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Diffstat (limited to 'runtimes/neurun/src/compiler/ConstantInitializer.cc')
| -rw-r--r-- | runtimes/neurun/src/compiler/ConstantInitializer.cc | 188 |
1 files changed, 0 insertions, 188 deletions
diff --git a/runtimes/neurun/src/compiler/ConstantInitializer.cc b/runtimes/neurun/src/compiler/ConstantInitializer.cc deleted file mode 100644 index d6d58e273..000000000 --- a/runtimes/neurun/src/compiler/ConstantInitializer.cc +++ /dev/null @@ -1,188 +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 "ConstantInitializer.h" - -#include "backend/interface/operand/IObject.h" -#include "backend/interface/IConfig.h" -#include "backend/BackendManager.h" -#include "model/operation/FullyConnectedNode.h" -#include "util/feature/nhwc/Reader.h" -#include "util/feature/nhwc/View.h" -#include "util/feature/nchw/View.h" -#include "misc/feature/IndexIterator.h" -#include "util/logging.h" - -namespace neurun -{ -namespace compiler -{ - -ConstantInitializer::ConstantInitializer(const graph::Graph &graph, Plan &plan) - : _graph{graph}, _plan{plan} -{ -} - -void ConstantInitializer::operator()() -{ - // Fill operand data - _plan.operands().iterate([&](int ind, neurun::backend::operand::IObject &obj) { - neurun::model::operand::Index index(ind); - const auto &model_obj = _graph.operands().at(index); - - // For only CONSTANTS - if (model_obj.getUsage() != neurun::model::operand::OperandUsage::CONSTANT) - return; - - // Only float32 is supported - auto type = model_obj.typeInfo().type(); - if (type != ::neurun::model::operand::DataType::TENSOR_FLOAT32) - throw std::runtime_error{"Unsupported data type. Only TENSOR_FLOAT32 is supported."}; - - VERBOSE(FillOperandData) << "Fill data for operand " << ind << std::endl; - - auto layout = - model_obj.lower_info()->def_backends().getOnlyElement()->config()->getOperandLayout(); - const auto shape = model_obj.shape(); - auto base = reinterpret_cast<const float *>(model_obj.data().base()); - auto size = model_obj.data().size(); - - obj.access([&](::neurun::backend::operand::ITensor &tensor) { - switch (shape.rank()) - { - case 1: - { - auto vec_size = shape.asVector(); - for (int32_t n = 0; n < vec_size; ++n) - { - const float *from = reinterpret_cast<const float *>(base) + n; - const auto value = *from; - - float *into = reinterpret_cast<float *>(tensor.buffer()) + n; - - *into = value; - } - break; - } - case 2: - { - // NOTE This is a WORKAROUND which supports FullyConnected weight only - // For FullyConnected, we must know the IFM shape to deduce 2D weight shape from 4D - // IFM. - // This is because of NHWC/NCHW layout, the order of mapping will be different. - // TODO Support general case - explicitly insert Reshape op for IFM as 2D - - // Find corresponding FullyConnected IFM - auto operation_index = _graph.operands().at(index).getUses().list().front(); - auto operation = &_graph.operations().at(operation_index); - auto fc_operation = - dynamic_cast<const neurun::model::operation::FullyConnectedNode *>(operation); - - if (fc_operation == nullptr) - break; - - auto ifm_index = fc_operation->getInputs().at( - neurun::model::operation::FullyConnectedNode::Input::INPUT); - const auto &ifm = _graph.operands().at(ifm_index); - const auto ifm_shape = ifm.shape().asFeature(); - const auto num_output = shape.dim(0); - - const ::nnfw::misc::feature::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H, - ifm_shape.W}; - const util::feature::nhwc::Reader<float> from{ker_shape, base, size}; - - if (layout == neurun::graph::operand::Layout::NHWC) - { - ::nnfw::misc::feature::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - - uint32_t offset = 0; - - // NNAPI uses NHWC ordering - offset += nth * ifm_shape.H * ifm_shape.W * ifm_shape.C; - offset += row * ifm_shape.W * ifm_shape.C; - offset += col * ifm_shape.C; - offset += ch; - - float *into = reinterpret_cast<float *>(tensor.buffer()) + offset; - - *into = value; - }; - } - else - { - assert(layout == neurun::graph::operand::Layout::NCHW); - - ::nnfw::misc::feature::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - - uint32_t offset = 0; - - // 'NCHW' ordering - offset += nth * ifm_shape.C * ifm_shape.H * ifm_shape.W; - offset += ch * ifm_shape.H * ifm_shape.W; - offset += row * ifm_shape.W; - offset += col; - - float *into = reinterpret_cast<float *>(tensor.buffer()) + offset; - - *into = value; - }; - } - - break; - } - case 4: - { - auto ker_shape = shape.asFeature(); - auto from = util::feature::nhwc::Reader<float>{ker_shape, base, size}; - - if (layout == neurun::graph::operand::Layout::NHWC) - { - auto into = util::feature::nhwc::View<float>{ - ker_shape, reinterpret_cast<float *>(tensor.buffer()), size}; - - ::nnfw::misc::feature::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - into.at(nth, ch, row, col) = value; - }; - } - else - { - assert(layout == neurun::graph::operand::Layout::NCHW); - - auto into = util::feature::nchw::View<float>{&tensor}; - - ::nnfw::misc::feature::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - into.at(nth, ch, row, col) = value; - }; - } - break; - } - default: - throw std::runtime_error{"Not yet supported"}; - } - }); - }); -} - -} // namespace codegen -} // namespace neurun |