#pragma once

#include "../taskflow.hpp"

/**

@file pipeline.hpp

@brief pipeline include file

*/

namespace tf {

// ----------------------------------------------------------------------------

// Class Definition: Pipeflow

// ----------------------------------------------------------------------------

/**

@class Pipeflow

@brief class to create a pipeflow object used by the pipe callable

Pipeflow represents a <i>scheduling token</i> in the pipeline scheduling

framework. A pipeflow is created by the pipeline scheduler at runtime to

pass to the pipe callable. Users can query the present statistics

of that scheduling token, including the line identifier, pipe identifier,

and token identifier, and build their application algorithms based on

these statistics.

At the first stage, users can explicitly call the stop method

to stop the pipeline scheduler.

@code{.cpp}

tf::Pipe{tf::PipeType::SERIAL, [](tf::Pipeflow& pf){

std::cout << "token id=" << pf.token()

<< " at line=" << pf.line()

<< " at pipe=" << pf.pipe()

<< '\n';

}};

@endcode

Pipeflow can only be created privately by the tf::Pipeline and

be used through the pipe callable.

*/

class Pipeflow {

template <typename... Ps>

friend class Pipeline;

template <typename P>

friend class ScalablePipeline;

template <typename... Ps>

friend class DataPipeline;

public:

/**

@brief default constructor

*/

Pipeflow() = default;

/**

@brief queries the line identifier of the present token

*/

size_t line() const {

return _line;

}

/**

@brief queries the pipe identifier of the present token

*/

size_t pipe() const {

return _pipe;

}

/**

@brief queries the token identifier

*/

size_t token() const {

return _token;

}

/**

@brief stops the pipeline scheduling

Only the first pipe can call this method to stop the pipeline.

Calling stop from other pipes will throw exception.

*/

void stop() {

if(_pipe != 0) {

TF_THROW("only the first pipe can stop the token");

}

_stop = true;

}

private:

// Regular data

size_t _line;

size_t _pipe;

size_t _token;

bool _stop;

};

// ----------------------------------------------------------------------------

// Class Definition: PipeType

// ----------------------------------------------------------------------------

/**

@enum PipeType

@brief enumeration of all pipe types

*/

enum class PipeType : int {

/** @brief parallel type */

PARALLEL = 1,

/** @brief serial type */

SERIAL = 2

};

// ----------------------------------------------------------------------------

// Class Definition: Pipe

// ----------------------------------------------------------------------------

/**

@class Pipe

@brief class to create a pipe object for a pipeline stage

@tparam C callable type

A pipe represents a stage of a pipeline. A pipe can be either

@em parallel direction or @em serial direction (specified by tf::PipeType)

and is coupled with a callable to invoke by the pipeline scheduler.

The callable must take a referenced tf::Pipeflow object in the first argument:

@code{.cpp}

Pipe{PipeType::SERIAL, [](tf::Pipeflow&){}}

@endcode

The pipeflow object is used to query the statistics of a scheduling token

in the pipeline, such as pipe, line, and token numbers.

*/

template <typename C = std::function<void(tf::Pipeflow&)>>

class Pipe {

template <typename... Ps>

friend class Pipeline;

template <typename P>

friend class ScalablePipeline;

public:

/**

@brief alias of the callable type

*/

using callable_t = C;

/**

@brief default constructor

*/

Pipe() = default;

/**

@brief constructs the pipe object

@param d pipe type (tf::PipeType)

@param callable callable type

The constructor constructs a pipe with the given direction

(tf::PipeType::SERIAL or tf::PipeType::PARALLEL) and the given callable.

The callable must take a referenced tf::Pipeflow object in the first argument.

@code{.cpp}

Pipe{PipeType::SERIAL, [](tf::Pipeflow&){}}

@endcode

When creating a pipeline, the direction of the first pipe must be serial

(tf::PipeType::SERIAL).

*/

Pipe(PipeType d, C&& callable) :

_type{d}, _callable{std::forward<C>(callable)} {

}

/**

@brief queries the type of the pipe

Returns the type of the callable.

*/

PipeType type() const {

return _type;

}

/**

@brief assigns a new type to the pipe

@param type a tf::PipeType variable

*/

void type(PipeType type) {

_type = type;

}

/**

@brief assigns a new callable to the pipe

@tparam U callable type

@param callable a callable object constructible from std::function<void(tf::Pipeflow&)>

Assigns a new callable to the pipe with universal forwarding.

