| /* |
| * Copyright (c) 2019-2021 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "src/core/cpu/kernels/CpuDepthwiseConv2dNativeKernel.h" |
| |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/ITensorInfo.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/NEON/wrapper/traits.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "support/ToolchainSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| constexpr auto data_layout = DataLayout::NHWC; |
| const size_t width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const size_t height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const size_t channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| constexpr auto dim_manual_loop = Window::Dimension(0, 0, 0); |
| constexpr auto dim_single_unit_step = Window::Dimension(0, 1, 1); |
| constexpr size_t vector_size = 8; |
| |
| struct DepthwiseConvolutionRunInfo |
| { |
| const size_t num_read_elements_per_iteration; |
| const uint32_t x_start; |
| const uint32_t x_end; |
| const uint32_t x_step; |
| const uint32_t x_leftover_start; |
| const size_t input_stride_y; |
| const size_t input_stride_z; |
| const size_t input_max_offset; |
| const size_t weights_width; |
| const size_t weights_height; |
| const size_t weights_stride_y; |
| const size_t weights_stride_z; |
| const size_t conv_stride_x; |
| const size_t conv_stride_y; |
| const size_t conv_pad_left; |
| const size_t conv_pad_top; |
| const size_t input_height; |
| const size_t input_width; |
| const size_t input_depth; |
| |
| DepthwiseConvolutionRunInfo(const ITensorInfo &input, const ITensorInfo &weights, const PadStrideInfo &conv_info, const Window &w, uint32_t depth_multiplier = 1) // NOLINT |
| : num_read_elements_per_iteration((depth_multiplier == 1 ? (vector_size / element_size_from_data_type(input.data_type())) : 1)), |
| x_start(w.x().start()), |
| x_end(w.x().end()), |
| x_step(static_cast<uint32_t>(num_read_elements_per_iteration * depth_multiplier)), |
| x_leftover_start(std::max(static_cast<int32_t>(w.x().end()) - static_cast<int32_t>(x_step) + 1, int32_t(0))), |
| input_stride_y(input.strides_in_bytes().y()), |
| input_stride_z(input.strides_in_bytes().z()), |
| input_max_offset(input.strides_in_bytes().z() * input.dimension(height_idx) - (input.padding().bottom + input.padding().top) * input.strides_in_bytes().y()), |
| weights_width(weights.dimension(width_idx)), |
| weights_height(weights.dimension(height_idx)), |
| weights_stride_y(weights.strides_in_bytes().y()), |
| weights_stride_z(weights.strides_in_bytes().z()), |
| conv_stride_x(conv_info.stride().first), |
| conv_stride_y(conv_info.stride().second), |
| conv_pad_left(conv_info.pad_left()), |
| conv_pad_top(conv_info.pad_top()), |
| input_height(input.dimension(height_idx)), |
| input_width(input.dimension(width_idx)), |
| input_depth(input.dimension(channel_idx)) |
| { |
| } |
| }; |
| |
| inline int32x4_t saturating_doubling_high_mul(const int32x4_t &a, const int32_t &b) |
| { |
| return vqrdmulhq_n_s32(a, b); |
| } |
| |
| inline int32_t saturating_doubling_high_mul(const int32_t &a, const int32_t &b) |
| { |
| return vget_lane_s32(vqrdmulh_n_s32(vdup_n_s32(a), b), 0); |
| } |
| |
| inline int32x4_t rounding_divide_by_exp2(const int32x4_t &x, const int exponent) |
| { |
| const int32x4_t shift = vdupq_n_s32(-exponent); |
| const int32x4_t fixup = vshrq_n_s32(vandq_s32(x, shift), 31); |
| const int32x4_t fixed = vqaddq_s32(x, fixup); |
| return vrshlq_s32(fixed, shift); |
| } |
| |
| inline int32x2_t rounding_divide_by_exp2(const int32x2_t &x, const int exponent) |
| { |
| const int32x2_t shift = vdup_n_s32(-exponent); |
| const int32x2_t fixup = vshr_n_s32(vand_s32(x, shift), 31); |
| const int32x2_t fixed = vqadd_s32(x, fixup); |
| return vrshl_s32(fixed, shift); |
| } |
| |
| inline int32_t rounding_divide_by_exp2(const int32_t &x, const int exponent) |
| { |
| const int32x2_t xs = vdup_n_s32(x); |
| return vget_lane_s32(rounding_divide_by_exp2(xs, exponent), 0); |
| } |
| |
| inline bool is_valid_input_region(int32_t base_w, uint32_t base_h, uint32_t w, uint32_t h, const DepthwiseConvolutionRunInfo &run_info, const Size2D &dilation) |
| { |
