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/*
* Copyright (c) 2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
#error "This experimental feature must be enabled with -DENABLE_EXPERIMENTAL_DYNAMIC_FUSION"
#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
#ifndef ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_DEPENDENCYGRAPH_H
#define ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_DEPENDENCYGRAPH_H
#include "arm_compute/core/Error.h"
#include <algorithm>
#include <map>
#include <vector>
namespace arm_compute
{
namespace experimental
{
namespace dynamic_fusion
{
template <typename T>
bool is_in(const T &v, const std::vector<T> &vec)
{
return std::find(std::begin(vec), std::end(vec), v) != std::end(vec);
}
/** The dependency graph of a workload, where the nodes are of 2 types: Tensor or Operator
* Represented as a doubly-linked adjacency list with the differentiation between source and destination
*
* A "Merge Tensor" is an external tensor associated with the tensor within the graph, and serve as a merge point
*/
class DependencyGraph
{
public:
/** A serial Id allocator
*
*/
class SerialIdAllocator
{
public:
using Id = int;
Id alloc()
{
return _counter++;
}
constexpr static Id empty()
{
return -1;
}
private:
Id _counter{ 0 };
};
using Id = SerialIdAllocator::Id;
/** Adjacency list
*
*/
using AdjList = std::map<Id, std::vector<Id>>;
/** A pack of operator including its input and output tensors, used by traversing through the graph in topological order
*
*/
struct OpPack
{
Id op{};
std::vector<Id> inputs{};
std::vector<Id> outputs{};
friend bool operator==(const OpPack &opp0, const OpPack &opp1)
{
return std::make_tuple(
opp0.op, opp0.inputs, opp0.outputs)
== std::make_tuple(
opp1.op, opp1.inputs, opp1.outputs);
}
};
public:
constexpr static Id empty_id()
{
return SerialIdAllocator::empty();
}
DependencyGraph() = default;
// Used in cases where two DependencyGraphs may want to share the same configuration of tensors
explicit DependencyGraph(const std::vector<Id> &imported_tensors);
// Testing only
DependencyGraph(const AdjList &adj_src_tensors, const AdjList &adj_dst_tensors, const AdjList &adj_src_ops, const AdjList &adj_dst_ops, std::map<Id, Id> merge_points = {});
/** Add a new tensor
*
* @param merge_tensor The external merge point associated with the tensor. Leave empty if not needed.
* @return Id The newly allocated tensor, or a previously added tensor associated with @p merge_tensor
*/
Id add_tensor(Id merge_tensor = empty_id());
void remove_tensor(Id tensor);
/** Add a new operator
*
* @param inputs Input tensors to the operator
* @param outputs Output tensors to the operator
* @return std::pair<Status, DependencyGraph::Id> where id is the newly allocated operator
*/
std::pair<Status, DependencyGraph::Id> add_operator(const std::vector<Id> &inputs, const std::vector<Id> &outputs);
void remove_operator(Id op);
/** Sort the graph in a topological order
*
* @return std::pair<Status, std::vector<OpPack>>
*/
std::pair<Status, std::vector<OpPack>> topological_sort() const;
std::vector<Id> src_ops(Id op) const;
std::vector<Id> dst_ops(Id op) const;
std::vector<Id> src_ops_from_tensor(Id tensor) const;
std::vector<Id> dst_ops_from_tensor(Id tensor) const;
/** Get the merge points object
*
* @return std::map<Id, Id>
*/
std::map<Id, Id> get_merge_points() const;
/** Get all root ops. Root ops can also be referred to as "src ops" of the whole graph
*
* @return std::vector<Id>
*/
std::vector<Id> get_root_ops() const;
/** Get all dst ops of the whole graph
*
* @return std::vector<Id>
*/
std::vector<Id> get_dst_ops() const;
/** Get source tensors to an operator
*
* @param op
* @return std::vector<Id>
*/
std::vector<Id> src_tensors(Id op) const;
/** Get destination tensors to an operator
*
* @param op
* @return std::vector<Id>
*/
std::vector<Id> dst_tensors(Id op) const;
/** Get source tensors of the whole graph
*
* @return std::vector<Id>
*/
std::vector<Id> src_tensors() const;
/** Get destination tensors of the whole graph
*
* @return std::vector<Id>
*/
std::vector<Id> dst_tensors() const;
/** Get all operators
*
* @return std::vector<Id>
*/
std::vector<Id> all_ops() const;
/** Get all tensors
*
* @return std::vector<Id>
*/
std::vector<Id> all_tensors() const;
/** Number of operators
*
* @return unsigned int
*/
unsigned int number_of_ops() const;
/** Number of tensors
*
* @return unsigned int
*/
unsigned int number_of_tensors() const;
/** Update @p merge_point to point to @p t_id
*
* @param t_id
* @param merge_point
*/
Status update_merge_point(Id t_id, Id merge_point);
/** Strict equality comparison (all internal ids and order of insertion matter).
* In the future this may be replaced with a topological comparison, allowing equivalent graphs with different internal ids to be equal
*
*
* @param g0
* @param g1
* @return true
* @return false
*/
friend bool operator==(const DependencyGraph &g0, const DependencyGraph &g1)
{
// Do not compare id allocators
return std::make_tuple(
g0._adj_src_tensors, g0._adj_dst_tensors, g0._adj_src_ops, g0._adj_dst_ops, g0._merge_to_internal)
== std::make_tuple(
g1._adj_src_tensors, g1._adj_dst_tensors, g1._adj_src_ops, g1._adj_dst_ops, g1._merge_to_internal);
}
void link_input(Id op, Id in_tensor);
void link_output(Id op, Id out_tensor);
/** Check if there's a path from @p src_tensor to @p dst_op
*
* @param src_tensor
* @param dst_op
* @return true
* @return false
*/
bool path_exists_from_tensor_to_op(Id src_tensor, Id dst_op) const;
/** Check if there's a path from @p src_op to @p dst_op
*
* @param src_op
* @param dst_op
* @return true
* @return false
*/
bool path_exists_from_op_to_op(Id src_op, Id dst_op) const;
/** Check if tensor is the src tensor of the entire graph
*
* @param tensor
* @return true
* @return false
*/
bool is_src_tensor(Id tensor) const;
/** Check if tensor is the dst tensor of the entire graph
*
* @param tensor
* @return true
* @return false
*/
bool is_dst_tensor(Id tensor) const;
private:
Id insert_new_tensor();
Id insert_new_op();
bool tensor_exists(Id tensor) const;
bool operator_exists(Id op) const;
bool is_src_tensor_of(Id op, Id tensor) const;
bool is_dst_tensor_of(Id op, Id tensor) const;
bool are_connected(Id op, Id tensor) const;
private:
AdjList _adj_src_tensors{};
AdjList _adj_dst_tensors{};
AdjList _adj_src_ops{};
AdjList _adj_dst_ops{};
std::map<Id, Id> _merge_to_internal{}; // From merge tensor to internal tensor
SerialIdAllocator _operator_id{};
SerialIdAllocator _tensor_id{};
};
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
#endif //ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_DEPENDENCYGRAPH_H
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