| /* |
| * Copyright (c) 2017 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/AccessWindowStatic.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 <algorithm> |
| #include <arm_neon.h> |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| template <unsigned int stridex> |
| qint16x8_t internal_vld1q(const qint16_t *in); |
| |
| template <> |
| qint16x8_t internal_vld1q<1>(const qint16_t *in) |
| { |
| return vld1q_qs16(in); |
| } |
| |
| template <> |
| qint16x8_t internal_vld1q<2>(const qint16_t *in) |
| { |
| const int16x8x2_t tmp = vld2q_s16(in); |
| return tmp.val[0]; |
| } |
| |
| template <> |
| qint16x8_t internal_vld1q<3>(const qint16_t *in) |
| { |
| const int16x8x3_t tmp = vld3q_s16(in); |
| return tmp.val[0]; |
| } |
| |
| inline qint16x8_t internal_vdupq_n(qint16_t v) |
| { |
| return vdupq_n_qs16(v); |
| } |
| |
| #ifdef ARM_COMPUTE_ENABLE_FP16 |
| 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, int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| return vmulq_f16(x, y); |
| } |
| |
| inline float16x8_t internal_vmlal(const float16x8_t &x, const float16x8_t &y, const float16x8_t &z, int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| return vaddq_f16(x, vmulq_f16(y, z)); |
| } |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| |
| 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, int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| return vmulq_f32(x, y); |
| } |
| |
| inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z, int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| return vmlaq_f32(x, y, z); |
| } |
| |
| template <unsigned int stridex> |
| qint8x8_t internal_vld1q(const qint8_t *in); |
| |
| template <> |
| qint8x8_t internal_vld1q<1>(const qint8_t *in) |
| { |
| return vld1_qs8(in); |
| } |
| |
| template <> |
| qint8x8_t internal_vld1q<2>(const qint8_t *in) |
| { |
| const qint8x8x2_t tmp = vld2_s8(in); |
| return tmp.val[0]; |
| } |
| |
| template <> |
| qint8x8_t internal_vld1q<3>(const qint8_t *in) |
| { |
| const qint8x8x3_t tmp = vld3_s8(in); |
| return tmp.val[0]; |
| } |
| |
| inline qint8x8_t internal_vdupq_n(qint8_t v) |
| { |
| return vdup_n_qs8(v); |
| } |
| |
| inline qint16x8_t internal_vmull(const qint8x8_t &x, const qint8x8_t &y, int fixed_point_position) |
| { |
| return vmull_qs8(x, y, fixed_point_position); |
| } |
| |
| inline qint16x8_t internal_vmlal(const qint16x8_t &x, const qint8x8_t &y, const qint8x8_t &z, int fixed_point_position) |
| { |
| return vqmlal_qs8(x, y, z, fixed_point_position); |
| } |
| |
| inline void internal_vst1q(qint16_t *p, const qint16x8_t &v) |
| { |
| vst1q_qs16(p, v); |
| } |
| |
| inline void internal_vst1q(int32_t *p, const qint32x4x2_t &v) |
| { |
| vst1q_s32(p, v.val[0]); |
| vst1q_s32(p + 4, v.val[1]); |
| } |
| |
| template <unsigned int stridex> |
| qint32x4x2_t internal_vld1q(const qint32_t *in); |
| |
| template <> |
| qint32x4x2_t internal_vld1q<1>(const qint32_t *in) |
| { |
| const qint32x4x2_t r = |
| { |
| { |
| vld1q_s32(in), |
| vld1q_s32(in + 4) |
| } |
| }; |
| return r; |
| } |
| |
| inline qint32x4x2_t internal_vmull(const qint16x8_t &x, const qint16x8_t &y, int fixed_point_position) |
| { |
| const qint32x4x2_t r = |
| { |
| { |
| vmull_qs16(vget_low_s16(x), vget_low_s16(y), fixed_point_position), |
| vmull_qs16(vget_high_s16(x), vget_high_s16(y), fixed_point_position), |
| } |
| }; |
| return r; |
| } |
| |
| inline qint32x4x2_t internal_vmlal(const qint32x4x2_t &x, const qint16x8_t &y, const qint16x8_t &z, int fixed_point_position) |
| { |
| const qint32x4x2_t r = |
| { |
| { |
| vqmlal_qs16(x.val[0], vget_low_s16(y), vget_low_s16(z), fixed_point_position), |
| vqmlal_qs16(x.val[1], vget_high_s16(y), vget_high_s16(z), fixed_point_position) |
| } |
| }; |
| return r; |
| } |
| |
| constexpr int SmallTensorSizeOptim = 8; |
| inline bool run_optim_small_tensor(const ITensor *t) |
| { |
| return t->info()->dimension(Window::DimX) <= SmallTensorSizeOptim && t->info()->dimension(Window::DimY) <= SmallTensorSizeOptim; |
| } |
| |
| // 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) > SmallTensorSizeOptim); |
| ARM_COMPUTE_ERROR_ON(input->info()->dimension(Window::DimY) > SmallTensorSizeOptim); |
| |
| 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()); |
| |
| // 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(); |
| uint8_t *out_ptr = out.ptr(); |
| int ih = 0; |
| int oh = 0; |
| float32x4_t accum0[SmallTensorSizeOptim] = { 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) }; |