| const int32_t current_h = base_h + h * dilation.y(); |
| const bool is_valid_h = current_h >= 0 && current_h < static_cast<int32_t>(run_info.input_height); |
| |
| const int32_t current_w = base_w + w * dilation.x(); |
| const bool is_valid_w = current_w >= 0 && current_w < static_cast<int32_t>(run_info.input_width); |
| |
| return is_valid_h && is_valid_w; |
| } |
| |
| template <typename T> |
| void depthwise_loop_multiplier1_fp(const ITensor *src, const ITensor *weights, const ITensor *biases, ITensor *dst, const PadStrideInfo &conv_info, |
| const Size2D &dilation, const Window &window, bool has_biases) |
| { |
| constexpr auto element_per_vector = vector_size / sizeof(T); |
| using VectorType = typename wrapper::traits::neon_vector<T, element_per_vector>::type; |
| using TagType = typename wrapper::traits::neon_vector<T, element_per_vector>::tag_type; |
| |
| const auto run_info = DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window); |
| |
| const VectorType zero_vector = wrapper::vdup_n(static_cast<T>(0), TagType{}); |
| |
| Window execution_window = window; |
| execution_window.set(Window::DimX, dim_single_unit_step); |
| |
| Window win_input = window; |
| win_input.set(Window::DimX, dim_manual_loop); |
| win_input.set(Window::DimY, dim_manual_loop); |
| win_input.set(Window::DimZ, dim_manual_loop); |
| |
| Window win_weights = win_input; |
| win_weights.set(Window::DimW, dim_manual_loop); |
| |
| Window win_output = window; |
| win_output.set(Window::DimX, dim_manual_loop); |
| |
| Iterator input_it(src, win_input); |
| Iterator weights_it(weights, win_weights); |
| Iterator output_it(dst, win_output); |
| Iterator biases_it{}; |
| |
| if(has_biases) |
| { |
| biases_it = Iterator(biases, win_weights); |
| } |
| |
| execute_window_loop(execution_window, [&](const Coordinates & id) |
| { |
| const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; |
| const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; |
| const int64_t base_input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; |
| |
| auto const base_weights_ptr = weights_it.ptr(); |
| uint32_t x = run_info.x_start; |
| |
| for(; x < run_info.x_leftover_start; x += run_info.x_step) |
| { |
| VectorType acc = zero_vector; |
| auto weights_ptr = base_weights_ptr; |
| int64_t input_offset = base_input_offset; |
| |
| for(uint32_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int64_t offs = input_offset + x * sizeof(T); |
| for(uint32_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_vals = is_valid_region ? |
| wrapper::vload(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))) : |
| zero_vector; |
| const auto weights_vals = wrapper::vload(reinterpret_cast<T *>(weights_ptr + w * run_info.weights_stride_y) + x); |
| acc = wrapper::vmla(acc, weights_vals, input_vals); |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| if(has_biases) |
| { |
| const auto biases_vals = wrapper::vload(reinterpret_cast<T *>(biases_it.ptr()) + x); |
| acc = wrapper::vadd(acc, biases_vals); |
| } |
| |
| wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()) + x, acc); |
| } |
| |
| for(; x < run_info.x_end; ++x) |
| { |
| auto acc_scalar = T{ 0 }; |
| auto weights_ptr = base_weights_ptr; |
| int64_t input_offset = base_input_offset; |
| |
| for(size_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int64_t offs = input_offset + x * sizeof(T); |
| for(size_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_vals = is_valid_region ? *reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset)) : 0; |
| const auto weights_vals = *(reinterpret_cast<T *>(weights_ptr + w * run_info.weights_stride_y) + x); |
| |
| acc_scalar += (input_vals * weights_vals); |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| if(has_biases) |
| { |
| const auto biases_vals = *(reinterpret_cast<T *>(biases_it.ptr()) + x); |
| acc_scalar += biases_vals; |
| } |
| *(reinterpret_cast<T *>(output_it.ptr()) + x) = acc_scalar; |
| } |
| }, |
| input_it, weights_it, biases_it, output_it); |
| } |
| |
| template <typename T> |
| void depthwise_loop_generic_fp(const ITensor *src, const ITensor *weights, const ITensor *biases, ITensor *dst, const PadStrideInfo &conv_info, |
