From 373b407558f99eb4bba632c170d03d807941dd2a Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Wed, 20 Jan 2021 16:41:12 +0000 Subject: Make Softmax kernels and operator stateless COMPMID-3997 Change-Id: I3a3cc76d8247dd769d9a5e6e171d718ea909312c Signed-off-by: Michalis Spyrou Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4986 Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- src/core/NEON/NEKernels.h | 1 - src/core/NEON/kernels/NESoftmaxLayerKernel.cpp | 380 ---------------------- src/core/NEON/kernels/NESoftmaxLayerKernel.h | 141 -------- src/core/NEON/kernels/softmax/impl/NEON/list.h | 425 ------------------------ src/core/NEON/kernels/softmax/impl/SVE/list.h | 429 ------------------------- 5 files changed, 1376 deletions(-) delete mode 100644 src/core/NEON/kernels/NESoftmaxLayerKernel.cpp delete mode 100644 src/core/NEON/kernels/NESoftmaxLayerKernel.h delete mode 100644 src/core/NEON/kernels/softmax/impl/NEON/list.h delete mode 100644 src/core/NEON/kernels/softmax/impl/SVE/list.h (limited to 'src/core/NEON') diff --git a/src/core/NEON/NEKernels.h b/src/core/NEON/NEKernels.h index c636e5b3be..66309f9296 100644 --- a/src/core/NEON/NEKernels.h +++ b/src/core/NEON/NEKernels.h @@ -117,7 +117,6 @@ #include "src/core/NEON/kernels/NESobel3x3Kernel.h" #include "src/core/NEON/kernels/NESobel5x5Kernel.h" #include "src/core/NEON/kernels/NESobel7x7Kernel.h" -#include "src/core/NEON/kernels/NESoftmaxLayerKernel.h" #include "src/core/NEON/kernels/NESpaceToBatchLayerKernel.h" #include "src/core/NEON/kernels/NESpaceToDepthLayerKernel.h" #include "src/core/NEON/kernels/NEStackLayerKernel.h" diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp deleted file mode 100644 index fe09f1ec59..0000000000 --- a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp +++ /dev/null @@ -1,380 +0,0 @@ -/* - * Copyright (c) 2017-2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "src/core/NEON/kernels/NESoftmaxLayerKernel.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" -#include "src/core/CPP/Validate.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include "src/core/NEON/kernels/softmax/impl/NEON/list.h" -#include "src/core/NEON/kernels/softmax/impl/SVE/list.h" -#include "src/core/common/Registrars.h" - -namespace arm_compute -{ -namespace -{ -struct SoftmaxSelectorData -{ - DataType dt; -}; -using SoftmaxSelectorPtr = std::add_pointer::type; -using SoftmaxLogits1DMaxKernelPtr = std::add_pointer::type; -using SoftmaxLogits1DKernelPtr = std::add_pointer::type; - -struct SoftmaxLogits1DKernel -{ - const char *name; - const SoftmaxSelectorPtr is_selected; - SoftmaxLogits1DKernelPtr ukernel; -}; - -struct SoftmaxLogits1DMaxKernel -{ - const char *name; - const SoftmaxSelectorPtr is_selected; - SoftmaxLogits1DMaxKernelPtr ukernel; -}; - -static const SoftmaxLogits1DKernel available_logits_1d_kernels[] = -{ -#if defined(__ARM_FEATURE_SVE) - { - "sve_softmax_logits_1d_float", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, - REGISTER_FP32_SVE(arm_compute::cpu::sve_softmax_logits_1d_float) - }, - { - "sve_softmax_logits_1d_float", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, - REGISTER_FP16_SVE(arm_compute::cpu::sve_softmax_logits_1d_float) - }, -#else /* !defined(__ARM_FEATURE_SVE) */ - { - "neon_softmax_logits_1d_float", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, - REGISTER_FP32_NEON(arm_compute::cpu::neon_softmax_logits_1d_float) - }, -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - { - "neon_softmax_logits_1d_float", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, - REGISTER_FP16_NEON(arm_compute::cpu::neon_softmax_logits_1d_float) - }, -#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */ -#endif /* defined(__ARM_FEATURE_SVE) */ - -#if defined(__ARM_FEATURE_SVE2) - { - "sve_softmax_logits_1d_quantized", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, - REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_softmax_logits_1d_quantized) - }, - { - "sve_softmax_logits_1d_quantized", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, - REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_softmax_logits_1d_quantized) - }, -#else /* !defined(__ARM_FEATURE_SVE2) */ - { - "neon_softmax_logits_1d_quantized", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, - REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_softmax_logits_1d_quantized) - }, - { - "neon_softmax_logits_1d_quantized", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, - REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_softmax_logits_1d_quantized) - }, -#endif /* defined(__ARM_FEATURE_SVE2) */ - -}; - -static const SoftmaxLogits1DMaxKernel available_logits_1d_max_kernels[] = -{ -#if defined(__ARM_FEATURE_SVE) - { - "sve_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, - REGISTER_FP32_SVE(arm_compute::cpu::sve_logits_1d_max) - }, - { - "sve_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, - REGISTER_FP16_SVE(arm_compute::cpu::sve_logits_1d_max) - }, - { - "sve_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, - REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_logits_1d_max) - }, - { - "sve_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, - REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_logits_1d_max) - }, -#else /* !defined(__ARM_FEATURE_SVE) */ - { - "neon_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, - REGISTER_FP32_NEON(arm_compute::cpu::neon_logits_1d_max) - }, -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - { - "neon_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, - REGISTER_FP16_NEON(arm_compute::cpu::neon_logits_1d_max) - }, -#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */ - { - "neon_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, - REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_logits_1d_max) - }, - { - "neon_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, - REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_logits_1d_max) - }, -#endif /* defined(__ARM_FEATURE_SVE) */ -}; - -const SoftmaxLogits1DKernel *get_implementation_logits(const SoftmaxSelectorData &data) -{ - for(const auto &uk : available_logits_1d_kernels) - { - if(uk.is_selected({ data.dt })) - { - return &uk; - } - } - return nullptr; -} - -const SoftmaxLogits1DMaxKernel *get_implementation_logits_max(const SoftmaxSelectorData &data) -{ - for(const auto &uk : available_logits_1d_max_kernels) - { - if(uk.is_selected({ data.dt })) - { - return &uk; - } - } - return nullptr; -} - -Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorInfo &output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - - // Validate in case of configured output - if(output.total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output.tensor_shape(), TensorShape(input.tensor_shape()).set(0, 1)); - } - - return Status{}; -} - -} // namespace - -NELogits1DMaxKernel::NELogits1DMaxKernel() - : _border_size() -{ -} - -BorderSize NELogits1DMaxKernel::border_size() const -{ - return _border_size; -} - -void NELogits1DMaxKernel::configure(const ITensor *input, ITensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), output->info()); - // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_1d_max(*input->info(), *output->info())); - // Configure kernel window - - // Softmax across the x dimension - const TensorShape output_shape = TensorShape(input->info()->tensor_shape()).set(0, 1); - // Output auto initialization if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->quantization_info()); - - Window win = calculate_max_window(*input->info(), Steps()); - Coordinates coord; - coord.set_num_dimensions(output->info()->num_dimensions()); - output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); - - _input = input; - _output = output; - - const int input_width = input->info()->valid_region().shape.x(); - const int num_elems_processed_per_iteration = 16U / data_size_from_type(input->info()->data_type()); - const int num_elems_read_per_iteration = ceil_to_multiple(input_width, num_elems_processed_per_iteration); - - _border_size = BorderSize(0, num_elems_read_per_iteration - input_width, 0, 0); - - INEKernel::configure(win); -} - -Status NELogits1DMaxKernel::validate(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_1d_max(*input, *output)); - - return Status{}; -} - -void NELogits1DMaxKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - const auto *uk = get_implementation_logits_max(SoftmaxSelectorData{ _input->info()->data_type() }); - uk->ukernel(_input, _output, window); -} - -namespace -{ -Status validate_arguments_logits_softmax(const ITensorInfo &input, const ITensorInfo &max, - const ITensorInfo &output, const float beta, const ITensorInfo &tmp, bool is_log) -{ - ARM_COMPUTE_UNUSED(beta); - // Check input - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - - const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input.data_type()); - - // Check max - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &max); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(TensorShape(input.tensor_shape()).set(0, 1), max.tensor_shape()); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &max); - - // Check output if configured - if(output.total_size() != 0) - { - const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(input.data_type(), is_log) : output.quantization_info(); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &output); - ARM_COMPUTE_RETURN_ERROR_ON(output.quantization_info() != output_quantization); - } - - // Check tmp if configured - if(tmp.total_size() != 0) - { - const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input.data_type(); - ARM_COMPUTE_RETURN_ERROR_ON(tmp.data_type() != tmp_data_type); - // We could potentially reduce tmp memory if we could predict or make an assumption - // on the maximum number of threads that will run in parallel. - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &tmp); - } - - return Status{}; -} -} // namespace - -template -NELogits1DSoftmaxKernel::NELogits1DSoftmaxKernel() - : _input(nullptr), _max(nullptr), _output(nullptr), _beta(1.0f), _tmp(nullptr) -{ -} - -template -void