| /* |
| * 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 SRC_DYNAMIC_FUSION_SKETCH_UTILS_DEPENDENCYGRAPH |
| #define SRC_DYNAMIC_FUSION_SKETCH_UTILS_DEPENDENCYGRAPH |
| |
| #include "arm_compute/core/Error.h" |
| #include <cstdint> |
| #include <map> |
| #include <set> |
| #include <tuple> |
| #include <vector> |
| |
| namespace arm_compute |
| { |
| namespace experimental |
| { |
| namespace dynamic_fusion |
| { |
| namespace |
| { |
| 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); |
| } |
| } // namespace |
| |
| /** A multi-input (tensors), multi-output (tensors) acyclic directed graph |
| * Represented as a doubly-linked adjacency list with the differentiation between source and destination |
| */ |
| class DependencyGraph |
| { |
| public: |
| using Id = int32_t; |
| using TensorId = Id; |
| using OperatorId = 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 |
| { |
| OperatorId op{}; |
| std::vector<TensorId> inputs{}; |
| std::vector<TensorId> 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: |
| DependencyGraph() = default; |
| friend std::ostream &operator<<(std::ostream &os, const DependencyGraph &); |
| |
| /** Try adding an operator (without actually adding it), while keeping the graph as a "linear sequence" / list |
| * |
| * Rule: If the new operator is not the first operator, at least one input tensor must be |
| * the output tensor of the last non-output operator. All other input tensors must be |
| * the global input of the graph (i.e. not the output of any operator). |
| * |
| * Rule: The output tensor of the new operator must not be the input tensor of any previously |
| * added operator. |
| * |
| * PRECONDITION: The current graph is already linear |
| * |
| * @return true If the operator can be added while keeping the graph as a linear sequence |
| * @return false Otherwise |
| */ |
| bool try_add_operator_as_linear(OperatorId op, const std::vector<TensorId> &inputs, const std::vector<TensorId> &outputs, bool is_output = false) const |
| { |
| ARM_COMPUTE_UNUSED(op, is_output); |
| if(all_ops().empty()) |
| { |
| return true; |
| } |
| |
| // If the new operator is not the first operator, at least one input tensor must be |
| // the output tensor of the last non-output operator. All other input tensors must be |
| // the global input of the graph (i.e. not the output of any operator). |
| if(_last_op_available) |
| { |
| auto use_input_from_last_op = false; |
| |
| for(auto src_tensor : inputs) |
| { |
| const auto src_ops = _adj_src_ops.find(src_tensor); |
| |
| if(src_ops != _adj_src_ops.end()) |
| { |
| ARM_COMPUTE_ERROR_ON(src_ops->second.size() > 1); |
| |
| if(!src_ops->second.empty()) |
| { |
| const auto src_op = src_ops->second[0]; |
| |
| if(src_op == _last_op) |
| { |
| if(use_input_from_last_op) |
| { |
| // To be safe, we also forbid using the output tensor |
| // of the last operator twice. |
| return false; |
| } |
| |
| use_input_from_last_op = true; |
| } |
| else |
| { |
| // The input tensor of this operator must not be the output tensor |
| // of any other operator except the last non-output operator. |
| return false; |
| } |
| } |
| } |
| } |
| |
| if(!use_input_from_last_op) |
| { |
| // At least one input tensor must be the output tensor of the last non-output operator. |
| return false; |
| } |
| } |
| |