| const Size2D &dilation, unsigned int depth_multiplier, const Window &window, bool has_biases) |
| { |
| const auto run_info = DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window, depth_multiplier); |
| |
| Window execution_window = window; |
| execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); |
| |
| Window win_input = execution_window; |
| win_input.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); |
| win_input.set(Window::DimY, dim_manual_loop); |
| win_input.set(Window::DimZ, dim_manual_loop); |
| |
| Window win_weights = window; |
| win_weights.set_dimension_step(Window::DimX, run_info.x_step); |
| win_weights.set(Window::DimY, dim_manual_loop); |
| win_weights.set(Window::DimZ, dim_manual_loop); |
| win_weights.set(Window::DimW, dim_manual_loop); |
| |
| Window win_output = window; |
| win_output.set_dimension_step(Window::DimX, run_info.x_step); |
| |
| Iterator input_it(src, win_input); |
| Iterator weights_it(weights, win_weights); |
| Iterator output_it(dst, win_output); |
| Iterator biases_it{}; |
| |
| if(has_biases) |
| { |
| biases_it = Iterator(biases, win_weights); |
| } |
| |
| execute_window_loop(execution_window, [&](const Coordinates & id) |
| { |
| std::vector<T> acc(depth_multiplier, static_cast<T>(0)); |
| |
| const int input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; |
| const int input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; |
| int input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; |
| |
| auto weights_ptr = weights_it.ptr(); |
| for(size_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int offs = input_offset; |
| for(size_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_val = is_valid_region ? *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))) : T(0); |
| |
| for(size_t m = 0; m < depth_multiplier; ++m) |
| { |
| const auto weights_val = *(reinterpret_cast<T *>(weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); |
| acc.at(m) = support::cpp11::fma(weights_val, input_val, acc.at(m)); |
| } |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| if(has_biases) |
| { |
| for(size_t m = 0; m < depth_multiplier; ++m) |
| { |
| const auto biases_val = *(reinterpret_cast<T *>(biases_it.ptr() + m * sizeof(T))); |
| *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m) + biases_val; |
| } |
| } |
| else |
| { |
| for(size_t m = 0; m < depth_multiplier; ++m) |
| { |
| *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m); |
| } |
| } |
| }, |
| input_it, weights_it, biases_it, output_it); |
| } |
| |
| template <typename T, typename TW> |
| void depthwise_loop_multiplier1_quantized(const ITensor *src, const ITensor *weights, const ITensor *biases, ITensor *dst, const PadStrideInfo &conv_info, |
| const Size2D &dilation, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window, bool has_biases) // NOLINT |
| { |
| ARM_COMPUTE_UNUSED(output_multiplier, output_shift); |
| constexpr auto element_per_vector = vector_size / sizeof(T); |
| using VectorType = typename wrapper::traits::neon_vector<T, element_per_vector>::type; |
| using TagType = typename wrapper::traits::neon_vector<T, element_per_vector>::tag_type; |
| using AccType = int32_t; |
| using AccArrayType = std::array<AccType, element_per_vector>; |
| |
| const auto out_of_bound_value = PixelValue(static_cast<uint64_t>(0), src->info()->data_type(), src->info()->quantization_info()).get<T>(); |
| const auto out_of_bound_vector = wrapper::vdup_n(static_cast<T>(out_of_bound_value), TagType{}); |
| |
| const auto run_info = DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window); |
| |
| const int32_t input_qoffset = src->info()->quantization_info().uniform().offset; |
| const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset; |
| const int32_t output_qoffset = dst->info()->quantization_info().uniform().offset; |
| const int32_t k_offset = run_info.weights_width * run_info.weights_height * input_qoffset * weights_qoffset; |
| |
| Window execution_window = window; |
| execution_window.set(Window::DimX, dim_single_unit_step); |
| |
| Window win_input = window; |
| win_input.set(Window::DimX, dim_manual_loop); |
