diff options
Diffstat (limited to 'src/runtime')
| -rw-r--r-- | src/runtime/NEON/functions/NEFillBorder.cpp | 7 | ||||
| -rw-r--r-- | src/runtime/NEON/functions/NESoftmaxLayer.cpp | 149 | ||||
| -rw-r--r-- | src/runtime/cpu/operators/CpuSoftmax.cpp | 204 | ||||
| -rw-r--r-- | src/runtime/cpu/operators/CpuSoftmax.h | 105 |
4 files changed, 376 insertions, 89 deletions
diff --git a/src/runtime/NEON/functions/NEFillBorder.cpp b/src/runtime/NEON/functions/NEFillBorder.cpp index bb57222eb4..256aad6d3f 100644 --- a/src/runtime/NEON/functions/NEFillBorder.cpp +++ b/src/runtime/NEON/functions/NEFillBorder.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2020 Arm Limited. + * Copyright (c) 2016-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -29,6 +29,11 @@ namespace arm_compute { +NEFillBorder::NEFillBorder() + : _border_handler(nullptr) +{ +} + void NEFillBorder::configure(ITensor *input, unsigned int border_width, BorderMode border_mode, const PixelValue &constant_border_value) { _border_handler = std::make_unique<NEFillBorderKernel>(); diff --git a/src/runtime/NEON/functions/NESoftmaxLayer.cpp b/src/runtime/NEON/functions/NESoftmaxLayer.cpp index 6be34ad1a4..3f1e43a8f2 100644 --- a/src/runtime/NEON/functions/NESoftmaxLayer.cpp +++ b/src/runtime/NEON/functions/NESoftmaxLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -22,49 +22,62 @@ * SOFTWARE. */ #include "arm_compute/runtime/NEON/functions/NESoftmaxLayer.h" - -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" -#include "src/core/NEON/kernels/NEFillBorderKernel.h" -#include "src/core/NEON/kernels/NESoftmaxLayerKernel.h" -#include "src/core/NEON/kernels/NESoftmaxLayerKernel.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/runtime/Tensor.h" +#include "src/core/cpu/kernels/CpuSoftmaxKernel.h" #include "src/core/helpers/SoftmaxHelpers.h" +#include "src/runtime/cpu/operators/CpuSoftmax.h" namespace arm_compute { template <bool IS_LOG> -NESoftmaxLayerGeneric<IS_LOG>::~NESoftmaxLayerGeneric() = default; +struct NESoftmaxLayerGeneric<IS_LOG>::Impl +{ + const ITensor *src{ nullptr }; + ITensor *dst{ nullptr }; + Tensor max{ nullptr }; + Tensor tmp{ nullptr }; + Tensor input_permuted{ nullptr }; + Tensor output_permuted{ nullptr }; + std::unique_ptr<cpu::CpuSoftmaxGeneric<IS_LOG>> op{ nullptr }; +}; template <bool IS_LOG> NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _permute_input(), _permute_output(), _max_kernel(), _softmax_kernel(), _fill_border_kernel(), _max(), _tmp(), _input_permuted(), _output_permuted(), - _needs_permute(false) + : _memory_group(std::move(memory_manager)), _impl(std::make_unique<Impl>()) { } template <bool IS_LOG> +NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(NESoftmaxLayerGeneric &&) = default; +template <bool IS_LOG> +NESoftmaxLayerGeneric<IS_LOG> &NESoftmaxLayerGeneric<IS_LOG>::operator=(NESoftmaxLayerGeneric &&) = default; +template <bool IS_LOG> +NESoftmaxLayerGeneric<IS_LOG>::~NESoftmaxLayerGeneric() = default; + +template <bool IS_LOG> void NESoftmaxLayerGeneric<IS_LOG>::configure(ITensor *input, ITensor *output, float beta, int32_t axis) { - // Perform validation step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(NESoftmaxLayerGeneric::validate(input->info(), output->info(), beta, axis)); - const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions()))); + _impl->src = input; + _impl->dst = output; + _impl->op = std::make_unique<cpu::CpuSoftmaxGeneric<IS_LOG>>(); + _impl->op->configure(input->info(), output->info(), beta, axis); - _needs_permute = actual_axis > 0; - - if(_needs_permute) + const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions()))); + const bool needs_permute = actual_axis > 0; + if(needs_permute) { // Add to the memory manager _input_permuted - _memory_group.manage(&_input_permuted); - - _permute_input.configure(input, &_input_permuted, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); + auto permute_input = std::make_unique<cpu::CpuPermute>(); + _memory_group.manage(&_impl->input_permuted); + permute_input->configure(input->info(), _impl->input_permuted.info(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); } // We