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/*
* Copyright (c) 2019 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 "DataflowExecutor.h"
#include <cassert>
#include "util/logging.h"
namespace neurun
{
namespace exec
{
int64_t DataflowExecutor::calculateRank(const std::vector<model::Element> &operations)
{
int64_t rank = 0;
if (!_indexed_ranks)
{
return rank;
}
for (const auto &element : operations)
{
auto it = _indexed_ranks->find(element.index);
if (it == _indexed_ranks->end())
{
assert(element.node->getName() == "Permute");
// assign int32_t::max to prevent integer overflow
rank += std::numeric_limits<int32_t>::max();
}
else
{
rank += it->second;
}
}
return rank;
}
void DataflowExecutor::emplaceToReadyJobs(const uint32_t &id)
{
auto &job = _waiting_jobs[id];
assert(job != nullptr);
auto &subg = _subgraphs->at(_job_to_subgraph[job->index()]);
auto rank = calculateRank(subg.operations());
_ready_jobs.emplace(rank, std::move(job));
}
void DataflowExecutor::notify(uint32_t finished_job_id)
{
for (auto id : _output_info[finished_job_id])
{
assert(_input_info[id] > 0);
auto count = --_input_info[id];
if (count == 0) // No dependent jobs left, ready for execution
{
emplaceToReadyJobs(id);
}
}
}
bool DataflowExecutor::noWaitingJobs()
{
return std::all_of(_waiting_jobs.begin(), _waiting_jobs.end(),
[](const std::unique_ptr<Job> &job) { return job == nullptr; });
}
DataflowExecutor::DataflowExecutor(const std::shared_ptr<const model::Model> &model,
std::unique_ptr<model::Subgraphs> subgraphs,
const std::shared_ptr<compiler::OperandContext> &operand_context,
std::unique_ptr<graph::LowerInfoMap> lower_info,
std::unique_ptr<backend::TensorManagerSet> tensor_mgrs,
CodeMap &&code_map)
: ExecutorBase{model, std::move(subgraphs), operand_context, std::move(lower_info),
std::move(tensor_mgrs)},
_code_map{std::move(code_map)}
{
VERBOSE(DataflowExecutor) << "Constructing Dataflow Executor" << std::endl;
assert(_subgraphs);
// Assign jobs convert SubgraphIndex to job index(uint32_t)
uint32_t next_job_index = 0;
std::unordered_map<model::SubgraphIndex, uint32_t> subgraph_to_job;
_subgraphs->iterate([&](const model::SubgraphIndex &subg_index, const model::Subgraph &) {
VERBOSE(DataflowExecutor) << "Create a job #" << next_job_index << " with SubgraphIndex "
<< subg_index.value() << std::endl;
_finished_jobs.emplace_back(
nnfw::cpp14::make_unique<Job>(next_job_index, _code_map.at(subg_index).get(),
_lower_info->operation.at(subg_index)->backend()));
subgraph_to_job[subg_index] = next_job_index++;
});
_waiting_jobs.resize(next_job_index);
_output_info.resize(next_job_index);
_initial_input_info.resize(next_job_index, 0);
_subgraphs->iterate([&](const model::SubgraphIndex &subg_index, const model::Subgraph &subg) {
auto job_index = subgraph_to_job[subg_index];
for (auto output : subg.getOutputs())
{
// Update output and input info
_subgraphs->iterate(
[&](const model::SubgraphIndex &subg_cur_index, const model::Subgraph &subg_cur) {
if (subg_cur.getInputs().contains(output))
{
auto dep_index = subgraph_to_job[subg_cur_index];
++_initial_input_info[dep_index];
_output_info[job_index].push_back(dep_index);
}
});
}
});
for (const auto &s : subgraph_to_job)
_job_to_subgraph.emplace(s.second, s.first);
_input_info = _initial_input_info;
}
void DataflowExecutor::executeImpl()
{
assert(noWaitingJobs());
// Execution setup
_waiting_jobs.swap(_finished_jobs); // Move finished jobs to waiting jobs
for (uint32_t i = 0; i < _waiting_jobs.size(); ++i)
{
if (_input_info[i] == 0)
{
emplaceToReadyJobs(i);
}
}
assert(!_ready_jobs.empty()); // Cannot begin if there is no initial jobs
bool is_profiling = util::getConfigBool(util::config::PROFILING_MODE);
// TODO Fix indentation
{
// Notifiy Execution Begin
for (auto &o : _observers)
{
o->handleBegin(this);
}
}
while (!_ready_jobs.empty())
{
auto job = std::move((_ready_jobs.begin())->second);
_ready_jobs.erase(_ready_jobs.begin());
auto job_index = job->index();
VERBOSE(DataflowExecutor) << "Run job #" << job_index << std::endl;
notifyJobBegin(job_index);
if (is_profiling)
job->fn()->runSync();
else
job->run();
notifyJobEnd(job_index);
notify(job_index);
_finished_jobs[job_index] = std::move(job);
}
assert(noWaitingJobs());
for (auto &o : _observers)
{
o->handleEnd(this);
}
// Reset input info for the next execution
_input_info = _initial_input_info;
}
void DataflowExecutor::notifyJobBegin(uint32_t job_index)
{
auto subgraph_index = _job_to_subgraph[job_index];
// Workaround - assumes only one operation
auto node = _subgraphs->at(subgraph_index).operations().at(0).node;
const backend::Backend *backend = _lower_info->operation.at(subgraph_index)->backend();
for (auto &o : _observers)
{
o->handleBegin(this, node, backend);
}
}
void DataflowExecutor::notifyJobEnd(uint32_t job_index)
{
auto subgraph_index = _job_to_subgraph[job_index];
// Workaround - assumes only one operation
auto node = _subgraphs->at(subgraph_index).operations().at(0).node;
const backend::Backend *backend = _lower_info->operation.at(subgraph_index)->backend();
for (auto &o : _observers)
{
o->handleEnd(this, node, backend);
}
}
} // namespace exec
} // namespace neurun
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