| /* |
| * 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. |
| */ |
| #include "src/cpu/kernels/CpuDirectConv3dKernel.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 "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include <algorithm> |
| |
| using namespace arm_compute::detail; |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv3dInfo &conv_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); |
| ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() != DataLayout::NDHWC); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); |
| |
| const DataLayout data_layout = src->data_layout(); |
| const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| // Weight layout is D, H, W, Cin, Cout |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 5); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) != src->dimension(channel_idx)); |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(0) != weights->dimension(0), |
| "biases size and number of output feature maps should match"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1, "biases should be one dimensional"); |
| } |
| |
| // Checks performed when output is configured |
| if(dst->total_size() != 0) |
| { |
| TensorShape output_shape = misc::shape_calculator::compute_conv3d_shape(src->tensor_shape(), weights->tensor_shape(), conv_info); |
| |
| DataType data_type = src->data_type(); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON(dst->data_type() != data_type); |
| } |
| |
| return Status{}; |
| } |
| |
| /** Reduce a vector to be a scalar by accumulating all lanes in the vector |
| * |
| * @param[in] v Vector to be reduced. |
| * |
| * @return the wrapped-around number. |
| */ |
| auto vreduce(const float32x4_t &v) |
| { |
| auto v0 = wrapper::vgethigh(v); |
| auto v1 = wrapper::vgetlow(v); |
| auto v_out = wrapper::vadd(v0, v1); |
| |
| float a = wrapper::vgetlane(v_out, 0); |
| float b = wrapper::vgetlane(v_out, 1); |
| return a + b; |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| auto vreduce(const float16x8_t &v) |
| { |
| auto v0 = wrapper::vgethigh(v); |
| auto v1 = wrapper::vgetlow(v); |
| auto v_out = wrapper::vadd(v0, v1); |
| |
| float16_t a = wrapper::vgetlane(v_out, 0); |
| float16_t b = wrapper::vgetlane(v_out, 1); |
| float16_t c = wrapper::vgetlane(v_out, 2); |
| float16_t d = wrapper::vgetlane(v_out, 3); |
| return a + b + c + d; |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| } |
| |
| template <typename T> |
| void CpuDirectConv3dKernel::convolve_ndhwc(const Window &window, const ITensor *src, const ITensor *weights, const ITensor *biases, ITensor *dst) |
| { |
| 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); |
| |
| // 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 T *biases_ptr = nullptr; |
| if(biases) |
| { |
| biases_ptr = reinterpret_cast<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()); |
| T out_temp = static_cast<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 out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); |
| vector_type w_vec = wrapper::vdup_n(static_cast<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(unsigned 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); |
| } |
| out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); |
| } |
| out_temp += vreduce(out_temp_vec); |
| 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); |
| const auto w_val = *(weights_ptr_mover); |
| out_temp += src_val * w_val; |
| } |
| } |
| } |
| } |
| *(reinterpret_cast<T *>(out_ptr + id_w[0])) = (biases) ? out_temp + biases_ptr[id_w[0]] : out_temp; |
| }, |
| wei); |
| }, |
| out); |
| } |
| |
| void CpuDirectConv3dKernel::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const Conv3dInfo &conv_info) |
| { |
| ARM_COMPUTE_UNUSED(biases); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); |
| |
| _conv_info = conv_info; |
| |
| // Get convolved dimensions |
| TensorShape output_shape = misc::shape_calculator::compute_conv3d_shape(src->tensor_shape(), weights->tensor_shape(), conv_info); |
| |
| DataType data_type = src->data_type(); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*dst, output_shape, 1, data_type); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info)); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*dst, Steps()); |
| ICpuKernel::configure(win); |
| } |
| |
| Status CpuDirectConv3dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv3dInfo &conv_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info)); |
| |
| return Status{}; |
| } |
| |
| void CpuDirectConv3dKernel::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); |
| |
| auto src = tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| auto biases = tensors.get_const_tensor(TensorType::ACL_SRC_2); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| switch(src->info()->data_type()) |
| { |
| case DataType::F32: |
| { |
| convolve_ndhwc<float>(window, src, weights, biases, dst); |
| break; |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| { |
| convolve_ndhwc<float16_t>(window, src, weights, biases, dst); |
| break; |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| } |
| |
| const char *CpuDirectConv3dKernel::name() const |
| { |
| return "CpuDirectConv3dKernel"; |
| } |
| } // namespace kernels |
| } // namespace cpu |
| } // namespace arm_compute |