| float32x4_t accum1[SmallTensorSizeOptim] = { 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_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 int fixed_point_position = input->info()->fixed_point_position(); |
| |
| // 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(); |
| 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), fixed_point_position)); |
| } |
| } |
| } |
| |
| // 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), fixed_point_position)); |
| } |
| } |
| } |
| } |
| }, |
| in, out); |
| } |
| }; |
| |
| #ifdef ARM_COMPUTE_ENABLE_FP16 |
| inline float16x8x3_t load_matrix_row(const float16_t *ptr) |
| { |
| /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes: |
| r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */ |
| const float16x8x3_t r = |
| { |
| { |
| vld1q_dup_f16(ptr), |
| vld1q_dup_f16(1 + ptr), |
| vld1q_dup_f16(2 + ptr) |
| } |
| }; |
| return r; |
| } |
| |
| template <unsigned int stridex> |
| float16x8x2_t convolve_3x3(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, |
| int fixed_point_position); |
| |
| template <> |
| float16x8x2_t convolve_3x3<1>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, |
| int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| |
| const float16x8x3_t vtop = |
| { |
| { |
| vld1q_f16(in_top), |
| vld1q_f16(in_top + 8), |
| vld1q_f16(in_top + 16) |
| } |
| }; |
| const float16x8x3_t vmid = |
| { |
| { |
| vld1q_f16(in_mid), |
| vld1q_f16(in_mid + 8), |
| vld1q_f16(in_mid + 16) |
| } |
| }; |
| const float16x8x3_t vlow = |
| { |
| { |
| vld1q_f16(in_low), |
| vld1q_f16(in_low + 8), |
| vld1q_f16(in_low + 16) |
| } |
| }; |
| float16x8x2_t out = |
| { |
| { |
| vmulq_f16(vtop.val[0], m0.val[0]), |
| vmulq_f16(vtop.val[1], m0.val[0]) |
| } |
| }; |
| out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop.val[1], 1), m0.val[1])); |
| out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop.val[1], 2), m0.val[2])); |
| out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vmid.val[0], m1.val[0])); |
| out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid.val[1], 1), m1.val[1])); |
| out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid.val[1], 2), m1.val[2])); |
| out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vlow.val[0], m2.val[0])); |
| out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow.val[1], 1), m2.val[1])); |
| out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow.val[1], 2), m2.val[2])); |
| out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vtop.val[1], vtop.val[2], 1), m0.val[1])); |
| out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vtop.val[1], vtop.val[2], 2), m0.val[2])); |
| out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vmid.val[1], m1.val[0])); |
| out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vmid.val[1], vmid.val[2], 1), m1.val[1])); |
| out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vmid.val[1], vmid.val[2], 2), m1.val[2])); |
| out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vlow.val[1], m2.val[0])); |
| out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vlow.val[1], vlow.val[2], 1), m2.val[1])); |
| out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vlow.val[1], vlow.val[2], 2), m2.val[2])); |
| return out; |
| } |
| |
| template <> |
| inline float16x8x2_t convolve_3x3<2>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, |
| int fixed_point_position) |
| { |
| float16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); |
| out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 2), out.val[0], 1); |
| out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 0), out.val[0], 2); |
| out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 2), out.val[0], 3); |
| return out; |
| } |
| |
| template <> |
| inline float16x8x2_t convolve_3x3<3>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, |
| int fixed_point_position) |
| { |
| float16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); |
| out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 3), out.val[0], 1); |
| return out; |
| } |
| |
| template <unsigned int stridex> |
| void store_results(float16_t *buffer, const float16x8x2_t &values); |
| |
| template <> |
| void store_results<1>(float16_t *buffer, const float16x8x2_t &values) |
| { |
| vst1q_f16(buffer, values.val[0]); |
| vst1q_f16(buffer + 8, values.val[1]); |
| } |
| |
| template <> |
| void store_results<2>(float16_t *buffer, const float16x8x2_t &values) |
| { |
| vst1q_f16(buffer, values.val[0]); |
| } |
| |
| template <> |
| void store_results<3>(float16_t *buffer, const float16x8x2_t &values) |
