| /* |
| * Copyright (c) 2017-2021 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/core/NEON/kernels/NEBatchNormalizationLayerKernel.h" |
| |
| #include "arm_compute/core/Helpers.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/core/CPP/Validate.h" |
| #include "src/core/NEON/NEFixedPoint.h" |
| #include "src/core/NEON/NEMath.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include "src/core/NEON/kernels/detail/NEActivationFunctionDetail.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| |
| #include "src/core/NEON/kernels/batchnormalization/impl/list.h" |
| #include "src/core/common/Registrars.h" |
| |
| #include <map> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| struct BatchNormalizationSelectorData |
| { |
| DataType dt; |
| }; |
| using BatchNormalizationSelectorPtr = std::add_pointer<bool(const BatchNormalizationSelectorData &data)>::type; |
| using BatchNormalizationKernelPtr = std::add_pointer<void(ITensor *, ITensor *, const ITensor *, const ITensor *, const ITensor *, const ITensor *, |
| float, ActivationLayerInfo &, const Window &)>::type; |
| |
| struct BatchNormalizationKernel |
| { |
| const char *name; |
| const BatchNormalizationSelectorPtr is_selected; |
| BatchNormalizationKernelPtr ukernel; |
| }; |
| |
| static const BatchNormalizationKernel available_kernels[] = |
| { |
| #if defined(__ARM_FEATURE_SVE) |
| { |
| "fp16_sve_batch_normalization", |
| [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F16; }, |
| REGISTER_FP16_SVE(arm_compute::cpu::fp16_sve_batch_normalization) |
| }, |
| { |
| "f32_sve_batch_normalization", |
| [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F32; }, |
| REGISTER_FP32_SVE(arm_compute::cpu::fp32_sve_batch_normalization) |
| }, |
| #else /* !defined(__ARM_FEATURE_SVE) */ |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) |
| { |
| "fp16_neon_batch_normalization", |
| [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F16; }, |
| REGISTER_FP16_NEON(arm_compute::cpu::fp16_neon_batch_normalization) |
| }, |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| { |
| "f32_neon_batch_normalization", |
| [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F32; }, |
| REGISTER_FP32_NEON(arm_compute::cpu::fp32_neon_batch_normalization) |
| }, |
| #endif /* !defined(__ARM_FEATURE_SVE) */ |
| }; |
| |
| const BatchNormalizationKernel *get_implementation(const BatchNormalizationSelectorData &data) |
| { |
| for(const auto &uk : available_kernels) |
| { |
| if(uk.is_selected(data)) |
| { |
| return &uk; |
| } |
| } |
| return nullptr; |
| } |
| |
| Status |
| validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, |
| const ITensorInfo *beta, const ITensorInfo *gamma, float epsilon, ActivationLayerInfo act_info) |
| { |
| ARM_COMPUTE_UNUSED(epsilon); |
| |
| const auto *uk = get_implementation(BatchNormalizationSelectorData{ input->data_type() }); |
| ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); |
| |
| if(act_info.enabled()) |
| { |
| ActivationLayerInfo::ActivationFunction act = act_info.activation(); |
| ARM_COMPUTE_RETURN_ERROR_ON(act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::RELU |
| && act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU |
| && act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); |
| ARM_COMPUTE_RETURN_ERROR_ON(act_info.b() > act_info.a()); |
| } |
| |
| if(nullptr != output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var); |
| if(beta != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta); |
| } |
| if(gamma != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)) != mean->dimension(0)); |
| |
| return Status{}; |
| } |
| } //namespace |
| |
| template <typename T, bool fused_activation, typename F> |
| void NEBatchNormalizationLayerKernel::batch_normalization_nchw(const Window &window) |
| { |
| /** Neon vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; |
| |
| const int window_step_x = 16 / sizeof(T); |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| Window win_to_use = window; |
| win_to_use.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(_input, win_to_use); |
| Iterator output(_output, win_to_use); |
| |
| F activation_functor(_act_info); |
| |
| // Hold information about the current feature map we are iterating. |
| // Only compute denominator and Neon vectors once per feature map. |
| int slice = -1; |
| |
| const auto input_mean = reinterpret_cast<const T *>(_mean->ptr_to_element(Coordinates(0, 0))); |
| const auto input_var = reinterpret_cast<const T *>(_var->ptr_to_element(Coordinates(0, 0))); |
| const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const T *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const T *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| |
| T mean = static_cast<T>(0); |
| T var = static_cast<T>(0); |
| T gamma = static_cast<T>(1); |
| T beta = static_cast<T>(0); |
| T denominator = static_cast<T>(0); |
| |
| auto mean_vec = wrapper::vdup_n(mean, ExactTagType{}); |
| auto var_vec = wrapper::vdup_n(var, ExactTagType{}); |
| auto gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); |
| auto beta_vec = wrapper::vdup_n(beta, ExactTagType{}); |
| auto denominator_vec = wrapper::vdup_n(denominator, ExactTagType{}); |
| const auto epsilon_vec = wrapper::vdup_n(static_cast<T>(_epsilon), ExactTagType{}); |
| execute_window_loop(win_to_use, [&](const Coordinates & id) |
| { |
| const auto input_ptr = reinterpret_cast<const T *>(input.ptr()); |
| const auto output_ptr = reinterpret_cast<T *>(output.ptr()); |
| |
| if(slice != id.z()) |
| { |
| mean = input_mean[id.z()]; |
| var = input_var[id.z()]; |