want to deal with a 2D input. Either it is the permuted version of the original input (4D case) // or it is the original input case (2D case) - ITensor *tmp_input = (_needs_permute ? &_input_permuted : input); + ITensor *tmp_input = (needs_permute ? &_impl->input_permuted : input); // Create intermediate tensors shapes const TensorInfo input_info = tmp_input->info()->clone()->reset_padding().set_is_resizable(true); @@ -74,80 +87,49 @@ void NESoftmaxLayerGeneric<IS_LOG>::configure(ITensor *input, ITensor *output, f // Init intermediate tensors TensorShape max_sum_shape = tmp_input->info()->tensor_shape(); max_sum_shape.set(0, 1); - _max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape)); - _tmp.allocator()->init(tensor_info_tmp); + _impl->max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape)); + _impl->tmp.allocator()->init(tensor_info_tmp); // Manage intermediate buffers - _memory_group.manage(&_max); - _memory_group.manage(&_tmp); + _memory_group.manage(&_impl->max); + _memory_group.manage(&_impl->tmp); // Configure kernels - _max_kernel = std::make_unique<NELogits1DMaxKernel>(); - _softmax_kernel = std::make_unique<NELogits1DSoftmaxKernel<IS_LOG>>(); - _max_kernel->configure(tmp_input, &_max); - if(_needs_permute) + auto max_kernel = std::make_unique<cpu::kernels::CpuLogits1DMaxKernel>(); + auto softmax_kernel = std::make_unique<cpu::kernels::CpuLogits1DSoftmaxKernel<IS_LOG>>(); + max_kernel->configure(tmp_input->info(), _impl->max.info()); + + if(needs_permute) { + auto permute_output = std::make_unique<cpu::CpuPermute>(); // Add to the memory manager _output_permuted - _memory_group.manage(&_output_permuted); + _memory_group.manage(&_impl->output_permuted); // The normalization kernel stores the result in a permuted output tensor - _softmax_kernel->configure(tmp_input, &_max, &_output_permuted, beta, &_tmp); - _input_permuted.allocator()->allocate(); + softmax_kernel->configure(tmp_input->info(), _impl->max.info(), _impl->output_permuted.info(), beta, _impl->tmp.info()); + _impl->input_permuted.allocator()->allocate(); // Re-permute the permuted output into the requested (4D) output - _permute_output.configure(&_output_permuted, output, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); + permute_output->configure(_impl->output_permuted.info(), output->info(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); // Allocate the intermediate permuted tensors - _output_permuted.allocator()->allocate(); + _impl->output_permuted.allocator()->allocate(); } else { - // Softmax 2D case - _fill_border_kernel = std::make_unique<NEFillBorderKernel>(); - _fill_border_kernel->configure(tmp_input, _max_kernel->border_size(), BorderMode::REPLICATE); - _softmax_kernel->configure(tmp_input, &_max, output, beta, &_tmp); + softmax_kernel->configure(tmp_input->info(), _impl->max.info(), output->info(), beta, _impl->tmp.info()); } // Allocate intermediate buffers - _max.allocator()->allocate(); - _tmp.allocator()->allocate(); + _impl->max.allocator()->allocate(); + _impl->tmp.allocator()->allocate(); } template <bool IS_LOG> Status NESoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_t axis) { - // Perform validation step ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported"); - ARM_COMPUTE_UNUSED(beta); - ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-input->num_dimensions()) || static_cast<int32_t>(input->num_dimensions()) <= axis); - - // Create intermediate tensor info - DataType tmp_data_type = input->data_type(); - const TensorInfo tensor_info_tmp(input->clone()->set_data_type(tmp_data_type).set_is_resizable(true)); - - TensorShape max_sum_shape = input->tensor_shape(); - max_sum_shape.set(0, 1); - const TensorInfo tensor_info_max_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(input->quantization_info()).set_is_resizable(true)); - const TensorInfo dont_care; - - const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->num_dimensions()))); - - const bool needs_permute = actual_axis > 0; - - if(needs_permute) - { - const PermutationVector permutation_vector = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis); - const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(*input, permutation_vector); - TensorInfo