| { |
| vst1_f16(buffer, vget_low_f16(values.val[0])); |
| } |
| |
| template <unsigned int stridex> |
| void accumulate_results(float16_t *buffer, const float16x8x2_t &values); |
| |
| template <> |
| void accumulate_results<1>(float16_t *buffer, const float16x8x2_t &values) |
| { |
| vst1q_f16(buffer, vaddq_f16(vld1q_f16(buffer), values.val[0])); |
| vst1q_f16(buffer + 8, vaddq_f16(vld1q_f16(buffer + 8), values.val[1])); |
| } |
| |
| template <> |
| void accumulate_results<2>(float16_t *buffer, const float16x8x2_t &values) |
| { |
| vst1q_f16(buffer, vaddq_f16(vld1q_f16(buffer), values.val[0])); |
| } |
| |
| template <> |
| void accumulate_results<3>(float16_t *buffer, const float16x8x2_t &values) |
| { |
| vst1_f16(buffer, vadd_f16(vld1_f16(buffer), vget_low_f16(values.val[0]))); |
| } |
| |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| |
| inline float32x4x3_t load_matrix_row(const float *ptr) |
| { |
| const float32x4x3_t r = |
| { |
| { |
| vld1q_dup_f32(ptr), |
| vld1q_dup_f32(1 + ptr), |
| vld1q_dup_f32(2 + ptr) |
| } |
| }; |
| return r; |
| } |
| inline qint8x8x3_t load_matrix_row(const qint8_t *ptr) |
| { |
| /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes: |
| r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */ |
| const qint8x8x3_t r = |
| { |
| { |
| vld1_dup_qs8(ptr), |
| vld1_dup_qs8(1 + ptr), |
| vld1_dup_qs8(2 + ptr) |
| } |
| }; |
| return r; |
| } |
| |
| 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, int fixed_point_position); |
| |
| 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, int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| 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, int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4, fixed_point_position); |
| 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, int fixed_point_position) |
| { |
| float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4, fixed_point_position); |
| out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1); |
| return out; |
| } |
| |
| template <unsigned int stridex> |
| float32x4x2_t convolve_3x3(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position); |
| |
| template <> |
| inline float32x4x2_t convolve_3x3<1>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| |
| const float32x4x3_t vtop = |
| { |
| { |
| vld1q_f32(in_top), |
| vld1q_f32(in_top + 4), |
| vld1q_f32(in_top + 8) |
| } |
| }; |
| const float32x4x3_t vmid = |
| { |
| { |
| vld1q_f32(in_mid), |
| vld1q_f32(in_mid + 4), |
| vld1q_f32(in_mid + 8) |
| } |
| }; |
| const float32x4x3_t vlow = |
| { |
| { |
| vld1q_f32(in_low), |
| vld1q_f32(in_low + 4), |
| vld1q_f32(in_low + 8) |
| } |
| }; |
| float32x4x2_t out = |
| { |
| { |
| vmulq_f32(vtop.val[0], m0.val[0]), |
| vmulq_f32(vtop.val[1], m0.val[0]) |
| } |
| }; |
| out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 1), m0.val[1]); |
| out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 2), m0.val[2]); |
| |
| out.val[0] = vmlaq_f32(out.val[0], vmid.val[0], m1.val[0]); |
| out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 1), m1.val[1]); |
| out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 2), m1.val[2]); |
| |
| out.val[0] = vmlaq_f32(out.val[0], vlow.val[0], m2.val[0]); |
| out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 1), m2.val[1]); |
| out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 2), m2.val[2]); |
| |
| out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 1), m0.val[1]); |
| out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 2), m0.val[2]); |
| |
| out.val[1] = vmlaq_f32(out.val[1], vmid.val[1], m1.val[0]); |
| out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 1), m1.val[1]); |
| out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 2), m1.val[2]); |
| |
| out.val[1] = vmlaq_f32(out.val[1], vlow.val[1], m2.val[0]); |
| out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 1), m2.val[1]); |
| out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 2), m2.val[2]); |
| return out; |
| } |
| |
| template <> |
| inline float32x4x2_t convolve_3x3<2>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position) |
| { |
| float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); |
| 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_3x3<3>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position) |
| { |
| float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); |
| out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1); |
| return out; |
| } |
| |
| template <unsigned int stridex> |