*/

template <typename U>

void callable(U&& callable) {

_callable = std::forward<U>(callable);

}

private:

PipeType _type;

C _callable;

};

// ----------------------------------------------------------------------------

// Class Definition: Pipeline

// ----------------------------------------------------------------------------

/**

@class Pipeline

@brief class to create a pipeline scheduling framework

@tparam Ps pipe types

A tf::Pipeline is a composable graph object that allows users to parallelize an application

using pipeline parallelism.

Unlike conventional pipeline programming frameworks (e.g., Intel TBB),

tf::Pipeline does not provide any data abstraction but a task-parallel framework

for users to customize their data layout when leveraging pipeline parallelism.

The following code creates a pipeline with four parallel lines that schedule

five tokens through three pipes of a serial-parallel-serial direction:

@code{.cpp}

tf::Taskflow taskflow;

tf::Executor executor;

const size_t num_lines = 4;

const size_t num_pipes = 3;

// create a custom data buffer

std::array<std::array<int, num_pipes>, num_lines> buffer;

// create a pipeline graph of four concurrent lines and three serial pipes

tf::Pipeline pipeline(num_lines,

// first pipe must define a serial direction

tf::Pipe{tf::PipeType::SERIAL, [&buffer](tf::Pipeflow& pf) {

// generate only 5 scheduling tokens

if(pf.token() == 5) {

pf.stop();

}

// save the token id into the buffer

else {

buffer[pf.line()][pf.pipe()] = pf.token();

}

}},

tf::Pipe{tf::PipeType::PARALLEL, [&buffer] (tf::Pipeflow& pf) {

// propagate the previous result to this pipe by adding one

buffer[pf.line()][pf.pipe()] = buffer[pf.line()][pf.pipe()-1] + 1;

}},

tf::Pipe{tf::PipeType::SERIAL, [&buffer](tf::Pipeflow& pf){

// propagate the previous result to this pipe by adding one

buffer[pf.line()][pf.pipe()] = buffer[pf.line()][pf.pipe()-1] + 1;

}}

);

// build the pipeline graph using composition

tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })

.name("starting pipeline");

tf::Task task = taskflow.composed_of(pipeline)

.name("pipeline");

tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })

.name("pipeline stopped");

// create task dependency

init.precede(task);

task.precede(stop);

// run the pipeline

executor.run(taskflow).wait();

@endcode

The five tokens will be scheduled across four parallel lines in a circular fashion,

as depicted below:

@dotfile images/pipeline_our_structure.dot

At each pipe stage, the program propagates the result to the next pipe

by adding one to the result stored in a custom data storage, @c buffer.

The pipeline scheduler will generate five scheduling tokens and then stop.

Internally, tf::Pipeline uses std::tuple to store the given sequence of pipes.

The definition of each pipe can be different, completely decided by the compiler

to optimize the object layout.

After a pipeline is constructed, it is not possible to change its pipes.

If applications need to change these pipes, please use tf::ScalablePipeline.

*/

template <typename... Ps>

class Pipeline {

static_assert(sizeof...(Ps)>0, "must have at least one pipe");

/**

@private

*/

struct Line {

std::atomic<size_t> join_counter;

};

/**

@private

*/

struct PipeMeta {

PipeType type;

};

public:

/**

@brief constructs a pipeline object

@param num_lines the number of parallel lines

@param ps a list of pipes

Constructs a pipeline of up to @c num_lines parallel lines to schedule

tokens through the given linear chain of pipes.

The first pipe must define a serial direction (tf::PipeType::SERIAL)

or an exception will be thrown.

*/

Pipeline(size_t num_lines, Ps&&... ps);

/**

@brief constructs a pipeline object

@param num_lines the number of parallel lines

@param ps a tuple of pipes

Constructs a pipeline of up to @c num_lines parallel lines to schedule

tokens through the given linear chain of pipes.

The first pipe must define a serial direction (tf::PipeType::SERIAL)

or an exception will be thrown.

*/

Pipeline(size_t num_lines, std::tuple<Ps...>&& ps);

/**

@brief queries the number of parallel lines

The function returns the number of parallel lines given by the user

upon the construction of the pipeline.

