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[Ansor][AutoTVM v2.0] Phase 1: Access Analyzer (apache#6103)
* add access analyzer * add test cases * move header files and polish comments * fix lint * update * fix lint * address comments * fix lint
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| /*r | ||
| * Licensed to the Apache Software Foundation (ASF) under one | ||
| * or more contributor license agreements. See the NOTICE file | ||
| * distributed with this work for additional information | ||
| * regarding copyright ownership. The ASF licenses this file | ||
| * to you 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. | ||
| */ | ||
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| /*! | ||
| * \file tvm/auto_scheduler/compute_dag.h | ||
| * \brief The auto-scheduler's computational graph and related program analyses. | ||
| * | ||
| * We convert a compute declaration described by `tvm.compute` (could be a single operator or a | ||
| * subgraph) to a ComputeDAG. It keeps the input/output tensors of the compute declaration, | ||
| * a list of all operations in the DAG as well as static analysis results for the DAG (e.g. the | ||
| * total float operation count, consumer/producer relations of each operation stage, whether an | ||
| * operation stage should be tiled/compute inlined ...). These analyses can help the search policy | ||
| * to make decisions during search process. | ||
| * ComputeDAG is also responsible for the interaction between TVM Auto-scheduler `LoopState` and | ||
| * TVM schedule (e.g. applying the `LoopState` transform steps to TVM schedule, providing | ||
| * `LoopState` with extra information got from TVM schedule ...). | ||
| */ | ||
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| #ifndef TVM_AUTO_SCHEDULER_COMPUTE_DAG_H_ | ||
| #define TVM_AUTO_SCHEDULER_COMPUTE_DAG_H_ | ||
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| #include <tvm/auto_scheduler/loop_state.h> | ||
| #include <tvm/runtime/c_runtime_api.h> | ||
| #include <tvm/te/schedule.h> | ||
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| #include <unordered_map> | ||
| #include <unordered_set> | ||
| #include <utility> | ||
| #include <vector> | ||
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| namespace tvm { | ||
| namespace auto_scheduler { | ||
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| /*! \brief Static analysis result for a ComputeDAG */ | ||
| class AccessAnalyzerNode : public Object { | ||
| public: | ||
| template <class T> | ||
| using OperationMap = std::unordered_map<te::Operation, T, ObjectPtrHash, ObjectPtrEqual>; | ||
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| /*! \brief Map an operation to all operations it reads from. | ||
| * For each operation pair, use a two-dimentional array to multiple multi-dimentional accesses | ||
| * The inner vector represents the indices of multi-dimensional access.*/ | ||
| OperationMap<OperationMap<std::vector<std::vector<PrimExpr>>>> read_from; | ||
| /*! \brief Map an operation to all operations it is read by. | ||
| * For each operation pair, use a two-dimentional array to multiple multi-dimentional accesses | ||
| * The inner vector represents the indices of multi-dimensional access.*/ | ||
| OperationMap<OperationMap<std::vector<std::vector<PrimExpr>>>> read_by; | ||
| /*! \brief Store the number of common outer iterators for operation pairs that have | ||
| * read-write relations. */ | ||
| OperationMap<OperationMap<int>> num_common_outer_iterators; | ||
| /*! \brief Store whether the operation is an op with only simple access. | ||
| * (e.g., injective, broadcast and elementwise ops without reduction) */ | ||
| OperationMap<bool> is_simple_access; | ||
| /*! \brief Store whether the operation is strictly-inlineable | ||
| * (e.g., injective, broadcast and elementwise without reduction, branch or expenive operations) | ||
| */ | ||
| OperationMap<bool> is_strict_inlineable; | ||
| /*! \brief Store whether the operation needs multi-level tiling | ||
| * (e.g., computation-intensive ops with data reuse opportunity like matmul, conv2d) */ | ||
