| /* |
| * Copyright (c) 2017-2020 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/NEON/kernels/NEDirectConvolutionLayerKernel.h" |
| #include "arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/CPP/Validate.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/NEON/NEFixedPoint.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 "arm_compute/core/NEON/wrapper/wrapper.h" |
| #include <algorithm> |
| #include <arm_neon.h> |
| |
| using namespace arm_compute; |
| using namespace arm_compute::detail; |
| |
| 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 NEON 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 *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON(input->info()->dimension(Window::DimX) > small_tensor_size_optim); |
| ARM_COMPUTE_ERROR_ON(input->info()->dimension(Window::DimY) > small_tensor_size_optim); |
| |
| const int input_stride_x = input->info()->strides_in_bytes().x(); |
| const int input_stride_y = input->info()->strides_in_bytes().y(); |
| const int input_stride_z = input->info()->strides_in_bytes().z(); |
| const int output_stride_y = output->info()->strides_in_bytes().y(); |
| const int output_stride_z = output->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 = output->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, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); |
| window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->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(output, window_out); |
| Iterator in(input, 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 *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| const int input_stride_x = input->info()->strides_in_bytes().x(); |
| const int input_stride_y = input->info()->strides_in_bytes().y(); |
| const int input_stride_z = input->info()->strides_in_bytes().z(); |
| const int output_stride_y = output->info()->strides_in_bytes().y(); |
| const int output_stride_z = output->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 = output->info()->dimension(0); |
| const int output_h = output->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, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); |
| window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->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(output, window_out); |
| Iterator in(input, 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> |
| class convolver_nhwc |
| { |
| public: |
| static void convolve(const Window &window, uint32_t kernel_size, unsigned int num_elems_read_per_iteration, |
| const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| const int input_width = input->info()->dimension(0); |
| const int input_depth = input->info()->dimension(2); |
| const int input_stride_x = input->info()->strides_in_bytes().x(); |
| const int input_stride_y = input->info()->strides_in_bytes().y(); |
| const int input_stride_z = input->info()->strides_in_bytes().z(); |
| const int output_stride_x = output->info()->strides_in_bytes().x(); |
| 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 conv_pad_top = conv_info.pad_top(); |
| const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); |
| const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); |
| const T1 zero = 0; |
| |
| // Setup input window for the input iterator |
| Window window_in = window; |
| 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)); |
| |
| // 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_k = calculate_max_window(*weights->info(), Steps()); |
| window_k.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| window_k.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| window_k.set(Window::DimZ, Window::Dimension(0, 1, 1)); |
| window_k.set(3, Window::Dimension(0, weights->info()->dimension(3), 1)); |
| |
| Iterator in(input, window_in); |
| Iterator out(output, window_out); |
| Iterator k(weights, window_k); |
| |
| execute_window_loop(window_k, [&](const Coordinates & id_k) |
| { |
| execute_window_loop(window_out, [&](const Coordinates & id) |
| { |
| const auto in_y = static_cast<int>(id.y() * conv_stride_x - conv_info.pad_left()); |
| const auto in_z = static_cast<int>(id.z() * conv_stride_y - conv_pad_top); |
| |
| const uint8_t *in_ptr = in.ptr() + in_y * input_stride_y + in_z * input_stride_z; |
| uint8_t *out_ptr = out.ptr() + id_k[3] * output_stride_x; |
| |
| T1 out_val = 0; |
| |
| auto in_addr_base0 = in_ptr; |
| auto we_addr_base0 = k.ptr(); |
| |
| for(uint32_t z = 0; z < kernel_size; ++z, in_addr_base0 += input_stride_z, we_addr_base0 += kernel_stride_z) |
| { |
| const int in_z = id.z() * conv_stride_y + z - conv_pad_top; |
| |
| if(in_z >= 0 && in_z < input_depth) // If false, pad top/bottom |
| { |
| auto in_addr_base1 = in_addr_base0; |
| auto we_addr_base1 = we_addr_base0; |