The number of lines represents the maximum parallelism this pipeline

can achieve.

*/

size_t num_lines() const noexcept;

/**

@brief queries the number of pipes

The Function returns the number of pipes given by the user

upon the construction of the pipeline.

*/

constexpr size_t num_pipes() const;

/**

@brief resets the pipeline

Resetting the pipeline to the initial state. After resetting a pipeline,

its token identifier will start from zero as if the pipeline was just

constructed.

*/

void reset();

/**

@brief queries the number of generated tokens in the pipeline

The number represents the total scheduling tokens that has been

generated by the pipeline so far.

*/

size_t num_tokens() const noexcept;

/**

@brief obtains the graph object associated with the pipeline construct

This method is primarily used as an opaque data structure for creating

a module task of the this pipeline.

*/

Graph& graph();

private:

Graph _graph;

size_t _num_tokens;

std::tuple<Ps...> _pipes;

std::array<PipeMeta, sizeof...(Ps)> _meta;

std::vector<std::array<Line, sizeof...(Ps)>> _lines;

std::vector<Task> _tasks;

std::vector<Pipeflow> _pipeflows;

template <size_t... I>

auto _gen_meta(std::tuple<Ps...>&&, std::index_sequence<I...>);

void _on_pipe(Pipeflow&, NonpreemptiveRuntime&);

void _build();

};

// constructor

template <typename... Ps>

Pipeline<Ps...>::Pipeline(size_t num_lines, Ps&&... ps) :

_pipes {std::make_tuple(std::forward<Ps>(ps)...)},

_meta {PipeMeta{ps.type()}...},

_lines (num_lines),

_tasks (num_lines + 1),

_pipeflows (num_lines) {

if(num_lines == 0) {

TF_THROW("must have at least one line");

}

if(std::get<0>(_pipes).type() != PipeType::SERIAL) {

TF_THROW("first pipe must be serial");

}

reset();

_build();

}

// constructor

template <typename... Ps>

Pipeline<Ps...>::Pipeline(size_t num_lines, std::tuple<Ps...>&& ps) :

_pipes {std::forward<std::tuple<Ps...>>(ps)},

_meta {_gen_meta(

std::forward<std::tuple<Ps...>>(ps), std::make_index_sequence<sizeof...(Ps)>{}

)},

_lines (num_lines),

_tasks (num_lines + 1),

_pipeflows (num_lines) {

if(num_lines == 0) {

TF_THROW("must have at least one line");

}

if(std::get<0>(_pipes).type() != PipeType::SERIAL) {

TF_THROW("first pipe must be serial");

}

reset();

_build();

}

// Function: _get_meta

template <typename... Ps>

template <size_t... I>

auto Pipeline<Ps...>::_gen_meta(std::tuple<Ps...>&& ps, std::index_sequence<I...>) {

return std::array{PipeMeta{std::get<I>(ps).type()}...};

}

// Function: num_lines

template <typename... Ps>

size_t Pipeline<Ps...>::num_lines() const noexcept {

return _pipeflows.size();

}

// Function: num_pipes

template <typename... Ps>

constexpr size_t Pipeline<Ps...>::num_pipes() const {

return sizeof...(Ps);

}

// Function: num_tokens

template <typename... Ps>

size_t Pipeline<Ps...>::num_tokens() const noexcept {

return _num_tokens;

}

// Function: graph

template <typename... Ps>

Graph& Pipeline<Ps...>::graph() {

return _graph;

}

// Function: reset

template <typename... Ps>

void Pipeline<Ps...>::reset() {

_num_tokens = 0;

for(size_t l = 0; l<num_lines(); l++) {

_pipeflows[l]._pipe = 0;

_pipeflows[l]._line = l;

}

_lines[0][0].join_counter.store(0, std::memory_order_relaxed);

for(size_t l=1; l<num_lines(); l++) {

for(size_t f=1; f<num_pipes(); f++) {

_lines[l][f].join_counter.store(

static_cast<size_t>(_meta[f].type), std::memory_order_relaxed

);

}

}

for(size_t f=1; f<num_pipes(); f++) {

_lines[0][f].join_counter.store(1, std::memory_order_relaxed);

}

for(size_t l=1; l<num_lines(); l++) {

_lines[l][0].join_counter.store(

static_cast<size_t>(_meta[0].type) - 1, std::memory_order_relaxed

);

