diff options
Diffstat (limited to 'src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp | 460 |
1 files changed, 163 insertions, 297 deletions
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp index afb08e5d1c..717fd11485 100644 --- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -28,13 +28,15 @@ #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 <map> @@ -43,24 +45,87 @@ namespace arm_compute { namespace { -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) +struct BatchNormalizationSelectorData +{ + 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; + +struct BatchNormalizationKernel +{ + const char *name; + const BatchNormalizationSelectorPtr is_selected; + BatchNormalizationKernelPtr ukernel; +}; + +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) + {"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 */ + {"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) + { + 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); - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - if(act_info.enabled()) + 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()) { 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); @@ -69,245 +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{}; } - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *mean, ITensorInfo *var, ITensorInfo *gamma, ITensorInfo *beta) -{ - ARM_COMPUTE_UNUSED(mean, var, gamma, beta); - - // Configure kernel window - Window win = calculate_max_window(*input, Steps()); - - if(output != nullptr) - { - // Output auto initialization if not yet initialized - auto_init_if_empty(*output, *input->clone()); - - // NEBatchNormalizationLayerKernel doesn't need padding so update_window_and_padding() can be skipped - Coordinates coord; - coord.set_num_dimensions(output->num_dimensions()); - output->set_valid_region(ValidRegion(coord, output->tensor_shape())); - } - - return std::make_pair(Status{}, win); -} } //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); -} - -template <typename T, bool fused_activation, typename F> -void NEBatchNormalizationLayerKernel::batch_normalization_nhwc(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_collapsed = window.collapse_if_possible(window, Window::DimZ); - win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input(_input, win_collapsed); - Iterator output(_output, win_collapsed); - - F activation_functor(_act_info); - - 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; - - const auto epsilon_vec = wrapper::vdup_n(static_cast<T>(_epsilon), ExactTagType{}); - execute_window_loop(win_collapsed, [&](const Coordinates &) - { - const auto input_ptr = reinterpret_cast<const T *>(input.ptr()); - const auto output_ptr = reinterpret_cast<T *>(output.ptr()); - - // Perform core calculations using vector operations - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Conctruct vectors - const auto mean_vec = wrapper::vloadq(input_mean + x); - const auto var_vec = wrapper::vloadq(input_var + x); - const auto gamma_vec = (input_gamma != nullptr) ? wrapper::vloadq(input_gamma + x) : wrapper::vdup_n(static_cast<T>(1.f), ExactTagType{}); - const auto beta_vec = (input_beta != nullptr) ? wrapper::vloadq(input_beta + x) : wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); - - // Calculate denominator - const auto denominator = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); - - // Calculate x bar - const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec); - const auto x_bar = wrapper::vmul(numerator, denominator); - 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) - { - // Conctruct vectors - const T gamma = (input_gamma != nullptr) ? input_gamma[x] : 1.f; - const T beta = (input_beta != nullptr) ? input_beta[x] : 0.f; - - const T denominator = sqrt(input_var[x] + _epsilon); - const T numerator = input_ptr[x] - input_mean[x]; - const T x_bar = numerator / denominator; - T res = beta + x_bar * gamma; - - // Perform fused activation - if(fused_activation) - { - activation_functor(res); - } - - // Store results - *reinterpret_cast<T *>(output_ptr + x) = res; - } - }, - input, output); -} - void NEBatchNormalizationLayerKernel::configure_non_fused() { - const bool is_nhwc = _input->info()->data_layout() == DataLayout::NHWC; - switch(_input->info()->data_type()) + switch (_input->info()->data_type()) { -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, false, detail::dummy<float16_t, 8>> : - &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 = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, false, detail::dummy<float, 4>> : - &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"); @@ -318,45 +170,30 @@ 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>> } - }; - // NHWC Fused Batched Normalization with activation functions : FP32 - static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nhwc = - { - { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, true, detail::relu<float, 4>> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, true, detail::brelu<float, 4>> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<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>> } - }; - // NHWC Fused Batched Normalization with activation functions : FP16 - static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nhwc = - { - { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, true, detail::relu<float16_t, 8>> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, true, detail::brelu<float16_t, 8>> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<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 = (_input->info()->data_layout() == DataLayout::NHWC) ? bn_fused_map_f16_nhwc[_act_info.activation()] : bn_fused_map_f16_nchw[_act_info.activation()]; + _func = bn_fused_map_f16_nchw[_act_info.activation()]; break; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: - _func = (_input->info()->data_layout() == DataLayout::NHWC) ? bn_fused_map_f32_nhwc[_act_info.activation()] : bn_fused_map_f32_nchw[_act_info.activation()]; + _func = bn_fused_map_f32_nchw[_act_info.activation()]; break; default: ARM_COMPUTE_ERROR("Element size not supported"); @@ -365,22 +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; @@ -392,37 +239,46 @@ 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 - if(_act_info.enabled()) - { - configure_fused(); - } - else + const bool is_nchw = _input->info()->data_layout() == DataLayout::NCHW; + if (is_nchw) { - configure_non_fused(); + if (_act_info.enabled()) + { + configure_fused(); + } + else + { + configure_non_fused(); + } } // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), (run_in_place) ? nullptr : output->info(), mean->info(), var->info(), (gamma != nullptr) ? gamma->info() : nullptr, - (beta != nullptr) ? beta->info() : nullptr); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); + 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()); + } } -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)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output ? output->clone().get() : nullptr, mean->clone().get(), var->clone().get(), - (gamma != nullptr) ? gamma->clone().get() : nullptr, (beta != nullptr) ? beta->clone().get() : nullptr) - .first); return Status{}; } @@ -432,8 +288,18 @@ void NEBatchNormalizationLayerKernel::run(const Window &window, const ThreadInfo 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); + ARM_COMPUTE_ERROR_ON(_func == nullptr && _input->info()->data_layout() == DataLayout::NCHW); - (this->*_func)(window); + const bool is_nchw = _input->info()->data_layout() == DataLayout::NCHW; + if (is_nchw) + { + (*_func)(window, _input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info); + } + else + { + const auto *uk = + get_implementation(BatchNormalizationSelectorData{_input->info()->data_type(), CPUInfo::get()}); + uk->ukernel(_input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info, window); + } } } // namespace arm_compute |