NELogits1DSoftmaxKernel::configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp); - ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), max->info(), output->info(), tmp->info()); - // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_softmax(*input->info(), *max->info(), *output->info(), beta, *tmp->info(), IS_LOG)); - - // Configure kernel window - const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type()); - - // Output auto initialization if not yet initialized - const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(input->info()->data_type(), IS_LOG) : output->info()->quantization_info(); - auto_init_if_empty(*output->info(), TensorInfo(*input->info()).set_quantization_info(output_quantization).reset_padding()); - - // Tmp auto initialization if not yet initialized - const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input->info()->data_type(); - auto_init_if_empty(*tmp->info(), TensorInfo(*input->info()).set_data_type(tmp_data_type).reset_padding()); - - // Configure kernel window - Window win = calculate_max_window(*max->info(), Steps()); - Coordinates coord; - coord.set_num_dimensions(output->info()->num_dimensions()); - output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); - - _input = input; - _max = max; - _output = output; - _beta = beta; - _tmp = tmp; - - INEKernel::configure(win); -} - -template -Status NELogits1DSoftmaxKernel::validate(const ITensorInfo *input, const ITensorInfo *max, - const ITensorInfo *output, const float beta, const ITensorInfo *tmp) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_softmax(*input, *max, *output, beta, *tmp, IS_LOG)); - - return Status{}; -} - -template -void NELogits1DSoftmaxKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - const unsigned int num_elems_processed_per_iteration = _input->info()->valid_region().shape.x(); - const unsigned int tmp_size_for_thread = _tmp->info()->element_size() * num_elems_processed_per_iteration; - - ARM_COMPUTE_ERROR_ON(_tmp->info()->total_size() < (info.num_threads * tmp_size_for_thread)); - - void *tmp_for_thread = _tmp->buffer() + (info.thread_id * tmp_size_for_thread); - - const auto *uk = get_implementation_logits(SoftmaxSelectorData{ _input->info()->data_type() }); - uk->ukernel(_input, _max, tmp_for_thread, _output, _beta, IS_LOG, window); -} - -template class NELogits1DSoftmaxKernel; -template class NELogits1DSoftmaxKernel; - -} // namespace arm_compute diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.h b/src/core/NEON/kernels/NESoftmaxLayerKernel.h deleted file mode 100644 index 70e2417fc2..0000000000 --- a/src/core/NEON/kernels/NESoftmaxLayerKernel.h +++ /dev/null @@ -1,141 +0,0 @@ -/* - * Copyright (c) 2017-2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef ARM_COMPUTE_NESOFTMAXLAYERKERNEL_H -#define ARM_COMPUTE_NESOFTMAXLAYERKERNEL_H - -#include "src/core/NEON/INEKernel.h" -#include "src/core/NEON/INESimpleKernel.h" - -namespace arm_compute -{ -class ITensor; - -/** Interface for the identifying the max value of 1D Logits */ -class NELogits1DMaxKernel : public INESimpleKernel -{ -public: - const char *name() const override - { - return "NELogits1DMaxKernel"; - } - /** Default constructor */ - NELogits1DMaxKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NELogits1DMaxKernel(const NELogits1DMaxKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NELogits1DMaxKernel &operator=(const NELogits1DMaxKernel &) = delete; - /** Allow instances of this class to be moved */ - NELogits1DMaxKernel(NELogits1DMaxKernel &&) = default; - /** Allow instances of this class to be moved */ - NELogits1DMaxKernel &operator=(NELogits1DMaxKernel &&) = default; - /** Default destructor */ - ~NELogits1DMaxKernel() = default; - /** Set the input and output tensors. - * - * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[out] output Destination tensor. Data types supported: same as @p input - */ - void configure(const ITensor *input, ITensor *output); - /** Static function to check if given info will lead to a valid configuration of @ref NELogits1DMaxKernel - * - * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] output Destination tensor. Data types supported: same as @p input - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - BorderSize border_size() const override; - -private: - BorderSize _border_size; -}; - -/** Interface for softmax computation for QASYMM8 with pre-computed max. */ -template -class NELogits1DSoftmaxKernel : public INEKernel -{ -public: - const char *name() const override - { - if(IS_LOG) - { - return "NELogits1DSoftmaxKernel"; - } - else - { - return "NELogits1DLogSoftmaxKernel"; - } - } - /** Default constructor */ - NELogits1DSoftmaxKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NELogits1DSoftmaxKernel(const NELogits1DSoftmaxKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NELogits1DSoftmaxKernel &operator=(const NELogits1DSoftmaxKernel &) = delete; - /** Allow instances of this class to be moved */ - NELogits1DSoftmaxKernel(NELogits1DSoftmaxKernel &&) = default; - /** Allow instances of this class