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Diffstat (limited to 'runtimes/neurun/src/backend/cpu/StageGenerator.cc')
| -rw-r--r-- | runtimes/neurun/src/backend/cpu/StageGenerator.cc | 547 |
1 files changed, 0 insertions, 547 deletions
diff --git a/runtimes/neurun/src/backend/cpu/StageGenerator.cc b/runtimes/neurun/src/backend/cpu/StageGenerator.cc deleted file mode 100644 index c53b320a4..000000000 --- a/runtimes/neurun/src/backend/cpu/StageGenerator.cc +++ /dev/null @@ -1,547 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "StageGenerator.h" - -#include <stdexcept> - -#include "cpp14/memory.h" -#include "util/Padding.h" -#include "kernel/cpu/OperationUtils.h" -#include "kernel/cpu/ConvolutionLayer.h" -#include "kernel/cpu/AvgPoolLayer.h" -#include "kernel/cpu/MaxPoolLayer.h" -#include "kernel/cpu/ConcatLayer.h" -#include "kernel/cpu/FullyConnectedLayer.h" -#include "kernel/cpu/ReshapeLayer.h" -#include "kernel/cpu/SoftMaxLayer.h" -#include "kernel/cpu/PermuteLayer.h" -#include "backend/BackendManager.h" -#include "backend/interface/IConfig.h" - -#include "util/logging.h" - -#include "util/Utils.h" - -namespace neurun -{ -namespace backend -{ -namespace cpu -{ - -StageGenerator::StageGenerator(const neurun::model::operand::Set &operand_ctx, - const std::shared_ptr<TensorBuilder> &tensor_builder) - : _ctx(operand_ctx), _tensor_builder(tensor_builder) -{ - // DO NOTHING -} - -void StageGenerator::visit(const model::operation::Conv2DNode &node) -{ - using model::operation::Conv2DNode; - - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(Conv2DNode::Input::INPUT)}; - const auto ker_index{node.getInputs().at(Conv2DNode::Input::KERNEL)}; - const auto bias_index{node.getInputs().at(Conv2DNode::Input::BIAS)}; - - const auto vstride_index{node.param().vstride_index}; - const auto hstride_index{node.param().hstride_index}; - - const auto padding_index{node.param().padding_index}; - const auto activation_index{node.param().activation_index}; - - const PaddingCode padding_type = - static_cast<PaddingCode>(_ctx.at(padding_index).asScalar<int32_t>()); - - assert((ANEURALNETWORKS_PADDING_SAME == padding_type) || - (ANEURALNETWORKS_PADDING_VALID == padding_type)); - - util::Stride stride; - - stride.vertical = _ctx.at(vstride_index).asScalar<int32_t>(); - stride.horizontal = _ctx.at(hstride_index).asScalar<int32_t>(); - - // Construct operation parameters - struct Param - { - model::operand::Index ofm_index; - model::operand::Index ifm_index; - model::operand::Index ker_index; - model::operand::Index bias_index; - - ::neurun::kernel::cpu::Shape ofm_shape; - ::neurun::kernel::cpu::Shape ifm_shape; - ::neurun::kernel::cpu::Shape ker_shape; - ::neurun::kernel::cpu::Shape bias_shape; - - util::Padding padding; - util::Stride stride; - - FuseCode activation; - }; - - Param param; - - param.ofm_index = ofm_index; - param.ifm_index = ifm_index; - param.ker_index = ker_index; - param.bias_index = bias_index; - - param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ofm_index)); - param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ifm_index)); - param.ker_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ker_index)); - param.bias_shape = ::neurun::kernel::cpu::getShape(_ctx.at(bias_index)); - - param.stride = stride; - param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? util::same_padding(_ctx.at(ifm_index).shape().asFeature(), - _ctx.at(ofm_index).shape().asFeature(), stride, - _ctx.at(ker_index).shape().asKernel().W, - _ctx.at(ker_index).shape().asKernel().H) - : util::valid_padding(); - - param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); - - auto tensors = _tensor_builder; - - returnStage([tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(param.ofm_index); - auto ifm_alloc = tensors->at(param.ifm_index); - auto ker_alloc = tensors->at(param.ker_index); - auto bias_alloc = tensors->at(param.bias_index); - - std::unique_ptr<::neurun::kernel::cpu::ConvolutionLayer> fn{ - new ::neurun::kernel::cpu::ConvolutionLayer}; - - fn->configure(ifm_alloc->buffer(), param.ifm_shape, ker_alloc->buffer(), param.ker_shape, - bias_alloc->buffer(), param.bias_shape, param.padding.left, param.padding.right, - param.padding.top, param.padding.bottom, param.stride.horizontal, - param.stride.vertical, param.activation, ofm_alloc->buffer(), param.ofm_shape); - - builder.append(std::move(fn)); - }); -} - -void StageGenerator::visit(const model::operation::MaxPool2DNode &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(model::operation::MaxPool2DNode::Input::INPUT)}; - - const auto kh_index{node.param().kh_index}; - const auto kw_index{node.param().kw_index}; - - const auto vstride_index{node.param().vstride_index}; - const auto hstride_index{node.param().hstride_index}; - - const auto padding_index{node.param().padding_index}; - const auto activation_index{node.param().activation_index}; - - const int32_t kh = _ctx.at(kh_index).asScalar<int32_t>(); - const int32_t kw = _ctx.at(kw_index).asScalar<int32_t>(); - - const int32_t vstride = _ctx.at(vstride_index).asScalar<int32_t>(); - const int32_t hstride = _ctx.at(hstride_index).asScalar<int32_t>(); - - const PaddingCode padding_type = - static_cast<PaddingCode>(_ctx.at(padding_index).asScalar<int32_t>()); - - // Construct operation parameters - struct Param - { - model::operand::Index ofm_index; - model::operand::Index ifm_index; - - uint32_t kw; - uint32_t kh; - - ::neurun::kernel::cpu::Shape ofm_shape; - ::neurun::kernel::cpu::Shape ifm_shape; - - util::Padding padding; - util::Stride stride; - - FuseCode activation; - }; - - Param param; - - param.ofm_index = ofm_index; - param.ifm_index = ifm_index; - - param.kh = kh; - param.kw = kw; - - param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ofm_index)); - param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ifm_index)); - - param.stride.vertical = vstride; - param.stride.horizontal = hstride; - - param.padding = - (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? util::same_padding(_ctx.at(ifm_index).shape().asFeature(), - _ctx.at(ofm_index).shape().asFeature(), param.stride, kw, kh) - : util::valid_padding(); - - param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); - - auto tensors = _tensor_builder; - - returnStage([tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(param.ofm_index).get(); - auto ifm_alloc = tensors->at(param.ifm_index).get(); - - std::unique_ptr<::neurun::kernel::cpu::MaxPoolLayer> fn{ - new ::neurun::kernel::cpu::MaxPoolLayer}; - - fn->configure(ifm_alloc->buffer(), param.ifm_shape, param.padding.left, param.padding.right, - param.padding.top, param.padding.bottom, param.stride.horizontal, - param.stride.vertical, param.kw, param.kh, param.activation, ofm_alloc->buffer(), - param.ofm_shape); - - builder.append(std::move(fn)); - }); -} - -void StageGenerator::visit(const model::operation::AvgPool2DNode &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(model::operation::AvgPool2DNode::Input::INPUT)}; - - const auto kh_index{node.param().kh_index}; - const auto kw_index{node.param().kw_index}; - - const auto vstride_index{node.param().vstride_index}; - const auto hstride_index{node.param().hstride_index}; - - const auto padding_index{node.param().padding_index}; - const auto activation_index{node.param().activation_index}; - - const int32_t kh = _ctx.at(kh_index).asScalar<int32_t>(); - const int32_t kw = _ctx.at(kw_index).asScalar<int32_t>(); - - const int32_t vstride = _ctx.at(vstride_index).asScalar<int32_t>(); - const int32_t hstride = _ctx.at(hstride_index).asScalar<int32_t>(); - - const PaddingCode padding_type = - static_cast<PaddingCode>(_ctx.at(padding_index).asScalar<int32_t>()); - - assert((ANEURALNETWORKS_PADDING_SAME == padding_type) || - (ANEURALNETWORKS_PADDING_VALID == padding_type)); - - // Construct operation parameters - struct Param - { - model::operand::Index ofm_index; - model::operand::Index ifm_index; - - uint32_t kw; - uint32_t kh; - - ::neurun::kernel::cpu::Shape ofm_shape; - ::neurun::kernel::cpu::Shape ifm_shape; - - util::Padding padding; - util::Stride stride; - - FuseCode activation; - }; - - Param param; - - param.ofm_index = ofm_index; - param.ifm_index = ifm_index; - - param.kh = kh; - param.kw = kw; - - param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ofm_index)); - param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ifm_index)); - - param.stride.vertical = vstride; - param.stride.horizontal = hstride; - - param.padding = - (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? util::same_padding(_ctx.at(ifm_index).shape().asFeature(), - _ctx.at(ofm_index).shape().asFeature(), param.stride, kw, kh) - : util::valid_padding(); - - param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); - - auto tensors = _tensor_builder; - - returnStage([tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(param.ofm_index).get(); - auto ifm_alloc = tensors->at(param.ifm_index).get(); - - std::unique_ptr<::neurun::kernel::cpu::AvgPoolLayer> fn{ - new ::neurun::kernel::cpu::AvgPoolLayer}; - - fn->configure(ifm_alloc->buffer(), param.ifm_shape, param.padding.left, param.padding.right, - param.padding.top, param.padding.bottom, param.stride.horizontal, - param.stride.vertical, param.kw, param.kh, param.activation, ofm_alloc->buffer(), - param.ofm_shape); - - builder.append(std::move(fn)); - }); -} - -void StageGenerator::visit(const model::operation::ConcatNode &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto axis_index{node.param().axis_index}; - - struct Param - { - model::operand::Index output_index; - std::vector<model::operand::Index> input_indexes; - - int32_t axis; - - ::neurun::kernel::cpu::Shape ofm_shape; - std::vector<::neurun::kernel::cpu::Shape> ifm_shapes; - }; - - Param param; - - param.output_index = ofm_index; - for (const auto &e : node.getInputs()) - { - param.input_indexes.emplace_back(e); - } - param.axis = _ctx.at(axis_index).asScalar<int32_t>(); - - param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ofm_index)); - - for (auto e : node.getInputs()) - { - param.ifm_shapes.emplace_back(::neurun::kernel::cpu::getShape(_ctx.at(e))); - } - - auto tensors = _tensor_builder; - - returnStage([tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(param.output_index).get(); - - std::vector<const uint8_t *> input_buffers; - for (auto ifm_ind : param.input_indexes) - { - input_buffers.emplace_back(tensors->at(ifm_ind).get()->buffer()); - } - - std::unique_ptr<::neurun::kernel::cpu::ConcatLayer> fn{new ::neurun::kernel::cpu::ConcatLayer}; - - fn->configure(input_buffers, param.ifm_shapes, param.axis, output_alloc->buffer(), - param.ofm_shape); - - builder.append(std::move(fn)); - }); -} - -void StageGenerator::visit(const model::operation::FullyConnectedNode &node) -{ - using model::operation::FullyConnectedNode; - - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(FullyConnectedNode::Input::INPUT)}; - const auto weight_index{node.getInputs().at(FullyConnectedNode::Input::WEIGHT)}; - const auto bias_index{node.getInputs().at(FullyConnectedNode::Input::BIAS)}; - const auto activation_index{node.param().activation_index}; - - // Construct operation parameters - struct Param - { - model::operand::Index output_index; - model::operand::Index input_index; - model::operand::Index weight_index; - model::operand::Index bias_index; - - ::neurun::kernel::cpu::Shape ofm_shape; - ::neurun::kernel::cpu::Shape ifm_shape; - ::neurun::kernel::cpu::Shape weight_shape; - ::neurun::kernel::cpu::Shape bias_shape; - - FuseCode activation; - }; - - Param param; - - param.output_index = output_index; - param.input_index = input_index; - param.weight_index = weight_index; - param.bias_index = bias_index; - - param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(output_index)); - param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(input_index)); - param.weight_shape = ::neurun::kernel::cpu::getShape(_ctx.at(weight_index)); - param.bias_shape = ::neurun::kernel::cpu::getShape(_ctx.at(bias_index)); - - param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); - - auto tensors = _tensor_builder; - - returnStage([tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(param.output_index).get(); - auto input_alloc = tensors->at(param.input_index).get(); - auto weight_alloc = tensors->at(param.weight_index).get(); - auto bias_alloc = tensors->at(param.bias_index).get(); - - std::unique_ptr<::neurun::kernel::cpu::FullyConnectedLayer> fn{ - new ::neurun::kernel::cpu::FullyConnectedLayer}; - - fn->configure(input_alloc->buffer(), param.ifm_shape, weight_alloc->buffer(), - param.weight_shape, bias_alloc->buffer(), param.bias_shape, param.activation, - output_alloc->buffer(), param.ofm_shape); - - builder.append(std::move(fn)); - }); -} - -void StageGenerator::visit(const model::operation::ReshapeNode &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(model::operation::ReshapeNode::Input::INPUT)}; - - struct Param - { - model::operand::Index output_index; - model::operand::Index input_index; - - ::neurun::kernel::cpu::Shape ofm_shape; - ::neurun::kernel::cpu::Shape ifm_shape; - }; - - Param param; - - param.output_index = output_index; - param.input_index = input_index; - - param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(output_index)); - param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(input_index)); - - auto tensors = _tensor_builder; - - returnStage([tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(param.output_index).get(); - auto input_alloc = tensors->at(param.input_index).get(); - - std::unique_ptr<::neurun::kernel::cpu::ReshapeLayer> fn{ - new ::neurun::kernel::cpu::ReshapeLayer}; - - fn->configure(input_alloc->buffer(), param.ifm_shape, output_alloc->buffer(), param.ofm_shape); - - builder.append(std::move(fn)); - }); -} - -void StageGenerator::visit(const model::operation::SoftmaxNode &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(model::operation::SoftmaxNode::Input::INPUT)}; - const auto scale_index{node.param().scale_index}; - - struct Param - { - model::operand::Index output_index; - model::operand::Index input_index; - - ::neurun::kernel::cpu::Shape ofm_shape; - ::neurun::kernel::cpu::Shape ifm_shape; - - float scale; - }; - - Param param; - - param.output_index = output_index; - param.input_index = input_index; - - param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(output_index)); - param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(input_index)); - - param.scale = _ctx.at(scale_index).asScalar<float>(); - - auto tensors = _tensor_builder; - - returnStage([tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(param.output_index).get(); - auto input_alloc = tensors->at(param.input_index).get(); - - std::unique_ptr<::neurun::kernel::cpu::SoftMaxLayer> fn{ - new ::neurun::kernel::cpu::SoftMaxLayer}; - - fn->configure(input_alloc->buffer(), param.ifm_shape, param.scale, output_alloc->buffer(), - param.ofm_shape); - - builder.append(std::move(fn)); - }); -} - -void StageGenerator::visit(const model::operation::PermuteNode &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(0)}; - - using PermuteType = model::operation::PermuteNode::Type; - - struct Param - { - model::operand::Index output_index; - model::operand::Index input_index; - - model::operand::Shape shape; - - PermuteType type{PermuteType::COPY}; - }; - - Param param; - - param.output_index = output_index; - param.input_index = input_index; - - param.shape = _ctx.at(output_index).shape(); - param.type = node.param().type; - - // assert(param.shape == _ctx.at(input_index)); - - const auto &input_li = _ctx.at(input_index).lower_info(); - const auto &output_li = _ctx.at(output_index).lower_info(); - const auto input_backend = input_li->def_backends().getOnlyElement(); - const auto output_backend = output_li->def_backends().getOnlyElement(); - - const auto input_tensors = input_backend->tensor_builder(); - const auto output_tensors = output_backend->tensor_builder(); - - returnStage([input_tensors, output_tensors, param](IExecutionBuilder &builder) { - auto output_object = output_tensors->wrapTensor(param.output_index); - auto input_object = input_tensors->wrapTensor(param.input_index); - - auto fn = nnfw::cpp14::make_unique<::neurun::kernel::cpu::PermuteLayer>(); - - fn->configure(input_object, output_object, param.shape, param.type); - - builder.append(std::move(fn)); - }); -} - -void StageGenerator::visit(const model::operation::AddNode &) { throw std::runtime_error("NYI"); } - -} // namespace neurun -} // namespace backend -} // namespace cpu |