diff options
Diffstat (limited to 'src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp | 309 |
1 files changed, 110 insertions, 199 deletions
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp index 92000bb2f6..717fd11485 100644 --- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -28,18 +28,17 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" + +#include "src/core/common/Registrars.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/batchnormalization/impl/list.h" #include "src/core/NEON/kernels/detail/NEActivationFunctionDetail.h" +#include "src/core/NEON/NEFixedPoint.h" +#include "src/core/NEON/NEMath.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 @@ -48,11 +47,19 @@ namespace { struct BatchNormalizationSelectorData { - DataType dt; + DataType dt; + const CPUInfo &ci; }; 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; +using BatchNormalizationKernelPtr = std::add_pointer<void(ITensor *, + ITensor *, + const ITensor *, + const ITensor *, + const ITensor *, + const ITensor *, + float, + ActivationLayerInfo &, + const Window &)>::type; struct BatchNormalizationKernel { @@ -61,41 +68,32 @@ struct BatchNormalizationKernel BatchNormalizationKernelPtr ukernel; }; -static const BatchNormalizationKernel available_kernels[] = -{ -#if defined(ENABLE_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) - }, -#endif /* !defined(ENABLE_SVE) */ -#if defined(ENABLE_NEON) +static const BatchNormalizationKernel available_kernels[] = { +#if defined(ARM_COMPUTE_ENABLE_SVE) + {"sve_fp16_batch_normalization", + [](const BatchNormalizationSelectorData &data) { return data.dt == DataType::F16 && data.ci.has_sve(); }, + REGISTER_FP16_SVE(arm_compute::cpu::fp16_sve_batch_normalization)}, + {"sve_fp32_batch_normalization", + [](const BatchNormalizationSelectorData &data) { return data.dt == DataType::F32 && data.ci.has_sve(); }, + REGISTER_FP32_SVE(arm_compute::cpu::fp32_sve_batch_normalization)}, +#endif /* !defined(ARM_COMPUTE_ENABLE_SVE) */ +#if defined(ARM_COMPUTE_ENABLE_NEON) #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) - }, + {"neon_fp16_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(ENABLE_NEON) */ + {"neon_fp32_batch_normalization", + [](const BatchNormalizationSelectorData &data) { return data.dt == DataType::F32; }, + REGISTER_FP32_NEON(arm_compute::cpu::fp32_neon_batch_normalization)}, +#endif /* !defined(ARM_COMPUTE_ENABLE_NEON) */ }; const BatchNormalizationKernel *get_implementation(const BatchNormalizationSelectorData &data) { - for(const auto &uk : available_kernels) + for (const auto &uk : available_kernels) { - if(uk.is_selected(data)) + if (uk.is_selected(data)) { return &uk; } @@ -103,25 +101,31 @@ const BatchNormalizationKernel *get_implementation(const BatchNormalizationSelec 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) +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() }); + const auto *uk = get_implementation(BatchNormalizationSelectorData{input->data_type(), CPUInfo::get()}); ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); - if(act_info.enabled()) + 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 != 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) + if (nullptr != output) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); @@ -130,139 +134,32 @@ validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const IT ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var); - if(beta != nullptr) + 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) + 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)); + 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) -{ - /** SIMD 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 constants 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()) + 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>>; + _func = REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused); break; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: - _func = &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, false, detail::dummy<float, 4>>; + _func = REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused); break; default: ARM_COMPUTE_ERROR("Element size not supported"); @@ -273,29 +170,28 @@ void NEBatchNormalizationLayerKernel::configure_non_fused() 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 + static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nchw = { + {ActivationLayerInfo::ActivationFunction::RELU, + REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_relu)}, + {ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, + REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_brelu)}, + {ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, + REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_lubrelu)}}; - switch(_input->info()->data_type()) + // NCHW Fused Batched Normalization with activation functions : FP16 + static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nchw = { + {ActivationLayerInfo::ActivationFunction::RELU, + REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused_relu)}, + {ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, + REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused_brelu)}, + {ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, + REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused_lubrelu)}}; + + 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; @@ -306,22 +202,32 @@ void NEBatchNormalizationLayerKernel::configure_fused() } NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel() - : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon(), _act_info() + : _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) +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)); + mean->info(), var->info(), (beta != nullptr) ? beta->info() : nullptr, + (gamma != nullptr) ? gamma->info() : nullptr, epsilon, act_info)); _input = input; _output = input; @@ -333,16 +239,16 @@ void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, _act_info = act_info; const bool run_in_place = (output == nullptr) || (output == input); - if(!run_in_place) + 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 (is_nchw) { - if(_act_info.enabled()) + if (_act_info.enabled()) { configure_fused(); } @@ -356,17 +262,21 @@ void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, Window win = calculate_max_window(*input->info(), Steps()); INEKernel::configure(win); - if(output != nullptr) + if (output != nullptr) { // Output auto initialization if not yet initialized auto_init_if_empty(*output->info(), *input->info()->clone()); } } -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) +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)); @@ -381,13 +291,14 @@ void NEBatchNormalizationLayerKernel::run(const Window &window, const ThreadInfo 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) + if (is_nchw) { - (this->*_func)(window); + (*_func)(window, _input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info); } else { - const auto *uk = get_implementation(BatchNormalizationSelectorData{ _input->info()->data_type() }); + const auto *uk = + get_implementation(BatchNormalizationSelectorData{_input->info()->data_type(), CPUInfo::get()}); uk->ukernel(_input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info, window); } } |