| |
| for(uint32_t y = 0; y < kernel_size; ++y, in_addr_base1 += input_stride_y, we_addr_base1 += kernel_stride_y) |
| { |
| auto out_values = internal_vdupq_n(zero); |
| |
| int x = 0; |
| int no_leftover = input_width - num_elems_read_per_iteration; |
| |
| for(; x < no_leftover; x += num_elems_read_per_iteration) |
| { |
| const auto in_addr = reinterpret_cast<const T1 *>(in_addr_base1 + x * input_stride_x); |
| const auto in_values = internal_vld1q<1>(in_addr); |
| |
| const auto we_addr = reinterpret_cast<const T1 *>(we_addr_base1 + x * kernel_stride_x); |
| const auto we_values = internal_vld1q<1>(we_addr); |
| |
| out_values = internal_vmlal(out_values, in_values, we_values); |
| } |
| |
| auto carry_addition = wrapper::vpadd(wrapper::vgethigh(out_values), wrapper::vgetlow(out_values)); |
| carry_addition = wrapper::vpadd(carry_addition, carry_addition); |
| out_val += wrapper::vgetlane(carry_addition, 0); |
| |
| // Leftover |
| for(; x < input_width; ++x) |
| { |
| const auto in_addr = reinterpret_cast<const T1 *>(in_addr_base1 + x * input_stride_x); |
| const auto in_value = *(in_addr); |
| |
| const auto we_addr = reinterpret_cast<const T1 *>(we_addr_base1 + x * kernel_stride_x); |
| const auto we_value = *(we_addr); |
| |
| out_val += in_value * we_value; |
| } |
| } |
| } |
| } |
| |
| *(reinterpret_cast<T1 *>(out_ptr)) = out_val; |
| }, |
| in, out); |
| }, |
| k); |
| } |
| }; |
| |
| 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 *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_UNUSED(num_elems_read_per_iteration); |
| const int input_stride_x = input->info()->strides_in_bytes().x(); |
| const int input_stride_y = input->info()->strides_in_bytes().y(); |
| const int input_stride_z = input->info()->strides_in_bytes().z(); |
| const int output_stride_y = output->info()->strides_in_bytes().y(); |
| const int output_stride_z = output->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 = output->info()->dimension(0); |
| const int output_h = output->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, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); |
| window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->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(output, window_out); |
| Iterator in(input, 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 *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_UNUSED(num_elems_read_per_iteration); |
| const int input_stride_x = input->info()->strides_in_bytes().x(); |
| const int input_stride_y = input->info()->strides_in_bytes().y(); |
| const int input_stride_z = input->info()->strides_in_bytes().z(); |
| const int output_stride_y = output->info()->strides_in_bytes().y(); |
| const int output_stride_z = output->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 = output->info()->dimension(0); |
| const int output_h = output->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, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); |
| window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->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(output, window_out); |
| Iterator in(input, 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); |
| } |
| }; |
| |
| inline void convolve_row1x9_nhwc(const float *row_ptr, const float *weights_ptr, size_t src_stride_y, size_t weights_stride_y, |
| float32x4_t &acc0, float32x4_t &acc1, float32x4_t &acc2, float32x4_t &acc3) |
| { |
| // Load 4 channels for each of the 12 inputs values along the same X spatial dimension |
| const float32x4_t src0 = wrapper::vloadq(row_ptr); |
| const float32x4_t src1 = wrapper::vloadq(row_ptr + 1 * src_stride_y); |
| const float32x4_t src2 = wrapper::vloadq(row_ptr + 2 * src_stride_y); |
| const float32x4_t src3 = wrapper::vloadq(row_ptr + 3 * src_stride_y); |
| const float32x4_t src4 = wrapper::vloadq(row_ptr + 4 * src_stride_y); |
| const float32x4_t src5 = wrapper::vloadq(row_ptr + 5 * src_stride_y); |
| const float32x4_t src6 = wrapper::vloadq(row_ptr + 6 * src_stride_y); |
| const float32x4_t src7 = wrapper::vloadq(row_ptr + 7 * src_stride_y); |
| const float32x4_t src8 = wrapper::vloadq(row_ptr + 8 * src_stride_y); |
| const float32x4_t src9 = wrapper::vloadq(row_ptr + 9 * src_stride_y); |
| const float32x4_t src10 = wrapper::vloadq(row_ptr + 10 * src_stride_y); |
| const float32x4_t src11 = wrapper::vloadq(row_ptr + 11 * src_stride_y); |
| |
| // Load 4 channels for each of the 9 weights values along the same X spatial dimension |
| const float32x4_t w0 = wrapper::vloadq(weights_ptr); |
| const float32x4_t w1 = wrapper::vloadq(weights_ptr + 1 * weights_stride_y); |
| const float32x4_t w2 = wrapper::vloadq(weights_ptr + 2 * weights_stride_y); |
| const float32x4_t w3 = wrapper::vloadq(weights_ptr + 3 * weights_stride_y); |
