| /* |
| * Copyright (c) 2017-2022 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/core/NEON/kernels/NEL2NormalizeLayerKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include "src/common/cpuinfo/CpuIsaInfo.h" |
| #include "src/core/common/Registrars.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "src/core/NEON/NEMath.h" |
| #include "src/cpu/kernels/l2normlayer/list.h" |
| |
| #include <arm_neon.h> |
| #include <cmath> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| constexpr int max_input_tensor_dim = 3; |
| |
| struct L2NormalizeLayerSelectorData |
| { |
| DataType dt; |
| unsigned int actual_axis; |
| cpuinfo::CpuIsaInfo isa; |
| }; |
| |
| using L2NormalizeLayerKernelSelctorPtr = std::add_pointer<bool(const L2NormalizeLayerSelectorData &data)>::type; |
| |
| using L2NormalizeLayerPtr = std::add_pointer<void( |
| const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis)>::type; |
| |
| struct L2NormalizeLayerKernel |
| { |
| const char *name; |
| const L2NormalizeLayerKernelSelctorPtr is_selected; |
| L2NormalizeLayerPtr ukernel; |
| }; |
| |
| static const L2NormalizeLayerKernel available_kernels[] = { |
| {"fp32_neon_l2normalize_x", |
| [](const L2NormalizeLayerSelectorData &data) |
| { return data.dt == DataType::F32 && data.actual_axis == Window::DimX; }, |
| REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_l2_normalize_x)}, |
| {"fp32_neon_l2normalize_yz", |
| [](const L2NormalizeLayerSelectorData &data) |
| { return data.dt == DataType::F32 && data.actual_axis != Window::DimX; }, |
| REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_l2_normalize_yz)}, |
| { |
| "fp16_neon_l2normalize_x", |
| [](const L2NormalizeLayerSelectorData &data) |
| { return data.dt == DataType::F16 && data.isa.fp16 && data.actual_axis == Window::DimX; }, |
| REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_l2_normalize_x), |
| }, |
| { |
| "fp16_neon_l2normalize_yz", |
| [](const L2NormalizeLayerSelectorData &data) |
| { return data.dt == DataType::F16 && data.isa.fp16 && data.actual_axis != Window::DimX; }, |
| REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_l2_normalize_yz), |
| }, |
| }; |
| |
| /** Micro-kernel selector |
| * |
| * @param[in] data Selection data passed to help pick the appropriate micro-kernel |
| * |
| * @return A matching micro-kernel else nullptr |
| */ |
| const L2NormalizeLayerKernel *get_implementation(const L2NormalizeLayerSelectorData &data) |
| { |
| for (const auto &uk : available_kernels) |
| { |
| if (uk.is_selected(data)) |
| { |
| return &uk; |
| } |
| } |
| return nullptr; |
| } |
| |
| Status |
| validate_arguments(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon) |
| { |
| ARM_COMPUTE_UNUSED(epsilon); |
| |
| const uint32_t actual_axis = wrap_around(axis, max_input_tensor_dim); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, sum, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, sum); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(actual_axis > 2, "Actual axis greater than 2 is not supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(actual_axis >= TensorShape::num_max_dimensions, |
| "Actual normalization axis greater than max number of dimensions"); |
| |
| // Reduce shape on axis |
| TensorShape sum_shape = input->tensor_shape(); |
| sum_shape.set(actual_axis, 1); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(sum->tensor_shape(), sum_shape); |
| |
| if (output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(input->tensor_shape(), output->tensor_shape()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) |
| { |
| Window win = calculate_max_window(*input, Steps()); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type()); |
| |
| // NEL2NormalizeLayerKernel doesn't need padding so update_window_and_padding() can be skipped |
| |
| return std::make_tuple(Status{}, win); |
| } |
| } // namespace |
| |
| NEL2NormalizeLayerKernel::NEL2NormalizeLayerKernel() |
| : _input(nullptr), _sum(nullptr), _output(nullptr), _actual_axis(0), _epsilon(1e-12) |
| { |
| } |
| |
| void NEL2NormalizeLayerKernel::configure( |
| const ITensor *input, const ITensor *sum, ITensor *output, int axis, float epsilon) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, sum, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), sum->info(), output->info(), axis, epsilon)); |
| |
| _input = input; |
| _sum = sum; |
| _output = output; |
| _actual_axis = wrap_around(axis, max_input_tensor_dim); |
| _epsilon = epsilon; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(_input->info(), _output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| INEKernel::configure(std::get<1>(win_config)); |
| } |
| |
| Status NEL2NormalizeLayerKernel::validate( |
| const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, sum, output, axis, epsilon)); |
| ARM_COMPUTE_RETURN_ON_ERROR( |
| std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get()))); |
| |
| return Status{}; |
| } |
| |
| void NEL2NormalizeLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| if (_actual_axis > 2) |
| { |
| ARM_COMPUTE_ERROR("Unsupported normalization axis"); |
| } |
| |
| const auto *uk = get_implementation( |
| L2NormalizeLayerSelectorData{_output->info()->data_type(), _actual_axis, CPUInfo::get().get_isa()}); |
| ARM_COMPUTE_ERROR_ON(uk == nullptr); |
| ARM_COMPUTE_ERROR_ON(uk->ukernel == nullptr); |
| |
| uk->ukernel(_input, _sum, _output, _epsilon, window, _actual_axis); |
| } |
| } // namespace arm_compute |