| qint16x8x2_t convolve_3x3(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position); |
| |
| template <> |
| inline qint16x8x2_t convolve_3x3<1>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position) |
| { |
| ARM_COMPUTE_UNUSED(fixed_point_position); |
| |
| const qint8x8x3_t vtop = |
| { |
| { |
| vld1_qs8(in_top), |
| vld1_qs8(in_top + 8), |
| vld1_qs8(in_top + 16) |
| } |
| }; |
| const qint8x8x3_t vmid = |
| { |
| { |
| vld1_qs8(in_mid), |
| vld1_qs8(in_mid + 8), |
| vld1_qs8(in_mid + 16) |
| } |
| }; |
| const qint8x8x3_t vlow = |
| { |
| { |
| vld1_qs8(in_low), |
| vld1_qs8(in_low + 8), |
| vld1_qs8(in_low + 16) |
| } |
| }; |
| qint16x8x2_t out = |
| { |
| { |
| vmull_qs8(vtop.val[0], m0.val[0], fixed_point_position), |
| vmull_qs8(vtop.val[1], m0.val[0], fixed_point_position) |
| } |
| }; |
| out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 1), m0.val[1], fixed_point_position); |
| out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 2), m0.val[2], fixed_point_position); |
| out.val[0] = vqmlal_qs8(out.val[0], vmid.val[0], m1.val[0], fixed_point_position); |
| out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 1), m1.val[1], fixed_point_position); |
| out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 2), m1.val[2], fixed_point_position); |
| out.val[0] = vqmlal_qs8(out.val[0], vlow.val[0], m2.val[0], fixed_point_position); |
| out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 1), m2.val[1], fixed_point_position); |
| out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 2), m2.val[2], fixed_point_position); |
| out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 1), m0.val[1], fixed_point_position); |
| out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 2), m0.val[2], fixed_point_position); |
| out.val[1] = vqmlal_qs8(out.val[1], vmid.val[1], m1.val[0], fixed_point_position); |
| out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 1), m1.val[1], fixed_point_position); |
| out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 2), m1.val[2], fixed_point_position); |
| out.val[1] = vqmlal_qs8(out.val[1], vlow.val[1], m2.val[0], fixed_point_position); |
| out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 1), m2.val[1], fixed_point_position); |
| out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 2), m2.val[2], fixed_point_position); |
| return out; |
| } |
| |
| template <> |
| inline qint16x8x2_t convolve_3x3<2>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position) |
| { |
| qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 2), out.val[0], 1); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 4), out.val[0], 2); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 3); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 0), out.val[0], 4); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 2), out.val[0], 5); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 4), out.val[0], 6); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 6), out.val[0], 7); |
| return out; |
| } |
| |
| template <> |
| inline qint16x8x2_t convolve_3x3<3>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position) |
| { |
| qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 3), out.val[0], 1); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 2); |
| out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 1), out.val[0], 3); |
| return out; |
| } |
| |
| template <unsigned int stridex> |
| void store_results(float *buffer, const float32x4x2_t &values); |
| |
| template <> |
| void store_results<1>(float *buffer, const float32x4x2_t &values) |
| { |
| vst1q_f32(buffer, values.val[0]); |
| vst1q_f32(buffer + 4, values.val[1]); |
| } |
| |
| template <> |
| void store_results<2>(float *buffer, const float32x4x2_t &values) |
| { |
| vst1q_f32(buffer, values.val[0]); |
| } |
| |
| template <> |
| void store_results<3>(float *buffer, const float32x4x2_t &values) |
| { |
| vst1_f32(buffer, vget_low_f32(values.val[0])); |
| } |
| |
| template <unsigned int stridex> |
| void store_results(qint16_t *buffer, const qint16x8x2_t &values); |
| |
| template <> |
| void store_results<1>(qint16_t *buffer, const qint16x8x2_t &values) |
| { |
| vst1q_qs16(buffer, values.val[0]); |
| vst1q_qs16(buffer + 8, values.val[1]); |
| } |
| |
| template <> |
| void store_results<2>(qint16_t *buffer, const qint16x8x2_t &values) |
| { |
| vst1q_qs16(buffer, values.val[0]); |
| } |
| |
| template <> |
| void store_results<3>(qint16_t *buffer, const qint16x8x2_t &values) |
| { |
| vst1_qs16(buffer, vget_low_s16(values.val[0])); |