| const float32x4_t w4 = wrapper::vloadq(weights_ptr + 4 * weights_stride_y); |
| const float32x4_t w5 = wrapper::vloadq(weights_ptr + 5 * weights_stride_y); |
| const float32x4_t w6 = wrapper::vloadq(weights_ptr + 6 * weights_stride_y); |
| const float32x4_t w7 = wrapper::vloadq(weights_ptr + 7 * weights_stride_y); |
| const float32x4_t w8 = wrapper::vloadq(weights_ptr + 8 * weights_stride_y); |
| |
| // Store 4 channels for each of the 4 output values along the same X spatial dimension |
| acc0 = wrapper::vmla(acc0, w0, src0); |
| acc0 = wrapper::vmla(acc0, w1, src1); |
| acc0 = wrapper::vmla(acc0, w2, src2); |
| acc0 = wrapper::vmla(acc0, w3, src3); |
| acc0 = wrapper::vmla(acc0, w4, src4); |
| acc0 = wrapper::vmla(acc0, w5, src5); |
| acc0 = wrapper::vmla(acc0, w6, src6); |
| acc0 = wrapper::vmla(acc0, w7, src7); |
| acc0 = wrapper::vmla(acc0, w8, src8); |
| |
| acc1 = wrapper::vmla(acc1, w0, src1); |
| acc1 = wrapper::vmla(acc1, w1, src2); |
| acc1 = wrapper::vmla(acc1, w2, src3); |
| acc1 = wrapper::vmla(acc1, w3, src4); |
| acc1 = wrapper::vmla(acc1, w4, src5); |
| acc1 = wrapper::vmla(acc1, w5, src6); |
| acc1 = wrapper::vmla(acc1, w6, src7); |
| acc1 = wrapper::vmla(acc1, w7, src8); |
| acc1 = wrapper::vmla(acc1, w8, src9); |
| |
| acc2 = wrapper::vmla(acc2, w0, src2); |
| acc2 = wrapper::vmla(acc2, w1, src3); |
| acc2 = wrapper::vmla(acc2, w2, src4); |
| acc2 = wrapper::vmla(acc2, w3, src5); |
| acc2 = wrapper::vmla(acc2, w4, src6); |
| acc2 = wrapper::vmla(acc2, w5, src7); |
| acc2 = wrapper::vmla(acc2, w6, src8); |
| acc2 = wrapper::vmla(acc2, w7, src9); |
| acc2 = wrapper::vmla(acc2, w8, src10); |
| |
| acc3 = wrapper::vmla(acc3, w0, src3); |
| acc3 = wrapper::vmla(acc3, w1, src4); |
| acc3 = wrapper::vmla(acc3, w2, src5); |
| acc3 = wrapper::vmla(acc3, w3, src6); |
| acc3 = wrapper::vmla(acc3, w4, src7); |
| acc3 = wrapper::vmla(acc3, w5, src8); |
| acc3 = wrapper::vmla(acc3, w6, src9); |
| acc3 = wrapper::vmla(acc3, w7, src10); |
| acc3 = wrapper::vmla(acc3, w8, src11); |
| } |
| |
| 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 V> |
| class convolver_9x9_nhwc |
| { |
| public: |
| static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, |
| const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| // Declare useful types |
| using vector_type = typename V::type; |
| using scalar_type = typename V::scalar_type; |
| using tag_type = typename V::tag_type; |
| |
| // Scalar quantities |
| const int element_size = input->info()->element_size(); |
| const int input_width = input->info()->dimension(0); |
| const int input_depth = input->info()->dimension(2); |
| const int input_stride_y = input->info()->strides_in_bytes().y() / element_size; |
| const int input_stride_z = input->info()->strides_in_bytes().z() / element_size; |
| const int input_stride_w = input->info()->strides_in_bytes()[3]; |
| const int output_stride_x = output->info()->strides_in_bytes().x(); |
| const int output_stride_y = output->info()->strides_in_bytes().y(); |
| const int kernel_stride_y = weights->info()->strides_in_bytes().y() / element_size; |
| const int kernel_stride_z = weights->info()->strides_in_bytes().z() / element_size; |
| const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); |
| const unsigned int conv_pad_top = conv_info.pad_top(); |
| const unsigned int conv_pad_left = conv_info.pad_left(); |
| |
| // Setup input window for the input iterator |
| Window window_in = window; |
| 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)); |
| |
| // 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_k = calculate_max_window(*weights->info(), Steps()); |
| window_k.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| window_k.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| window_k.set(Window::DimZ, Window::Dimension(0, 1, 1)); |
| window_k.set(3, Window::Dimension(0, weights->info()->dimension(3), 1)); |
| |
| Iterator in(input, window_in); |
| Iterator out(output, window_out); |
| Iterator k(weights, window_k); |
| |
| // Calculate the max_offset. |
| // max_offset is the offset for the last NOT valid value in the Z dimension (spatial dimension Y for NHWC) |
| // |******************| |
| // | pad_top | |
| // |******************| |
| // | | |
| // | plane0 | |
| // | batch0 | |
| // |__________________| |
| // |******************| Batch 0 |
| // | pad_bottom | |
| // | pad_top | |
| // |******************| |
| // | | |
| // | plane1 | |
| // | batch0 | |
| // |__________________|-----> max_offset |
| // |******************| |
| // | pad_bottom | |
| // | pad_top | |
| // |******************| |
| // | | |
| // | plane0 | |
| // | batch1 | |
| // |__________________| |
| // |******************| Batch 1 |
| // | pad_bottom | |