| // The output tensor of the new operator must not be the input tensor of any previously |
| // added operator. |
| for(auto dst_tensor : outputs) |
| { |
| if(_adj_dst_ops.find(dst_tensor) != _adj_dst_ops.end()) |
| { |
| return false; |
| } |
| } |
| |
| return true; |
| } |
| /** Add an operator, while keeping the graph as a "linear sequence" |
| * |
| * PRECONDITION: The current graph is already linear |
| * INVARIANT: The list can only grow from head to tail |
| * INVARIANT: POSTCONDITION: The graph is linear |
| */ |
| void add_operator_as_linear(OperatorId op, const std::vector<TensorId> &inputs, const std::vector<TensorId> &outputs, bool is_output = false) |
| { |
| const auto success = add_operator(op, inputs, outputs, is_output); |
| ARM_COMPUTE_UNUSED(success); |
| ARM_COMPUTE_ERROR_ON(!success); |
| } |
| /** Add a new operator |
| * Return invalid if it violates the DAG invariant |
| * Invalid operation will not change the graph |
| * |
| * @param[in] op Operator to add |
| * @param[in] inputs Input tensors to the operator |
| * @param[in] outputs Output tensors to the operator |
| * @param[in] is_output Whether this is an output operator |
| */ |
| bool add_operator(OperatorId op, const std::vector<TensorId> &inputs, const std::vector<TensorId> &outputs, bool is_output = false) |
| { |
| if(operator_exists(op)) |
| { |
| return false; |
| } |
| _adj_src_tensors[op] = {}; |
| _adj_dst_tensors[op] = {}; |
| for(auto in_tensor : inputs) |
| { |
| // Linking input tensor to operator node will never create a cycle / loop because we guarantee |
| // each op is newly created, so every <input, op> pair / edge is new |
| link_input(op, in_tensor); |
| } |
| for(auto out_tensor : outputs) |
| { |
| // If there exists a back path from op's output tensor to op already, then linking the two will create a loop / cycle |
| if(path_exists_from_tensor_to_op(out_tensor, op)) |
| { |
| remove_operator(op); |
| return false; |
| } |
| else |
| { |
| link_output(op, out_tensor); |
| } |
| } |
| |
| if(!is_output) |
| { |
| _last_op_available = true; |
| _last_op = op; |
| } |
| |
| return true; |
| } |
| |
| /** Build a sequence of operators from the acyclic graph of operators. |
| * |
| * The graph will be visited in depth-first strategy. The operator can only be added to |
| * the sequence when all operators that supply the input tensors have been added. Otherwise, |
| * the operator will be ignored and later visited again. In other words, the dependency between |
| * operators will be preserved in the sequence. |
| */ |
| std::vector<OpPack> build_operators_sequence() const |
| { |
| std::vector<OpPack> ops_seq; |
| std::set<Id> done_ops; |
| std::set<Id> done_tensors; |
| |
| const auto input_tensors = global_src_tensors(); |
| |
| for(auto tensor : input_tensors) |
| { |
| done_tensors.insert(tensor); |
| |
| for(auto op : _adj_dst_ops.at(tensor)) |
| { |
| build_operators_sequence_from_op(op, ops_seq, done_ops, done_tensors); |
| } |
| } |
| |
| return ops_seq; |
| } |
| |
| /** 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[in] g0 |
| * @param[in] g1 |
| * @return true If the same |
| * @return false Otherwise |
| */ |
| 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) |
| == std::make_tuple( |
| g1._adj_src_tensors, g1._adj_dst_tensors, g1._adj_src_ops, g1._adj_dst_ops); |
| } |
| std::vector<OperatorId> src_ops_from_tensor(TensorId tensor) const |
