diff options
Diffstat (limited to 'src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp | 516 |
1 files changed, 172 insertions, 344 deletions
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp index 6bd30ee845..717fd11485 100644 --- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,42 +21,111 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h" +#include "src/core/NEON/kernels/NEBatchNormalizationLayerKernel.h" -#include "arm_compute/core/CPP/Validate.h" #include "arm_compute/core/Helpers.h" -#include "arm_compute/core/NEON/NEFixedPoint.h" -#include "arm_compute/core/NEON/NEMath.h" -#include "arm_compute/core/NEON/kernels/detail/NEActivationFunctionDetail.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" -#include <map> +#include "src/core/common/Registrars.h" +#include "src/core/CPP/Validate.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" -using namespace arm_compute; +#include <map> +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); @@ -65,288 +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) -{ - if(output != nullptr) - { - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, *input->clone()); - } - - unsigned int num_elems_processed_per_iteration = 16 / input->element_size(); - - Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); - AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - bool window_changed = update_window_and_padding(win, input_access); - - if(output != nullptr) - { - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); - window_changed |= update_window_and_padding(win, output_access); - output_access.set_valid_region(win, input->valid_region()); - } - - // Mean, var, gamma and beta get parallelized for the NHWC case as they follow the channel dimension, which is along the first axis - if(input->data_layout() == DataLayout::NHWC) - { - AccessWindowHorizontal mean_access(mean, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal var_access(var, 0, num_elems_processed_per_iteration); - window_changed |= update_window_and_padding(win, mean_access, var_access); - - if(gamma != nullptr) - { - AccessWindowHorizontal gamma_access(gamma, 0, num_elems_processed_per_iteration); - window_changed |= update_window_and_padding(win, gamma_access); - } - if(beta != nullptr) - { - AccessWindowHorizontal beta_access(beta, 0, num_elems_processed_per_iteration); - window_changed |= update_window_and_padding(win, beta_access); - } - } - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} } //namespace -template <bool fused_activation, typename F> -void NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw(const Window &window) -{ - ARM_COMPUTE_UNUSED(window); -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - Iterator input(_input, window); - Iterator output(_output, window); - - 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 float16_t *>(_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast<const float16_t *>(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float16_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; - - float16x8_t mean_vec = vdupq_n_f16(0.0); - float16x8_t var_vec = vdupq_n_f16(0.0); - float16x8_t gamma_vec = vdupq_n_f16(1.0); - float16x8_t beta_vec = vdupq_n_f16(0.0); - float16x8_t denominator = vdupq_n_f16(0.0); - const float16x8_t epsilon_vec = vdupq_n_f16(_epsilon); - execute_window_loop(window, [&](const Coordinates & id) - { - if(slice != id.z()) - { - // Conctruct vectors - mean_vec = vdupq_n_f16(*(input_mean + id.z())); - var_vec = vdupq_n_f16(*(input_var + id.z())); - if(input_gamma != nullptr) - { - gamma_vec = vdupq_n_f16(*(input_gamma + id.z())); - } - if(input_beta != nullptr) - { - beta_vec = vdupq_n_f16(*(input_beta + id.z())); - } - - // Calculate denominator - denominator = vinvsqrtq_f16(vaddq_f16(var_vec, epsilon_vec)); - slice = id.z(); - } - - // Calculate x bar and store results - const float16x8_t numerator = vsubq_f16(vld1q_f16(reinterpret_cast<const float16_t *>(input.ptr())), mean_vec); - const float16x8_t x_bar = vmulq_f16(numerator, denominator); - float16x8_t res = vaddq_f16(beta_vec, vmulq_f16(x_bar, gamma_vec)); - - // Perform fused activation - if(fused_activation) - { - activation_functor(res); - } - - vst1q_f16(reinterpret_cast<float16_t *>(output.ptr()), res); - }, - input, output); -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ -} - -template <bool fused_activation, typename F> -void NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc(const Window &window) -{ - ARM_COMPUTE_UNUSED(window); -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - Iterator input(_input, window); - Iterator output(_output, window); - - F activation_functor(_act_info); - - const auto input_mean = reinterpret_cast<const float16_t *>(_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast<const float16_t *>(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float16_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; - - const float16x8_t epsilon_vec = vdupq_n_f16(_epsilon); - execute_window_loop(window, [&](const Coordinates & id) - { - // Conctruct vectors - const float16x8_t mean_vec = vld1q_f16(input_mean + id.x()); - const float16x8_t var_vec = vld1q_f16(input_var + id.x()); - const float16x8_t gamma_vec = (input_gamma != nullptr) ? vld1q_f16(input_gamma + id.x()) : vdupq_n_f16(1.0); - const float16x8_t beta_vec = (input_beta != nullptr) ? vld1q_f16(input_beta + id.x()) : vdupq_n_f16(0.0); - // Calculate denominator - const float16x8_t denominator = vinvsqrtq_f16(vaddq_f16(var_vec, epsilon_vec)); - - // Calculate x bar and store results - const float16x8_t numerator = vsubq_f16(vld1q_f16(reinterpret_cast<const float16_t *>(input.ptr())), mean_vec); - const float16x8_t x_bar = vmulq_f16(numerator, denominator); - float16x8_t res = vaddq_f16(beta_vec, vmulq_f16(x_bar, gamma_vec)); - - // Perform fused activation - if(fused_activation) - { - activation_functor(res); - } - - vst1q_f16(reinterpret_cast<float16_t *>(output.ptr()), res); - }, - input, output); -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ -} - -template <bool fused_activation, typename F> -void NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw(const Window &window) -{ - Iterator input(_input, window); - Iterator output(_output, window); - - 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 float *>(_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast<const float *>(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; - - float32x4_t mean_vec = vdupq_n_f32(0.0); - float32x4_t var_vec = vdupq_n_f32(0.0); - float32x4_t gamma_vec = vdupq_n_f32(1.0); - float32x4_t beta_vec = vdupq_n_f32(0.0); - float32x4_t denominator = vdupq_n_f32(0.0); - const float32x4_t epsilon_vec = vdupq_n_f32(_epsilon); - execute_window_loop(window, [&](const Coordinates & id) - { - if(slice != id.z()) - { - // Conctruct vectors - mean_vec = vdupq_n_f32(*(input_mean + id.z())); - var_vec = vdupq_n_f32(*(input_var + id.z())); - if(input_gamma != nullptr) - { - gamma_vec = vdupq_n_f32(*(input_gamma + id.z())); - } - if(input_beta != nullptr) - { - beta_vec = vdupq_n_f32(*(input_beta + id.z())); - } - - // Calculate denominator - denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec)); - slice = id.z(); - } - - // Calculate x bar - const float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec); - const float32x4_t x_bar = vmulq_f32(numerator, denominator); - float32x4_t res = vmlaq_f32(beta_vec, x_bar, gamma_vec); - - // Perform fused activation - if(fused_activation) - { - activation_functor(res); - } - - // Store results - vst1q_f32(reinterpret_cast<float *>(output.ptr()), res); - }, - input, output); -} - -template <bool fused_activation, typename F> -void NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc(const Window &window) -{ - Iterator input(_input, window); - Iterator output(_output, window); - - F activation_functor(_act_info); - - const auto input_mean = reinterpret_cast<const float *>(_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast<const float *>(_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const float *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const float *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; - - const float32x4_t epsilon_vec = vdupq_n_f32(_epsilon); - execute_window_loop(window, [&](const Coordinates & id) - { - // Conctruct vectors - const float32x4_t mean_vec = vld1q_f32(input_mean + id.x()); - const float32x4_t var_vec = vld1q_f32(input_var + id.x()); - const float32x4_t gamma_vec = (input_gamma != nullptr) ? vld1q_f32(input_gamma + id.x()) : vdupq_n_f32(1.0); - const float32x4_t beta_vec = (input_beta != nullptr) ? vld1q_f32(input_beta + id.x()) : vdupq_n_f32(0.0); - // Calculate denominator - const float32x4_t denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec)); - - // Calculate x bar - const float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec); - const float32x4_t x_bar = vmulq_f32(numerator, denominator); - float32x4_t res = vmlaq_f32(beta_vec, x_bar, gamma_vec); - - // Perform fused activation - if(fused_activation) - { - activation_functor(res); - } - - // Store results - vst1q_f32(reinterpret_cast<float *>(output.ptr()), 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_fp16_nhwc<false, ::detail::dummy<float16_t, 8>> : - &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<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_fp32_nhwc<false, ::detail::dummy<float, 4>> : - &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<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"); @@ -357,45 +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_fp32_nchw<true, ::detail::relu<float, 4>> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<true, ::detail::brelu<float, 4>> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<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_fp32_nhwc<true, ::detail::relu<float, 4>> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<true, ::detail::brelu<float, 4>> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<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_fp16_nchw<true, ::detail::relu<float16_t, 8>> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<true, ::detail::brelu<float16_t, 8>> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<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_fp16_nhwc<true, ::detail::relu<float16_t, 8>> }, - { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<true, ::detail::brelu<float16_t, 8>> }, - { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<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"); @@ -404,22 +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; @@ -431,37 +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{}; } @@ -471,7 +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 |