| win_input.set(Window::DimY, dim_manual_loop); |
| win_input.set(Window::DimZ, dim_manual_loop); |
| |
| Window win_weights = win_input; |
| win_weights.set(Window::DimW, dim_manual_loop); |
| |
| Window win_output = window; |
| win_output.set(Window::DimX, dim_manual_loop); |
| |
| Iterator input_it(src, win_input); |
| Iterator weights_it(weights, win_weights); |
| Iterator output_it(dst, win_output); |
| Iterator biases_it{}; |
| |
| if(has_biases) |
| { |
| biases_it = Iterator(biases, win_weights); |
| } |
| |
| execute_window_loop(execution_window, [&](const Coordinates & id) |
| { |
| const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; |
| const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; |
| const int64_t base_input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; |
| auto const base_weights_ptr = weights_it.ptr(); |
| size_t x = run_info.x_start; |
| |
| for(; x < run_info.x_leftover_start; x += run_info.x_step) |
| { |
| AccArrayType acc{}; |
| AccArrayType in_sum{}; |
| AccArrayType we_sum{}; |
| |
| auto weights_ptr = base_weights_ptr; |
| auto input_offset = base_input_offset; |
| |
| for(size_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int64_t offs = input_offset + x * sizeof(T); |
| for(size_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_vals = is_valid_region ? |
| wrapper::vload(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))) : |
| out_of_bound_vector; |
| const auto weights_vals = wrapper::vload(reinterpret_cast<TW *>(weights_ptr + w * run_info.weights_stride_y) + x); |
| |
| for(size_t i = 0; i < element_per_vector; ++i) |
| { |
| acc.at(i) += input_vals[i] * weights_vals[i]; |
| in_sum.at(i) += input_vals[i]; |
| we_sum.at(i) += weights_vals[i]; |
| } |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| VectorType out_vals = wrapper::vdup_n(static_cast<T>(0), TagType{}); |
| for(size_t i = 0; i < element_per_vector; ++i) |
| { |
| acc.at(i) -= in_sum.at(i) * weights_qoffset; |
| acc.at(i) -= we_sum.at(i) * input_qoffset; |
| acc.at(i) += k_offset; |
| |
| if(has_biases) |
| { |
| acc.at(i) += *(reinterpret_cast<int32_t *>(biases_it.ptr() + i * sizeof(int32_t)) + x); |
| } |
| |
| const int32_t out_mul = output_multiplier.at(x + i); |
| const int32_t out_shift = output_shift.at(x + i); |
| if(out_shift < 0) |
| { |
| acc.at(i) = saturating_doubling_high_mul(acc.at(i) * (1 << (-out_shift)), out_mul) + output_qoffset; |
| } |
| else |
| { |
| acc.at(i) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(i), out_mul), out_shift) + output_qoffset; |
| } |
| out_vals[i] = static_cast<T>(utility::clamp<AccType, T>(acc.at(i))); |
| } |
| |
| wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()) + x, out_vals); |
| } |
| |
| // left-over |
| for(; x < run_info.x_end; ++x) |
| { |
| AccType acc = 0; |
| AccType in_sum = 0; |
| AccType we_sum = 0; |
| |
| auto weights_ptr = base_weights_ptr; |
| auto input_offset = base_input_offset; |
| |
| for(size_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int64_t offs = input_offset + x * sizeof(T); |
| for(size_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_val = is_valid_region ? |
| *reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset)) : |
| out_of_bound_value; |
| const auto weights_val = *(reinterpret_cast<TW *>(weights_ptr + w * run_info.weights_stride_y) + x); |
| |
| acc += input_val * weights_val; |
| in_sum += input_val; |
| we_sum += weights_val; |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| T out_vals{ 0 }; |
| |
| acc -= in_sum * weights_qoffset; |
| acc -= we_sum * input_qoffset; |
| acc += k_offset; |
| |
| if(has_biases) |
| { |
| acc += *(reinterpret_cast<int32_t *>(biases_it.ptr()) + x); |
| } |
| |
| const int32_t out_mul = output_multiplier.at(x); |
| const int32_t out_shift = output_shift.at(x); |
| |
| if(out_shift < 0) |
| { |
| acc = saturating_doubling_high_mul(acc * (1 << (-out_shift)), out_mul) + output_qoffset; |
| } |
| else |
| { |
| acc = rounding_divide_by_exp2(saturating_doubling_high_mul(acc, out_mul), out_shift) + output_qoffset; |
| } |
| |
| out_vals = static_cast<T>(utility::clamp<AccType, T>(acc)); |
| *(reinterpret_cast<T *>(output_it.ptr()) + x) = out_vals; |
