diff options
Diffstat (limited to 'src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp')
-rw-r--r-- | src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp | 138 |
1 files changed, 11 insertions, 127 deletions
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp index deb89996a9..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 * @@ -151,128 +151,15 @@ Status validate_arguments(const ITensorInfo *input, } } //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()) { -#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"); @@ -285,29 +172,26 @@ 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>>}, + REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_relu)}, {ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, - &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::brelu<float, 4>>}, + REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_brelu)}, {ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, - &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::lubrelu<float, 4>>}}; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_lubrelu)}}; + // 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>>}, + REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused_relu)}, {ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, - &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::brelu<float16_t, 8>>}, + REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused_brelu)}, {ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, - &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::lubrelu<float16_t, 8>>}}; -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + 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; @@ -409,7 +293,7 @@ void NEBatchNormalizationLayerKernel::run(const Window &window, const ThreadInfo const bool is_nchw = _input->info()->data_layout() == DataLayout::NCHW; if (is_nchw) { - (this->*_func)(window); + (*_func)(window, _input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info); } else { |