}

}

// Procedure: _on_pipe

template <typename... Ps>

void Pipeline<Ps...>::_on_pipe(Pipeflow& pf, NonpreemptiveRuntime& rt) {

visit_tuple([&](auto&& pipe){

using callable_t = typename std::decay_t<decltype(pipe)>::callable_t;

if constexpr (std::is_invocable_v<callable_t, Pipeflow&>) {

pipe._callable(pf);

}

else if constexpr(std::is_invocable_v<callable_t, Pipeflow&, NonpreemptiveRuntime&>) {

pipe._callable(pf, rt);

}

else {

static_assert(dependent_false_v<callable_t>, "un-supported pipe callable type");

}

}, _pipes, pf._pipe);

}

// Procedure: _build

template <typename... Ps>

void Pipeline<Ps...>::_build() {

using namespace std::literals::string_literals;

FlowBuilder fb(_graph);

// init task

_tasks[0] = fb.emplace([this]() {

return static_cast<int>(_num_tokens % num_lines());

}).name("cond");

// line task

for(size_t l = 0; l < num_lines(); l++) {

_tasks[l + 1] = fb.emplace([this, l] (tf::NonpreemptiveRuntime& rt) mutable {

auto pf = &_pipeflows[l];

pipeline:

_lines[pf->_line][pf->_pipe].join_counter.store(

static_cast<size_t>(_meta[pf->_pipe].type), std::memory_order_relaxed

);

// First pipe does all jobs of initialization and token dependencies

if (pf->_pipe == 0) {

pf->_token = _num_tokens;

if (pf->_stop = false, _on_pipe(*pf, rt); pf->_stop == true) {

// here, the pipeline is not stopped yet because other

// lines of tasks may still be running their last stages

return;

}

++_num_tokens;

}

else {

_on_pipe(*pf, rt);

}

size_t c_f = pf->_pipe;

size_t n_f = (pf->_pipe + 1) % num_pipes();

size_t n_l = (pf->_line + 1) % num_lines();

pf->_pipe = n_f;

// ---- scheduling starts here ----

// Notice that the shared variable f must not be changed after this

// point because it can result in data race due to the following

// condition:

//

// a -> b

// | |

// v v

// c -> d

//

// d will be spawned by either c or b, so if c changes f but b spawns d

// then data race on f will happen

std::array<int, 2> retval;

size_t n = 0;

// downward dependency

if(_meta[c_f].type == PipeType::SERIAL &&

_lines[n_l][c_f].join_counter.fetch_sub(

1, std::memory_order_acq_rel) == 1

) {

retval[n++] = 1;

}

// forward dependency

if(_lines[pf->_line][n_f].join_counter.fetch_sub(

1, std::memory_order_acq_rel) == 1

) {

retval[n++] = 0;

}

// notice that the task index starts from 1

switch(n) {

case 2: {

rt.schedule(_tasks[n_l+1]);

goto pipeline;

}

case 1: {

// downward dependency

if (retval[0] == 1) {

pf = &_pipeflows[n_l];

}

// forward dependency

goto pipeline;

}

}

}).name("nprt-"s + std::to_string(l));

_tasks[0].precede(_tasks[l+1]);

}

}

// ----------------------------------------------------------------------------

// Class Definition: ScalablePipeline

// ----------------------------------------------------------------------------

/**

@class ScalablePipeline

@brief class to create a scalable pipeline object

@tparam P type of the iterator to a range of pipes

A scalable pipeline is a composable graph object for users to create a

<i>pipeline scheduling framework</i> using a module task in a taskflow.

Unlike tf::Pipeline that instantiates all pipes upon the construction time,

tf::ScalablePipeline allows variable assignments of pipes using range iterators.

