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Diffstat (limited to 'src/cpu/kernels/CpuDirectConv3dKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuDirectConv3dKernel.cpp | 324 |
1 files changed, 324 insertions, 0 deletions
diff --git a/src/cpu/kernels/CpuDirectConv3dKernel.cpp b/src/cpu/kernels/CpuDirectConv3dKernel.cpp new file mode 100644 index 0000000000..fecdb2bcae --- /dev/null +++ b/src/cpu/kernels/CpuDirectConv3dKernel.cpp @@ -0,0 +1,324 @@ +/* + * 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
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