| // | pad_top | |
| // |******************| |
| // | | |
| // | plane1 | |
| // | batch1 | |
| // |__________________| |
| // | pad_bottom | |
| // |******************| |
| const int max_offset = input_stride_z * input_depth - (input->info()->padding().bottom + input->info()->padding().top) * input_stride_y; |
| execute_window_loop(window_k, [&](const Coordinates & id_k) // loop on the batch size |
| { |
| |
| execute_window_loop(window_out, [&](const Coordinates & id) |
| { |
| const auto y_offset = int(id.y() - conv_pad_left) * input_stride_y; |
| |
| // Buffer pointers |
| const scalar_type *in_ptr = reinterpret_cast<scalar_type *>(input->buffer() + input->info()->offset_first_element_in_bytes() + id[3] * input_stride_w); |
| const scalar_type *weights_ptr = reinterpret_cast<scalar_type *>(k.ptr()); |
| uint8_t *out_ptr = out.ptr() + id_k[3] * output_stride_x; |
| |
| // Output elements |
| vector_type out0 = wrapper::vdup_n(scalar_type(0), tag_type()); |
| vector_type out1 = wrapper::vdup_n(scalar_type(0), tag_type()); |
| vector_type out2 = wrapper::vdup_n(scalar_type(0), tag_type()); |
| vector_type out3 = wrapper::vdup_n(scalar_type(0), tag_type()); |
| |
| // Reduce along the feature maps |
| for(int x = 0; x < input_width; x += num_elems_read_per_iteration) |
| { |
| // z == 0 |
| auto in_z = static_cast<int>(id.z() * conv_stride_y - conv_pad_top); |
| in_z = std::min(static_cast<unsigned int>(in_z), static_cast<unsigned int>(input_depth)); |
| auto offset = y_offset + in_z * input_stride_z; |
| offset = std::min(offset, max_offset); |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 0 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| |
| // z == 1 |
| in_z = static_cast<int>(id.z() * conv_stride_y - conv_pad_top + 1); |
| in_z = std::min(static_cast<unsigned int>(in_z), static_cast<unsigned int>(input_depth)); |
| offset = y_offset + in_z * input_stride_z; |
| offset = std::min(offset, max_offset); |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 1 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| |
| // z == 2 |
| in_z = static_cast<int>(id.z() * conv_stride_y - conv_pad_top + 2); |
| in_z = std::min(static_cast<unsigned int>(in_z), static_cast<unsigned int>(input_depth)); |
| offset = y_offset + in_z * input_stride_z; |
| offset = std::min(offset, max_offset); |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 2 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| |
| // z == 3 |
| in_z = static_cast<int>(id.z() * conv_stride_y - conv_pad_top + 3); |
| offset = y_offset + in_z * input_stride_z; |
| offset = std::min(offset, max_offset); |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 3 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| |
| // z == 4 |
| in_z = static_cast<int>(id.z() * conv_stride_y - conv_pad_top + 4); |
| offset = y_offset + in_z * input_stride_z; |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 4 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| |
| // z == 5 |
| offset += input_stride_z; |
| offset = std::min(offset, max_offset); |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 5 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| |
| // z == 6 |
| offset += input_stride_z; |
| offset = std::min(offset, max_offset); |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 6 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| |
| // z == 7 |
| offset += input_stride_z; |
| offset = std::min(offset, max_offset); |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 7 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| |
| // z == 8 |
| offset += input_stride_z; |
| offset = std::min(offset, max_offset); |
| convolve_row1x9_nhwc(in_ptr + offset + x, weights_ptr + 8 * kernel_stride_z + x, input_stride_y, kernel_stride_y, out0, out1, out2, out3); |
| } |
| |
| *(reinterpret_cast<scalar_type *>(out_ptr + 0 * output_stride_y)) = vreduce(out0); |
| *(reinterpret_cast<scalar_type *>(out_ptr + 1 * output_stride_y)) = vreduce(out1); |
| *(reinterpret_cast<scalar_type *>(out_ptr + 2 * output_stride_y)) = vreduce(out2); |
| *(reinterpret_cast<scalar_type *>(out_ptr + 3 * output_stride_y)) = vreduce(out3); |
| }, |
| in, out); |
| }, |
| k); |
| } |
| }; |
| |
| 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 *input, const ITensor *weights, ITensor *output, 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, input, weights, output, conv_info); |
| break; |
| case 2: |
| convolver_1x1<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); |
| break; |
| case 3: |
| convolver_1x1<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, 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 *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); |
| if(run_optim_small_tensor(input)) |
| { |
| switch(conv_stride_x) |
