diff options
Diffstat (limited to 'src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp')
-rw-r--r-- | src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp | 302 |
1 files changed, 164 insertions, 138 deletions
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp index 108b199df7..deb89996a9 100644 --- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp @@ -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 @@ -52,8 +51,15 @@ struct BatchNormalizationSelectorData 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 { @@ -62,41 +68,32 @@ struct BatchNormalizationKernel BatchNormalizationKernelPtr ukernel; }; -static const BatchNormalizationKernel available_kernels[] = -{ +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) - }, + {"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) - }, + {"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) - }, + {"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; } @@ -104,25 +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(), CPUInfo::get() }); + 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); @@ -131,17 +134,18 @@ 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{}; } @@ -169,10 +173,12 @@ void NEBatchNormalizationLayerKernel::batch_normalization_nchw(const Window &win // 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; + 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); @@ -186,80 +192,83 @@ void NEBatchNormalizationLayerKernel::batch_normalization_nchw(const Window &win 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()) + execute_window_loop( + win_to_use, + [&](const Coordinates &id) { - 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) + const auto input_ptr = reinterpret_cast<const T *>(input.ptr()); + const auto output_ptr = reinterpret_cast<T *>(output.ptr()); + + if (slice != id.z()) { - beta = input_beta[id.z()]; - beta_vec = wrapper::vdup_n(beta, ExactTagType{}); + 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(); } - // 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) + // Perform core calculations using vector operations + int x = window_start_x; + for (; x <= (window_end_x - window_step_x); x += window_step_x) { - activation_functor(res); + // 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); } - // 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) + // Compute left-over elements + for (; x < window_end_x; ++x) { - activation_functor(res); + 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; } - - // Store results - *(output_ptr + x) = res; - } - }, - input, output); + }, + 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 = &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, false, + detail::dummy<float16_t, 8>>; break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: @@ -274,23 +283,25 @@ 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>> } - }; + 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>> } - }; + 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()) + switch (_input->info()->data_type()) { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: @@ -307,22 +318,32 @@ 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; @@ -334,16 +355,16 @@ 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(); } @@ -357,17 +378,21 @@ 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)); @@ -382,13 +407,14 @@ 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); } else { - const auto *uk = get_implementation(BatchNormalizationSelectorData{ _input->info()->data_type(), CPUInfo::get() }); + const auto *uk = + get_implementation(BatchNormalizationSelectorData{_input->info()->data_type(), CPUInfo::get()}); uk->ukernel(_input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info, window); } } |