| mean_vec = wrapper::vdup_n(mean, ExactTagType{}); |
| var_vec = wrapper::vdup_n(var, ExactTagType{}); |
| if(input_gamma != nullptr) |
| { |
| gamma = input_gamma[id.z()]; |
| gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); |
| } |
| if(input_beta != nullptr) |
| { |
| beta = input_beta[id.z()]; |
| beta_vec = wrapper::vdup_n(beta, ExactTagType{}); |
| } |
| |
| // Calculate denominator |
| denominator_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); |
| denominator = wrapper::vgetlane(denominator_vec, 0); |
| slice = id.z(); |
| } |
| |
| // Perform core calculations using vector operations |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| // Calculate x bar |
| const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec); |
| const auto x_bar = wrapper::vmul(numerator, denominator_vec); |
| auto res = wrapper::vmla(beta_vec, x_bar, gamma_vec); |
| |
| // Perform fused activation |
| if(fused_activation) |
| { |
| activation_functor(res); |
| } |
| |
| // Store results |
| wrapper::vstore(output_ptr + x, res); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const T numerator = input_ptr[x] - mean; |
| const T x_bar = numerator * denominator; |
| T res = beta + x_bar * gamma; |
| |
| // Perform fused activation |
| if(fused_activation) |
| { |
| activation_functor(res); |
| } |
| |
| // Store results |
| *(output_ptr + x) = res; |
| } |
| }, |
| input, output); |
| } |
| |
| void NEBatchNormalizationLayerKernel::configure_non_fused() |
| { |
| switch(_input->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, false, detail::dummy<float16_t, 8>>; |
| break; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| _func = &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, false, detail::dummy<float, 4>>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Element size not supported"); |
| break; |
| } |
| } |
| |
| void NEBatchNormalizationLayerKernel::configure_fused() |
| { |
| // NCHW Fused Batched Normalization with activation functions : FP32 |
| static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nchw = |
| { |
| { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::relu<float, 4>> }, |
| { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::brelu<float, 4>> }, |
| { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::lubrelu<float, 4>> } |
| }; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| // NCHW Fused Batched Normalization with activation functions : FP16 |
| static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nchw = |
| { |
| { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::relu<float16_t, 8>> }, |
| { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::brelu<float16_t, 8>> }, |
| { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::lubrelu<float16_t, 8>> } |
| }; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| |
| switch(_input->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = bn_fused_map_f16_nchw[_act_info.activation()]; |
| break; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| _func = bn_fused_map_f32_nchw[_act_info.activation()]; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Element size not supported"); |
| break; |
| } |
| } |
| |
| NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel() |
| : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon(), _act_info() |
| { |
| } |
| |
| void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, |
| const ITensor *mean, const ITensor *var, |
| const ITensor *beta, const ITensor *gamma, |
| float epsilon, ActivationLayerInfo act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr, |
| mean->info(), var->info(), |
| (beta != nullptr) ? beta->info() : nullptr, |
| (gamma != nullptr) ? gamma->info() : nullptr, |
| epsilon, act_info)); |
| |
| _input = input; |
| _output = input; |
| _mean = mean; |
| _var = var; |
| _gamma = gamma; |
| _beta = beta; |
| _epsilon = epsilon; |
| _act_info = act_info; |
| |
| const bool run_in_place = (output == nullptr) || (output == input); |
| if(!run_in_place) |
| { |
| _output = output; |
| } |
| |
| // Configure activation function to run |
| const bool is_nchw = _input->info()->data_layout() == DataLayout::NCHW; |
| if(is_nchw) |
| { |
| if(_act_info.enabled()) |
| { |
| configure_fused(); |
| } |
| else |
| { |
| configure_non_fused(); |
| } |
| } |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input->info(), Steps()); |
| INEKernel::configure(win); |
| |
| if(output != nullptr) |
| { |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), *input->info()->clone()); |
| |
| Coordinates coord; |
| coord.set_num_dimensions(output->info()->num_dimensions()); |
| output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); |
| } |
| } |
| |
| Status NEBatchNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, |
| const ITensorInfo *mean, const ITensorInfo *var, |
| const ITensorInfo *beta, const ITensorInfo *gamma, |
| float epsilon, ActivationLayerInfo act_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, var, beta, gamma, epsilon, act_info)); |
| |
| return Status{}; |
| } |
| |
| void NEBatchNormalizationLayerKernel::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); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr && _input->info()->data_layout() == DataLayout::NCHW); |
| |
| const bool is_nchw = _input->info()->data_layout() == DataLayout::NCHW; |
| if(is_nchw) |
| { |
| (this->*_func)(window); |
| } |
| else |
| { |
| const auto *uk = get_implementation(BatchNormalizationSelectorData{ _input->info()->data_type() }); |
| uk->ukernel(_input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info, window); |
| } |
| } |
| } // namespace arm_compute |