| { |
| case 1: |
| convolver_w1x1_i8x8_f32<1>::convolve(window, input, weights, output, conv_info); |
| break; |
| case 2: |
| convolver_w1x1_i8x8_f32<2>::convolve(window, input, weights, output, conv_info); |
| break; |
| case 3: |
| convolver_w1x1_i8x8_f32<3>::convolve(window, input, weights, output, 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, input, weights, output, conv_info); |
| break; |
| case 2: |
| convolver_1x1<float, float, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); |
| break; |
| case 3: |
| convolver_1x1<float, float, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, 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 *input, const ITensor *weights, ITensor *output, 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, input, weights, output, conv_info); |
| break; |
| case 2: |
| convolver_3x3<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); |
| break; |
| case 3: |
| convolver_3x3<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, 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 *input, const ITensor *weights, ITensor *output, 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, input, weights, output, conv_info); |
| break; |
| case 2: |
| convolver_5x5<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); |
| break; |
| case 3: |
| convolver_5x5<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not implemented"); |
| } |
| } |
| |
| template <typename V> |
| inline void convolve_9x9_nhwc(const Window &window, unsigned int num_elems_read_per_iteration, |
| const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); |
| switch(conv_stride_x) |
| { |
| case 1: |
| convolver_9x9_nhwc<V>::convolve(window, num_elems_read_per_iteration, input, weights, output, conv_info); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not implemented"); |
| } |
| } |
| |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| |
| const DataLayout data_layout = input->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) != input->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 && input->data_type() != DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(width_idx) > 3) && (input->data_type() == DataType::F16)); |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info); |
| |
| DataType data_type = input->data_type(); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON(output->data_type() != data_type); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, 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(input->data_layout() == DataLayout::UNKNOWN); |
| |
| const DataLayout data_layout = input->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 = input->dimension(width_idx); |
| |
| Window win{}; |
| bool window_changed = false; |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| switch(kernel_size) |
| { |
| case 1: |
| { |
| switch(input->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(input)) |
| { |
| 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(input->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(input->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>(output->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(*output, Steps(num_elems_written_per_iteration)); |
| |
| AccessWindowRectangle input_access(input, -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(output, 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(), output->tensor_shape())); |
| } |
| else |
| { |
| if(kernel_size == 9) |
| { |
| border_size.left = 0; |
| border_size.top = conv_info.pad_left(); |
| |
| const int num_elems_read_per_iteration_x = 4; |
| const int num_elems_written_per_iteration_x = 1; |
| const int num_elems_read_per_iteration_y = 12; |
| const int num_elems_written_per_iteration_y = 4; |
| |
| num_elems_read_per_iteration = num_elems_read_per_iteration_x; |
| num_elems_written_per_iteration = num_elems_written_per_iteration_x; |
| |
| border_size.right = num_elems_read_per_iteration_x; |
| if((conv_info.pad_bottom() != 0) || (conv_info.pad_top() != 0)) |
| { |
| // If bottom or top padding are set, we need to read num_elems_read_per_iteration_y rows to zero. |
| // Since num_elems_read_per_iteration_y is always greater than conv_info.pad_right() we can set |
| // the bottom padding to num_elems_read_per_iteration_y |
| border_size.bottom = num_elems_read_per_iteration_y; |
| } |
| else if(conv_info.pad_right() != 0) |
| { |
| // Convetional border padding. Fill the bottom paddings so that we can read in batch of num_elems_read_per_iteration_y |
| border_size.bottom = ceil_to_multiple(input->dimension(1) + conv_info.pad_right(), num_elems_read_per_iteration_y) - input->dimension(1); |
| } |
| else |
| { |
| // No padding |
| border_size.bottom = 0; |
| } |
| |