| } |
| }, |
| input_it, weights_it, biases_it, output_it); |
| } |
| |
| template <typename T, typename TW> |
| void depthwise_loop_generic_quantized(const ITensor *src, const ITensor *weights, const ITensor *biases, ITensor *dst, const PadStrideInfo &conv_info, |
| const Size2D &dilation, unsigned int depth_multiplier, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window, bool has_biases) // NOLINT |
| { |
| using AccType = int32_t; |
| |
| const auto run_info = DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window, depth_multiplier); |
| |
| const auto out_of_bound_value = PixelValue(static_cast<uint64_t>(0), src->info()->data_type(), src->info()->quantization_info()).get<T>(); |
| |
| const int32_t input_qoffset = src->info()->quantization_info().uniform().offset; |
| const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset; |
| const int32_t output_qoffset = dst->info()->quantization_info().uniform().offset; |
| const int32_t k_offset = run_info.weights_width * run_info.weights_height * input_qoffset * weights_qoffset; |
| |
| Window execution_window = window; |
| execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); |
| |
| Window win_input = execution_window; |
| win_input.set(Window::DimY, dim_manual_loop); |
| win_input.set(Window::DimZ, dim_manual_loop); |
| |
| Window win_weights = window; |
| win_weights.set_dimension_step(Window::DimX, run_info.x_step); |
| win_weights.set(Window::DimY, dim_manual_loop); |
| win_weights.set(Window::DimZ, dim_manual_loop); |
| win_weights.set(Window::DimW, dim_manual_loop); |
| |
| Window win_output = window; |
| win_output.set_dimension_step(Window::DimX, run_info.x_step); |
| |
| Iterator input_it(src, win_input); |
| Iterator weights_it(weights, win_weights); |
| Iterator output_it(dst, win_output); |
| Iterator biases_it{}; |
| |
| if(has_biases) |
| { |
| biases_it = Iterator(biases, win_weights); |
| } |
| |
| execute_window_loop(execution_window, [&](const Coordinates & id) |
| { |
| std::vector<AccType> acc(depth_multiplier, 0); |
| std::vector<AccType> we_sum(depth_multiplier, 0); |
| AccType in_sum = 0; |
| |
| const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; |
| const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; |
| int64_t input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; |
| |
| auto weights_ptr = weights_it.ptr(); |
| for(size_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int offs = input_offset; |
| for(size_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_val = is_valid_region ? *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))) : out_of_bound_value; |
| |
| for(size_t m = 0; m < depth_multiplier; ++m) |
| { |
| const auto weights_val = *(reinterpret_cast<TW *>(weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); |
| acc.at(m) += input_val * weights_val; |
| |
| we_sum.at(m) += weights_val; |
| } |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| in_sum += input_val; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| for(size_t m = 0; m < depth_multiplier; ++m) |
| { |
| acc.at(m) -= in_sum * weights_qoffset; |
| acc.at(m) -= we_sum.at(m) * input_qoffset; |
| acc.at(m) += k_offset; |
| |
| if(has_biases) |
| { |
| acc.at(m) += *(reinterpret_cast<int32_t *>(biases_it.ptr() + m * sizeof(int32_t))); |
| } |
| |
| const int32_t out_mul = output_multiplier.at(id.x() * depth_multiplier + m); |
| const int32_t out_shift = output_shift.at(id.x() * depth_multiplier + m); |
| if(out_shift < 0) |
| { |
| acc.at(m) = saturating_doubling_high_mul(acc.at(m) * (1 << (-out_shift)), out_mul) + output_qoffset; |
| } |
| else |
| { |
| acc.at(m) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(m), out_mul), out_shift) + output_qoffset; |
| } |
| *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = static_cast<T>(utility::clamp<AccType, T>(acc.at(m))); |
| } |
| }, |
| input_it, weights_it, biases_it, output_it); |
| } |
| |
| template <typename T, typename TW> |
| void depthwise_loop_pow2_quantized_per_tensor(const ITensor *src, const ITensor *weights, const ITensor *biases, ITensor *dst, const PadStrideInfo &conv_info, |
