_start; index_h < index_h_end; ++index_h, in_ptr_row += input_stride_h, weights_ptr_row += kernel_stride_h) + { + const T *in_ptr_mover = in_ptr_row; + int index_wc = index_wc_start; + vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); + for(; index_wc <= index_wc_end - num_elems_read_per_iteration; index_wc += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration) + { + const auto src_vec = wrapper::vloadq(in_ptr_mover); + const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wc); + out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); + } + out_temp += vreduce(out_temp_vec); + for(; index_wc < index_wc_end; ++index_wc, ++in_ptr_mover) + { + const auto src_val = *(in_ptr_mover); + const auto w_val = *(weights_ptr_row + index_wc); + out_temp += src_val * w_val; + } + } + *(reinterpret_cast<T *>(out_ptr)) = out_temp; + }, + wei); + }, + out); +} + +template <typename T> +void CpuDirectConvolutionKernel::convolve_nhwc(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst) +{ + // Declare useful types + using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>; + using vector_type = typename vtype::type; + using tag_type = typename vtype::tag_type; + + // Scalar quantities + const int element_size = src->info()->element_size(); + const int input_stride_w = src->info()->strides_in_bytes().y() / element_size; + const int input_stride_h = src->info()->strides_in_bytes().z() / element_size; + const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size; + const int input_dim_w = src->info()->dimension(1); + const int input_dim_h = src->info()->dimension(2); + + const int output_stride_c = dst->info()->strides_in_bytes().x(); + + const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().y() / element_size; + const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().z() / element_size; + const int kernel_dim_w = weights->info()->dimension(1); + const int kernel_dim_h = weights->info()->dimension(2); + + const int conv_pad_top = _conv_info.pad_top(); + const int conv_pad_left = _conv_info.pad_left(); + const int conv_stride_w = std::get<0>(_conv_info.stride()); + const int conv_stride_h = std::get<1>(_conv_info.stride()); + + // Setup input window for the output iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + // Setup input window for the weights iterator + Window window_w = calculate_max_window(*weights->info(), Steps()); + window_w.set(Window::DimX, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimY, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimZ, Window::Dimension(0, 1, 1)); + + Iterator out(dst, window_out); + Iterator wei(weights, window_w); + + constexpr int num_elems_read_per_iteration = 16 / sizeof(T); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + // We are computing the theoretical starting input starting points + const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left; + const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top; + const int in_w_end_t = in_w_start_t + kernel_dim_w; + const int in_h_end_t = in_h_start_t + kernel_dim_h; + + // We are computing the valid initial and ending input points by checking the borders + const int in_w_start = std::max(in_w_start_t, 0); + const int in_h_start = std::max(in_h_start_t, 0); + const int in_w_end = std::min(in_w_end_t, input_dim_w); + const int in_h_end = std::min(in_h_end_t, input_dim_h); + + // We use the input points to select the valid weight points to use + const int wei_w_start = in_w_start - in_w_start_t; + const int wei_h_start = in_h_start - in_h_start_t; + const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end); + const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end); + + const int index_c_end = weights->info()->dimension(0); + const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n; + + execute_window_loop(window_w, [&](const Coordinates & id_w) + { + const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr()); + uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c; + + T out_temp = static_cast<T>(0); + for(int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; ++index_wei_h, ++index_in_h) + { + const T *const in_ptr_row = in_ptr_start + index_in_h * input_stride_h; + const T *const weights_ptr_row = weights_ptr_start + index_wei_h * kernel_stride_h; + for(int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end; ++index_wei_w, ++index_in_w) + { + const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w; + const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w; + int index_c = 0; + vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); + for(; index_c <= index_c_end - num_elems_read_per_iteration; index_c += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration, weights_ptr_mover += num_elems_read_per_iteration) + { + const auto src_vec = wrapper::vloadq(in_ptr_mover); + const auto w_vec = wrapper::vloadq(weights_ptr_mover); + out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); + } + out_temp += vreduce(out_temp_vec); + for(; index_c < index_c_end; ++index_c, ++in_ptr_mover, ++weights_ptr_mover) + { + const auto src_val = *(in_ptr_mover); + const auto w_val = *(weights_ptr_mover); + out_temp += src_val * w_val; + } + } + } + *(reinterpret_cast<T *>(out_ptr)) = out_temp; + }, + wei); + }, + out); +} + +BorderSize CpuDirectConvolutionKernel::border_size() const +{ + return _border_size; +} + +void CpuDirectConvolutionKernel::configure(ITensorInfo *src, ITensorInfo *weights, ITensorInfo *dst, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); + + _conv_info = conv_info; + _data_layout = src->data_layout(); + _kernel_size = weights->dimension(get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH)); + + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); + const unsigned int conv_pad_right = conv_info.pad_right(); + const unsigned int conv_pad_bottom = conv_info.pad_bottom(); + if(_data_layout == DataLayout::NCHW) + { + _border_size = BorderSize(conv_pad_top, conv_pad_right, conv_pad_bottom, conv_pad_left); + } + else + { + _border_size = BorderSize(0); + } + + // Get convolved dimensions + TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); + + DataType data_type = src->data_type(); + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*dst, output_shape, 1, data_type); + + // Perform validation step + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, dst, conv_info)); + + // Configure kernel window + auto win_config = validate_and_configure_window(src, weights, dst, conv_info, _num_weight_elems_read_per_row, + _num_elems_read_per_iteration, _num_elems_written_per_iteration, _border_size); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICpuKernel::configure(win_config.second); +} + +Status CpuDirectConvolutionKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info) +{ + unsigned int num_weight_elems_read_per_row = 0; + unsigned int num_elems_read_per_iteration = 0; + unsigned int num_elems_written_per_iteration = 0; + BorderSize border_size = {}; + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, dst, conv_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), + weights->clone().get(), + dst->clone().get(), + conv_info, + num_weight_elems_read_per_row, + num_elems_read_per_iteration, + num_elems_written_per_iteration, + border_size) + .first); + + return Status{}; +} + +void CpuDirectConvolutionKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); + + auto src = tensors.get_const_tensor(TensorType::ACL_SRC_0); + auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); + auto dst = tensors.get_tensor(TensorType::ACL_DST); + const int kernel_size = weights->info()->dimension(get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH)); + + if(_data_layout == DataLayout::NCHW) + { + switch(kernel_size) + { + case 1: + { + switch(src->info()->data_type()) + { + case DataType::F32: + convolve_1x1<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, src, weights, dst, _conv_info); + break; +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F16: + convolve_1x1<float16_t, float16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, src, weights, dst, _conv_info); + break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + default: + ARM_COMPUTE_ERROR("Data type not supported"); + break; + } + break; + } + case 3: + { + switch(src->info()->data_type()) + { + case DataType::F32: + convolve_3x3<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, src, weights, dst, _conv_info); + break; +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F16: + convolve_3x3<float16_t, float16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, src, weights, dst, _conv_info); + break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + default: + ARM_COMPUTE_ERROR("Data type not supported"); + break; + } + break; + } + case 5: + { + switch(src->info()->data_type()) + { + case DataType::F32: + convolve_5x5<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, src, weights, dst, _conv_info); + break; + default: + ARM_COMPUTE_ERROR("Data type not supported"); + break; + } + break; + } + default: + { + ARM_COMPUTE_ERROR("Only kernel sizes 1x1, 3x3 and 5x5 are supported."); + break; + } + } + } + else + { + switch(src->info()->data_type()) + { + case DataType::F32: + { + if(have_zero_x_internal_padding(src->info(), weights->info())) + { + convolve_nhwc_optimized<float>(window, src, weights, dst); + } + else + { + convolve_nhwc<float>(window, src, weights, dst); + } + break; + } + default: + ARM_COMPUTE_ERROR("Data type not supported"); + break; + } + } +} +const char *CpuDirectConvolutionKernel::name() const +{ + return "CpuDirectConvolutionLayerKernel"; +} +} // namespace kernels +} // namespace cpu +} // namespace arm_compute |