to be moved */ - NELogits1DSoftmaxKernel &operator=(NELogits1DSoftmaxKernel &&) = default; - /** Default destructor */ - ~NELogits1DSoftmaxKernel() = default; - /** Set the input and output tensors. - * - * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] max Max values tensor. Same shape as input with dimension 0 set to 1. - * Data types supported: same as @p input. - * @param[out] output Destination tensor. Data types supported: same as @p input. - * @param[in] beta A scaling factor for the exponent. - * - * @param tmp Auxiliary tensor. Must be type F32 and same shape as the input. - */ - void configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp); - /** Static function to check if given info will lead to a valid configuration of @ref NELogits1DSoftmaxKernel - * - * @param[in] input Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] max Max values tensor info. Same shape as input with dimension 0 set to 1. - * Data types supported: same as @p input. - * @param[in] output Destination tensor info. Data types supported: same as @p input. - * @param[in] beta A scaling factor for the exponent. - * @param[in] tmp Tensor info of auxiliary. Must be type F32 and same shape as the input. - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *max, - const ITensorInfo *output, const float beta, const ITensorInfo *tmp); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - const ITensor *_input; - const ITensor *_max; - ITensor *_output; - float _beta; - ITensor *_tmp; //Temporary. Used internally -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_NESOFTMAXLAYERKERNEL_H */ diff --git a/src/core/NEON/kernels/softmax/impl/NEON/list.h b/src/core/NEON/kernels/softmax/impl/NEON/list.h deleted file mode 100644 index a8f781f439..0000000000 --- a/src/core/NEON/kernels/softmax/impl/NEON/list.h +++ /dev/null @@ -1,425 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H -#define SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H - -#include "src/core/NEON/wrapper/wrapper.h" -#include "support/SaturateCast.h" -#include "src/core/NEON/NEFixedPoint.h" -#include "src/core/NEON/NEMath.h" - -namespace arm_compute -{ -namespace cpu -{ -namespace -{ -template -int_vec_type convert_float_to_int(const float_vec_type &in); - -template -float_vec_type convert_int_to_float(const int_vec_type &in); - -template <> -uint8x16_t convert_float_to_int(const float32x4x4_t &in) -{ - uint8x16_t out; - convert_float32x4x4_to_uint8x16(in, out); - return out; -} - -template <> -int8x16_t convert_float_to_int(const float32x4x4_t &in) -{ - int8x16_t out; - convert_float32x4x4_to_int8x16(in, out); - return out; -} - -template <> -float32x4x4_t convert_int_to_float(const uint8x16_t &in) -{ - return convert_uint8x16_to_float32x4x4(in); -} - -template <> -float32x4x4_t convert_int_to_float(const int8x16_t &in) -{ - return convert_int8x16_to_float32x4x4(in); -} -} // namespace - -template -void neon_logits_1d_max(const ITensor *in, ITensor *out, const Window &window) -{ - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t; - - constexpr int window_step_x = 16 / sizeof(T); - const auto window_start_x = static_cast(window.x().start()); - const auto window_end_x = static_cast(window.x().end()); - - Window win{ window }; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - Iterator input(in, win); - Iterator output(out, win); - - const int sum_stages = log2(window_step_x / 2); - execute_window_loop(win, [&](const Coordinates &) - { - // Get pointers - const auto in_ptr = reinterpret_cast(input.ptr()); - const auto out_ptr = reinterpret_cast(output.ptr()); - - // Init max value - auto vec_max = wrapper::vdup_n(support::cpp11::lowest(), ExactTagType{}); - int x = window_start_x; - - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto current_value = wrapper::vloadq(in_ptr + x); - vec_max = wrapper::vmax(vec_max, current_value); - } - auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max)); - - for(int i = 0; i < sum_stages; ++i) - { - carry_max = wrapper::vpmax(carry_max, carry_max); - } - T max_val = wrapper::vgetlane(carry_max, 0); - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val; - } - - *out_ptr = max_val; - }, - input, output); -} - -template -void neon_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp, - ITensor *out, float beta, bool is_log, const Window &window) -{ - static_assert(std::is_same::value - || std::is_same::value, - "quantized type should be either qasymm8_t or qasymm8_signed_t."); - - const int start_x = in->info()->valid_region().anchor.x(); - const int input_width = in->info()->valid_region().shape.x(); - - const float scale_beta = -beta * in->info()->quantization_info().uniform().scale; - const auto scale_beta_vec = vdupq_n_f32(scale_beta); - - Iterator in_it(in, window); - Iterator max_it(max, window); - Iterator out_it(out, window); - constexpr int vec_size = 16; - - execute_window_loop(window, [&](const Coordinates &) - { - /* Get pointers */ - const auto in_ptr = reinterpret_cast(in_it.ptr()) + start_x; - const auto out_ptr = reinterpret_cast(out_it.ptr()) + start_x; - const