| OperationMap<bool> needs_multi_level_tiling; | ||
| /*! \brief Store whether the operation is an output operation */ | ||
| OperationMap<bool> is_output; | ||
| /*! \brief Store the topological order of operations */ | ||
| Array<te::Operation> ops_topo_order; | ||
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| static constexpr const char* _type_key = "auto_scheduler.AccessAnalyzer"; | ||
| TVM_DECLARE_FINAL_OBJECT_INFO(AccessAnalyzerNode, Object); | ||
| }; | ||
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| /*! | ||
| * \brief Managed reference to AccessAnalyzerNode. | ||
| * \sa AccessAnalyzerNode | ||
| */ | ||
| class AccessAnalyzer : public ObjectRef { | ||
| public: | ||
| explicit AccessAnalyzer(const Array<te::Tensor>& tensors); | ||
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| /*! | ||
| * \brief Return whether this operation is an injective operation | ||
| * (e.g., injective, broadcast and elementwise ops without reduction) | ||
| * \param op The operation | ||
| */ | ||
| TVM_DLL bool IsSimpleAccess(const te::Operation& op) const; | ||
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| /*! | ||
| * \brief Return whether this operation is strictly inlinable | ||
| * (e.g., injective, broadcast and elementwise without reduction, branch or expenive operations) | ||
| * \param op The operation | ||
| */ | ||
| TVM_DLL bool IsStrictInlineable(const te::Operation& op) const; | ||
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| /*! | ||
| * \brief Return whether this operation needs multi-level tiling | ||
| * (e.g., computation-intensive ops with data reuse opportunity like matmul, conv2d) | ||
| * \param op The operation | ||
| */ | ||
| TVM_DLL bool NeedsMultiLevelTiling(const te::Operation& op) const; | ||
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| /*! | ||
| * \brief Return whether this operation is an output op | ||
| * \param op The operation | ||
| */ | ||
| TVM_DLL bool IsOutput(const te::Operation& op) const; | ||
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| /*! | ||
| * \brief Get all consumers of on operation | ||
| * \param state The current loop state | ||
| * \param op The operation | ||
| * \return The set of consumers | ||
| * \note This function propagates the relation for inlined ops | ||
| */ | ||
| TVM_DLL std::unordered_set<te::Operation, ObjectHash, ObjectEqual> GetConsumers( | ||
| const State& state, const te::Operation& op) const; | ||
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| /*! | ||
| * \brief Get all producers of on operation | ||
| * \param state The current loop state | ||
| * \param op The operation | ||
| * \return The set of producers | ||
| * \note This function propagates the relation for inlined ops | ||
| */ | ||
| TVM_DLL std::unordered_set<te::Operation, ObjectHash, ObjectEqual> GetProducers( | ||
| const State& state, const te::Operation& op) const; | ||
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| /*! | ||
| * \brief Get all direct producers of on operation | ||
| * \param op The operation | ||
| * \return The set of direct producers | ||
| * \note This function DOES NOT propagate the relation for inlined ops | ||
| */ | ||
| TVM_DLL std::unordered_set<te::Operation, ObjectHash, ObjectEqual> GetDirectProducers( | ||
| const te::Operation& op) const; | ||
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| /*! | ||
| * \brief Get the number of common outer iterators. | ||
| * \param op The operation | ||
| * \param target_op The target operation | ||
| * \note This function propagates the relation for chains with multiple ops. | ||
| */ | ||
| TVM_DLL int GetNumCommonOuterIterator(const te::Operation& op, | ||
| const te::Operation& target_op) const; | ||
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| /*! | ||
| * \brief Return whether two operations are elementwise-matched | ||
| * (e.g. conv2d and relu are elementwise matched) | ||
| * \note This function propagates the relation for chains with multiple ops. | ||
| */ | ||
| TVM_DLL bool ElementWiseMatch(const te::Operation& op, const te::Operation& target_op) const; | ||