| } |
| |
| template <unsigned int stridex> |
| void accumulate_results(float *buffer, const float32x4x2_t &values); |
| |
| template <> |
| void accumulate_results<1>(float *buffer, const float32x4x2_t &values) |
| { |
| vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0])); |
| vst1q_f32(buffer + 4, vaddq_f32(vld1q_f32(buffer + 4), values.val[1])); |
| } |
| |
| template <> |
| void accumulate_results<2>(float *buffer, const float32x4x2_t &values) |
| { |
| vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0])); |
| } |
| |
| template <> |
| void accumulate_results<3>(float *buffer, const float32x4x2_t &values) |
| { |
| vst1_f32(buffer, vadd_f32(vld1_f32(buffer), vget_low_f32(values.val[0]))); |
| } |
| |
| template <unsigned int stridex> |
| void accumulate_results(qint16_t *buffer, const qint16x8x2_t &values); |
| |
| template <> |
| void accumulate_results<1>(qint16_t *buffer, const qint16x8x2_t &values) |
| { |
| vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0])); |
| vst1q_qs16(buffer + 8, vqaddq_qs16(vld1q_qs16(buffer + 8), values.val[1])); |
| } |
| |
| template <> |
| void accumulate_results<2>(qint16_t *buffer, const qint16x8x2_t &values) |
| { |
| vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0])); |
| } |
| |
| template <> |
| void accumulate_results<3>(qint16_t *buffer, const qint16x8x2_t &values) |
| { |
| vst1_qs16(buffer, vqadd_qs16(vld1_qs16(buffer), vget_low_s16(values.val[0]))); |
| } |
| |
| template <unsigned int stridex> |
| int get_input_num_elems_processed(unsigned int num_elems_written_per_iteration); |
| |
| template <> |
| int get_input_num_elems_processed<1>(unsigned int num_elems_written_per_iteration) |
| { |
| return num_elems_written_per_iteration; |
| } |
| |
| template <> |
| int get_input_num_elems_processed<2>(unsigned int num_elems_written_per_iteration) |
| { |
| return num_elems_written_per_iteration << 1; |
| } |
| |
| template <> |
| int get_input_num_elems_processed<3>(unsigned int num_elems_written_per_iteration) |
| { |
| return num_elems_written_per_iteration * 3; |
| } |
| |
| 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<stridex>(num_elems_written_per_iteration); |
| 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_x = std::get<0>(conv_info.pad()); |
| const unsigned int conv_pad_y = std::get<1>(conv_info.pad()); |
| const int fixed_point_position = input->info()->fixed_point_position(); |
| |
| // 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_x * input_stride_x - conv_pad_y * 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) |
| { |
| auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position); |
| store_results<stridex>(p_out, vres); |
| } |
| } |
| } |
| // 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) |
| { |
| auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position); |
| accumulate_results<stridex>(p_out, vres); |
| } |
| } |
| } |
| } |
| }, |
| 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<stridex>(num_elems_written_per_iteration); |
| 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_x = std::get<0>(conv_info.pad()); |
| const unsigned int conv_pad_y = std::get<1>(conv_info.pad()); |
| const int fixed_point_position = input->info()->fixed_point_position(); |
| |
| // 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_x * input_stride_x - conv_pad_y * 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, fixed_point_position); |
| 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, fixed_point_position); |
| accumulate_results<stridex>(p_out, vres); |
| } |
| } |
| } |
| } |
| }, |
| in, out); |
| } |
| }; |
| |
| 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"); |
| } |
| } |
| |
| } // 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_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F16, DataType::QS16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 1 && (std::get<0>(conv_info.pad()) || std::get<1>(conv_info.pad())), |
| "Pad > 0 not supported for 1x1 weights"); |
| ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 3 && (std::get<0>(conv_info.pad()) > 1 || std::get<1>(conv_info.pad()) > 1), |
| "Pad > 1 not supported for 3x3 weights"); |
| ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 5 && (std::get<0>(conv_info.pad()) > 2 || std::get<1>(conv_info.pad()) > 2), |
| "Pad > 2 not supported for 5x5 weights"); |
| |
| ARM_COMPUTE_ERROR_ON_MSG(std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported."); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1)); |
| ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); |