| const Size2D &dilation, unsigned int depth_multiplier, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window, bool has_biases) // NOLINT |
| { |
| constexpr int half_vec = vector_size / 2; |
| |
| using AccType = int32_t; |
| using AccVectorType = typename wrapper::traits::neon_vector<AccType, half_vec>::type; |
| using AccVectorTagType = typename wrapper::traits::neon_vector<AccType, half_vec>::tag_type; |
| using TagType = typename wrapper::traits::neon_vector<T, vector_size>::tag_type; |
| |
| const auto run_info = DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window, depth_multiplier); |
| |
| const auto input_qoffset_vec = wrapper::vreinterpret(wrapper::vmovl(wrapper::vdup_n(static_cast<T>(src->info()->quantization_info().uniform().offset), TagType{}))); |
| const auto weights_qoffset_vec = wrapper::vreinterpret(wrapper::vmovl(wrapper::vdup_n(static_cast<TW>(weights->info()->quantization_info().uniform().offset), TagType{}))); |
| const auto output_qoffset_vec = wrapper::vdup_n(dst->info()->quantization_info().uniform().offset, arm_compute::wrapper::traits::vector_128_tag{}); |
| |
| const auto lower = wrapper::vdup_n(static_cast<AccType>(std::numeric_limits<T>::lowest()), AccVectorTagType{}); |
| const auto upper = wrapper::vdup_n(static_cast<AccType>(std::numeric_limits<T>::max()), AccVectorTagType{}); |
| const auto zero = wrapper::vdup_n(static_cast<AccType>(0), AccVectorTagType{}); |
| |
| const auto out_mul = output_multiplier.at(0); |
| const auto out_shift = output_shift.at(0); |
| |
| Window execution_window = window; |
| execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); |
| |
| Window win_input = execution_window; |
| win_input.set(Window::DimY, dim_manual_loop); |
| win_input.set(Window::DimZ, dim_manual_loop); |
| |
| Window win_weights = window; |
| win_weights.set_dimension_step(Window::DimX, run_info.x_step); |
| win_weights.set(Window::DimY, dim_manual_loop); |
| win_weights.set(Window::DimZ, dim_manual_loop); |
| win_weights.set(Window::DimW, dim_manual_loop); |
| |
| Window win_output = window; |
| win_output.set_dimension_step(Window::DimX, run_info.x_step); |
| |
| Iterator input_it(src, win_input); |
| Iterator weights_it(weights, win_weights); |
| Iterator output_it(dst, win_output); |
| Iterator biases_it{}; |
| |
| if(has_biases) |
| { |
| biases_it = Iterator(biases, win_weights); |
| } |
| |
| std::vector<AccVectorType> acc0(depth_multiplier / vector_size); |
| std::vector<AccVectorType> acc1(depth_multiplier / vector_size); |
| |
| execute_window_loop(execution_window, [&](const Coordinates & id) |
| { |
| std::fill(begin(acc0), end(acc0), zero); |
| std::fill(begin(acc1), end(acc1), zero); |
| |
| const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; |
| const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; |
| int64_t input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; |
| |
| auto weights_ptr = weights_it.ptr(); |
| for(size_t h = 0; h < run_info.weights_height; ++h) |
| { |
| const int32_t current_h = input_z + h * dilation.y(); |
| if(current_h >= 0 && current_h < static_cast<int32_t>(run_info.input_height)) |
| { |
| int offs = input_offset; |
| for(size_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const int32_t current_w = input_y + w * dilation.x(); |
| if(current_w >= 0 && current_w < static_cast<int32_t>(run_info.input_width)) |
| { |
| const auto input_8x8 = wrapper::vdup_n(*(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))), TagType{}); |
| const auto input_s16x8 = wrapper::vreinterpret(wrapper::vmovl(input_8x8)); |
| const auto input_no_offs = wrapper::vsub(input_s16x8, input_qoffset_vec); |
| |
| for(size_t m = 0, i = 0; m < depth_multiplier; m += vector_size, ++i) |
| { |
| const auto weights_8x8 = wrapper::vload(reinterpret_cast<TW *>(weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); |
| const auto weights_s16x8 = wrapper::vreinterpret(wrapper::vmovl(weights_8x8)); |
| const auto weights_no_offs = wrapper::vsub(weights_s16x8, weights_qoffset_vec); |
| |
| acc0.at(i) = wrapper::vmlal(acc0.at(i), wrapper::vgetlow(input_no_offs), wrapper::vgetlow(weights_no_offs)); |