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| TVM_DEFINE_OBJECT_REF_METHODS(AccessAnalyzer, ObjectRef, AccessAnalyzerNode); | ||
| }; | ||
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| /*! \brief The TVM Auto-scheduler computational graph and related program analyses. */ | ||
| class ComputeDAGNode : public Object { | ||
| public: | ||
| /*! | ||
| * \brief Input and output tensors. | ||
| * This is used as the input of `tvm.lower` or `tvm.build`. | ||
| */ | ||
| Array<te::Tensor> tensors; | ||
| /*! \brief All related operations in topo order. */ | ||
| Array<te::Operation> ops; | ||
| /*! \brief The number of total float operations for this ComputeDAG. */ | ||
| double flop_ct; | ||
| /*! \brief The initial state without any transform steps. */ | ||
| State init_state; | ||
| /*! \brief The static read-write access analyzer */ | ||
| AccessAnalyzer access_analyzer; | ||
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| void VisitAttrs(tvm::AttrVisitor* v) { | ||
| v->Visit("tensors", &tensors); | ||
| v->Visit("ops", &ops); | ||
| v->Visit("flop_ct", &flop_ct); | ||
| v->Visit("init_state", &init_state); | ||
| } | ||
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| static constexpr const char* _type_key = "auto_scheduler.ComputeDAG"; | ||
| TVM_DECLARE_FINAL_OBJECT_INFO(ComputeDAGNode, Object); | ||
| }; | ||
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| /*! | ||
| * \brief Managed reference to ComputeDAGNode. | ||
| * \sa ComputeDAGNode | ||
| */ | ||
| class ComputeDAG : public ObjectRef { | ||
| public: | ||
| /*! \brief The constructor. | ||
| * \param tensors `te::Tensor`s for a compute declaration. | ||
| */ | ||
| TVM_DLL explicit ComputeDAG(Array<te::Tensor> tensors); | ||
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| /*! | ||
| * \brief Apply the history transform steps to get a TVM schedule. | ||
| * \param transform_steps Transform steps of a state. | ||
| * \param stages The list of stages after applying the steps. | ||
| * Pass a valid pointer if this information needs to be used outside this function. | ||
| * \param stage_to_axes The map that stores all axes for one stage. | ||
| * Pass a valid pointer if this information needs to be used outside this function. | ||
| * \return A `te.schedule` and the an Array of `te.Tensor` to be used in `tvm.lower` | ||
| * or `tvm.build`. | ||
| */ | ||
| std::pair<te::Schedule, Array<te::Tensor>> ApplySteps( | ||
| const Array<Step>& transform_steps, Array<te::Stage>* stages = nullptr, | ||
| StageToAxesMap* stage_to_axes = nullptr) const; | ||
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| /*! | ||
| * \brief Print transform steps as equivalent python schedule API. | ||
| * This can be used for debugging. | ||
| * \param transform_steps Transform steps of a state. | ||
| * \return The Python schedule code. | ||
| */ | ||
| String PrintStepsAsPython(const Array<Step>& transform_steps) const; | ||
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| /*! | ||
| * \brief Fill the correct bound information for a given state by calling ir_pass::InferBound. | ||
| * The states can lose complete bound information after some transform steps (e.g., compute_at). | ||
| * We can call this function to infer and fill all the bound information. | ||
| * This function calls TVM InferBound pass internally to get the bound. | ||
| * The returned state of this function is guaranteed to have complete bound information. | ||
| * \param state The input state. | ||
| * \return The State with complete bound information | ||
| */ | ||
| State InferBound(const State& state) const; | ||
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| TVM_DEFINE_OBJECT_REF_METHODS(ComputeDAG, ObjectRef, ComputeDAGNode); | ||
| TVM_DEFINE_OBJECT_REF_COW_METHOD(ComputeDAGNode); | ||
| }; | ||
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| } // namespace auto_scheduler | ||
| } // namespace tvm | ||
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| #endif // TVM_AUTO_SCHEDULER_COMPUTE_DAG_H_ |
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