| { |
| return _adj_src_ops.at(tensor); |
| } |
| std::vector<OperatorId> dst_ops_from_tensor(TensorId tensor) const |
| { |
| return _adj_dst_ops.at(tensor); |
| } |
| /** Get all tensors |
| * |
| * @return std::vector<TensorId> |
| */ |
| std::vector<TensorId> all_tensors() const |
| { |
| std::vector<TensorId> tensors{}; |
| std::transform(std::begin(_adj_src_ops), std::end(_adj_src_ops), std::back_inserter(tensors), [](const auto & it) |
| { |
| return it.first; |
| }); |
| return tensors; |
| } |
| /** Get source tensors of the whole graph |
| * |
| * @return std::vector<TensorId> |
| */ |
| std::vector<TensorId> global_src_tensors() const |
| { |
| std::vector<TensorId> tensors; |
| for(auto tensor_src_ops : _adj_src_ops) |
| { |
| if(tensor_src_ops.second.empty()) |
| { |
| tensors.push_back(tensor_src_ops.first); |
| } |
| } |
| return tensors; |
| } |
| /** Get destination tensors of the whole graph |
| * |
| * @return std::vector<TensorId> |
| */ |
| std::vector<TensorId> global_dst_tensors() const |
| { |
| std::vector<TensorId> tensors; |
| for(auto tensor_dst_ops : _adj_dst_ops) |
| { |
| if(tensor_dst_ops.second.empty()) |
| { |
| tensors.push_back(tensor_dst_ops.first); |
| } |
| } |
| return tensors; |
| } |
| /** Get intermediate tensors of the whole graph. |
| * |
| * @return std::vector<TensorId> |
| */ |
| std::vector<TensorId> intermediate_tensors() const |
| { |
| std::vector<TensorId> tensors; |
| |
| // If a tensor is used to connect the input of an operator and the output of another operator, |
| // it is not allocated in the memory. The tensor exists as a temporary variable only. |
| for(auto src_tensor : _adj_src_ops) |
| { |
| if(!src_tensor.second.empty()) |
| { |
| const auto dst_tensor = _adj_dst_ops.find(src_tensor.first); |
| if(dst_tensor != _adj_dst_ops.end()) |
| { |
| if(!dst_tensor->second.empty()) |
| { |
| tensors.push_back(src_tensor.first); |
| } |
| } |
| } |
| } |
| |
| return tensors; |
| } |
| /** Get all root ops. Root ops can also be referred to as "src ops" of the whole graph |
| * |
| * @return std::vector<OperatorId> |
| */ |
| std::vector<OperatorId> get_root_ops() const |
| { |
| std::vector<OperatorId> ops{}; |
| const auto op_list = all_ops(); |
| |
| for(auto op : op_list) |
| { |
| if(src_ops(op).empty()) |
| { |
| ops.emplace_back(op); |
| } |
| } |
| return ops; |
| } |
| |
| private: |
| void link_input(OperatorId op, TensorId in_tensor) |
| { |
| ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| if(!tensor_exists(in_tensor)) |
| { |
| insert_new_tensor(in_tensor); |
| } |
| ARM_COMPUTE_ERROR_ON(are_connected(op, in_tensor)); // Prevent repetitive linking |
| _adj_src_tensors[op].push_back(in_tensor); |
| _adj_dst_ops[in_tensor].push_back(op); |
| } |
| void link_output(OperatorId op, TensorId out_tensor) |
| { |
| ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| if(!tensor_exists(out_tensor)) |
| { |
| insert_new_tensor(out_tensor); |
| } |
| ARM_COMPUTE_ERROR_ON(are_connected(op, out_tensor)); // Prevent repetitive linking |
| _adj_dst_tensors[op].push_back(out_tensor); |
| _adj_src_ops[out_tensor].push_back(op); |
| } |
| |
| std::vector<OperatorId> src_ops(OperatorId op) const |
| { |
| ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| std::vector<OperatorId> ops{}; |
| for(TensorId src_tensor : src_tensors(op)) |