Users can also reset a scalable pipeline to a different range of pipes

between runs. The following code creates a scalable pipeline of four

parallel lines to schedule tokens through three serial pipes in a custom storage,

then resetting the pipeline to a new range of five serial pipes:

@code{.cpp}

tf::Taskflow taskflow("pipeline");

tf::Executor executor;

const size_t num_lines = 4;

// create data storage

std::array<int, num_lines> buffer;

// define the pipe callable

auto pipe_callable = [&buffer] (tf::Pipeflow& pf) mutable {

switch(pf.pipe()) {

// first stage generates only 5 scheduling tokens and saves the

// token number into the buffer.

case 0: {

if(pf.token() == 5) {

pf.stop();

}

else {

printf("stage 1: input token = %zu\n", pf.token());

buffer[pf.line()] = pf.token();

}

return;

}

break;

// other stages propagate the previous result to this pipe and

// increment it by one

default: {

printf(

"stage %zu: input buffer[%zu] = %d\n", pf.pipe(), pf.line(), buffer[pf.line()]

);

buffer[pf.line()] = buffer[pf.line()] + 1;

}

break;

}

};

// create a vector of three pipes

std::vector< tf::Pipe<std::function<void(tf::Pipeflow&)>> > pipes;

for(size_t i=0; i<3; i++) {

pipes.emplace_back(tf::PipeType::SERIAL, pipe_callable);

}

// create a pipeline of four parallel lines based on the given vector of pipes

tf::ScalablePipeline pl(num_lines, pipes.begin(), pipes.end());

// build the pipeline graph using composition

tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })

.name("starting pipeline");

tf::Task task = taskflow.composed_of(pl)

.name("pipeline");

tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })

.name("pipeline stopped");

// create task dependency

init.precede(task);

task.precede(stop);

// dump the pipeline graph structure (with composition)

taskflow.dump(std::cout);

// run the pipeline

executor.run(taskflow).wait();

// reset the pipeline to a new range of five pipes and starts from

// the initial state (i.e., token counts from zero)

for(size_t i=0; i<2; i++) {

pipes.emplace_back(tf::PipeType::SERIAL, pipe_callable);

}

pl.reset(pipes.begin(), pipes.end());

executor.run(taskflow).wait();

@endcode

The above example creates a pipeline graph that schedules five tokens over

four parallel lines in a circular fashion, first going through three serial pipes

and then five serial pipes:

@code{.bash}

# initial construction of three serial pipes

o -> o -> o

| | |

v v v

o -> o -> o

| | |

v v v

o -> o -> o

| | |

v v v

o -> o -> o

# resetting to a new range of five serial pipes

o -> o -> o -> o -> o

| | | | |

v v v v v

o -> o -> o -> o -> o

| | | | |

v v v v v

o -> o -> o -> o -> o

| | | | |

v v v v v

o -> o -> o -> o -> o

@endcode

Each pipe has the same type of `%tf::Pipe<%std::function<void(%tf::Pipeflow&)>>`

and is kept in a vector that is amenable to change.

We construct the scalable pipeline using two range iterators pointing to the

beginning and the end of the vector.

At each pipe stage, the program propagates the result to the next pipe

by adding one to the result stored in a custom data storage, @c buffer.

The pipeline scheduler will generate five scheduling tokens and then stop.

A scalable pipeline is move-only.

*/

template <typename P>

class ScalablePipeline {

/**

@private

*/

struct Line {

std::atomic<size_t> join_counter;

};

public:

/**

@brief pipe type

*/

using pipe_t = typename std::iterator_traits<P>::value_type;

/**

@brief default constructor

*/

ScalablePipeline() = default;

/**

@brief constructs an empty scalable pipeline object

@param num_lines the number of parallel lines

An empty scalable pipeline does not have any pipes.

The pipeline needs to be reset to a valid range of pipes

before running.

*/

ScalablePipeline(size_t num_lines);

/**

@brief constructs a scalable pipeline object

@param num_lines the number of parallel lines

@param first iterator to the beginning of the range

@param last iterator to the end of the range

Constructs a pipeline from the given range of pipes specified in

<tt>[first, last)</tt> using @c num_lines parallel lines.

The first pipe must define a serial direction (tf::PipeType::SERIAL)

or an exception will be thrown.

Internally, the scalable pipeline copies the iterators

from the specified range. Those pipe callables pointed to by

these iterators must remain valid during the execution of the pipeline.

*/

ScalablePipeline(size_t num_lines, P first, P last);

/**

@brief disabled copy constructor

*/

ScalablePipeline(const ScalablePipeline&) = delete;

/**

@brief move constructor

Constructs a pipeline from the given @c rhs using move semantics

(i.e. the data in @c rhs is moved into this pipeline).

After the move, @c rhs is in a state as if it is just constructed.

