diff options
Diffstat (limited to 'src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp | 238 |
1 files changed, 97 insertions, 141 deletions
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp index afb08e5d1c..b4cac74dc4 100644 --- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp @@ -37,19 +37,77 @@ #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) */ + { + "fp16_neon_batch_normalization", + [](const BatchNormalizationSelectorData & data) { return data.dt == DataType::F16; }, + REGISTER_FP16_NEON(arm_compute::cpu::fp16_neon_batch_normalization) + }, + { + "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); - 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); + + const auto *uk = get_implementation(BatchNormalizationSelectorData{ input->data_type() }); + ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); if(act_info.enabled()) { @@ -83,27 +141,6 @@ validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const IT 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> @@ -212,102 +249,17 @@ void NEBatchNormalizationLayerKernel::batch_normalization_nchw(const Window &win 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()) { #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 = &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, false, detail::dummy<float16_t, 8>>; 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 = &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, false, detail::dummy<float, 4>>; break; default: ARM_COMPUTE_ERROR("Element size not supported"); @@ -324,13 +276,6 @@ void NEBatchNormalizationLayerKernel::configure_fused() { 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 = @@ -339,24 +284,17 @@ void NEBatchNormalizationLayerKernel::configure_fused() { 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 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"); @@ -398,20 +336,32 @@ void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, } // Configure activation function to run - if(_act_info.enabled()) + const bool is_nchw = _input->info()->data_layout() == DataLayout::NCHW; + if(is_nchw) { - configure_fused(); - } - else - { - 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()); + + 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, @@ -420,9 +370,6 @@ Status NEBatchNormalizationLayerKernel::validate(const ITensorInfo *input, const 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 +379,17 @@ 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) + { + (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 |