From c9e519d2ea4780297d71e68cccc5de9c7bb7c0b4 Mon Sep 17 00:00:00 2001 From: alerah01 Date: Mon, 31 Jan 2022 19:04:10 +0200 Subject: Decouple CpuDirectConv2dKernel Resolves COMPMID-4626 Exclude SVE & SVE2 paths from android.bp NDK version does not support these extensions. Change-Id: I49b147d2a84819975d3225f2920106fa1a0d742f Signed-off-by: alerah01 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7136 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Giorgio Arena --- src/cpu/kernels/directconv2d/nchw/all.cpp | 179 ++++++++++++++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 src/cpu/kernels/directconv2d/nchw/all.cpp (limited to 'src/cpu/kernels/directconv2d/nchw/all.cpp') diff --git a/src/cpu/kernels/directconv2d/nchw/all.cpp b/src/cpu/kernels/directconv2d/nchw/all.cpp new file mode 100644 index 0000000000..a719fa50d6 --- /dev/null +++ b/src/cpu/kernels/directconv2d/nchw/all.cpp @@ -0,0 +1,179 @@ +/* + * Copyright (c) 2022 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/cpu/kernels/directconv2d/nhwc/neon/impl.h" + +#include "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" +#include "src/core/NEON/wrapper/wrapper.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/IAccessWindow.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "src/core/helpers/WindowHelpers.h" + +#include + +namespace arm_compute +{ +namespace cpu +{ +namespace kernels +{ +template +void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) +void neon_fp16_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +{ + convolve_nchw(window, src, weights, dst, conv_info); +} +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +void neon_fp32_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +{ + convolve_nchw(window, src, weights, dst, conv_info); +} + +template +void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_UNUSED(conv_info); + + // Declare useful types + using vtype = wrapper::traits::neon_bitvector; + using vector_type = typename vtype::type; + using tag_type = typename vtype::tag_type; + + // Scalar quantities + const int element_size = src->info()->element_size(); + const int input_stride_w = src->info()->strides_in_bytes()[0] / element_size; + const int input_stride_h = src->info()->strides_in_bytes()[1] / element_size; + const int input_stride_c = src->info()->strides_in_bytes()[2] / element_size; + const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size; + + const int input_dim_w = src->info()->dimension(0); + const int input_dim_h = src->info()->dimension(1); + + const int output_stride_c = dst->info()->strides_in_bytes()[2]; + + const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().x() / element_size; + const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().y() / element_size; + const unsigned int kernel_stride_c = weights->info()->strides_in_bytes().z() / element_size; + + const int kernel_dim_w = weights->info()->dimension(0); + const int kernel_dim_h = weights->info()->dimension(1); + + const int conv_pad_top = conv_info.pad_top(); + const int conv_pad_left = conv_info.pad_left(); + const int conv_stride_w = std::get<0>(conv_info.stride()); + const int conv_stride_h = std::get<1>(conv_info.stride()); + + // Setup input window for the output iterator + Window window_out = window; + window_out.set(Window::DimZ, Window::Dimension(0, 1, 1)); + + // Setup input window for the weights iterator + Window window_w = calculate_max_window(*weights->info(), Steps()); + window_w.set(Window::DimX, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimY, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimZ, Window::Dimension(0, 1, 1)); + + Iterator out(dst, window_out); + Iterator wei(weights, window_w); + + constexpr int num_elems_read_per_iteration = 16 / sizeof(T); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + // We are computing the theoretical starting input starting points + const int in_w_start_t = static_cast(id.x()) * conv_stride_w - conv_pad_left; + const int in_h_start_t = static_cast(id.y()) * conv_stride_h - conv_pad_top; + const int in_w_end_t = in_w_start_t + kernel_dim_w; + const int in_h_end_t = in_h_start_t + kernel_dim_h; + + // We are computing the valid initial and ending input points by checking the borders + const int in_w_start = std::max(in_w_start_t, 0); + const int in_h_start = std::max(in_h_start_t, 0); + const int in_w_end = std::min(in_w_end_t, input_dim_w); + const int in_h_end = std::min(in_h_end_t, input_dim_h); + + // We use the input points to select the valid weight points to use + const int wei_w_start = in_w_start - in_w_start_t; + const int wei_h_start = in_h_start - in_h_start_t; + const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end); + + const int index_c_end = weights->info()->dimension(2); + const T *const in_ptr_start = reinterpret_cast(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n; + execute_window_loop(window_w, [&](const Coordinates & id_w) + { + const T *const weights_ptr_start = reinterpret_cast(wei.ptr()); + uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c; + T out_temp = static_cast(0); + + for(int index_wei_c = 0, index_in_c = 0; index_wei_c < index_c_end; ++index_wei_c, ++index_in_c) + { + const T *const in_ptr_row_0 = in_ptr_start + index_in_c * input_stride_c; + const T *const weights_ptr_row_0 = weights_ptr_start + index_wei_c * kernel_stride_c; + 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 *in_ptr_row = in_ptr_row_0 + index_in_h * input_stride_h; + const T *weights_ptr_row = weights_ptr_row_0 + index_wei_h * kernel_stride_h; + int index_w = in_w_start; + int index_wei_w = wei_w_start; + vector_type out_temp_vec = wrapper::vdup_n(static_cast(0), tag_type()); + for(; index_w <= ((in_w_end - num_elems_read_per_iteration)); index_w += num_elems_read_per_iteration, index_wei_w += num_elems_read_per_iteration) + { + const auto src_vec = wrapper::vloadq(in_ptr_row + index_w * input_stride_w); + const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wei_w * kernel_stride_w); + out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); + } + out_temp += vreduce(out_temp_vec); + for(; index_w < in_w_end; ++index_w, ++index_wei_w) + { + const auto src_val = *(in_ptr_row + index_w * input_stride_w); + const auto w_val = *(weights_ptr_row + index_wei_w * kernel_stride_w); + out_temp += src_val * w_val; + } + } + } + *(reinterpret_cast(out_ptr)) = out_temp; + + }, + wei); + }, + out); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +template void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); + +} // namespace kernels +} // namespace cpu +} // namespace arm_compute -- cgit v1.2.1