| acc1.at(i) = wrapper::vmlal(acc1.at(i), wrapper::vgethigh(input_no_offs), wrapper::vgethigh(weights_no_offs)); |
| } |
| } |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| for(size_t m = 0, i = 0; m < depth_multiplier; m += vector_size, ++i) |
| { |
| if(has_biases) |
| { |
| const auto bias_val0 = wrapper::vloadq(reinterpret_cast<int32_t *>(biases_it.ptr() + m * sizeof(int32_t))); |
| const auto bias_val1 = wrapper::vloadq(reinterpret_cast<int32_t *>(biases_it.ptr() + (m + half_vec) * sizeof(int32_t))); |
| |
| acc0.at(i) = wrapper::vadd(acc0.at(i), bias_val0); |
| acc1.at(i) = wrapper::vadd(acc1.at(i), bias_val1); |
| } |
| |
| if(out_shift < 0) |
| { |
| acc0.at(i) = wrapper::vadd(saturating_doubling_high_mul(acc0.at(i) * (1 << (-out_shift)), out_mul), output_qoffset_vec); |
| acc1.at(i) = wrapper::vadd(saturating_doubling_high_mul(acc1.at(i) * (1 << (-out_shift)), out_mul), output_qoffset_vec); |
| } |
| else |
| { |
| acc0.at(i) = wrapper::vadd(rounding_divide_by_exp2(saturating_doubling_high_mul(acc0.at(i), out_mul), out_shift), output_qoffset_vec); |
| acc1.at(i) = wrapper::vadd(rounding_divide_by_exp2(saturating_doubling_high_mul(acc1.at(i), out_mul), out_shift), output_qoffset_vec); |
| } |
| |
| acc0.at(i) = wrapper::vmin(wrapper::vmax(acc0.at(i), lower), upper); |
| acc1.at(i) = wrapper::vmin(wrapper::vmax(acc1.at(i), lower), upper); |
| |
| const auto out_val = wrapper::vcombine(wrapper::vmovn(acc0.at(i)), |
| wrapper::vmovn(acc1.at(i))); |
| |
| if(std::is_same<T, uint8_t>::value) |
| { |
| wrapper::vstore(reinterpret_cast<uint8_t *>(output_it.ptr() + m * sizeof(uint8_t)), wrapper::vqmovn(vreinterpretq_u16_s16(out_val))); |
| } |
| else |
| { |
| wrapper::vstore(reinterpret_cast<int8_t *>(output_it.ptr() + m * sizeof(int8_t)), wrapper::vqmovn(out_val)); |
| } |
| } |
| }, |
| input_it, weights_it, biases_it, output_it); |
| } |
| |
| Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ConvolutionInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src); |
| ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(info.depth_multiplier == 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) + (weights->dimension(1) - 1) * (info.dilation.x() - 1) > src->dimension(1) + info.pad_stride_info.pad_left() + info.pad_stride_info.pad_right()); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) + (weights->dimension(2) - 1) * (info.dilation.y() - 1) > src->dimension(2) + info.pad_stride_info.pad_top() + info.pad_stride_info.pad_bottom()); |
| ARM_COMPUTE_RETURN_ERROR_ON((src->dimension(0) * info.depth_multiplier) != weights->dimension(0)); |
| ARM_COMPUTE_RETURN_ERROR_ON((info.dilation.x() < 1) || (info.dilation.y() < 1)); |
| ARM_COMPUTE_RETURN_ERROR_ON((info.pad_stride_info.stride().first < 1) || (info.pad_stride_info.stride().second < 1)); |
| |
| if(is_data_type_quantized_per_channel(weights->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->quantization_info().scale().size()); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); |
| } |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0)); |
| |
| if(is_data_type_quantized_asymmetric(src->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); |
| } |
| } |
| |
| if(dst->total_size() != 0) |
| { |
| const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*src, *weights, info); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| void CpuDepthwiseConv2dNativeKernel::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ConvolutionInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, (biases != nullptr) ? biases : nullptr, dst, info)); |
| |
| _conv_info = info.pad_stride_info; |
| _depth_multiplier = info.depth_multiplier; |
| _dilation = info.dilation; |
| _has_biases = (biases != nullptr); |
| |
| if(is_data_type_quantized(src->data_type())) |
| { |
| const auto input_scale = src->quantization_info().uniform().scale; |
| const auto output_scale = dst->quantization_info().uniform().scale; |
| |
| auto weights_scale = weights->quantization_info().scale(); |
| if(!is_data_type_quantized_per_channel(weights->data_type())) |
| { |
| for(size_t i = 1; i < weights->dimension(channel_idx); ++i) |
| { |
| weights_scale.push_back(weights_scale.front()); |
| } |
| } |
| |