The behavior is undefined if @c rhs is running during the move.

*/

ScalablePipeline(ScalablePipeline&& rhs);

/**

@brief disabled copy assignment operator

*/

ScalablePipeline& operator = (const ScalablePipeline&) = delete;

/**

@brief move constructor

Replaces the contents with those of @c rhs using move semantics

(i.e. the data in @c rhs is moved into this pipeline).

After the move, @c rhs is in a state as if it is just constructed.

The behavior is undefined if @c rhs is running during the move.

*/

ScalablePipeline& operator = (ScalablePipeline&& rhs);

/**

@brief queries the number of parallel lines

The function returns the number of parallel lines given by the user

upon the construction of the pipeline.

The number of lines represents the maximum parallelism this pipeline

can achieve.

*/

size_t num_lines() const noexcept;

/**

@brief queries the number of pipes

The Function returns the number of pipes given by the user

upon the construction of the pipeline.

*/

size_t num_pipes() const noexcept;

/**

@brief resets the pipeline

Resets the pipeline to the initial state. After resetting a pipeline,

its token identifier will start from zero.

*/

void reset();

/**

@brief resets the pipeline with a new range of pipes

@param first iterator to the beginning of the range

@param last iterator to the end of the range

The member function assigns the pipeline to a new range of pipes

specified in <tt>[first, last)</tt> and resets the pipeline to the

initial state. After resetting a pipeline, its token identifier will

start from zero.

Internally, the scalable pipeline copies the iterators

from the specified range. Those pipe callables pointed to by

these iterators must remain valid during the execution of the pipeline.

*/

void reset(P first, P last);

/**

@brief resets the pipeline to a new line number and a

new range of pipes

@param num_lines number of parallel lines

@param first iterator to the beginning of the range

@param last iterator to the end of the range

The member function resets the pipeline to a new number of

parallel lines and a new range of pipes specified in

<tt>[first, last)</tt>, as if the pipeline is just constructed.

After resetting a pipeline, its token identifier will start from zero.

Internally, the scalable pipeline copies the iterators

from the specified range. Those pipe callables pointed to by

these iterators must remain valid during the execution of the pipeline.

*/

void reset(size_t num_lines, P first, P last);

/**

@brief queries the number of generated tokens in the pipeline

The number represents the total scheduling tokens that has been

generated by the pipeline so far.

*/

size_t num_tokens() const noexcept;

/**

@brief obtains the graph object associated with the pipeline construct

This method is primarily used as an opaque data structure for creating

a module task of the this pipeline.

*/

Graph& graph();

private:

Graph _graph;

size_t _num_tokens{0};

std::vector<P> _pipes;

std::vector<Task> _tasks;

std::vector<Pipeflow> _pipeflows;

std::unique_ptr<Line[]> _lines;

void _on_pipe(Pipeflow&, NonpreemptiveRuntime&);

void _build();

Line& _line(size_t, size_t);

};

// constructor

template <typename P>

ScalablePipeline<P>::ScalablePipeline(size_t num_lines) :

_tasks (num_lines + 1),

_pipeflows (num_lines) {

if(num_lines == 0) {

TF_THROW("must have at least one line");

}

_build();

}

// constructor

template <typename P>

ScalablePipeline<P>::ScalablePipeline(size_t num_lines, P first, P last) :

_tasks (num_lines + 1),

_pipeflows (num_lines) {

if(num_lines == 0) {

TF_THROW("must have at least one line");

}

reset(first, last);

_build();

}

// move constructor

template <typename P>

ScalablePipeline<P>::ScalablePipeline(ScalablePipeline&& rhs):

_num_tokens {rhs._num_tokens},

_pipes {std::move(rhs._pipes)},

_pipeflows {std::move(rhs._pipeflows)},

_lines {std::move(rhs._lines)} {

_graph.clear();

_tasks.resize(_pipeflows.size()+1);

rhs._num_tokens = 0;

rhs._tasks.clear();

_build();

}

// move assignment operator

template <typename P>

ScalablePipeline<P>& ScalablePipeline<P>::operator = (ScalablePipeline&& rhs) {

_num_tokens = rhs._num_tokens;

_pipes = std::move(rhs._pipes);

_pipeflows = std::move(rhs._pipeflows);

_lines = std::move(rhs._lines);