diff options
Diffstat (limited to 'src/cpu/kernels/CpuDirectConv3dKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuDirectConv3dKernel.cpp | 303 |
1 files changed, 88 insertions, 215 deletions
diff --git a/src/cpu/kernels/CpuDirectConv3dKernel.cpp b/src/cpu/kernels/CpuDirectConv3dKernel.cpp index fecdb2bcae..595b5f1330 100644 --- a/src/cpu/kernels/CpuDirectConv3dKernel.cpp +++ b/src/cpu/kernels/CpuDirectConv3dKernel.cpp @@ -23,9 +23,6 @@ */ #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" @@ -35,8 +32,10 @@ #include "arm_compute/core/Validate.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "src/core/CPP/Validate.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "src/core/common/Registrars.h" #include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/conv3d/neon/list.h" #include <algorithm> @@ -50,236 +49,126 @@ namespace kernels { namespace { -Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv3dInfo &conv_info) +struct DirectConv3dSelectorData { - 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)); + DataType dt; + const CPUInfo &ci; +}; +using DirectConv3dSelectorPtr = std::add_pointer<bool(const DirectConv3dSelectorData &data)>::type; +using DirectConv3dKernelPtr = std::add_pointer<void(const ITensor *, const ITensor *, const ITensor *, ITensor *, const Conv3dInfo &, const Window &)>::type; +struct DirectConv3dKernel +{ + const char *name; + const DirectConv3dSelectorPtr is_selected; + DirectConv3dKernelPtr ukernel; +}; - if(biases != nullptr) +static const DirectConv3dKernel available_kernels[] = +{ +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) { - 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) + "neon_fp16_directconv3d", + [](const DirectConv3dSelectorData & data) { return data.dt == DataType::F16 && data.ci.has_fp16(); }, + REGISTER_FP16_NEON(arm_compute::cpu::directconv3d_float_neon_ndhwc<float16_t>) + }, +#endif /* !defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */ { - 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); + "neon_fp32_directconv3d", + [](const DirectConv3dSelectorData & data) { return data.dt == DataType::F32; }, + REGISTER_FP32_NEON(arm_compute::cpu::directconv3d_float_neon_ndhwc<float>) } +}; - return Status{}; -} - -/** Reduce a vector to be a scalar by accumulating all lanes in the vector +/** Micro-kernel selector * - * @param[in] v Vector to be reduced. + * @param[in] data Selection data passed to help pick the appropriate micro-kernel * - * @return the wrapped-around number. + * @return A matching micro-kernel else nullptr */ -auto vreduce(const float32x4_t &v) +const DirectConv3dKernel *get_implementation(const DirectConv3dSelectorData &data) { - 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 + for(const auto &uk : available_kernels) + { + if(uk.is_selected(data)) + { + return &uk; + } + } + return nullptr; } -template <typename T> -void CpuDirectConv3dKernel::convolve_ndhwc(const Window &window, const ITensor *src, const ITensor *weights, const ITensor *biases, ITensor *dst) +Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, const Conv3dInfo &conv_info) { - 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; + const auto *uk = get_implementation(DirectConv3dSelectorData{ src0->data_type(), CPUInfo::get() }); + ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); - // Setup input window for the output iterator - Window window_out = window; - window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ERROR_ON(src0->data_layout() != DataLayout::NDHWC); + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src0); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); - // 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)); + const DataLayout data_layout = src0->data_layout(); + const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - Iterator out(dst, window_out); - Iterator wei(weights, window_w); + // Weight layout is D, H, W, Cin, Cout + ARM_COMPUTE_RETURN_ERROR_ON(src1->num_dimensions() > 5); + ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(1) != src0->dimension(channel_idx)); - const T *biases_ptr = nullptr; - if(biases) + if(src2 != nullptr) { - biases_ptr = reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes()); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, src2); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src2->dimension(0) != src1->dimension(0), + "biases size and number of output feature maps should match"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src2->num_dimensions() > 1, "biases should be one dimensional"); } - 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); + // Checks performed when output is configured + if(dst->total_size() != 0) + { + TensorShape output_shape = misc::shape_calculator::compute_conv3d_shape(src0->tensor_shape(), src1->tensor_shape(), conv_info); - // 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); + DataType data_type = src0->data_type(); - 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; + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), output_shape); + ARM_COMPUTE_RETURN_ERROR_ON(dst->data_type() != data_type); + } - 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); + return Status{}; +} } -void CpuDirectConv3dKernel::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const Conv3dInfo &conv_info) +void CpuDirectConv3dKernel::configure(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, ITensorInfo *dst, const Conv3dInfo &conv_info) { - ARM_COMPUTE_UNUSED(biases); - ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); + ARM_COMPUTE_UNUSED(src2); + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); - _conv_info = conv_info; + const auto *uk = get_implementation(DirectConv3dSelectorData{ src0->data_type(), CPUInfo::get() }); + ARM_COMPUTE_ERROR_ON_NULLPTR(uk); + + _conv_info = conv_info; + _run_method = uk->ukernel; + _name = std::string("CpuDirectConv3dKernel").append("/").append(uk->name); // Get convolved dimensions - TensorShape output_shape = misc::shape_calculator::compute_conv3d_shape(src->tensor_shape(), weights->tensor_shape(), conv_info); + TensorShape output_shape = misc::shape_calculator::compute_conv3d_shape(src0->tensor_shape(), src1->tensor_shape(), conv_info); - DataType data_type = src->data_type(); + DataType data_type = src0->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)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, 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) +Status CpuDirectConv3dKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, const Conv3dInfo &conv_info) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, conv_info)); return Status{}; } @@ -289,35 +178,19 @@ void CpuDirectConv3dKernel::run_op(ITensorPack &tensors, const Window &window, c ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); + ARM_COMPUTE_ERROR_ON(_run_method == nullptr); - 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); + auto src0 = tensors.get_const_tensor(TensorType::ACL_SRC_0); + auto src1 = tensors.get_const_tensor(TensorType::ACL_SRC_1); + auto src2 = 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; - } + _run_method(src0, src1, src2, dst, _conv_info, window); } const char *CpuDirectConv3dKernel::name() const { - return "CpuDirectConv3dKernel"; + return _name.c_str(); } } // namespace kernels } // namespace cpu |