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Diffstat (limited to 'src/cpu/kernels/conv3d/neon/quantized.h')
| -rw-r--r-- | src/cpu/kernels/conv3d/neon/quantized.h | 275 |
1 files changed, 275 insertions, 0 deletions
diff --git a/src/cpu/kernels/conv3d/neon/quantized.h b/src/cpu/kernels/conv3d/neon/quantized.h new file mode 100644 index 0000000000..f0fc9b5a71 --- /dev/null +++ b/src/cpu/kernels/conv3d/neon/quantized.h @@ -0,0 +1,275 @@ +/* + * Copyright (c) 2021 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef SRC_CORE_NEON_KERNELS_CONV3D_QUANTIZED_H +#define SRC_CORE_NEON_KERNELS_CONV3D_QUANTIZED_H + +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/runtime/FunctionDescriptors.h" + +#include "src/core/helpers/WindowHelpers.h" +#include "src/core/NEON/NEAsymm.h" +#include "src/core/NEON/wrapper/wrapper.h" + +namespace arm_compute +{ +namespace cpu +{ +template <typename T> +void directconv3d_quantized_neon_ndhwc(const ITensor *src0, + const ITensor *src1, + const ITensor *src2, + ITensor *dst, + const Conv3dInfo &conv_info, + const Window &window) +{ + const ITensor *src = src0; + const ITensor *weights = src1; + const ITensor *biases = src2; + + using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>; + using vector_type = typename vtype::type; + using tag_type = typename vtype::tag_type; + constexpr int num_elems_read_per_iteration = 16 / sizeof(T); + using q16_t = typename wrapper::traits::promote_t<T>; + using q32_t = typename wrapper::traits::promote_t<q16_t>; + using q32x4_t = typename wrapper::traits::neon_vector<q32_t, 4>::type; + + const int32_t input_offset = -src->info()->quantization_info().uniform().offset; + const float input_scale = src->info()->quantization_info().uniform().scale; + const int32_t weights_offset = -weights->info()->quantization_info().uniform().offset; + const float weights_scale = weights->info()->quantization_info().uniform().scale; + const int32_t output_offset = dst->info()->quantization_info().uniform().offset; + const float output_scale = dst->info()->quantization_info().uniform().scale; + + int32_t output_multiplier = 0; + int32_t output_shift = 0; + const float multiplier = input_scale * weights_scale / output_scale; + arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); + + // Scalar quantities (N D H W Cin) + const int element_size = src->info()->element_size(); + const int input_stride_w = src->info()->strides_in_bytes().y() / element_size; + const int input_stride_h = src->info()->strides_in_bytes().z() / element_size; + const int input_stride_d = src->info()->strides_in_bytes()[3] / element_size; + const int input_stride_n = src->info()->strides_in_bytes()[4] / element_size; + const int input_dim_w = src->info()->dimension(1); + const int input_dim_h = src->info()->dimension(2); + const int input_dim_d = src->info()->dimension(3); + + // Kernel info (D H W Cin Cout) + const unsigned int kernel_stride_w = weights->info()->strides_in_bytes()[2] / element_size; + const unsigned int kernel_stride_h = weights->info()->strides_in_bytes()[3] / element_size; + const unsigned int kernel_stride_d = weights->info()->strides_in_bytes()[4] / element_size; + const int kernel_dim_w = weights->info()->dimension(2); + const int kernel_dim_h = weights->info()->dimension(3); + const int kernel_dim_d = weights->info()->dimension(4); + + // Convolution padding and stride + const int conv_pad_top = conv_info.padding.top; + const int conv_pad_left = conv_info.padding.left; + const int conv_pad_front = conv_info.padding.front; + const int conv_stride_w = conv_info.stride.width; + const int conv_stride_h = conv_info.stride.height; + const int conv_stride_d = conv_info.stride.depth; + + // Setup input window for the output iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + // Setup input window for the weights iterator + Window window_w = calculate_max_window(*weights->info(), Steps()); + window_w.set(Window::DimY, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimZ, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimW, Window::Dimension(0, 1, 1)); + window_w.set(4, Window::Dimension(0, 1, 1)); + + Iterator out(dst, window_out); + Iterator wei(weights, window_w); + + const int32_t *biases_ptr = nullptr; + if (biases != nullptr) + { + biases_ptr = reinterpret_cast<int32_t *>(biases->buffer() + biases->info()->offset_first_element_in_bytes()); + } + execute_window_loop( + window_out, + [&](const Coordinates &id) + { + // We are computing the theoretical input starting points + const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left; + const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top; + const int in_d_start_t = static_cast<int>(id[3]) * conv_stride_d - conv_pad_front; + const int in_w_end_t = in_w_start_t + kernel_dim_w; + const int in_h_end_t = in_h_start_t + kernel_dim_h; + const int in_d_end_t = in_d_start_t + kernel_dim_d; + + // We are computing the valid initial and ending input points by checking the borders + const int in_w_start = std::max(in_w_start_t, 0); + const int in_h_start = std::max(in_h_start_t, 0); + const int in_d_start = std::max(in_d_start_t, 0); + const int in_w_end = std::min(in_w_end_t, input_dim_w); + const int in_h_end = std::min(in_h_end_t, input_dim_h); + const int in_d_end = std::min(in_d_end_t, input_dim_d); + + // We use the input points to select the valid weight points to use + const int wei_w_start = in_w_start - in_w_start_t; + const int wei_h_start = in_h_start - in_h_start_t; + const int wei_d_start = in_d_start - in_d_start_t; + const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end); + const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end); + const int wei_d_end = kernel_dim_d - (in_d_end_t - in_d_end); + + const int index_c_out_end = weights->info()->dimension(0); + const int index_c_in_end = weights->info()->dimension(1); + const T *const in_ptr_start = + reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + + id[4] * input_stride_n; + + execute_window_loop( + window_w, + [&](const Coordinates &id_w) + { + /* + * This is the loop in the weights, and it goes along OFM (output feature map) + */ + const auto weights_ptr_start = reinterpret_cast<const T *>(wei.ptr()); + int32_t acc = static_cast<int32_t>(0); + T *out_ptr = reinterpret_cast<T *>(out.ptr()); + for (int index_wei_d = wei_d_start, index_in_d = in_d_start; index_wei_d < wei_d_end; + ++index_wei_d, ++index_in_d) + { + const auto in_ptr_d = in_ptr_start + index_in_d * input_stride_d; + const auto weights_ptr_d = weights_ptr_start + index_wei_d * kernel_stride_d; + for (int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; + ++index_wei_h, ++index_in_h) + { + const T *const in_ptr_row = in_ptr_d + index_in_h * input_stride_h; + const T *const weights_ptr_row = weights_ptr_d + index_wei_h * kernel_stride_h; + for (int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end; + ++index_wei_w, ++index_in_w) + { + const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w; + const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w; + int index_c_in = 0; + vector_type w_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); + + q32x4_t acc_q32_0 = wrapper::vdup_n(static_cast<q32_t>(0), tag_type()); + q32x4_t acc_q32_1 = wrapper::vdup_n(static_cast<q32_t>(0), tag_type()); + q32x4_t acc_q32_2 = wrapper::vdup_n(static_cast<q32_t>(0), tag_type()); + q32x4_t acc_q32_3 = wrapper::vdup_n(static_cast<q32_t>(0), tag_type()); + + for (; index_c_in <= index_c_in_end - num_elems_read_per_iteration; + index_c_in += num_elems_read_per_iteration, + in_ptr_mover += num_elems_read_per_iteration) + { + const auto src_vec = wrapper::vloadq(in_ptr_mover); + //Load Cin weights + for (int k = 0; k < num_elems_read_per_iteration; + ++k, weights_ptr_mover += index_c_out_end) + { + w_vec = wrapper::vsetlane(*weights_ptr_mover, w_vec, k); + } + q32x4_t src_q32_0 = wrapper::vdup_n(static_cast<q32_t>(input_offset), tag_type()); + q32x4_t src_q32_1 = wrapper::vdup_n(static_cast<q32_t>(input_offset), tag_type()); + q32x4_t src_q32_2 = wrapper::vdup_n(static_cast<q32_t>(input_offset), tag_type()); + q32x4_t src_q32_3 = wrapper::vdup_n(static_cast<q32_t>(input_offset), tag_type()); + + q32x4_t wei_q32_0 = wrapper::vdup_n(static_cast<q32_t>(weights_offset), tag_type()); + q32x4_t wei_q32_1 = wrapper::vdup_n(static_cast<q32_t>(weights_offset), tag_type()); + q32x4_t wei_q32_2 = wrapper::vdup_n(static_cast<q32_t>(weights_offset), tag_type()); + q32x4_t wei_q32_3 = wrapper::vdup_n(static_cast<q32_t>(weights_offset), tag_type()); + + const auto src_q16_0 = wrapper::vmovl(wrapper::vgetlow(src_vec)); + const auto src_q16_1 = wrapper::vmovl(wrapper::vgethigh(src_vec)); + const auto wei_q16_0 = wrapper::vmovl(wrapper::vgetlow(w_vec)); + const auto wei_q16_1 = wrapper::vmovl(wrapper::vgethigh(w_vec)); + + src_q32_0 = wrapper::vadd(src_q32_0, wrapper::vmovl(wrapper::vgetlow(src_q16_0))); + src_q32_1 = wrapper::vadd(src_q32_1, wrapper::vmovl(wrapper::vgethigh(src_q16_0))); + src_q32_2 = wrapper::vadd(src_q32_2, wrapper::vmovl(wrapper::vgetlow(src_q16_1))); + src_q32_3 = wrapper::vadd(src_q32_3, wrapper::vmovl(wrapper::vgethigh(src_q16_1))); + + wei_q32_0 = wrapper::vadd(wei_q32_0, wrapper::vmovl(wrapper::vgetlow(wei_q16_0))); + wei_q32_1 = wrapper::vadd(wei_q32_1, wrapper::vmovl(wrapper::vgethigh(wei_q16_0))); + wei_q32_2 = wrapper::vadd(wei_q32_2, wrapper::vmovl(wrapper::vgetlow(wei_q16_1))); + wei_q32_3 = wrapper::vadd(wei_q32_3, wrapper::vmovl(wrapper::vgethigh(wei_q16_1))); + + acc_q32_0 = wrapper::vmla(acc_q32_0, wei_q32_0, src_q32_0); + acc_q32_1 = wrapper::vmla(acc_q32_1, wei_q32_1, src_q32_1); + acc_q32_2 = wrapper::vmla(acc_q32_2, wei_q32_2, src_q32_2); + acc_q32_3 = wrapper::vmla(acc_q32_3, wei_q32_3, src_q32_3); + } +#if defined(__aarch64__) + acc += wrapper::vaddv(acc_q32_0); + acc += wrapper::vaddv(acc_q32_1); + acc += wrapper::vaddv(acc_q32_2); + acc += wrapper::vaddv(acc_q32_3); +#else // __aarch64__ + auto temp = wrapper::vpadd(wrapper::vgethigh(acc_q32_0), wrapper::vgetlow(acc_q32_0)); + temp = wrapper::vpadd(temp, temp); + acc += wrapper::vgetlane(temp, 0); + + temp = wrapper::vpadd(wrapper::vgethigh(acc_q32_1), wrapper::vgetlow(acc_q32_1)); + temp = wrapper::vpadd(temp, temp); + acc += wrapper::vgetlane(temp, 0); + + temp = wrapper::vpadd(wrapper::vgethigh(acc_q32_2), wrapper::vgetlow(acc_q32_2)); + temp = wrapper::vpadd(temp, temp); + acc += wrapper::vgetlane(temp, 0); + + temp = wrapper::vpadd(wrapper::vgethigh(acc_q32_3), wrapper::vgetlow(acc_q32_3)); + temp = wrapper::vpadd(temp, temp); + acc += wrapper::vgetlane(temp, 0); + +#endif // __aarch64__ + + for (; index_c_in < index_c_in_end; + ++index_c_in, ++in_ptr_mover, weights_ptr_mover += index_c_out_end) + { + const auto src_val = *(in_ptr_mover) + input_offset; + const auto w_val = *(weights_ptr_mover) + weights_offset; + acc += src_val * w_val; + } + } + } + } + + if (biases) + { + acc += *reinterpret_cast<const int32_t *>(biases_ptr + id_w[0]); + } + + T out_val = + finalize_quantization(acc, output_multiplier, output_shift, output_offset, T(0), T(0), false); + *(reinterpret_cast<T *>(out_ptr + id_w[0])) = out_val; + }, + wei); + }, + out); +} +} // namespace cpu +} // namespace arm_compute +#endif // SRC_CORE_NEON_KERNELS_CONV3D_QUANTIZED_H |