| for(const auto &s : weights_scale) |
| { |
| int32_t out_mult = 0; |
| int32_t out_shift = 0; |
| const float multiplier = input_scale * s / output_scale; |
| arm_compute::quantization::calculate_quantized_multiplier(multiplier, &out_mult, &out_shift); |
| |
| _output_multiplier.push_back(out_mult); |
| _output_shift.push_back(out_shift); |
| } |
| } |
| |
| switch(weights->data_type()) |
| { |
| case DataType::QASYMM8: |
| _func = &CpuDepthwiseConv2dNativeKernel::run_depthwise<uint8_t, uint8_t>; |
| break; |
| case DataType::QASYMM8_SIGNED: |
| _func = &CpuDepthwiseConv2dNativeKernel::run_depthwise<int8_t, int8_t>; |
| break; |
| case DataType::QSYMM8_PER_CHANNEL: |
| if(src->data_type() == DataType::QASYMM8) |
| { |
| _func = &CpuDepthwiseConv2dNativeKernel::run_depthwise<uint8_t, int8_t>; |
| } |
| else |
| { |
| _func = &CpuDepthwiseConv2dNativeKernel::run_depthwise<int8_t, int8_t>; |
| } |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = &CpuDepthwiseConv2dNativeKernel::run_depthwise<float16_t, float16_t>; |
| break; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| _func = &CpuDepthwiseConv2dNativeKernel::run_depthwise<float, float>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| |
| const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*src, *weights, info); |
| auto_init_if_empty(*dst, src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_quantization_info(dst->quantization_info())); |
| |
| Window win = calculate_max_window(*dst, Steps()); |
| ICpuKernel::configure(win); |
| } |
| |
| Status CpuDepthwiseConv2dNativeKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ConvolutionInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, info)); |
| return Status{}; |
| } |
| |
| template <typename T, typename TW, CpuDepthwiseConv2dNativeKernel::FloatEnalber<T>> |
| void CpuDepthwiseConv2dNativeKernel::run_depthwise(const ITensor *src, const ITensor *weights, const ITensor *biases, |
| ITensor *dst, const Window &window, bool has_biases) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); |
| |
| if(_depth_multiplier == 1) |
| { |
| depthwise_loop_multiplier1_fp<T>(src, weights, biases, dst, _conv_info, _dilation, window, has_biases); |
| } |
| else |
| { |
| depthwise_loop_generic_fp<T>(src, weights, biases, dst, _conv_info, _dilation, _depth_multiplier, window, has_biases); |
| } |
| } |
| |
| template <typename T, typename TW, CpuDepthwiseConv2dNativeKernel::Quantized8bitEnalber<T>> |
| void CpuDepthwiseConv2dNativeKernel::run_depthwise(const ITensor *src, const ITensor *weights, const ITensor *biases, |
| ITensor *dst, const Window &window, bool has_biases) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); |
| |
| if(_depth_multiplier == 1) |
| { |
| depthwise_loop_multiplier1_quantized<T, TW>(src, weights, biases, dst, _conv_info, _dilation, _output_multiplier, _output_shift, window, has_biases); |
| } |
| else |
| { |
| const bool is_pow2 = ((_depth_multiplier & (_depth_multiplier - 1)) == 0); |
| const bool is_quantized_per_tensor = !(is_data_type_quantized_per_channel(weights->info()->data_type())); |
| |
| if(is_pow2 && is_quantized_per_tensor && _depth_multiplier >= 8) |
| { |
| depthwise_loop_pow2_quantized_per_tensor<T, TW>(src, weights, biases, dst, _conv_info, _dilation, _depth_multiplier, _output_multiplier, _output_shift, window, has_biases); |
| } |
| else |
| { |
| depthwise_loop_generic_quantized<T, TW>(src, weights, biases, dst, _conv_info, _dilation, _depth_multiplier, _output_multiplier, _output_shift, window, has_biases); |
| } |
| } |
| } |
| |
| void CpuDepthwiseConv2dNativeKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| const auto src = tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| const auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| const auto biases = tensors.get_const_tensor(TensorType::ACL_SRC_2); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| (this->*_func)(src, weights, biases, dst, window, _has_biases); |
| } |
| |
| const char *CpuDepthwiseConv2dNativeKernel::name() const |
| { |
| return "CpuDepthwiseConv2dNativeKernel"; |
| } |
| } // namespace kernels |
| } // namespace cpu |
| } // namespace arm_compute |