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Diffstat (limited to 'src/dynamic_fusion/sketch/utils/DependencyGraph.h')
| -rw-r--r-- | src/dynamic_fusion/sketch/utils/DependencyGraph.h | 648 |
1 files changed, 648 insertions, 0 deletions
diff --git a/src/dynamic_fusion/sketch/utils/DependencyGraph.h b/src/dynamic_fusion/sketch/utils/DependencyGraph.h new file mode 100644 index 0000000000..c157c2b21c --- /dev/null +++ b/src/dynamic_fusion/sketch/utils/DependencyGraph.h @@ -0,0 +1,648 @@ +/* + * 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 */ |