diff options
Diffstat (limited to 'src/cpu/kernels/directconv2d/impl.h')
| -rw-r--r-- | src/cpu/kernels/directconv2d/impl.h | 389 |
1 files changed, 389 insertions, 0 deletions
diff --git a/src/cpu/kernels/directconv2d/impl.h b/src/cpu/kernels/directconv2d/impl.h new file mode 100644 index 0000000000..d3965326cd --- /dev/null +++ b/src/cpu/kernels/directconv2d/impl.h @@ -0,0 +1,389 @@ +/* + * Copyright (c) 2022-2023 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 ACL_SRC_CPU_KERNELS_DIRECTCONV2D_IMPL_H +#define ACL_SRC_CPU_KERNELS_DIRECTCONV2D_IMPL_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 "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" +#include "src/core/NEON/wrapper/wrapper.h" + +#include <algorithm> + +namespace arm_compute +{ +namespace cpu +{ +namespace kernels +{ +template <typename T, bool has_pads> +void linearize_volume_nchw(const uint8_t *const in_ptr, + T *out_ptr, + bool has_bias, + int top_left_x, + int top_left_y, + int kernel_width, + int kernel_height, + int kernel_depth, + int input_w, + int input_h, + int input_stride_x, + int input_stride_y, + int input_stride_z, + int pad_value, + int dilation_x, + int dilation_y) +{ + const int kernel_size2 = kernel_width * kernel_height; + const int x_e = top_left_x + kernel_width * dilation_x; + const int y_e = top_left_y + kernel_height * dilation_y; + + // Linearize volume + int d = 0; + // This for loop linearize a volume with 3 slices. This allows: + // 1) to reduce the iterations of the outer for loop "d" + // 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs + for (; d <= (kernel_depth - 3); d += 3) + { + for (int y = top_left_y; y < y_e; y += dilation_y) + { + if ((y < 0 || y >= input_h) && has_pads) + { + // All the values will be the offset (will be zeros when not quantized) + for (int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + { + *(out_ptr + 0 * kernel_size2) = pad_value; + *(out_ptr + 1 * kernel_size2) = pad_value; + *(out_ptr + 2 * kernel_size2) = pad_value; + } + } + else + { + for (int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + { + if ((x < 0 || x >= input_w) && has_pads) + { + *(out_ptr + 0 * kernel_size2) = pad_value; + *(out_ptr + 1 * kernel_size2) = pad_value; + *(out_ptr + 2 * kernel_size2) = pad_value; + } + else + { + *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast<const T *>( + in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x))); + *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast<const T *>( + in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x))); + *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast<const T *>( + in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x))); + } + } + } + } + out_ptr += 2 * kernel_size2; + } + + // Left over + for (; d < kernel_depth; d++) + { + for (int y = top_left_y; y < y_e; y += dilation_y) + { + if ((y < 0 || y >= input_h) && has_pads) + { + // All the values will be the offset (will be zeros when not quantized) + memset(static_cast<void *>(out_ptr), pad_value, kernel_width * sizeof(T)); + out_ptr += kernel_width; + } + else + { + for (int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + { + if ((x < 0 || x >= input_w) && has_pads) + { + *out_ptr = pad_value; + } + else + { + *out_ptr = *(reinterpret_cast<const T *>( + in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x))); + } + } + } + } + } + + // Append 1 if the convolution layer has biases + if (has_bias) + { + *out_ptr = static_cast<T>(1); + } +} + +template <typename T, bool has_pads> +void linearize_volume_nhwc(const uint8_t *const in_ptr, + T *out_ptr, + bool has_bias, + int start_x, + int start_y, + int kernel_width, + int kernel_height, + int input_w, + int input_h, + int input_c, + int input_stride_y, + int input_stride_z, + int pad_value, + int dilation_x, + int dilation_y) +{ + const int end_x = start_x + kernel_width * dilation_x; + const int end_y = start_y + kernel_height * dilation_y; + const int pad_quant = kernel_width * input_c; + const int element_size = static_cast<int>(sizeof(T)); + if ((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && + (input_stride_y == input_c * element_size)) + { + for (int y = start_y; y < end_y; y += dilation_y) + { + //optimized for no dilation and no boundary pixels + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), + input_c * kernel_width * element_size); + out_ptr += input_c * kernel_width; + } + } + else + { + for (int y = start_y; y < end_y; y += dilation_y) + { + if (y < 0 || y >= input_h) + { + memset(static_cast<void *>(out_ptr), pad_value, pad_quant * element_size); + out_ptr += pad_quant; + } + else if (dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != input_c * element_size) + { + for (int x = start_x; x < end_x; x += dilation_x) + { + if (x < 0 || x >= input_w) + { + memset(static_cast<void *>(out_ptr), pad_value, input_c * element_size); + out_ptr += input_c; + } + else + { + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), + input_c * element_size); + out_ptr += input_c; + } + } + } + else + { + //optimized for no dilation and no boundary pixels + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), + input_c * kernel_width * element_size); + out_ptr += input_c * kernel_width; + } + } + } + // Append 1 if the convolution layer has biases + if (has_bias) + { + *out_ptr = static_cast<T>(1); + } +} + +template <typename T, bool has_pads> +void linearize_volume_nhwc(const uint8_t *const in_ptr, + T *out_ptr, + bool has_bias, + int start_x, + int start_y, + int kernel_width, + int kernel_height, + int input_w, + int input_h, + int input_c, + int input_stride_y, + int input_stride_z, + int pad_value, + int dilation_x, + int dilation_y, + int pad_right) +{ + const int end_x = start_x + kernel_width * dilation_x; + const int end_y = start_y + kernel_height * dilation_y; + const int pad_quant = kernel_width * (input_c + pad_right); + const int element_size = static_cast<int>(sizeof(T)); + const int channel_chunk_size = input_c * element_size; + + if ((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && + (input_stride_y == channel_chunk_size)) + { + for (int y = start_y; y < end_y; y += dilation_y) + { + const uint8_t *offset_ptr = in_ptr + (y * input_stride_z + start_x * input_stride_y); + for (int e = 0; e < kernel_width; e++) + { + memcpy(out_ptr, reinterpret_cast<const T *>(offset_ptr + e * channel_chunk_size), channel_chunk_size); + out_ptr += input_c + pad_right; + } + } + } + else + { + for (int y = start_y; y < end_y; y += dilation_y) + { + if (y < 0 || y >= input_h) + { + memset(static_cast<void *>(out_ptr), pad_value, pad_quant * element_size); + out_ptr += pad_quant; + } + else if (dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != channel_chunk_size) + { + for (int x = start_x; x < end_x; x += dilation_x) + { + if (x < 0 || x >= input_w) + { + memset(static_cast<void *>(out_ptr), pad_value, (input_c + pad_right) * element_size); + out_ptr += input_c + pad_right; + } + else + { + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), + channel_chunk_size); + out_ptr += input_c + pad_right; + } + } + } + else + { + const uint8_t *offset_ptr = in_ptr + (y * input_stride_z + start_x * input_stride_y); + for (int e = 0; e < kernel_width; e++) + { + memcpy(out_ptr, reinterpret_cast<const T *>(offset_ptr + e * channel_chunk_size), + channel_chunk_size); + out_ptr += input_c + pad_right; + } + } + } + } + // Append 1 if the convolution layer has biases + if (has_bias) + { + *out_ptr = static_cast<T>(1); + } +} + +template <typename T, bool has_pads, bool is_nchw> +void run_im2col(const ITensor *src, + ITensor *dst, + const Window &window, + DataLayout data_layout, + const PadStrideInfo &conv_info, + std::pair<unsigned int, unsigned int> convolved_dims, + const Size2D &kernel_dims, + const Size2D &dilation, + uint32_t input_pad_right, + bool has_bias) +{ + const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + + const int input_w = src->info()->dimension(width_idx); + const int input_h = src->info()->dimension(height_idx); + const int input_c = src->info()->dimension(channel_idx); + const int input_stride_x = src->info()->strides_in_bytes().x(); + const int input_stride_y = src->info()->strides_in_bytes().y(); + const int input_stride_z = src->info()->strides_in_bytes().z(); + const int pad_left = conv_info.pad_left(); + const int pad_top = conv_info.pad_top(); + const int stride_x = conv_info.stride().first; + const int stride_y = conv_info.stride().second; + const int pad_value = + is_data_type_quantized(src->info()->data_type()) ? src->info()->quantization_info().uniform().offset : 0; + + const auto kernel_width = kernel_dims.width; + const auto kernel_height = kernel_dims.height; + + Window window_in_out(window); + // The first three dimensions of the input and output are increased by the inner loops + window_in_out.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in_out.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + // Create iterators + Iterator in(src, window_in_out); + Iterator out(dst, window_in_out); + + execute_window_loop( + window, + [&](const Coordinates &id) + { + const int start_w = id[width_idx] * stride_x - pad_left; + const int start_h = id[height_idx] * stride_y - pad_top; + + // Get pointers + const uint8_t *const input_ptr = in.ptr(); + auto output_ptr = + reinterpret_cast<T *>(out.ptr() + (id[width_idx] + id[height_idx] * convolved_dims.first) * + dst->info()->strides_in_bytes().y()); + + // Linearize volume + if (is_nchw) + { + linearize_volume_nchw<T, has_pads>( + input_ptr, output_ptr, has_bias, start_w, start_h, kernel_width, kernel_height, input_c, input_w, + input_h, input_stride_x, input_stride_y, input_stride_z, pad_value, dilation.x(), dilation.y()); + } + else + { + if (input_pad_right > 0) + { + linearize_volume_nhwc<T, has_pads>(input_ptr, output_ptr, has_bias, start_w, start_h, kernel_width, + kernel_height, input_w, input_h, input_c, input_stride_y, + input_stride_z, pad_value, dilation.x(), dilation.y(), + input_pad_right); + } + else + { + linearize_volume_nhwc<T, has_pads>(input_ptr, output_ptr, has_bias, start_w, start_h, kernel_width, + kernel_height, input_w, input_h, input_c, input_stride_y, + input_stride_z, pad_value, dilation.x(), dilation.y()); + } + } + }, + in, out); +} + +} // namespace kernels +} // namespace cpu +} // namespace arm_compute +#endif // ACL_SRC_CPU_KERNELS_DIRECTCONV2D_IMPL_H |