| { |
| ops.insert(ops.end(), std::begin(_adj_src_ops.at(src_tensor)), std::end(_adj_src_ops.at(src_tensor))); |
| } |
| return ops; |
| } |
| std::vector<OperatorId> dst_ops(OperatorId op) const |
| { |
| ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| std::vector<OperatorId> ops{}; |
| for(TensorId dst_tensor : _adj_dst_tensors.at(op)) |
| { |
| ops.insert(ops.end(), std::begin(_adj_dst_ops.at(dst_tensor)), std::end(_adj_dst_ops.at(dst_tensor))); |
| } |
| return ops; |
| } |
| |
| /** Get source tensors to an operator |
| * |
| * @param[in] op |
| * @return std::vector<TensorId> |
| */ |
| std::vector<TensorId> src_tensors(OperatorId op) const |
| { |
| ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| return _adj_src_tensors.at(op); |
| } |
| /** Get destination tensors to an operator |
| * |
| * @param[in] op |
| * @return std::vector<TensorId> |
| */ |
| std::vector<TensorId> dst_tensors(OperatorId op) const |
| { |
| ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| return _adj_dst_tensors.at(op); |
| } |
| /** Get all operators |
| * |
| * @return std::vector<OperatorId> |
| */ |
| std::vector<OperatorId> all_ops() const |
| { |
| std::vector<OperatorId> ops{}; |
| std::transform(std::begin(_adj_src_tensors), std::end(_adj_src_tensors), std::back_inserter(ops), [](const auto & it) |
| { |
| return it.first; |
| }); |
| return ops; |
| } |
| /** Remove an operator from graph. |
| * |
| * @param[in] op |
| */ |
| void remove_operator(OperatorId op) |
| { |
| for(auto src_tensor : _adj_src_tensors.at(op)) |
| { |
| auto &dst_ops = _adj_dst_ops.at(src_tensor); |
| dst_ops.erase( |
| std::remove(std::begin(dst_ops), std::end(dst_ops), op), |
| std::end(dst_ops)); |
| } |
| for(auto dst_tensor : _adj_dst_tensors.at(op)) |
| { |
| auto &src_ops = _adj_src_ops.at(dst_tensor); |
| src_ops.erase( |
| std::remove(std::begin(src_ops), std::end(src_ops), op), |
| std::end(src_ops)); |
| } |
| // Remove any isolated tensors |
| // An isolated tensor is one where both its _adj_src_ops and _adj_dst_ops are empty |
| for(auto t : all_tensors()) |
| { |
| if(_adj_src_ops.at(t).empty() && _adj_dst_ops.at(t).empty()) |
| { |
| _adj_src_ops.erase(t); |
| _adj_dst_ops.erase(t); |
| } |
| } |
| _adj_src_tensors.erase(op); |
| _adj_dst_tensors.erase(op); |
| } |
| void insert_new_tensor(TensorId tensor) |
| { |
| _adj_src_ops[tensor] = {}; |
| _adj_dst_ops[tensor] = {}; |
| } |
| bool tensor_exists(TensorId tensor) const |
| { |
| return _adj_src_ops.find(tensor) != _adj_src_ops.end() && _adj_dst_ops.find(tensor) != _adj_dst_ops.end(); |
| } |
| bool operator_exists(OperatorId op) const |
| { |
| return _adj_src_tensors.find(op) != _adj_src_tensors.end() && _adj_dst_tensors.find(op) != _adj_dst_tensors.end(); |
| } |
| bool is_src_tensor_of(OperatorId op, TensorId tensor) const |
| { |
| if(!operator_exists(op) || !tensor_exists(tensor)) |
| { |
| return false; |
| } |
| const auto op_inputs = src_tensors(op); |
| return std::find(op_inputs.begin(), op_inputs.end(), tensor) != op_inputs.end(); |
| } |
| bool is_dst_tensor_of(OperatorId op, TensorId tensor) const |
| { |
| if(!operator_exists(op) || !tensor_exists(tensor)) |
| { |
| return false; |
| } |
| const auto op_outputs = dst_tensors(op); |
| return std::find(op_outputs.begin(), op_outputs.end(), tensor) != op_outputs.end(); |
| } |