input_permuted(input->clone()->set_tensor_shape(permuted_shape)); - ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(input, &input_permuted, permutation_vector)); - TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape)); - ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(&output_permuted, output, permutation_vector)); - } - - ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DMaxKernel::validate(input, &tensor_info_max_sum)); - ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DSoftmaxKernel<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, output, beta, &dont_care)); - + ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuSoftmaxGeneric<IS_LOG>::validate(input, output, beta, axis)); return Status{}; } @@ -155,23 +137,14 @@ template <bool IS_LOG> void NESoftmaxLayerGeneric<IS_LOG>::run() { MemoryGroupResourceScope scope_mg(_memory_group); - - if(_needs_permute) - { - _permute_input.run(); - } - else - { - NEScheduler::get().schedule(_fill_border_kernel.get(), Window::DimY); - } - - NEScheduler::get().schedule(_max_kernel.get(), Window::DimY); - NEScheduler::get().schedule(_softmax_kernel.get(), Window::DimY); - - if(_needs_permute) - { - _permute_output.run(); - } + ITensorPack pack; + pack.add_tensor(TensorType::ACL_SRC, _impl->src); + pack.add_tensor(TensorType::ACL_DST, _impl->dst); + pack.add_tensor(TensorType::ACL_INT_0, &_impl->tmp); + pack.add_tensor(TensorType::ACL_INT_1, &_impl->max); + pack.add_tensor(TensorType::ACL_INT_2, &_impl->input_permuted); + pack.add_tensor(TensorType::ACL_INT_3, &_impl->output_permuted); + _impl->op->run(pack); } template class NESoftmaxLayerGeneric<false>; diff --git a/src/runtime/cpu/operators/CpuSoftmax.cpp b/src/runtime/cpu/operators/CpuSoftmax.cpp new file mode 100644 index 0000000000..0e1bcd5c69 --- /dev/null +++ b/src/runtime/cpu/operators/CpuSoftmax.cpp @@ -0,0 +1,204 @@ +/* + * 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. + */ +#include "src/runtime/cpu/operators/CpuSoftmax.h" + +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" +#include "src/core/cpu/kernels/CpuSoftmaxKernel.h" +#include "src/core/helpers/SoftmaxHelpers.h" + +namespace arm_compute +{ +namespace cpu +{ +template <bool IS_LOG> +CpuSoftmaxGeneric<IS_LOG>::CpuSoftmaxGeneric() + : _permute_input(), _permute_output(), _max_kernel(), _softmax_kernel(), _max(nullptr), _tmp(nullptr), _input_permuted(nullptr), _output_permuted(nullptr), _needs_permute(false) +{ +} + +template <bool IS_LOG> +void CpuSoftmaxGeneric<IS_LOG>::configure(const ITensorInfo *src, ITensorInfo *dst, float beta, int32_t axis) +{ + // Perform validation step + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); + ARM_COMPUTE_ERROR_THROW_ON(CpuSoftmaxGeneric::validate(src, dst, beta, axis)); + + const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(src->num_dimensions()))); + + _needs_permute = actual_axis > 0; + + if(_needs_permute) + { + _input_permuted = std::make_unique<TensorInfo>(); + _permute_input.configure(src, _input_permuted.get(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); + } + + // We want to deal with a 2D input. Either it is the permuted version of the original input (4D case) + // or it is the original input case (2D case) + const ITensorInfo *tmp_input = (_needs_permute ? _input_permuted.get() : src); + + // Create intermediate tensors shapes + TensorShape max_sum_shape = tmp_input->tensor_shape(); + max_sum_shape.set(0, 1); + const TensorInfo input_info = tmp_input->clone()->reset_padding().set_is_resizable(true); + DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->data_type()) ? DataType::F32 : tmp_input->data_type(); + TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type)); + TensorInfo max_info(tmp_input->clone()->set_tensor_shape(max_sum_shape)); + + // Init intermediate tensors + _max = std::make_unique<TensorInfo>(max_info); + _tmp = std::make_unique<TensorInfo>(tensor_info_tmp); + + // Configure kernels + auto mk = std::make_unique<kernels::CpuLogits1DMaxKernel>(); + mk->configure(tmp_input, _max.get()); + _max_kernel = std::move(mk); + + auto sm = std::make_unique<kernels::CpuLogits1DSoftmaxKernel<IS_LOG>>(); + if(_needs_permute) + { + _output_permuted = std::make_unique<TensorInfo>(); + + // The normalization kernel stores the result in a permuted output tensor + sm->configure(tmp_input, _max.get(), _output_permuted.get(), beta, _tmp.get()); + + // Re-permute the permuted output into the requested (4D) output + _permute_output.configure(_output_permuted.get(), dst, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); + } + else + { + // Softmax 2D case + sm->configure(tmp_input, _max.get(), dst, beta, _tmp.get()); + } + _softmax_kernel = std::move(sm); +} + +template <bool IS_LOG> +Status CpuSoftmaxGeneric<IS_LOG>::validate(const ITensorInfo *src, const ITensorInfo *dst, float beta, int32_t axis) +{ + // Perform validation step + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->num_dimensions() > 4, "Only up to 4 dimensions are supported"); + ARM_COMPUTE_UNUSED(beta); + ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-src->num_dimensions()) || static_cast<int32_t>(src->num_dimensions()) <= axis); + + // Create intermediate tensor info + DataType tmp_data_type = src->data_type(); + const TensorInfo tensor_info_tmp(src->clone()->set_data_type(tmp_data_type).set_is_resizable(true)); + + TensorShape max_sum_shape = src->tensor_shape(); + max_sum_shape.set(0, 1); + const TensorInfo tensor_info_max_sum(src->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(src->quantization_info()).set_is_resizable(true)); + const TensorInfo dont_care; + + const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(src->num_dimensions()))); + + const bool needs_permute = actual_axis > 0; + + if(needs_permute) + { + const PermutationVector permutation_vector = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis); + const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(*src, permutation_vector); + TensorInfo input_permuted(src->clone()->set_tensor_shape(permuted_shape)); + ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(src, &input_permuted, permutation_vector)); + TensorInfo output_permuted(dst->clone()->set_tensor_shape(permuted_shape)); + ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(&output_permuted, dst, permutation_vector)); + } + + ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuLogits1DMaxKernel::validate(src, &tensor_info_max_sum)); + ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuLogits1DSoftmaxKernel<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, dst, beta, &dont_care)); + + return Status{}; +} + +template <bool IS_LOG> +void CpuSoftmaxGeneric<IS_LOG>::run(ITensorPack &tensors) +{ + ARM_COMPUTE_ERROR_ON_MSG(tensors.empty(), "No inputs provided"); + + ITensorPack max_pack; + ITensorPack softmax_pack; + + if(_needs_permute) + { + ITensorPack permute_in_pack; + permute_in_pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC)); + permute_in_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_INT_2)); + _permute_input.run(permute_in_pack); + + max_pack.add_tensor(TensorType::ACL_SRC, tensors.get_tensor(ACL_INT_2)); + + softmax_pack.add_tensor(TensorType::ACL_SRC_0, tensors.get_tensor(ACL_INT_2)); + softmax_pack.add_tensor(TensorType::ACL_SRC_1, tensors.get_tensor(ACL_INT_1)); + softmax_pack.add_tensor(TensorType::ACL_DST_0, tensors.get_tensor(ACL_INT_3)); + softmax_pack.add_tensor(TensorType::ACL_DST_1, tensors.get_tensor(ACL_INT_0)); + } + else + { + max_pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC)); + softmax_pack.add_tensor(TensorType::ACL_SRC_0, tensors.get_const_tensor(ACL_SRC)); + softmax_pack.add_tensor(TensorType::ACL_SRC_1, tensors.get_tensor(ACL_INT_1)); + softmax_pack.add_tensor(TensorType::ACL_DST_0, tensors.get_tensor(ACL_DST)); + softmax_pack.add_tensor(TensorType::ACL_DST_1, tensors.get_tensor(ACL_INT_0)); + } + + max_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_INT_1)); + + NEScheduler::get().schedule_op(_max_kernel.get(), Window::DimY, _max_kernel->window(), max_pack); + NEScheduler::get().schedule_op(_softmax_kernel.get(), Window::DimY, _softmax_kernel->window(), softmax_pack); + + if(_needs_permute) + { + ITensorPack