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/*
* 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 "backend/acl_cl/InitializerGenerator.h"
#include <arm_compute/core/Coordinates.h>
#include "backend/acl_cl/kernel/View.h"
#include "internal/nnapi/kernel/Reader.h"
#include "util/kernel/IndexIterator.h"
namespace neurun
{
namespace backend
{
namespace acl_cl
{
InitializerGenerator::InitializerGenerator(const neurun::graph::operand::Set &ctx) : _ctx(ctx)
{
// DO NOTHING
}
Initializer
InitializerGenerator::generateWeight(const graph::operation::Conv2D::Implicit::Node &node)
{
const ::neurun::graph::operand::Index ker_index{node.getInputs().at(1)};
const auto ker_shape = _ctx.at(ker_index).shape().asKernel();
auto ker_base = _ctx.at(ker_index).data().base();
auto ker_size = _ctx.at(ker_index).data().size();
return [ker_shape, ker_base, ker_size](::arm_compute::ITensor &tensor) {
const ::internal::nnapi::kernel::Reader<float> from{ker_shape, ker_base, ker_size};
::internal::arm_compute::kernel::View<float> into{&tensor};
::nnfw::util::kernel::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;
};
};
}
Initializer InitializerGenerator::generateWeight(const graph::operation::FullyConnected::Node &node)
{
const ::neurun::graph::operand::Index weight_index{node.getInputs().at(1)};
const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)};
const auto num_output = _ctx.at(weight_index).shape().dim(0);
auto weight_base = _ctx.at(weight_index).data().base();
auto weight_size = _ctx.at(weight_index).data().size();
// NOTE We assume that input is a feature map
// TODO Remove this restriction!
const auto ifm_shape = _ctx.at(input_index).shape().asFeature();
return [num_output, ifm_shape, weight_base, weight_size](::arm_compute::ITensor &tensor) {
const ::nnfw::util::kernel::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H, ifm_shape.W};
const ::internal::nnapi::kernel::Reader<float> from{ker_shape, weight_base, weight_size};
::nnfw::util::kernel::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;
// ARM Compute Library uses '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;
const ::arm_compute::Coordinates coordinate{offset};
auto into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
*into = value;
};
};
}
Initializer InitializerGenerator::generateBias(const graph::operation::Conv2D::Implicit::Node &node)
{
// TODO Refactor so we can reuse the common code
const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)};
auto bias_base = _ctx.at(bias_index).data().base();
const auto bias_size = _ctx.at(bias_index).shape().asVector();
return [bias_base, bias_size](::arm_compute::ITensor &tensor) {
for (int32_t n = 0; n < bias_size; ++n)
{
const ::arm_compute::Coordinates coordinate{n};
float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
const float *from = reinterpret_cast<const float *>(bias_base) + n;
const auto value = *from;
*into = value;
}
};
}
Initializer InitializerGenerator::generateBias(const graph::operation::FullyConnected::Node &node)
{
const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)};
auto bias_base = _ctx.at(bias_index).data().base();
const auto bias_size = _ctx.at(bias_index).shape().asVector();
return [bias_base, bias_size](::arm_compute::ITensor &tensor) {
for (int32_t n = 0; n < bias_size; ++n)
{
const ::arm_compute::Coordinates coordinate{n};
float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
const float *from = reinterpret_cast<const float *>(bias_base) + n;
const auto value = *from;
*into = value;
}
};
}
} // namespace acl_cl
} // namespace backend
} // namespace neurun
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