auto tmp_ptr = reinterpret_cast(tmp); - - float sum{}; - float sum_inversed{}; - - /* Compute exponentials and sum */ - { - /* Get max value */ - const auto max_val = *reinterpret_cast(max_it.ptr()); - const auto vec_max = wrapper::vdup_n(max_val, wrapper::traits::vector_128_tag{}); - - /* Init sum to zero */ - float32x4x4_t vec_sum = - { - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - }; - - /* Loop over row and compute exponentials and sum */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_elements = wrapper::vloadq(in_ptr + x); - vec_elements = wrapper::vqsub(vec_max, vec_elements); - auto vec_elements_flt = convert_int_to_float(vec_elements); - - if(is_log) - { - vec_elements_flt.val[0] = vmulq_f32(vec_elements_flt.val[0], scale_beta_vec); - vec_elements_flt.val[1] = vmulq_f32(vec_elements_flt.val[1], scale_beta_vec); - vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec); - vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec); - vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0])); - vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1])); - vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2])); - vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3])); - } - else - { - vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec)); - vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec)); - vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec)); - vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec)); - vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]); - vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]); - vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]); - vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]); - } - - vst4q_f32(tmp_ptr + x, vec_elements_flt); - } - - /* Reduce sum */ - const auto sum_16_byte = vaddq_f32(vaddq_f32(vec_sum.val[0], vec_sum.val[1]), vaddq_f32(vec_sum.val[2], vec_sum.val[3])); - auto sum_res = vpadd_f32(vget_high_f32(sum_16_byte), vget_low_f32(sum_16_byte)); - sum_res = vpadd_f32(sum_res, sum_res); - sum = wrapper::vgetlane(sum_res, 0); - - /* Run remaining elements */ - for(; x < input_width; ++x) - { - float element{}; - if(is_log) - { - element = (max_val - in_ptr[x]) * scale_beta; - sum += std::exp(element); - } - else - { - element = std::exp((max_val - in_ptr[x]) * scale_beta); - sum += element; - } - - tmp_ptr[x] = element; - } - - if(!is_log) - { - sum_inversed = 256.f / sum; - } - else - { - sum = std::log(sum); - } - } - - /* Normalize exponentials */ - { - constexpr bool is_qasymm8_signed = std::is_same::value; - /* Loop over row and compute softmax */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - using int_vec_type = wrapper::traits::neon_vector_t; - float32x4x4_t vec_in = vld4q_f32(tmp_ptr + x); - int_vec_type normalized_value{}; - if(is_log) - { - const float32x4x4_t sub = - { - vsubq_f32(vec_in.val[0], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[1], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[2], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[3], vdupq_n_f32(sum)), - }; - normalized_value = convert_float_to_int(sub); - } - else - { - float32x4x4_t mul = - { - vmulq_f32(vec_in.val[0], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[1], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[2], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[3], vdupq_n_f32(sum_inversed)), - }; - - if(is_qasymm8_signed) - { - const auto offset_vec = wrapper::vdup_n(128.f, wrapper::traits::vector_128_tag{}); - mul.val[0] = wrapper::vsub(mul.val[0], offset_vec); - mul.val[1] = wrapper::vsub(mul.val[1], offset_vec); - mul.val[2] = wrapper::vsub(mul.val[2], offset_vec); - mul.val[3] = wrapper::vsub(mul.val[3], offset_vec); - } - - normalized_value = convert_float_to_int(mul); - } - wrapper::vstore(out_ptr + x, normalized_value); - } - /* Run remaining elements */ - for(; x < input_width; ++x) - { - if(is_log) - { - out_ptr[x] = utils::cast::saturate_cast(tmp_ptr[x] - sum); - } - else - { - out_ptr[x] = utils::cast::saturate_cast((tmp_ptr[x] * sum_inversed) - (is_qasymm8_signed ? 128.f : 0)); - } - } - } - }, - in_it, max_it, out_it); -} - -template -void neon_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp, - ITensor *out, const float beta, bool is_log, const Window &window) -{ - const int start_x = in->info()->valid_region().anchor.x(); - const int input_width = in->info()->valid_region().shape.x(); - - Iterator in_it(in, window); - Iterator max_it(max, window); - Iterator out_it(out, window); - - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t; - - constexpr int vec_size = 16 / sizeof(T); - const int sum_stages = log2(vec_size / 2); - - execute_window_loop(window, [&](const Coordinates &) - { - /* Get pointers */ - const auto in_ptr = reinterpret_cast(in_it.ptr()) + start_x; - const auto out_ptr = reinterpret_cast(out_it.ptr()) + start_x; - const auto tmp_ptr = reinterpret_cast(tmp); - - T sum{}; - T sum_inversed{}; - - /* Compute exponentials and sum */ - { - /* Get max value */ - const auto