| |
| const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); |
| const unsigned int conv_pad_x = std::get<0>(conv_info.pad()); |
| const unsigned int conv_pad_y = std::get<1>(conv_info.pad()); |
| |
| _input = input; |
| _weights = weights; |
| _output = output; |
| _conv_info = conv_info; |
| _kernel_size = weights->info()->dimension(0); |
| _border_size = BorderSize(conv_pad_y, conv_pad_x); |
| |
| const unsigned int kernel_size = weights->info()->dimension(0); |
| |
| // Get convolved dimensions |
| unsigned int output_width = 0; |
| unsigned int output_height = 0; |
| std::tie(output_width, output_height) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_size, kernel_size, conv_info); |
| |
| TensorShape output_shape = input->info()->tensor_shape(); |
| output_shape.set(0, output_width); |
| output_shape.set(1, output_height); |
| output_shape.set(2, weights->info()->dimension(3)); |
| |
| DataType data_type = input->info()->data_type(); |
| |
| if(is_data_type_fixed_point(data_type)) |
| { |
| // Promote data type in case of fixed point |
| data_type = ((data_type == DataType::QS8) ? DataType::QS16 : DataType::QS32); |
| } |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, data_type, input->info()->fixed_point_position()); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, output->info()->data_type()); |
| |
| switch(_kernel_size) |
| { |
| case 1: |
| { |
| switch(input->info()->data_type()) |
| { |
| #ifdef ARM_COMPUTE_ENABLE_FP16 |
| case DataType::F16: |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| case DataType::QS8: |
| case DataType::QS16: |
| _num_elems_written_per_iteration = 8; |
| break; |
| case DataType::F32: |
| if(run_optim_small_tensor(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: |
| case 5: |
| { |
| switch(input->info()->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_COMPUTE_ENABLE_FP16 |
| case DataType::F16: |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| case DataType::QS8: |
| case DataType::QS16: |
| _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; |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported."); |
| break; |
| } |
| } |
| break; |
| default: |
| { |
| ARM_COMPUTE_ERROR("Not implemented"); |
| break; |
| } |
| } |
| |
| // Calculate right and bottom border |
| const unsigned int conv_stride_y = std::get<1>(_conv_info.stride()); |
| const int input_width = input->info()->dimension(0); |
| const int input_height = input->info()->dimension(1); |
| const int upper_bound_w = ceil_to_multiple(((output->info()->dimension(0) - 1) * conv_stride_x + _kernel_size), _num_elems_read_per_iteration) - conv_pad_x - input_width; |
| const int upper_bound_h = ((output->info()->dimension(1) - 1) * conv_stride_y - conv_pad_y + _kernel_size) - input_height; |
| _border_size.right = std::max(upper_bound_w, static_cast<int>(_kernel_size)); |
| _border_size.bottom = std::max(upper_bound_h, static_cast<int>(_kernel_size)); |
| Window win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration)); |
| AccessWindowStatic input_access(input->info(), -conv_pad_x, -conv_pad_y, input_width + _border_size.right, input_height + _border_size.bottom); |
| AccessWindowStatic weights_access(weights->info(), 0, 0, _num_weight_elems_read_per_row, _kernel_size); |
| AccessWindowHorizontal output_access(output->info(), 0, _num_elems_written_per_iteration); |
| update_window_and_padding(win, input_access, weights_access, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| INEKernel::configure(win); |
| } |
| |
| 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(0); |
| |
| switch(kernel_size) |
| { |
| case 1: |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::QS8: |
| convolve_1x1<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); |
| break; |
| case DataType::QS16: |
| convolve_1x1<qint16_t, qint32_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); |
| break; |
| 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_COMPUTE_ENABLE_FP16 |
| 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_COMPUTE_ENABLE_FP16 */ |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| break; |
| } |
| case 3: |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::QS8: |
| convolve_3x3<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); |
| break; |
| 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_COMPUTE_ENABLE_FP16 |
| 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_COMPUTE_ENABLE_FP16 */ |
| 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; |
| } |
| } |
| } |
