diff options
Diffstat (limited to 'src/cpu/kernels/conv3d/neon/list.h')
| -rw-r--r-- | src/cpu/kernels/conv3d/neon/list.h | 165 |
1 files changed, 92 insertions, 73 deletions
diff --git a/src/cpu/kernels/conv3d/neon/list.h b/src/cpu/kernels/conv3d/neon/list.h index 3bfa124dc3..082c60be29 100644 --- a/src/cpu/kernels/conv3d/neon/list.h +++ b/src/cpu/kernels/conv3d/neon/list.h @@ -27,8 +27,9 @@ #include "arm_compute/core/Types.h" #include "arm_compute/core/utils/misc/Traits.h" #include "arm_compute/runtime/FunctionDescriptors.h" -#include "src/core/NEON/wrapper/wrapper.h" + #include "src/core/helpers/WindowHelpers.h" +#include "src/core/NEON/wrapper/wrapper.h" #include "src/cpu/kernels/conv3d/neon/quantized.h" namespace arm_compute @@ -36,7 +37,12 @@ namespace arm_compute namespace cpu { template <typename T> -void directconv3d_float_neon_ndhwc(const ITensor *src0, const ITensor *src1, const ITensor *src2, ITensor *dst, const Conv3dInfo &conv_info, const Window &window) +void directconv3d_float_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; @@ -88,91 +94,104 @@ void directconv3d_float_neon_ndhwc(const ITensor *src0, const ITensor *src1, con Iterator wei(weights, window_w); const T *biases_ptr = nullptr; - if(biases != nullptr) + if (biases != nullptr) { 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) + 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 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 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 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 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) + 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) { - w_vec = wrapper::vsetlane(*weights_ptr_mover, w_vec, k); + 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 (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; + } } - 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_ptr != nullptr) ? out_temp + biases_ptr[id_w[0]] : out_temp; + *(reinterpret_cast<T *>(out_ptr + id_w[0])) = + (biases_ptr != nullptr) ? out_temp + biases_ptr[id_w[0]] : out_temp; + }, + wei); }, - wei); - }, - out); + out); } } // namespace cpu } // namespace arm_compute -#endif // SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
\ No newline at end of file +#endif // SRC_CORE_NEON_KERNELS_CONV3D_LIST_H |