max_val = *reinterpret_cast(max_it.ptr()); - const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{}); - - /* Init sum to zero */ - auto vec_sum = wrapper::vdup_n(static_cast(0), ExactTagType{}); - - /* Loop over row and compute exponentials and sum */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_elements = wrapper::vloadq(in_ptr + x); - vec_elements = wrapper::vsub(vec_elements, vec_max); - if(is_log) - { - vec_elements = wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast(beta), ExactTagType{})); - vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements)); - } - else - { - vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast(beta), ExactTagType{}))); - vec_sum = wrapper::vadd(vec_sum, vec_elements); - } - wrapper::vstore(tmp_ptr + x, vec_elements); - } - - /* Reduce sum */ - auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum)); - for(int i = 0; i < sum_stages; ++i) - { - sum_res = wrapper::vpadd(sum_res, sum_res); - } - sum = wrapper::vgetlane(sum_res, 0); - - /* Run remaining elements */ - for(; x < input_width; ++x) - { - T element{}; - - if(is_log) - { - element = (in_ptr[x] - max_val) * beta; - sum += std::exp(element); - } - else - { - element = std::exp((in_ptr[x] - max_val) * beta); - sum += element; - } - tmp_ptr[x] = element; - } - - if(!is_log) - { - sum_inversed = T(1) / sum; - } - else - { - sum = static_cast(std::log(sum)); - } - } - - /* Normalize exponentials */ - { - /* Loop over row and compute softmax */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_in = wrapper::vloadq(tmp_ptr + x); - auto normalized_value = wrapper::vdup_n(static_cast(0), ExactTagType{}); - if(is_log) - { - normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast(sum), ExactTagType{})); - } - else - { - normalized_value = wrapper::vmul(vec_in, wrapper::vdup_n(static_cast(sum_inversed), ExactTagType{})); - } - wrapper::vstore(out_ptr + x, normalized_value); - } - /* Run remaining elements */ - for(; x < input_width; ++x) - { - if(is_log) - { - out_ptr[x] = tmp_ptr[x] - sum; - } - else - { - out_ptr[x] = tmp_ptr[x] * sum_inversed; - } - } - } - }, - in_it, max_it, out_it); -} - -} // namespace cpu -} // namespace arm_compute - -#endif /* SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H */ diff --git a/src/core/NEON/kernels/softmax/impl/SVE/list.h b/src/core/NEON/kernels/softmax/impl/SVE/list.h deleted file mode 100644 index 0936bd5a56..0000000000 --- a/src/core/NEON/kernels/softmax/impl/SVE/list.h +++ /dev/null @@ -1,429 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H -#define SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H - -#if defined(__ARM_FEATURE_SVE) -#include "arm_compute/core/Types.h" -#include "arm_compute/core/utils/misc/Traits.h" -#include "src/core/NEON/SVEMath.h" -#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" -#include - -namespace arm_compute -{ -namespace cpu -{ -namespace -{ -#if defined(__ARM_FEATURE_SVE2) -template -int_vec_type convert_float_to_int(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3); - -template <> -svuint8_t convert_float_to_int(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3) -{ - svuint8_t out; - const auto all_true_pg = svptrue_b32(); - auto tmp_0 = svcvt_u32_f32_z(all_true_pg, in_0); - auto tmp_1 = svcvt_u32_f32_z(all_true_pg, in_1); - auto tmp_2 = svcvt_u32_f32_z(all_true_pg, in_2); - auto tmp_3 = svcvt_u32_f32_z(all_true_pg, in_3); - - auto tmp_16_0 = svqxtnt_u32(svqxtnb_u32(tmp_0), tmp_1); - auto tmp_16_1 = svqxtnt_u32(svqxtnb_u32(tmp_2), tmp_3); - - auto tmp_16_uzp_0 = svuzp1(tmp_16_0, tmp_16_0); - auto tmp_16_uzp_1 = svuzp2(tmp_16_0, tmp_16_0); - auto tmp_16_uzp_2 = svuzp1(tmp_16_1, tmp_16_1); - auto tmp_16_uzp_3 = svuzp2(tmp_16_1, tmp_16_1); - - auto pg = svwhilelt_b16_s32(0, svcnth() / 2); - - tmp_16_0 = svsplice(pg, tmp_16_uzp_0, tmp_16_uzp_1); - tmp_16_1 = svsplice(pg, tmp_16_uzp_2, tmp_16_uzp_3); - - out = svqxtnt_u16(svqxtnb_u16(tmp_16_0), tmp_16_1); - - auto out_uzp_0 = svuzp1(out, out); - auto out_uzp_1 = svuzp2(out, out); - - pg = svwhilelt_b8_s32(0, svcntb() / 2); - out = svsplice(pg, out_uzp_0, out_uzp_1); - - return out; -} - -template <> -svint8_t convert_float_to_int(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3) -{ - svint8_t out; - const auto all_true_pg = svptrue_b32(); - auto tmp_0 = svcvt_s32_f32_z(all_true_pg, in_0); - auto tmp_1 = svcvt_s32_f32_z(all_true_pg, in_1); - auto tmp_2 = svcvt_s32_f32_z(all_true_pg, in_2); - auto tmp_3 = svcvt_s32_f32_z(all_true_pg, in_3); - - auto tmp_16_0 = svqxtnt_s32(svqxtnb_s32(tmp_0), tmp_1); - auto tmp_16_1 = svqxtnt_s32(svqxtnb_s32(tmp_2), tmp_3); - - auto tmp_16_uzp_0 = svuzp1(tmp_16_0, tmp_16_0); - auto tmp_16_uzp_1 = svuzp2(tmp_16_0, tmp_16_0); - auto tmp_16_uzp_2 = svuzp1(tmp_16_1, tmp_16_1); - auto tmp_16_uzp_3 = svuzp2(tmp_16_1, tmp_16_1); - - auto pg = svwhilelt_b16_s32(0, svcnth() / 2); - - tmp_16_0 = svsplice(pg, tmp_16_uzp_0, tmp_16_uzp_1); - tmp_16_1 = svsplice(pg, tmp_16_uzp_2, tmp_16_uzp_3); - - out = svqxtnt_s16(svqxtnb_s16(tmp_16_0), tmp_16_1); - - auto out_uzp_0 = svuzp1(out, out); - auto out_uzp_1 = svuzp2(out, out); - - pg = svwhilelt_b8_s32(0, svcntb() / 2); - out = svsplice(pg, out_uzp_0, out_uzp_1); - - return out; -} -#endif /* defined(__ARM_FEATURE_SVE2) */ -} // namespace - -template -void sve_logits_1d_max(const ITensor *in, ITensor *out, const Window &window) -{ - const auto all_true_pg = wrapper::svptrue(); - const auto window_start_x = static_cast(window.x().start()); - const auto window_end_x = static_cast(window.x().end()); - - Window win{ window }; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - Iterator input(in, win); - Iterator output(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - // Get pointers - const auto in_ptr = reinterpret_cast(input.ptr()); - const auto out_ptr = reinterpret_cast(output.ptr()); - - // Init max value - auto vec_max = wrapper::svdup_n(support::cpp11::lowest()); - - int x = window_start_x; - svbool_t pg = wrapper::svwhilelt(x, window_end_x); - do - { - const auto current_value = svld1(pg, in_ptr + x); - vec_max = svmax_m(pg, vec_max, current_value); - - x += wrapper::svcnt(); - pg = wrapper::svwhilelt(x, window_end_x); - } - while(svptest_any(all_true_pg, pg)); - - auto max_val = svmaxv(all_true_pg, vec_max); - - *out_ptr = max_val; - }, - input, output); -} - -#if defined(__ARM_FEATURE_SVE2) -template -void sve_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp, - ITensor *out, float beta, bool is_log, const Window &window) -{ - const int start_x = in->info()->valid_region().anchor.x(); - const int input_width = in->info()->valid_region().shape.x(); - - const float scale_beta = -beta * in->info()->quantization_info().uniform().scale; - const auto scale_beta_vec = svdup_n_f32(scale_beta); - - Iterator in_it(in, window); - Iterator max_it(max, window); - Iterator out_it(out, window); - const auto all_true_pg = wrapper::svptrue(); - using SVEType = typename wrapper::traits::sve_vector::type; - - const int inc_1 = static_cast(svcntw()); - const int inc_2 = static_cast(2 * svcntw()); - const int inc_3 = static_cast(3 * svcntw()); - - execute_window_loop(window, [&](const Coordinates &) - { - /* Get pointers */ - const auto in_ptr = reinterpret_cast(in_it.ptr()) + start_x; - const auto out_ptr = reinterpret_cast(out_it.ptr()) + start_x; - const auto tmp_ptr = reinterpret_cast(tmp); - - float sum{}; - - /* Compute exponentials and sum */ - { - /* Get max value */ - const auto max_val = *reinterpret_cast(max_it.ptr()); - const auto vec_max = wrapper::svdup_n(max_val); - - /* Init sum to zero */ - auto vec_sum_0 = svdup_n_f32(0.f); - auto vec_sum_1 = svdup_n_f32(0.f); - auto vec_sum_2 = svdup_n_f32(0.f); - auto vec_sum_3 = svdup_n_f32(0.f); - - /* Loop over row and compute exponentials and sum */ - int x = 0; - svbool_t pg = wrapper::svwhilelt(x, input_width); - svbool_t pg_0 = svunpklo(svunpklo(pg)); - svbool_t pg_1 = svunpkhi(svunpklo(pg)); - svbool_t pg_2 = svunpklo(svunpkhi(pg)); - svbool_t pg_3 = svunpkhi(svunpkhi(pg)); - do - { - auto vec_elements = svld1(pg, in_ptr + x); - vec_elements = svsub_z(pg, vec_max, vec_elements); - - auto vec_elements_flt_0 = svcvt_f32_z(pg_0, svunpklo(svunpklo(vec_elements))); - auto vec_elements_flt_1 = svcvt_f32_z(pg_1, svunpkhi(svunpklo(vec_elements))); - auto vec_elements_flt_2 = svcvt_f32_z(pg_2, svunpklo(svunpkhi(vec_elements))); - auto vec_elements_flt_3 = svcvt_f32_z(pg_3, svunpkhi(svunpkhi(vec_elements))); - - if(is_log) - { - vec_elements_flt_0 = svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec); - vec_elements_flt_1 = svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec); - vec_elements_flt_2 = svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec); - vec_elements_flt_3 = svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec); - vec_sum_0 = svadd_f32_m(pg_0, vec_sum_0, svexp_f32_z(pg_0, vec_elements_flt_0)); - vec_sum_1 = svadd_f32_m(pg_1, vec_sum_1, svexp_f32_z(pg_1, vec_elements_flt_1)); - vec_sum_2 = svadd_f32_m(pg_2, vec_sum_2, svexp_f32_z(pg_2, vec_elements_flt_2)); - vec_sum_3 = svadd_f32_m(pg_3, vec_sum_3, svexp_f32_z(pg_3, vec_elements_flt_3)); - } - else - { - vec_elements_flt_0 = svexp_f32_z(pg_0, svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec)); - vec_elements_flt_1 = svexp_f32_z(pg_1, svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec)); - vec_elements_flt_2 = svexp_f32_z(pg_2, svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec)); - vec_elements_flt_3 = svexp_f32_z(pg_3, svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec)); - vec_sum_0 = svadd_f32_m(pg_0, vec_sum_0, vec_elements_flt_0); - vec_sum_1 = svadd_f32_m(pg_1, vec_sum_1, vec_elements_flt_1); - vec_sum_2 = svadd_f32_m(pg_2, vec_sum_2, vec_elements_flt_2); - vec_sum_3 = svadd_f32_m(pg_3, vec_sum_3, vec_elements_flt_3); - } - - svst1_f32(pg_0, tmp_ptr + x, vec_elements_flt_0); - svst1_f32(pg_1, tmp_ptr + x + inc_1, vec_elements_flt_1); - svst1_f32(pg_2, tmp_ptr + x + inc_2, vec_elements_flt_2); - svst1_f32(pg_3, tmp_ptr + x + inc_3, vec_elements_flt_3); - - x += wrapper::svcnt(); - pg = wrapper::svwhilelt(x, input_width); - pg_0 = svunpklo(svunpklo(pg)); - pg_1 = svunpkhi(svunpklo(pg)); - pg_2 = svunpklo(svunpkhi(pg)); - pg_3 = svunpkhi(svunpkhi(pg)); - } - while(svptest_any(all_true_pg, pg)); - - /* Reduce sum */ - const auto vec_sum = svadd_f32_z(all_true_pg, svadd_f32_z(all_true_pg, vec_sum_0, vec_sum_1), svadd_f32_z(all_true_pg, vec_sum_2, vec_sum_3)); - sum = svaddv_f32(all_true_pg, vec_sum); - - /* Run remaining elements */ - x = 0; - if(is_log) - { - sum = std::log(sum); - } - else - { - sum = 256.f / sum; - } - } - - /* Normalize exponentials */ - { - constexpr bool is_qasymm8_signed = std::is_same::value; - /* Loop over row and compute softmax */ - int x = 0; - svbool_t pg = wrapper::svwhilelt(x, input_width); - svbool_t pg_0 = svunpklo(svunpklo(pg)); - svbool_t pg_1 = svunpkhi(svunpklo(pg)); - svbool_t pg_2 = svunpklo(svunpkhi(pg)); - svbool_t pg_3 = svunpkhi(svunpkhi(pg)); - do - { - auto vec_in_0 = svld1_f32(pg_0, tmp_ptr + x); - auto vec_in_1 = svld1_f32(pg_1, tmp_ptr + x + inc_1); - auto vec_in_2 = svld1_f32(pg_2, tmp_ptr + x + inc_2); - auto vec_in_3 = svld1_f32(pg_3, tmp_ptr + x + inc_3); - - svfloat32_t res_0{}; - svfloat32_t res_1{}; - svfloat32_t res_2{}; - svfloat32_t res_3{}; - - if(is_log) - { - res_0 = svsub_f32_z(pg_0, vec_in_0, svdup_n_f32(sum)); - res_1 = svsub_f32_z(pg_1, vec_in_1, svdup_n_f32(sum)); - res_2 = svsub_f32_z(pg_2, vec_in_2, svdup_n_f32(sum)); - res_3 = svsub_f32_z(pg_3, vec_in_3, svdup_n_f32(sum)); - } - else - { - res_0 = svmul_f32_z(pg_0, vec_in_0, svdup_n_f32(sum)); - res_1 = svmul_f32_z(pg_1, vec_in_1, svdup_n_f32(sum)); - res_2 = svmul_f32_z(pg_2, vec_in_2, svdup_n_f32(sum)); - res_3 = svmul_f32_z(pg_3, vec_in_3, svdup_n_f32(sum)); - - if(is_qasymm8_signed) - { - const auto offset_vec = svdup_n_f32(128.f); - res_0 = svsub_z(pg_0, vec_in_0, offset_vec); - res_1 = svsub_z(pg_1, vec_in_1, offset_vec); - res_2 = svsub_z(pg_2, vec_in_2, offset_vec); - res_3 = svsub_z(pg_3, vec_in_3, offset_vec); - } - } - - // Store value - const auto out = convert_float_to_int(res_0, res_1, res_2, res_3); - svst1(pg, out_ptr + x, out); - x += wrapper::svcnt(); - pg = wrapper::svwhilelt(x, input_width); - pg_0 = svunpklo(svunpklo(pg)); - pg_1 = svunpkhi(svunpklo(pg)); - pg_2 = svunpklo(svunpkhi(pg)); - pg_3 = svunpkhi(svunpkhi(pg)); - } - while(svptest_any(all_true_pg, pg)); - } - }, - in_it, max_it, out_it); -} -#endif /* defined(__ARM_FEATURE_SVE2) */ - -template -void sve_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp, - ITensor *out, const float beta, bool is_log, const Window &window) -{ - const int start_x = in->info()->valid_region().anchor.x(); - const int input_width = in->info()->valid_region().shape.x(); - - Iterator in_it(in, window); - Iterator max_it(max, window); - Iterator out_it(out, window); - - const auto all_true_pg = wrapper::svptrue(); - - execute_window_loop(window, [&](const Coordinates &) - { - /* Get pointers */ - const auto in_ptr = reinterpret_cast(in_it.ptr()) + start_x; - const auto out_ptr = reinterpret_cast(out_it.ptr()) + start_x; - const auto tmp_ptr = reinterpret_cast(tmp); - - ScalarType sum{ 0 }; - - /* Compute exponentials and sum */ - { - /* Get max value */ - const auto max_val = *reinterpret_cast(max_it.ptr()); - const auto vec_max = wrapper::svdup_n(max_val); - - /* Init sum to zero */ - auto vec_sum = wrapper::svdup_n(static_cast(0)); - - /* Loop over row and compute exponentials and sum */ - int x = 0; - svbool_t pg = wrapper::svwhilelt(x, input_width); - do - { - auto vec_elements = svld1(pg, in_ptr + x); - vec_elements = svsub_z(pg, vec_elements, vec_max); - if(is_log) - { - vec_elements = svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast(beta))); - vec_sum = svadd_m(pg, vec_sum, wrapper::svexp_z(pg, vec_elements)); - } - else - { - vec_elements = wrapper::svexp_z(pg, svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast(beta)))); - vec_sum = svadd_m(pg, vec_sum, vec_elements); - } - svst1(pg, tmp_ptr + x, vec_elements); - - x += wrapper::svcnt(); - pg = wrapper::svwhilelt(x, input_width); - } - while(svptest_any(all_true_pg, pg)); - - /* Reduce sum */ - sum = svaddv(all_true_pg, vec_sum); - - if(is_log) - { - sum = static_cast(std::log(sum)); - } - else - { - sum = ScalarType(1) / sum; - } - } - - /* Normalize exponentials */ - { - /* Loop over row and compute softmax */ - int x = 0; - svbool_t pg = wrapper::svwhilelt(x, input_width); - do - { - auto vec_in = svld1(pg, tmp_ptr + x); - auto normalized_value = wrapper::svdup_n(static_cast(0)); - if(is_log) - { - normalized_value = svsub_z(pg, vec_in, wrapper::svdup_n(static_cast(sum))); - } - else - { - normalized_value = svmul_z(pg, vec_in, wrapper::svdup_n(static_cast(sum))); - } - svst1(pg, out_ptr + x, normalized_value); - - x += wrapper::svcnt(); - pg = wrapper::svwhilelt(x, input_width); - } - while(svptest_any(all_true_pg, pg)); - } - }, - in_it, max_it, out_it); -} - -} // namespace cpu -} // namespace arm_compute -#endif /* defined(__ARM_FEATURE_SVE) */ - -#endif /* SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H */ -- cgit v1.2.1