| bool are_connected(OperatorId op, TensorId tensor) const |
| { |
| return is_src_tensor_of(op, tensor) || is_dst_tensor_of(op, tensor); |
| } |
| /** If op is the destination / leaf operator of the whole graph |
| * |
| * @param[in] op |
| * @return true |
| * @return false |
| */ |
| bool is_dst_op(OperatorId op) const |
| { |
| return dst_ops(op).empty(); |
| } |
| std::vector<OperatorId> get_dst_ops() const |
| { |
| std::vector<OperatorId> ops{}; |
| const auto op_list = all_ops(); |
| |
| for(auto op : op_list) |
| { |
| if(is_dst_op(op)) |
| { |
| ops.emplace_back(op); |
| } |
| } |
| return ops; |
| } |
| bool path_exists_from_tensor_to_op(TensorId src_tensor, OperatorId dst_op) const |
| { |
| if(!tensor_exists(src_tensor) || !operator_exists(dst_op)) |
| { |
| return false; |
| } |
| for(auto child_op : dst_ops_from_tensor(src_tensor)) |
| { |
| if(path_exists_from_op_to_op(child_op, dst_op)) |
| { |
| return true; |
| } |
| } |
| return false; |
| } |
| |
| bool path_exists_from_op_to_op(OperatorId src_op, OperatorId dst_op) const |
| { |
| if(!operator_exists(src_op) || !operator_exists(dst_op)) |
| { |
| return false; |
| } |
| if(src_op == dst_op) |
| { |
| return true; |
| } |
| if(is_in(src_op, get_dst_ops())) |
| { |
| return false; |
| } |
| for(auto child_tensor : dst_tensors(src_op)) |
| { |
| if(path_exists_from_tensor_to_op(child_tensor, dst_op)) |
| { |
| return true; |
| } |
| } |
| return false; |
| } |
| |
| void build_operators_sequence_from_op( |
| Id op, |
| std::vector<OpPack> &ops_seq, |
| std::set<Id> &done_ops, |
| std::set<Id> &done_tensors) const |
| { |
| while(true) |
| { |
| // If the operator has been added to the sequence, ignore it. |
| if(done_ops.find(op) != done_ops.end()) |
| { |
| return; |
| } |
| |
| // If not all the input tensors of the operator are available, this operator cannot be |
| // added to the sequence for now. It will be visited again after the source operator |
| // is added to the sequence. |
| const auto src_tensors = _adj_src_tensors.at(op); |
| |
| for(auto src : src_tensors) |
| { |
| if(done_tensors.find(src) == done_tensors.end()) |
| { |
| return; |
| } |
| } |
| |
| // This operator is ready to be added to the sequence. |
| const auto dst_tensors = _adj_dst_tensors.at(op); |
| |
| done_ops.insert(op); |
| |
| OpPack pack{ op, src_tensors, dst_tensors }; |
| ops_seq.push_back(pack); |
| |
| done_tensors.insert(dst_tensors.begin(), dst_tensors.end()); |
| |
| // Visit all the sink operators. |
| // Call this function recursively unless there is only one sink. |
| if(dst_tensors.size() == 1 && _adj_dst_ops.at(dst_tensors[0]).size() == 1) |
| { |
| op = _adj_dst_ops.at(dst_tensors[0])[0]; |
| } |
| else |
| { |
| for(auto dst_tensor : dst_tensors) |
| { |
| const auto dst_ops = _adj_dst_ops.at(dst_tensor); |
| |
| for(auto dst_op : dst_ops) |
| { |
| build_operators_sequence_from_op(dst_op, ops_seq, done_ops, done_tensors); |
| } |
| } |
| |
| return; |
| } |
| } |
| } |
| |
| private: |
| AdjList _adj_src_tensors{}; |
| AdjList _adj_dst_tensors{}; |
| AdjList _adj_src_ops{}; |
| AdjList _adj_dst_ops{}; |
| |
| bool _last_op_available{ false }; |
| OperatorId _last_op{ 0 }; |
| }; |
| |
| } // namespace dynamic_fusion |
| } // namespace experimental |
| } // namespace arm_compute |
| #endif /* SRC_DYNAMIC_FUSION_SKETCH_UTILS_DEPENDENCYGRAPH */ |