| win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); |
| |
| AccessWindowStatic input_access(input, 0, -border_size.top, |
| ceil_to_multiple(input->dimension(0), num_elems_read_per_iteration_x), |
| input->dimension(1) + border_size.bottom); |
| |
| AccessWindowStatic weights_access(weights, 0, 0, ceil_to_multiple(weights->dimension(0), num_elems_read_per_iteration_x), weights->dimension(1)); |
| AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); |
| window_changed = update_window_and_padding(win, input_access, weights_access, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); |
| } |
| else |
| { |
| border_size.left = 0; |
| border_size.top = conv_info.pad_left(); |
| border_size.right = 0; |
| border_size.bottom = conv_info.pad_right(); |
| num_elems_read_per_iteration = 16 / element_size_from_data_type(input->data_type()); |
| win = calculate_max_window(*output, Steps()); |
| |
| AccessWindowRectangle input_access(input, 0, -border_size.top, num_elems_read_per_iteration, kernel_size, 1.f, conv_stride_x); |
| AccessWindowRectangle weights_access(weights, 0, 0, num_elems_read_per_iteration, kernel_size); |
| window_changed = update_window_and_padding(win, input_access, weights_access); |
| } |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| NEDirectConvolutionLayerKernel::NEDirectConvolutionLayerKernel() |
| : _input(nullptr), _weights(nullptr), _output(nullptr), _conv_info(), _border_size(0), _kernel_size(0), _num_weight_elems_read_per_row(0), _num_elems_read_per_iteration(0), |
| _num_elems_written_per_iteration(0) |
| { |
| } |
| |
| BorderSize NEDirectConvolutionLayerKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void NEDirectConvolutionLayerKernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| |
| _input = input; |
| _weights = weights; |
| _output = output; |
| _conv_info = conv_info; |
| _kernel_size = weights->info()->dimension(get_data_layout_dimension_index(weights->info()->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(); |
| _border_size = BorderSize(conv_pad_top, conv_pad_right, conv_pad_bottom, conv_pad_left); |
| |
| // Get convolved dimensions |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input->info(), *weights->info(), conv_info); |
| |
| DataType data_type = input->info()->data_type(); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, data_type); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info)); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), 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); |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEDirectConvolutionLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, 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(input, weights, output, conv_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), |
| weights->clone().get(), |
| output->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 NEDirectConvolutionLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr); |
| |
| const int kernel_size = _weights->info()->dimension(get_data_layout_dimension_index(_weights->info()->data_layout(), DataLayoutDimension::WIDTH)); |
| |
| if(_input->info()->data_layout() == DataLayout::NCHW) |
| { |
| switch(kernel_size) |
| { |
| case 1: |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| convolve_1x1<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _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, _input, _weights, _output, _conv_info); |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| break; |
| } |
| case 3: |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| convolve_3x3<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _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, _input, _weights, _output, _conv_info); |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| break; |
| } |
| case 5: |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| convolve_5x5<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _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 |
| { |
| const int kernel_size = _weights->info()->dimension(get_data_layout_dimension_index(_weights->info()->data_layout(), DataLayoutDimension::WIDTH)); |
| const int stride_x = std::get<0>(_conv_info.stride()); |
| const int stride_y = std::get<1>(_conv_info.stride()); |
| |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| { |
| if(kernel_size == 9 && stride_x == 1 && stride_y == 1) |
| { |
| using vtype = wrapper::traits::neon_vector<float, 4>; |
| convolve_9x9_nhwc<vtype>(window, _num_elems_read_per_iteration, _input, _weights, _output, _conv_info); |
| } |
| else |
| { |
| convolver_nhwc<float>::convolve(window, kernel_size, _num_elems_read_per_iteration, _input, _weights, _output, _conv_info); |
| } |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| } |
| } |