permute_out_pack; + permute_out_pack.add_tensor(TensorType::ACL_SRC, tensors.get_tensor(ACL_INT_3)); + permute_out_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_DST)); + _permute_output.run(permute_out_pack); + } +} + +template <bool IS_LOG> +experimental::MemoryRequirements CpuSoftmaxGeneric<IS_LOG>::workspace() const +{ + experimental::MemoryRequirements req{}; + + req.push_back({ TensorType::ACL_INT_0, _tmp->total_size(), 0 }); + req.push_back({ TensorType::ACL_INT_1, _max->total_size(), 0 }); + + if(_needs_permute) + { + req.push_back({ TensorType::ACL_INT_2, _input_permuted->total_size(), 0 }); + req.push_back({ TensorType::ACL_INT_3, _output_permuted->total_size(), 0 }); + } + + return req; +} + +template class CpuSoftmaxGeneric<false>; +template class CpuSoftmaxGeneric<true>; +} // namespace cpu +} // namespace arm_compute diff --git a/src/runtime/cpu/operators/CpuSoftmax.h b/src/runtime/cpu/operators/CpuSoftmax.h new file mode 100644 index 0000000000..9f18e0e4c5 --- /dev/null +++ b/src/runtime/cpu/operators/CpuSoftmax.h @@ -0,0 +1,105 @@ +/* + * 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 ARM_COMPUTE_CPU_SOFTMAX_H +#define ARM_COMPUTE_CPU_SOFTMAX_H + +#include "arm_compute/core/ITensorInfo.h" +#include "arm_compute/core/experimental/Types.h" +#include "src/core/cpu/ICpuKernel.h" +#include "src/runtime/cpu/ICpuOperator.h" +#include "src/runtime/cpu/operators/CpuPermute.h" +#include <memory> + +namespace arm_compute +{ +namespace cpu +{ +class CpuLogits1DMaxKernel; +template <bool IS_LOG> +class CpuLogits1DSoftmaxKernel; + +/** Basic function to compute a SoftmaxLayer and a Log SoftmaxLayer. + * + * Softmax is calculated by : + * @f[ out = exp((x - max(x)) * beta) / sum(exp((x - max(x)) * beta)) @f] + * + * Log Softmax is calculated by : + * @f[ out = (x - max(x) * beta) - log(\sum{e^{x - max(x) * beta}}) @f] + * + * This function runs the following function/kernels: + * -# If axis is not 0: + * -# @ref CpuPermute + * -# @ref kernels::CpuLogits1DMaxKernel + * -# @ref kernels::CpuLogits1DSoftmaxKernel + */ +template <bool IS_LOG = false> +class CpuSoftmaxGeneric : public ICpuOperator +{ +public: + /** Constructor */ + CpuSoftmaxGeneric(); + /** Set the input and output tensors. + * + * @param[in,out] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. + * last value of each row to the nearest multiple. + * @param[out] dst Destination tensor ifo. Data types supported: same as @p input. + * @param[in] beta (Optional) A scaling factor for the exponent. + * @param[in] axis (Optional) The dimension in which to apply the function. E.g. for input of shape 4x5x6 and + * axis=1, softmax will be applied to 4x6=24 vectors of size 5. Defaults to 0 + */ + void configure(const ITensorInfo *src, ITensorInfo *dst, float beta = 1.0f, int32_t axis = 0); + + /** Static function to check if given info will lead to a valid configuration of @ref CpuSoftmax + * + * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. + * @param[in] dst Destination tensor info. Data types supported: same as @p input + * @param[in] beta (Optional) A scaling factor for the exponent. + * @param[in] axis (Optional) The dimension in which to apply the function. E.g. for input of shape 4x5x6 and + * axis=1, softmax will be applied to 4x6=24 vectors of size 5. Defaults to 0 + * + * @return a status + */ + static Status validate(const ITensorInfo *src, const ITensorInfo *dst, float beta = 1.0f, int32_t axis = 0); + + // Inherited methods overridden: + void run(ITensorPack &tensors) override; + experimental::MemoryRequirements workspace() const override; + +private: + CpuPermute _permute_input; + CpuPermute _permute_output; + std::unique_ptr<ICpuKernel> _max_kernel; + std::unique_ptr<ICpuKernel> _softmax_kernel; + std::unique_ptr<ITensorInfo> _max; + std::unique_ptr<ITensorInfo> _tmp; + std::unique_ptr<ITensorInfo> _input_permuted; + std::unique_ptr<ITensorInfo> _output_permuted; + bool _needs_permute; +}; +using CpuSoftmax = CpuSoftmaxGeneric<false>; +using CpuLogSoftmax = CpuSoftmaxGeneric<true>; + +} // namespace cpu +} // namespace arm_compute +#endif /* ARM_COMPUTE_CPU_SOFTMAX_H */ |